Question: Regarding the implementation of ISO 26262 in the design of control systems for autonomous vehicles.

This paper presents an innovative method utilizing apprenticeship learning for motion planning, specifically tailored for parking lot navigation in automated vehicles. Traditional motion and path-planning algorithms frequently employ intricate cost functions for global navigation and local trajectory refinement. Achieving optimal outcomes, however, typically necessitates meticulous manual tuning of the balance among various cost components. In real-world scenarios, demonstrating a few successful trajectories is often a more straightforward task. Our proposed algorithm, when provided with a limited set of trajectory demonstrations, is capable of autonomously determining the ideal weightings between the distinct costs. Through our experimental evaluation, we have successfully applied this algorithm to the challenge of guiding a robotic vehicle through a parking lot, showcasing its effectiveness in automating the cost function calibration process for motion planning tasks.

**Question**: The Impact of Blockchain Technology on Supply Chain Management

**Documents**: Blockchain-based supply chain traceability: a case study in the Australian wine industry
Blockchain technology is a distributed database that can be used to record transactions between two parties efficiently and in a verifiable and permanent way. This paper presents a case study of the application of blockchain technology in the Australian wine industry. The study found that blockchain technology can provide a secure and transparent system for tracking the origin and movement of wine products from the vineyard to the consumer. This can help to prevent fraud, reduce counterfeiting, and improve the traceability of wine products. The case study also identified several challenges in the implementation of blockchain technology in the wine industry, including the need for standardization, the lack of technical expertise, and the high cost of implementation.

**Similar Documents**: This study explores the transformative potential of blockchain technology in enhancing supply chain traceability, focusing on a detailed case analysis within the Australian wine sector. Blockchain, characterized as a decentralized database, offers a robust mechanism for recording transactions between entities in an efficient, verifiable, and enduring manner. Our investigation into the application of blockchain within the Australian wine industry reveals its capability to establish a secure and transparent framework for monitoring the journey of wine products, spanning from vineyard cultivation to consumer delivery. This system significantly curtails fraudulent activities, diminishes counterfeiting, and fortifies the traceability of wine products. However, the study also delineates several hurdles to blockchain implementation in the wine industry, such as the imperative for standardization, the scarcity of technical proficiency, and the considerable financial outlay associated with deployment. These findings underscore the multifaceted impact of blockchain technology on supply chain management within the wine industry.

An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.

Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high density scenarios.

In the automotive industry, the usage of microcontrollers has been increasing rapidly. Many mechanical parts of automobiles are being replaced with microcontrollers, and these microcontrollers are controlling various parts of the automobile, so communications between microcontrollers must be reliable. Until now, several protocols for automotive communications have been introduced, and LIN, CAN, and FlexRay are the most widely known protocols. Different vendors of automobiles use different protocols and each protocol possess different features. This paper presents a network gateway system between LIN, low-speed CAN, high-speed CAN, and FlexRay.

This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.

Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.

This article introduces Periodically Controlled Hybrid Automata (PCHA) for modular specification of embedded control systems. In a PCHA, control actions that change the control input to the plant occur roughly periodically, while other actions that update the state of the controller may occur in the interim. Such actions could model, for example, sensor updates and information received from higher-level planning modules that change the set point of the controller. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariant properties of PCHAs is presented. For PCHAs with polynomial continuous vector fields, it is possible to check these conditions automatically using, for example, quantifier elimination or sum of squares decomposition. We examine the feasibility of this automatic approach on a small example. The proposed technique is also used to manually verify safety and progress properties of a fairly complex planner-controller subsystem of an autonomous ground vehicle. Geometric properties of planner-generated paths are derived which guarantee that such paths can be safely followed by the controller.

The usefulness of a system specification depends in part on the completeness of the requirements. However, enumerating all necessary requirements is difficult, especially when requirements interact with an unpredictable environment. A specification built with an idealized environmental view is incomplete if it does not include requirements to handle non-idealized behavior. Often incomplete requirements are not detected until implementation, testing, or worse, after deployment. Even when performed during requirements analysis, detecting incomplete requirements is typically an error prone, tedious, and manual task. This paper introduces Ares, a design-time approach for detecting incomplete requirements decomposition using symbolic analysis of hierarchical requirements models. We illustrate our approach by applying Ares to a requirements model of an industry-based automotive adaptive cruise control system. Ares is able to automatically detect specific instances of incomplete requirements decompositions at design-time, many of which are subtle and would be difficult to detect, either manually or with testing.

A prerequisite to foster proliferation of automated driving is common system acceptance. However, different users groups (novice, enthusiasts) decline automation, which could be, in turn, problematic for a successful market launch. We see a feasible solution in the combination of the advantages of manual (autonomy) and automated (increased safety) driving. Hence, we've developed the Hotzenplotz interface, combining possibility-driven design with psychological user needs. A simulator study (N=30) was carried-out to assess user experience with subjective criteria (Need Scale, PANAS/-X, HEMA, AttrakDiff) and quantitative measures (driving behavior, HR/HRV) in different conditions. Our results confirm that pure AD is significantly less able to satisfy user needs compared to manual driving and make people feeling bored/out of control. In contrast, the Hotzenplotz interface has proven to reduce the negative effects of AD. Our implication is that drivers should be provided with different control options to secure acceptance and avoid deskilling.

