Question: Leveraging automated test case generators for GUI testing in the industry context.

This paper offers an extensive review of the leading methods for automated software test case generation, a critical and time-consuming aspect of software testing that significantly influences its effectiveness and efficiency. Due to its importance, this topic has been a focal point for extensive research, leading to the development of various strategies and tools. The paper comprehensively covers the most significant techniques for automatic software test case generation, with each method detailed in a separate section. The methodologies explored include: (a) structural testing through symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its advanced form, adaptive random testing, and (e) search-based testing. Each section is authored by leading experts in the respective technique, providing an in-depth look at the foundational concepts, the latest advancements, open research questions, and a critical analysis of search-based software testing. This orchestrated survey serves as a valuable resource for researchers and practitioners seeking to understand and implement automated test case generation in the field of software testing.

Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.

Surprising as it may seem, many of the early adopters of the object-oriented paradigm already face a number of problems typically encountered in large-scale legacy systems. Consequently, reverse engineering techniques are relevant in an object-oriented context as well. This paper investigates a hybrid approach, combining the immediate appeal of visualisations with the scalability of metrics. We validate such a hybrid approach by showing how CodeCrawler —the experimental platform we built— allowed us to understand the program structure of, and identify potential design anomalies in a public domain software system.

A tutorial introduction to designs in unifying theories of programming p. 40 An integration of program analysis and automated theorem proving p. 67 Verifying controlled components p. 87 Efficient CSP[subscript Z] data abstraction p. 108 State/event-based software model checking p. 128 Formalising behaviour trees with CSP p. 148 Generating MSCs from an integrated formal specification language p. 168 UML to B : formal verification of object-oriented models p. 187 Software verification with integrated data type refinement for integer arithmetic p. 207

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.

We introduce interaction proxies as a strategy for runtime repair and enhancement of the accessibility of mobile applications. Conceptually, interaction proxies are inserted between an application's original interface and the manifest interface that a person uses to perceive and manipulate the application. This strategy allows third-party developers and researchers to modify an interaction without an application's source code, without rooting the phone, without otherwise modifying an application, while retaining all capabilities of the system (e.g., Android's full implementation of the TalkBack screen reader). This paper introduces interaction proxies, defines a design space of interaction re-mappings, identifies necessary implementation abstractions, presents details of implementing those abstractions in Android, and demonstrates a set of Android implementations of interaction proxies from throughout our design space. We then present a set of interviews with blind and low-vision people interacting with our prototype interaction proxies, using these interviews to explore the seamlessness of interaction, the perceived usefulness and potential of interaction proxies, and visions of how such enhancements could gain broad usage. By allowing third-party developers and researchers to improve an interaction, interaction proxies offer a new approach to personalizing mobile application accessibility and a new approach to catalyzing development, deployment, and evaluation of mobile accessibility enhancements.

We survey promising directions for research in the area of formal methods and its applications, including fundamental questions about the combination of formal methods and machine learning, and applications to cyberphysical systems and education.

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks.
 We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.

A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of system rules. The cornerstone of our approach is inferring programmer “beliefs” that we then cross-check for contradictions. Beliefs are facts implied by code: a dereference of a pointer, p, implies a belief that p is non-null, a call to “unlock(l)” implies that l was locked, etc. For beliefs we know the programmer must hold, such as the pointer dereference above, we immediately flag contradictions as errors. For beliefs that the programmer may hold, we can assume these beliefs hold and use a statistical analysis to rank the resulting errors from most to least likely. For example, a call to “spin lock” followed once by a call to “spin unlock” implies that the programmer may have paired these calls by coincidence. If the pairing happens 999 out of 1000 times, though, then it is probably a valid belief and the sole deviation a probable error. The key feature of this approach is that it requires no a priori knowledge of truth: if two beliefs contradict, we know that one is an error without knowing what the correct belief is. Conceptually, our checkers extract beliefs by tailoring rule “templates” to a system – for example, finding all functions that fit the rule template “<a> must be paired with <b>.” We have developed six checkers that follow this conceptual framework. They find hundreds of bugs in real systems such as Linux and OpenBSD. From our experience, they give a dramatic reduction in the manual effort needed to check a large system. Compared to our previous work [9], these template checkers find ten to one hundred times more rule instances and derive properties we found impractical to specify manually.