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

In order to gain their customers' trust, software vendors can certify their products according to security standards, e.g., the Common Criteria (ISO 15408). However, a Common Criteria certification requires a comprehensible documentation of the software product. The creation of this documentation results in high costs in terms of time and money.
 We propose a software development process that supports the creation of the required documentation for a Common Criteria certification. Hence, we do not need to create the documentation after the software is built. Furthermore, we propose to use an enhanced version of the requirements-driven software engineering process called ADIT to discover possible problems with the establishment of Common Criteria documents. We aim to detect these issues before the certification process. Thus, we avoid expensive delays of the certification effort. ADIT provides a seamless development approach that allows consistency checks between different kinds of UML models. ADIT also supports traceability from security requirements to design documents. We illustrate our approach with the development of a smart metering gateway system.


Question: Evaluating thermal and visual indicators of deceit for a non-contact deception identification technique.

This study explores the potential of thermal imaging as a tool for detecting deception, focusing on the thermal variations in the periorbital region of the face. By analyzing the heat radiation patterns, we aim to identify distinctive thermal signatures that could indicate dishonesty. We begin with a comprehensive review of automated deception detection methods and introduce a new approach, which we rigorously test on a dataset comprising 492 thermal responses from 25 participants, equally divided between truthful and deceptive statements. The key innovation of our study is the extensive questioning of each subject, allowing for a detailed within-person analysis. We also propose a framework for assessing the decision-making process and accurately evaluating the model's generalization capabilities. Our methodology involves using a $k$-nearest neighbor classifier to categorize the thermal responses based on various data representation strategies. Our findings reveal an 87% accuracy in distinguishing between lies and truths using a within-person analysis and fivefold cross-validation. However, our results also highlight the limitations of the between-person approach in accurately modeling deception across different datasets.

Designing systems able to interact with students in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the continuous Audio/Visual Emotion Challenge (AVEC'12) whose goal is to predict four affective signals describing human emotions. The proposed method uses Fourier spectra to extract multi-scale dynamic descriptions of signals characterizing face appearance, head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. We also propose a particularly fast regressor-level fusion framework to merge systems based on different modalities. Experiments have proven the efficiency of each key point of the proposed method and our results on challenge data were the highest among 10 international research teams. Key-words Affective computing, Dynamic features, Multimodal fusion, Feature selection, Facial expressions. ACM Classification

The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning. keywords : Facial expression analysis, Action unit recognition, Active Appearance Models, temporal clustering.

Synopsis Few systematic studies have examined individual differences in women's use of facial make-up or the possible psychosocial effects of such use. In the present investigation, the researchers developed several measures of the amount and the situational and temporal patterning of cosmetics use. Among forty-two female college students, differential use was associated with a number of selected personality variables-public self-consciousness, public body-consciousness, social anxiety, and various body-image factors. Through the imagery-induction of cosmetics use and non-use conditions, cosmetics users' self-evaluative responses were studied. In a variety of imagined situations, subjects reported being more self-confident and sociable when wearing as opposed to not wearing their customary cosmetics. Findings were discussed in the context of the role of cosmetics in self-image enhancement and social impression management.

We present a noncontact method to monitor blood oxygen saturation (SpO2). The method uses a CMOS camera with a trigger control to allow recording of photoplethysmography (PPG) signals alternatively at two particular wavelengths, and determines the SpO2 from the measured ratios of the pulsatile to the nonpulsatile components of the PPG signals at these wavelengths. The signal-to-noise ratio (SNR) of the SpO2 value depends on the choice of the wavelengths. We found that the combination of orange (λ = 611 nm) and near infrared (λ = 880 nm) provides the best SNR for the noncontact video-based detection method. This combination is different from that used in traditional contact-based SpO2 measurement since the PPG signal strengths and camera quantum efficiencies at these wavelengths are more amenable to SpO2 measurement using a noncontact method. We also conducted a small pilot study to validate the noncontact method over an SpO2 range of 83%-98%. This study results are consistent with those measured using a reference contact SpO2 device (r = 0.936, p <; 0.001). The presented method is particularly suitable for tracking one's health and wellness at home under free-living conditions, and for those who cannot use traditional contact-based PPG devices.

In this article, we examine how emotional and perceptual stimulus factors influence visual search efficiency. In an initial task, we run a visual search task, using a large number of target/distractor emotion combinations. In two subsequent tasks, we then assess measures of perceptual (rated and computational distances) and emotional (rated valence, arousal and potency) stimulus properties. In a series of regression analyses, we then explore the degree to which target salience (the size of target/distractor dissimilarities) on these emotional and perceptual measures predict the outcome on search efficiency measures (response times and accuracy) from the visual search task. The results show that both emotional and perceptual stimulus salience contribute to visual search efficiency. The results show that among the emotional measures, salience on arousal measures was more influential than valence salience. The importance of the arousal factor may be a contributing factor to contradictory history of results within this field.

In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.

In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn. 2008 Published by Elsevier B.V.

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.

0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.01.009 ⇑ Corresponding author at: Dipartimento di Informa of Naples Federico II, via Claudio 21, 80125 Naples, It E-mail addresses: emanuela.marasco@unina.it (E. (C. Sansone). It has been showed that, by employing fake fingers, the existing fingerprint recognition systems may be easily deceived. So, there is an urgent need for improving their security. Software-based liveness detection algorithms typically exploit morphological and perspiration-based characteristics separately to measure the vitality. Both such features provide discriminant information about live and fake fingers, then, it is reasonable to investigate also their joint contribution. In this paper, we combine a set of the most robust morphological and perspiration-based measures. The effectiveness of the proposed approach has been assessed through a comparison with several state-ofthe-art techniques for liveness detection. Experiments have been carried out, for the first time, by adopting standard databases. They have been taken from the Liveness Detection Competition 2009 whose data have been acquired by using three different optical sensors. Further, we have analyzed how the performance of our algorithm changes when the material employed for the spoof attack is not available during the training of the system. 2012 Elsevier B.V. All rights reserved.

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.


Question: Evaluating thermal and visual indicators of deceit for a non-contact deception identification method.

This paper explores an innovative multimodal strategy for the automatic detection of deceit. Our methodology integrates diverse data sources, including a microphone for audio capture, a thermal camera for visual recording, and physiological sensors to monitor bodily responses. Specifically, we concentrate on three key modalities: language analysis, physiological indicators, and thermal imaging. To the best of our knowledge, this is the pioneering effort to unify these particular modalities in the pursuit of deception detection. We conduct a series of experiments to identify salient features from each modality and subsequently test the efficacy of combined models that incorporate multiple modalities. The outcomes highlight that the amalgamation of features across different modalities markedly enhances the accuracy of detecting deceptive behavior compared to relying on a single modality alone. Notably, the utilization of non-contact modalities, such as thermal imaging and language use, has been found to be as effective, if not more so, than conventional contact-based methods. The proposed multimodal approach paves the way for a more efficient, automated, and non-intrusive system for identifying deceit, minimizing the need for human intervention in the detection process.

EE K. BuRGOON IS Professor of Communication and Director of the Center for the Management of Information at the University of Arizona. She is the author or coauthor of seven books and nearly two hundred articles and chapters on topics related to interpersonal and nonverbal communication, deception and credibility, mass media, and new communication technologies. Her current research focuses on interactivity, adaptation, and attunement in communication.

Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk.
 In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

This paper proposes a critical survey of crowd analysis techniques using visual and non-visual sensors. Automatic crowd understanding has a massive impact on several applications including surveillance and security, situation awareness, crowd management, public space design, intelligent and virtual environments. In case of emergency, it enables practical safety applications by identifying crowd situational context information. This survey identifies different approaches as well as relevant work on crowd analysis by means of visual and non-visual techniques. Multidisciplinary research groups are addressing crowd phenomenon and its dynamics ranging from social, and psychological aspects to computational perspectives. The possibility to use smartphones as sensing devices and fuse this information with video sensors data, allows to better describe crowd dynamics and behaviors. Eventually, challenges and further research opportunities with reference to crowd analysis are exposed.

Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and ‘‘safe’’ interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information. 2014 Elsevier Ltd. All rights reserved.

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.

Visual speech information plays an important role in automatic speech recognition (ASR) especially when audio is corrupted or even inaccessible. Despite the success of audio-based ASR, the problem of visual speech decoding remains widely open. This paper provides a detailed review of recent advances in this research area. In comparison with the previous survey [97] which covers the whole ASR system that uses visual speech information, we focus on the important questions asked by researchers and summarize the recent studies that attempt to answer them. In particular, there are three questions related to the extraction of visual features, concerning speaker dependency, pose variation and temporal information, respectively. Another question is about audio-visual speech fusion, considering the dynamic changes of modality reliabilities encountered in practice. In addition, the state-of-the-art on facial landmark localization is briefly introduced in this paper. Those advanced techniques can be used to improve the region-of-interest detection, but have been largely ignored when building a visual-based ASR system. We also provide details of audio-visual speech databases. Finally, we discuss the remaining challenges and offer our insights into the future research on visual speech decoding.

In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.

We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of 10 characteristic points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles and mouth shape). The method defines and detects the six basic emotions (plus the neutral one) in terms of this information and has been fine-tuned with a data-base of 399 images. For the moment, the method is applied to static images. Application to sequences is being now developed. The extraction of such information about the user is of great interest for the development of new multimodal user interfaces.

In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.

This work explores the feasibility of using sensors embedded in Google Glass, a head-mounted wearable device, to measure physiological signals of the wearer. In particular, we develop new methods to use Glass's accelerometer, gyroscope, and camera to extract pulse and respiratory rates of 12 participants during a controlled experiment. We show it is possible to achieve a mean absolute error of 0.83 beats per minute (STD: 2.02) for heart rate and 1.18 breaths per minute (STD: 2.04) for respiration rate when considering different combinations of sensors. These results included testing across sitting, supine, and standing still postures before and after physical exercise.


Question: Evaluating thermal and visual indicators of deceit for a contactless deception identification method.