Context: Several usability guidelines have been proposed to improve the usability of smartphone apps. These guidelines can be classified into three disjoint sets: platform specific guidelines, genre specific guidelines, and generic guidelines. However, smartphone applications are usually developed for multiple platforms and targeted for a variety of users. Hence the usefulness of existing guidelines is severally limited. Objective: This study aims to develop a comprehensive list of usability guidelines suitable for multiple platforms and genres of smartphone applications.

to Abstract This paper discusses practical issues involved in app ing logic built-in self-test (BIST) to four large industria designs. These multi-clock designs, ranging in size fr 200K to 800K gates, pose significant challenges to log BIST methodology, flow, and tools. The paper presents process of generating a BIST-compliant core along with t logic BIST controller for at-speed testing. Comparativ data on fault grades and area overhead between autom test pattern generation (ATPG) and logic BIST a reported. The experimental results demonstrate that w automation of the proposed solutions, logic BIST c achieve test quality approaching that of ATPG with min mal area overhead and few changes to the design flow.


Question: Utilizing automated test case generators for GUI testing in the industry setting.

Introducing Pex: Automated White Box Test Generation for .NET Frameworks

Pex is a groundbreaking tool that automatically generates a compact test suite capable of achieving high code coverage for .NET programs. This is accomplished through a sophisticated analysis technique known as dynamic symbolic execution, which bears a resemblance to path-bounded model checking. Pex meticulously monitors execution traces to understand the program's behavior and leverages a constraint solver to craft novel test inputs that explore a variety of program states. The outcome is an automatically generated, succinct test suite that frequently attains impressive code coverage levels.

In a notable case study, Pex was applied to a critical component of the .NET runtime, a module that had undergone extensive testing over multiple years. Despite this prior scrutiny, Pex was able to uncover previously undetected bugs, including a significant issue that could have had far-reaching consequences. This demonstrates the efficacy of Pex in enhancing the reliability and robustness of .NET software through its advanced automated testing capabilities.

Software inspections, reviews, and walkthroughs have become a standard process component in many software development domains. Maturity level 3 of the CMM-I requires establishment of peer reviews [12] and substantial sustained improvements in quality and productivity have been reported as a result of using reviews ([16], [21], [22], [27]). The NSF Impact project identifies the degree to which these industrial success cases have been instigated and improved by research in software engineering.
 This research identifies that there is widespread adoption of inspections, reviews or walkthroughs but that companies do not generally exploit their full potential. However there exist sustained industrial success cases with respect to the wide-spread and measurably successful application of them. It also identifies research in software engineering that can be credibly documented as having influenced the industrial success cases. Credible documentation may exist in the form of publications or documented reports by witnesses. Due to the semi-formal nature of inspections, reviews, and walkthroughs, a specific focus is given to empirical research results as motivators for adoption. Through the examination of one detailed case study, it is shown that software engineering research has had a significant impact on practice and that the impact can be traced in this case from research to that practice. The case study chosen provides evidence of both success and failure regarding sustained application in practice.
 Thus the analysis of historic impact chains of research reveals a clear impact of software engineering research on sustained industrial success for inspections, reviews and walkthroughs. More importantly, in impact chains where the empirical results have not been established, we conclude that success has not been achieved or has not been sustained.
 The paper closes with (1) lessons learned for creating the sustained use and impact of semi-formal software engineering processes, (2) a request for researchers and practitioners to further consider how their work can improve the effectiveness of research and practice, and (3) a request to contribute additional success cases and impact factors to the authors database for future enhancements of this paper.