This document presents a pioneering study that applies computational techniques to assess the veracity of testimonies in real-life court trials, aiming to augment the decision-making process in high-stakes legal settings. Recognizing the critical importance of witness and defendant statements in judicial outcomes, we have developed a unique dataset derived from public court proceedings, featuring video recordings. Our research focuses on the integration of verbal and non-verbal cues to construct a sophisticated multimodal system for deception detection, designed to distinguish between honest and deceptive assertions made by court participants. By employing a model that meticulously analyzes linguistic content and gesture patterns, we have achieved classification accuracies ranging from 60% to 75%. Furthermore, we conducted a comparative analysis, evaluating the human capacity for deception detection in court hearings. Our findings reveal that the performance of our automated system significantly surpasses human judgment in discerning deceitful from truthful statements.

With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eyecontact with a device is proposed in [16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing ; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.

In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.

We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.

Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.

The Kinect has primarily been used as a gesture-driven device for motion-based controls. To date, Kinect-based research has predominantly focused on improving tracking and gesture recognition across a wide base of users. In this paper, we propose to use the Kinect for biometrics; rather than accommodating a wide range of users we exploit each user's uniqueness in terms of gestures. Unlike pure biometrics, such as iris scanners, face detectors, and fingerprint recognition which depend on irrevocable biometric data, the Kinect can provide additional revocable gesture information. We propose a dynamic time-warping (DTW) based framework applied to the Kinect's skeletal information for user access control. Our approach is validated in two scenarios: user identification, and user authentication on a dataset of 20 individuals performing 8 unique gestures. We obtain an overall 4.14%, and 1.89% Equal Error Rate (EER) in user identification, and user authentication, respectively, for a gesture and consistently outperform related work on this dataset. Given the natural noise present in the real-time depth sensor this yields promising results.

Substantial literature exists regarding how eye-tracking systems can be used to measure cognitive load and how these measurements can be useful for adapting training in real time. Much of the published literature discusses the applications and limitations of the research and typically provides recommendations for improvement. This review assesses these articles collectively to provide a clearer solution for implementing eye-tracking systems into a training environment. Although limitations exist for using eye tracking as an interface tool, gaze and pupillary response have been successfully used to reflect changes in cognitive load and are starting to be incorporated into adaptive training systems, although issues are still present with differentiating pupil responses from simultaneous psychological effects. Additionally, current eye-tracking systems and data analysis software have proven accurate enough for general use, but issues including system cost and software integration prevent this technology from becoming commercialized for use in common instructional settings.

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.

Liveness detection is a way to detect whether the person is live or not during submission of his/her biometric trait. It is mandatory in order to prevent face spoofing attacks. Therefore, in this paper, we proposed a robust face structure analysis mechanism to detect the liveness by exploiting face shape information. 3D structure/shape of the face is measured on the basis of disparity map between left and right image taken by stereoscopic vision. A gradient-based eight neighbour feature extraction technique has been proposed to extract unique features from these disparity images. It produces minimal computational cost by taking subset of the overall image. We have applied linear discriminant analysis (LDA), C-means algorithms on these features while principal component analysis (PCA) is applied on raw disparity images. We have achieved a recognition rate of 91.6%, 97.5% and 98.3% using PCA, LDA and C-means respectively, which strengthened the confidence of our proposed feature extraction technique.

EE K. BuRGOON IS Professor of Communication and Director of the Center for the Management of Information at the University of Arizona. She is the author or coauthor of seven books and nearly two hundred articles and chapters on topics related to interpersonal and nonverbal communication, deception and credibility, mass media, and new communication technologies. Her current research focuses on interactivity, adaptation, and attunement in communication.

With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.


Question: Evaluating thermal and visual indicators of deceit for a non-contact deception identification method.

This paper delves into the exploration of thermal and visual cues as indicators of deception, aiming to develop a non-contact deception detection methodology. Unlike traditional approaches that rely on physical contact or direct interaction with the subject, this innovative technique employs remote sensing technologies to analyze subtle changes in body temperature and visual behaviors associated with lying. Thermal imaging captures variations in facial heat patterns, which can reveal physiological responses indicative of deception. Concurrently, visual analysis tracks eye movements, facial expressions, and other non-verbal cues that often accompany dishonesty. By integrating these multimodal signals, the proposed method aims to enhance the accuracy of deception detection without the need for direct physical interaction, thereby offering a non-intrusive alternative to existing techniques. This approach not only expands the capabilities of deception detection systems but also paves the way for applications in various fields, including security, law enforcement, and psychology, where non-verbal cues can provide crucial insights into human behavior.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An advanced direct search algorithm for solving complex economic dispatch problems in power systems is presented. This method specifically addresses the valve-point effect, a nonlinear phenomenon that complicates the optimization of power generation costs. The proposed algorithm employs a robust search mechanism to navigate the solution space efficiently, ensuring that the dispatch solution is both economically viable and operationally feasible. Key features of the algorithm include a dynamic step adjustment strategy, which adapts the search steps based on the proximity to the optimal solution, and a local refinement phase that fine-tunes the solution to achieve higher precision. The effectiveness of the proposed method is validated through extensive simulations on standard test systems, demonstrating its capability to outperform conventional methods in terms of solution quality and computational efficiency. This research contributes significantly to the field of power system optimization by providing a practical and efficient solution to the valve-point effect challenge.

**Similar Documents**: This document introduces an enhanced direct search algorithm designed to tackle the intricate economic dispatch problem in power systems, with a specific focus on mitigating the valve-point effect. This nonlinear characteristic poses significant challenges in optimizing power generation costs due to its irregular impact on fuel consumption. The proposed algorithm utilizes an advanced search technique to explore the solution space effectively, ensuring that the dispatch strategy is both cost-effective and operationally sound. Among its notable attributes are a sophisticated step adjustment mechanism, which dynamically modifies the search steps in response to the distance from the optimal solution, and a detailed refinement stage that polishes the solution for enhanced accuracy. The algorithm's efficacy is substantiated through

This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.

In this paper we describe a method for detecting buried objects of interest using a forward looking infrared camera (FLIR) installed on a moving vehicle. Infrared (IR) detection of buried targets is based on the thermal gradient between the object and the surrounding soil. The processing of FILR images consists in a spot-finding procedure that includes edge detection, opening and closing. Each spot is then described using texture features such as histogram of gradients (HOG) and local binary patterns (LBP) and assigned a target confidence using a support vector machine (SVM) classifier. Next, each spot together with its confidence is projected and summed in the UTM space. To validate our approach, we present results obtained on 6 one mile long runs recorded with a long wave IR (LWIR) camera installed on a moving vehicle.

We investigate the accuracy of a set of surface patterns in identifying ironic sentences in comments submitted by users to an on-line newspaper. The initial focus is on identifying irony in sentences containing positive predicates since these sentences are more exposed to irony, making their true polarity harder to recognize. We show that it is possible to find ironic sentences with relatively high precision (from 45% to 85%) by exploring certain oral or gestural clues in user comments, such as emoticons, onomatopoeic expressions for laughter, heavy punctuation marks, quotation marks and positive interjections. We also demonstrate that clues based on deeper linguistic information are relatively inefficient in capturing irony in user-generated content, which points to the need for exploring additional types of oral clues.

Advances in biosensing make it possible to include heart rate monitoring in applications and several studies have suggested that heart rate communication has potential for improving social connectedness. However, it is not known how people understand heart rate feedback, or what issues need to be taken into account when designing technologies including heart rate feedback. To explore this, we created a heart rate communication probe that was used in two qualitative in-lab studies and a two-week field trial in participants' homes. Results show that heart rate feedback is a strong connectedness cue that affects the interaction in various ways, depending on a number of interrelated factors. In particular, we found two distinct categories of effects: heart rate as information and heart rate as connection. We propose two mechanisms that could explain these observations and draw out the implications they have for future use of heartbeat communication to support social connectedness or other aspects of social interaction.

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.

This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.

Men's, more than women's, sexual responses may include a coordination of several physiological indices in order to build their sexual arousal to relevant targets. Here, for the first time, genital arousal and pupil dilation to sexual stimuli were simultaneously assessed. These measures corresponded more strongly with each other, subjective sexual arousal, and self-reported sexual orientation in men than women. Bisexual arousal is more prevalent in women than men. We therefore predicted that if bisexual-identified men show bisexual arousal, the correspondence of their arousal indices would be more female-typical, thus weaker, than for other men. Homosexual women show more male-typical arousal than other women; hence, their correspondence of arousal indices should be stronger than for other women. Findings, albeit weak in effect, supported these predictions. Thus, if sex-specific patterns are reversed within one sex, they might affect more than one aspect of sexual arousal. Because pupillary responses reflected sexual orientation similar to genital responses, they offer a less invasive alternative for the measurement of sexual arousal.

The application of eye tracking for the evaluation of humans' viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with visualization environments. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions of future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.


Question: Evaluating thermal and visual indicators of deceit for a contactless deception identification technique.

This paper explores the multifaceted question of whether individuals exhibit distinct behavioral patterns when lying versus when they are truthful. Drawing from a comprehensive analysis of 1,338 estimates encompassing 158 cues to deception, the findings reveal that, in certain aspects, individuals who lie exhibit less openness than those who tell the truth. Liars are observed to construct less convincing narratives, making a less favorable impression and displaying higher levels of tension. Their narratives often lack the ordinary nuances and unique elements typically found in truthful accounts. However, it is noteworthy that many behaviors did not exhibit any significant or only slight correlations with deceit. The cues to deception were found to be more pronounced under conditions of high motivation, particularly when the motivation was rooted in personal identity rather than financial or material incentives. Moreover, cues to deception were found to be more evident when the lies pertained to acts of transgression.

This study underscores the complexity of detecting deception through behavioral cues and highlights the importance of considering situational factors, such as motivation, in the interpretation of these cues. It suggests that while some behaviors may indeed be indicative of deceit, the context in which they occur plays a critical role in their interpretation. This research contributes to the ongoing debate on the reliability of behavioral cues as a means of deception detection, particularly in scenarios where personal identity is at stake and where the subject matter involves acts of wrongdoing.

Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.

The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

Keystroke inference attacks pose an increasing threat to ubiquitous mobile devices. This paper presents EyeTell, a novel video-assisted attack that can infer a victim's keystrokes on his touchscreen device from a video capturing his eye movements. EyeTell explores the observation that human eyes naturally focus on and follow the keys they type, so a typing sequence on a soft keyboard results in a unique gaze trace of continuous eye movements. In contrast to prior work, EyeTell requires neither the attacker to visually observe the victim's inputting process nor the victim device to be placed on a static holder. Comprehensive experiments on iOS and Android devices confirm the high efficacy of EyeTell for inferring PINs, lock patterns, and English words under various environmental conditions.