A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital issue in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of projects, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies on the influence the most important problem attributes have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management. 2007 Elsevier Inc. All rights reserved.

Today’s applications such as ubiquitous systems are more and more aware of user’s habits and the context of use. The features of products and the context of use will affect the human’s experiences and preferences about the use of device. Thus, user experience in user-product interaction has been regarded as an important research topic in the mobile application design area. The purpose of this paper is to clarify how user experience can be evaluated in adaptive mobile applications. The user experience evaluations were performed through interviews and observation while test users were using PDA-based adaptive mobile application prototypes. As a result, this paper presents the analysis of the test methods for further user experience evaluations. CR Categories: J.m [Computer Applications]: Miscellaneous; Experimentation; Human Factors

Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixes to propose fixes of current/future bugs. Specifically, we propose Ratchet, a corrective patch generation system using neural machine translation. By learning corresponding pre-correction and post-correction code in past fixes with a neural sequence-to-sequence model, Ratchet is able to generate a fix code for a given bug-prone code query. We perform an empirical study with five open source projects, namely Ambari, Camel, Hadoop, Jetty and Wicket, to evaluate the effectiveness of Ratchet. Our findings show that Ratchet can generate syntactically valid statements 98.7% of the time, and achieve an F1-measure between 0.41-0.83 with respect to the actual fixes adopted in the code base. In addition, we perform a qualitative validation using 20 participants to see whether the generated statements can be helpful in correcting bugs. Our survey showed that Ratchet’s output was considered to be helpful in fixing the bugs on many occasions, even if fix was not 100%

A tutorial introduction to designs in unifying theories of programming p. 40 An integration of program analysis and automated theorem proving p. 67 Verifying controlled components p. 87 Efficient CSP[subscript Z] data abstraction p. 108 State/event-based software model checking p. 128 Formalising behaviour trees with CSP p. 148 Generating MSCs from an integrated formal specification language p. 168 UML to B : formal verification of object-oriented models p. 187 Software verification with integrated data type refinement for integer arithmetic p. 207

The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.

In the past, most studies used the technology acceptance model (TAM) to survey the subjective perception of users in using information technology. The usability test was also used to assess the ease of use of user interfaces. This study introduces a conceptual framework to explore the relationship between user’s beliefs of TAM and usability testing attributes. Usability testing was conducted on an eCampus learning system with a mobile device. TAM data was collected from the participants for analyzing a possible relationship. The findings of this study reveal that TAM results contradict the usability test results in certain areas. The focus of our proposed research model is supported from the causality between perceived ease of use and usability; however, the correlation between perceived usefulness and usability remains unclear.

Multicore hardware is making concurrent programs pervasive. Unfortunately, concurrent programs are prone to bugs. Among different types of concurrency bugs, atomicity violation bugs are common and important. Existing techniques to detect atomicity violation bugs suffer from one limitation: requiring bugs to manifest during monitored runs, which is an open problem in concurrent program testing.
 This paper makes two contributions. First, it studies the interleaving characteristics of the common practice in concurrent program testing (i.e., running a program over and over) to understand why atomicity violation bugs are hard to expose. Second, it proposes CTrigger to effectively and efficiently expose atomicity violation bugs in large programs. CTrigger focuses on a special type of interleavings (i.e., unserializable interleavings) that are inherently correlated to atomicity violation bugs, and uses trace analysis to systematically identify (likely) feasible unserializable interleavings with low occurrence-probability. CTrigger then uses minimum execution perturbation to exercise low-probability interleavings and expose difficult-to-catch atomicity violation.
 We evaluate CTrigger with real-world atomicity violation bugs from four sever/desktop applications (Apache, MySQL, Mozilla, and PBZIP2) and three SPLASH2 applications on 8-core machines. CTrigger efficiently exposes the tested bugs within 1--235 seconds, two to four orders of magnitude faster than stress testing. Without CTrigger, some of these bugs do not manifest even after 7 full days of stress testing. In addition, without deterministic replay support, once a bug is exposed, CTrigger can help programmers reliably reproduce it for diagnosis. Our tested bugs are reproduced by CTrigger mostly within 5 seconds, 300 to over 60000 times faster than stress testing.