A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required; the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack.

Eye-gaze is an input mode which has the potential of an efficient computer interface. Eye movement has been the focus of research in this area. Non-intrusive eyegaze tracking that allows slight head movement is addressed in this paper. A small 2D mark is employed as a reference to compensate for this movement. The iris center has been chosen for purposes of measuring eye movement. The gaze point is estimated after acquiring the eye movement data. Preliminary experimental results are given through a screen pointing application.

A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.

In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempospatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate. & 2012 Elsevier Ltd. All rights reserved.

Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F1 score of the classification was about 94.0% and the accuracy above 89%. We believe this approach will be helpful for designing a non-intrusive and un-obtrusive eyewear-based ingestive behavior monitoring system.

We present Gazture, a light-weight gaze based real-time gesture control system on commercial tablets. Unlike existing approaches that require dedicated hardware (e.g., high resolution camera), high computation overhead (powerful CPU) or specific user behavior (keeping head steady), Gazture provides gesture recognition based on easy-to-control user gaze input with a small overhead. To achieve this goal, Gazture incorporates a two-layer structure: The first layer focuses on real-time gaze estimation with acceptable tracking accuracy while incurring a small overhead. The second layer implements a robust gesture recognition algorithm while compensating gaze estimation error. To address user posture change while using mobile device, we design a online transfer function based method to convert current eye features into corresponding eye features in reference posture, which then facilitates efficient gaze position estimation. We implement Gazture on Lenovo Tab3 8 Plus tablet with Android 6.0.1, and evaluate its performance in different scenarios. The evaluation results show that Gazture can achieve a high accuracy in gesture recognition while incurring a low overhead.

It is well established that heart rate variability (HRV) plays an important role in social communication. Polyvagal theory suggests that HRV may provide a sensitive marker of one's ability to respond and recognize social cues. The aim of the present study was to directly test this hypothesis. Resting-state HRV was collected and performance on the Reading the Mind in the Eyes Test was assessed in 65 volunteers. HRV was positively associated with performance on this emotion recognition task confirming our hypothesis and these findings were retained after controlling for a variety of confounding variables known to influence HRV - sex, BMI, smoking habits, physical activity levels, depression, anxiety, and stress. Our data suggests that increased HRV may provide a novel marker of one's ability to recognize emotions in humans. Implications for understanding the biological basis of emotion recognition, and social impairment in humans are discussed.


Question: A state-conscious black-box web vulnerability scanner: Enemy of the State.

This paper introduces a comprehensive strategy for enhancing the security of web application code through a combination of static analysis and runtime protection. With the web's growing role in facilitating various transactions, security concerns have become a significant barrier, particularly due to the rise in remotely exploitable vulnerabilities linked to web application flaws. Inspired by the success of verification tools in uncovering vulnerabilities in legacy C programs, we propose a robust approach to secure web applications. Framing web application vulnerabilities as a secure information flow issue, we have developed a lattice-based static analysis algorithm that draws from type systems and typestate. This algorithm is sound and aims to identify vulnerable code sections, which are then equipped with runtime guards, ensuring web application security without requiring user intervention. Through strategic annotations, we minimize runtime overhead, achieving zero overhead under optimal conditions. To validate our approach, we have created a tool, WebSSARI (Web application Security by Static Analysis and Runtime Inspection), and applied it to 230 open-source web application projects from SourceForge.net, chosen for their varying degrees of maturity, popularity, and scale. Our analysis revealed that 69 of these projects contained vulnerabilities. After notifying the respective developers, 38 confirmed our findings and expressed their intentions to release patches. Our data further indicates that static analysis significantly reduces potential runtime overhead by 98.4%, underscoring the efficacy of our integrated security solution.

Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.

While third-party tracking on the web has garnered much attention, its workings remain poorly understood. Our goal is to dissect how mainstream web tracking occurs in the wild. We develop a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state. We run our detection system while browsing the web and observe a rich ecosystem, with over 500 unique trackers in our measurements alone. We find that most commercial pages are tracked by multiple parties, trackers vary widely in their coverage with a small number being widely deployed, and many trackers exhibit a combination of tracking behaviors. Based on web search traces taken from AOL data, we estimate that several trackers can each capture more than 20% of a user’s browsing behavior. We further assess the impact of defenses on tracking and find that no existing browser mechanisms prevent tracking by social media sites via widgets while still allowing those widgets to achieve their utility goals, which leads us to develop a new defense. To the best of our knowledge, our work is the most complete study of web tracking to date.

The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.

Businesses often interact with users via web-browsers and applications on mobile devices, and host services on cloud servers they may not own. Such highly-exposed environments employ white-box cryptography (WBC) for security protection. WBC operates on a security model far different from the traditional black-box model. The modern business world includes large commercial segments in which end-users are directly exposed to business application software hosted on web browsers, mobile phones, web-connected tablets, and an increasing number of other devices: the `internet of things' (IoT). Software applications and their communication activities now dominate much of the commercial world, and there have been countless hacks on such software, and on devices hosting it, with targets as diverse as mobile phones, web browser applications, vehicles, and even refrigerators! The business advantages of deploying computational power near the user encourage software migration to exposed network end-points, but this increasing exposure provides an ever growing attack surface. Here, we discuss goals and challenges of white-box cryptography and emerging approaches in a continual attempt to stay at least one step ahead of the attackers. We list some WBC techniques, both traditional and recent, indicating how they might be incorporated into a WBC AES implementation.

We analyze the problem of designing pattern recognition systems in adversarial settings, under an engineering viewpoint, motivated by their increasing exploitation in security-sensitive applications like spam and malware detection, despite their vulnerability to potential attacks has not yet been deeply understood. We first review previous work and report examples of how a complex system may be evaded either by leveraging on trivial vulnerabilities of its untrained components, e.g., parsing errors in the pre-processing steps, or by exploiting more subtle vulnerabilities of learning algorithms. We then discuss the need of exploiting both reactive and proactive security paradigms complementarily to improve the security by design. Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.

SQL Injection vulnerabilities still exist even after almost two decades that it first appeared. In spite of numerous prevention methodologies being used today, web applications still tend to be vulnerable to SQL injection attacks. Technology has improved drastically over the past few years and computers have certainly brought a great impact on our lifestyle. The computer applications and their usage over the web are myriad. It is quite evident that the in the near future, the usage of computers would relatively be higher than what we are witnessing today. A wide variety of data such as credit information, military data, human communication data, and countless types of data is shared over the far-flung computer networks. As the usage and reliability on computers increase, the threat to sensitive data likewise increases. The challenges with the cyber security when dealing with sensitive information is now a nightmare. To help understand the threats and the severity of exploits deployed, the paper provides proof of concepts for exploits carried out to compromise web applications and how the databases are exploited using the SQL injection methodologies. The SQL injection vulnerabilities in the web applications are surprisingly very vast and this is definitely is a huge security threat to personal data of people that is stored on web. In this paper, the methods used in information gathering, how the security is breached, and how payloads are used to exploit web applications are explained using the Kali Linux. In addition, an analysis is carried out on how the websites are comprised. Advanced methods on how to defend SQL injections are briefly justified. For the readers to understand better, a real time scenario of a penetration tester and a database server is set up with a few suppositions, and the commands that dodge the security characteristics and manipulate the databases are explicated.

In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box testing, we propose a methodology based on machine learning that has shown promising results on a case study.

We present PANDA, an open-source tool that has been purpose-built to support whole system reverse engineering. It is built upon the QEMU whole system emulator, and so analyses have access to all code executing in the guest and all data. PANDA adds the ability to record and replay executions, enabling iterative, deep, whole system analyses. Further, the replay log files are compact and shareable, allowing for repeatable experiments. A nine billion instruction boot of FreeBSD, e.g., is represented by only a few hundred MB. PANDA leverages QEMU's support of thirteen different CPU architectures to make analyses of those diverse instruction sets possible within the LLVM IR. In this way, PANDA can have a single dynamic taint analysis, for example, that precisely supports many CPUs. PANDA analyses are written in a simple plugin architecture which includes a mechanism to share functionality between plugins, increasing analysis code re-use and simplifying complex analysis development. We demonstrate PANDA's effectiveness via a number of use cases, including enabling an old but legitimately purchased game to run despite a lost CD key, in-depth diagnosis of an Internet Explorer crash, and uncovering the censorship activities and mechanisms of an IM client.

Firewalls are the mainstay of enterprise security and the most widely adopted technology for protecting private networks. The quality of protection provided by a firewall directly depends on the quality of its policy (i.e., configuration). Due to the lack of tools for analyzing firewall policies, most firewalls on the Internet have been plagued with policy errors. A firewall policy error either creates security holes that will allow malicious traffic to sneak into a private network or blocks legitimate traffic and disrupts normal business processes, which in turn could lead to irreparable, if not tragic, consequences. A major source of policy errors stem from policy changes. Firewall policies often need to be changed as networks evolve and new threats emerge. In this paper, we first present the theory and algorithms for firewall policy change-impact analysis. Our algorithms take as input a firewall policy and a proposed change, then output the accurate impact of the change. Thus, a firewall administrator can verify a proposed change before committing it.

The consideration of targeted security attacks is not yet common in the automotive domain where primarily safety requirements are considered. Hence this survey addresses the relatively new field of IT-security in automotive technology in order to motivate further research. With the emergence of automotive technologies like car-to-car (c2c) communication, the challenges increase. In order to show the necessity for such research we describe how in theory, a potential attack on the Electronic Throttle Control (ETC) could succeed, if no countermeasures are taken, with potentially drastic consequences on the safety of the vehicle, its occupants and its environment as a result. Also an attack on the power window system is shown using the implementation techniques already employed in the design of automotive components. Our goal is to show, that such exploited security vulnerabilities are likely to have serious consequences with respect to the vehicles safety. Therefore such attacks need to be considered and suitable countermeasures have to be identified. In order to achieve this, the connection of the two disciplines Safety and Security for automotive IT components needs to be discussed. We introduce two exemplary attacks, the Trojan Horse targeting at the Electronic Throttle Control System (ETC) and a replay attack on the electric window lift. The first attack is mainly discussed in theory, the second is elaborated in a practical simulation scenario. Finally we introduce a general investigation of potential ways of intrusion and present some first practical results from tests with current automotive hardware.