In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media response analysis and claim that even using a simple query-based extractive approach can dramatically save the processing time of the employees without significantly reducing the quality of their work.

Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).


Question: Utilizing automated test case generators for GUI testing in the industry setting.

This paper introduces SMART (Systematic Modular Automated Random Testing), an innovative approach to dynamic test generation that overcomes the scalability challenges faced by traditional methods when applied to large-scale, complex programs. Inspired by interprocedural static analysis techniques, SMART extends the concept of Directed Automated Random Testing (DART), which integrates dynamic test generation with model checking to execute program paths comprehensively and detect runtime errors. However, DART's exhaustive exploration of all possible paths becomes impractical for extensive programs. SMART addresses this issue by employing a compositional strategy, testing functions in isolation and then summarizing test outcomes as preconditions and postconditions. These summaries are utilized when evaluating higher-level functions, ensuring comprehensive coverage without the exponential growth in execution time. SMART is shown to be sound and complete compared to DART, meaning it can achieve the same level of path coverage but with significantly reduced computational demands. For programs with a bounded number of feasible paths per function, SMART's execution count grows linearly with the bound, in stark contrast to DART's exponential increase. The paper provides illustrative examples of C programs and preliminary experimental data that substantiate the theoretical advantages of SMART's compositional dynamic test generation approach.

Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.

Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the softwore. A regression test selection technique choses, from an existing test set, thests that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a precedure or program and its modified version and use these graphs to select tests that execute changed code from the original test suite. We prove that, under certain conditions, the set of tests our technique selects includes every test from the original test suite that con expose faults in the modified procedfdure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at lease as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modifications. We have implemented our algorithms; initial empirical studies indicate that our technique can significantly reduce the cost of regression testing modified software.

Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.

This paper describes the genesis of Gualzru, a robot commissioned by a large Spanish technological company to provide advertisement services in open public spaces. Gualzru has to stand by at an interactive panel observing the people passing by and, at some point, select a promising candidate and approach her to initiate a conversation. After a small verbal interaction, the robot is supposed to convince the passerby to walk back to the panel, leaving the rest of the selling task to an interactive software embedded in it. The whole design and building process took less than three years of team composed of five groups at different geographical locations. We describe here the lessons learned during this period of time, from different points of view including the hardware, software, architectural decisions and team collaboration issues.

Software defects due to coding errors continue to plague the industry with disastrous impact, especially in the enterprise application software category. Identifying how much of these defects are specifically due to coding errors is a challenging problem. Defect prevention is the most vivid but usually neglected aspect of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a high quality product. In order to reduce the time and cost, we will focus on finding the total number of defects if the test case shows that the software process not executing properly. That has occurred in the software development process. The proposed system classifying various defects using decision tree based defect classification technique, which is used to group the defects after identification. The classification can be done by employing algorithms such as ID3 or C4.5 etc. After the classification the defect patterns will be measured by employing pattern mining technique. Finally the quality will be assured by using various quality metrics such as defect density, etc. The proposed system will be implemented in JAVA.