Question: A state-conscious black-box web vulnerability scanner: An adversary of hidden security flaws.

This document outlines an enhanced method for penetration testing, focusing on improved input vector identification to bolster web application security. Penetration testing is a critical practice for identifying potential vulnerabilities by mimicking attacks from unauthorized users. Central to this process is the accurate identification of input vectors—the points where an attack can be initiated—and the subsequent analysis of attack outcomes to determine their success. However, existing methods for pinpointing input vectors and assessing attack results are often inconsistent and incomplete, potentially leaving parts of the application unexamined and vulnerabilities undetected. To address these shortcomings, we present a novel penetration testing strategy that incorporates two cutting-edge analysis techniques. The first technique is employed to systematically identify all possible input vectors within a web application, ensuring comprehensive coverage. The second technique automates the detection of whether an attack has led to an injection, enhancing the efficiency and accuracy of the testing process. To substantiate the effectiveness of our proposed method, we conduct a comparative analysis against a leading alternative technique. The empirical results confirm that our approach yields a more exhaustive penetration testing regimen, uncovering a greater number of security vulnerabilities in the process.

Rogue devices are an increasingly dangerous reality in the insider threat problem domain. Industry, government, and academia need to be aware of this problem and promote state-of-the-art detection methods.

Over the past decade, the need to build secure software has become a dominant goal in software development. Consequently, software researchers and practitioners have identified ways that malicious users can exploit software and how developers can fix the vulnerabilities. They have also built a variety of source code security checking software applications to partially automate the task of performing a security analysis of a program. Although great advances have been made in this area, the core problem of how the security vulnerabilities occur still exists. An answer to this problem could be a paradigm shift from imperative to functional programming techniques. This may hold the key to removing software vulnerabilities altogether.

Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.

A intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100’s of users on 1000’s of hosts. More than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data. Six research groups participated in a blind evaluation and results were analyzed for probe, denialof-service (DoS), remote-to-local (R2L), and user to root (U2R) attacks. The best systems detected old attacks included in the training data, at moderate detection rates ranging from 63% to 93% at a false alarm rate of 10 false alarms per day. Detection rates were much worse for new and novel R2L and DoS attacks included only in the test data. The best systems failed to detect roughly half these new attacks which included damaging access to root-level privileges by remote users. These results suggest that further research should focus on developing techniques to find new attacks instead of extending existing rule-based

In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.

In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-TermMemory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and furthermining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.

The ability to trap the execution of a binary program at desired instructions is essential in many security scenarios such as malware analysis and attack provenance. However, an increasing percent of both malicious and legitimate programs are equipped with anti-debugging and anti-instrumentation techniques, which render existing debuggers and instrumentation tools inadequate. In this paper, we present Spider, a stealthy program instrumentation framework which enables transparent, efficient and flexible instruction-level trapping based on hardware virtualization. Spider uses invisible breakpoint, a novel primitive we develop that inherits the efficiency and flexibility of software breakpoint, and utilizes hardware virtualization to hide its side-effects from the guest. We have implemented a prototype of Spider on KVM. Our evaluation shows that Spider succeeds in remaining transparent against state-of-the-art anti-debugging and anti-instrumentation techniques; the overhead of invisible breakpoint is comparable with traditional hardware breakpoint. We also demonstrate Spider's usage in various security applications.

Many algorithms can take a variable amount of time to complete depending on the data being processed. These timing differences can sometimes disclose confidential information. Indeed, researchers have been able to reconstruct an RSA private key purely by querying an SSL Web server and timing the results. Our work analyzes the limits of attacks based on accurately measuring network response times and jitter over a local network and across the Internet. We present the design of filters to significantly reduce the effects of jitter, allowing an attacker to measure events with 15-100μs accuracy across the Internet, and as good as 100ns over a local network. Notably, security-related algorithms on Web servers and other network servers need to be carefully engineered to avoid timing channel leaks at the accuracy demonstrated in this article.

An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting ("in the box"), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out of the box"). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the "in the box" approach, thus leading to a technical challenge known as the semantic gap.
 In this paper, we present the design, implementation, and evaluation of VMwatcher - an "out-of-the-box" approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to systematically reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM from the outside in a non-intrusive manner. Specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. With the semantic gap bridged, we identify two unique malware detection capabilities: (1) view comparison-based malware detection and its demonstration in rootkit detection and (2) "out-of-the-box" deployment of host-based anti-malware software with improved detection accuracy and tamper-resistance. We have implemented a proof-of-concept prototype on both Linux and Windows platforms and our experimental results with real-world malware, including elusive kernel-level rootkits, demonstrate its practicality and effectiveness.

This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.We implemented the DIDUCE system for Java programs and applied it to four programs of significant size and complexity. DIDUCE succeeded in identifying the root causes of programming errors in each of the programs quickly and automatically. In particular, DIDUCE is effective in isolating a timing-dependent bug in a released JSSE (Java Secure Socket Extension) library, which would have taken an experienced programmer days to find. Our experience suggests that detecting and checking program invariants dynamically is a simple and effective methodology for debugging many different kinds of program errors across a wide variety of application domains.