Automated program repair (APR) has attracted great research attention, and various techniques have been proposed. Search-based APR is one of the most important categories among these techniques. Existing researches focus on the design of effective mutation operators and searching algorithms to better find the correct patch. Despite various efforts, the effectiveness of these techniques are still limited by the search space explosion problem. One of the key factors attribute to this problem is the quality of fault spaces as reported by existing studies. This motivates us to study the importance of the fault space to the success of finding a correct patch. Our empirical study aims to answer three questions. Does the fault space significantly correlate with the performance of search-based APR? If so, are there any indicative measurements to approximate the accuracy of the fault space before applying expensive APR techniques? Are there any automatic methods that can improve the accuracy of the fault space? We observe that the accuracy of the fault space affects the effectiveness and efficiency of search-based APR techniques, e.g., the failure rate of GenProg could be as high as 60% when the real fix location is ranked lower than 10 even though the correct patch is in the search space. Besides, GenProg is able to find more correct patches and with fewer trials when given a fault space with a higher accuracy. We also find that the negative mutation coverage, which is designed in this study to measure the capability of a test suite to kill the mutants created on the statements executed by failing tests, is the most indicative measurement to estimate the efficiency of search-based APR. Finally, we confirm that automated generated test cases can help improve the accuracy of fault spaces, and further improve the performance of search-based APR techniques. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. c © 2017 ACM. ISBN 978-1-4503-2138-9. DOI: 10.1145/1235 CCS Concepts •Software and its engineering→Automatic programming; Empirical software validation;

A tutorial introduction to designs in unifying theories of programming p. 40 An integration of program analysis and automated theorem proving p. 67 Verifying controlled components p. 87 Efficient CSP[subscript Z] data abstraction p. 108 State/event-based software model checking p. 128 Formalising behaviour trees with CSP p. 148 Generating MSCs from an integrated formal specification language p. 168 UML to B : formal verification of object-oriented models p. 187 Software verification with integrated data type refinement for integer arithmetic p. 207

Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all testtakers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.

e emerging blockchain technology supports decentralized computing paradigm shi and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of soware, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specic languages and its testability. ere have been high-prole incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating nancial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. is implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specic goal to more readily realize the application of blockchain smart contracts in security and privacy, we should rst understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specic programming language, Solidity, to provide the rst body of knowledge for creating more secure blockchainbased soware. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Game developers are facing an increasing demand for new games every year. Game development tools can be of great help, but require highly specialized professionals. Also, just as any software development effort, game development has some challenges. Model-Driven Game Development (MDGD) is suggested as a means to solve some of these challenges, but with a loss in flexibility. We propose a MDGD approach that combines multiple domain-specific languages (DSLs) with design patterns to provide flexibility and allow generated code to be integrated with manual code. After experimentation, we observed that, with the approach, less experienced developers can create games faster and more easily, and the product of code generation can be customized with manually written code, providing flexibility. However, with MDGD, developers become less familiar with the code, making manual codification more difficult.


Question: Leveraging automated test case generators for GUI testing in the industry context.

This paper explores the landscape of software testing research, highlighting its achievements, challenges, and future aspirations. Software engineering encompasses various disciplines aimed at preventing and correcting faults to ensure proper system functionality. Testing, a widely used validation technique in the industry, has been historically characterized by its ad hoc nature, high costs, and variable effectiveness. Given its broad scope across the development lifecycle, software testing research confronts a multitude of challenges. The paper proposes a roadmap to address these challenges, anchored in past milestones and directed towards four ambitious but elusive goals. Connecting these achievements to the aspirational objectives are a series of significant research challenges, which are explored in detail alongside promising current work. The focus is on advancing automated test case generators for graphical user interface (GUI) testing in industrial settings, recognizing the critical role of GUI testing in software quality assurance. The paper discusses how automated test case generators can enhance GUI testing efficiency and reliability, thereby addressing the challenges of cost, time, and effectiveness that have traditionally plagued the testing domain. Through an examination of current research and practical applications, the paper aims to bridge the gap between software testing achievements and the dream of fully automated, effective, and cost-efficient GUI testing solutions.

The software industry has invested the substantial effort to improve the quality of its products like ISO, CMMI and TMMI. Although applying of TMMI maturity criteria has a positive impact on product quality, test engineering productivity, and cycle-time effort. But it requires heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with it. At the same time, agile methods deal with changing requirements and continuous delivery of valuable software by short time iterations. So that the aim of this paper is to improve the testing process by applying a detailed mapping between TMMI and Scrum practices and verifying this mapping with a study providing empirical evidence of the obtained results. The research has been evaluated on a sample of 2 large-sized TMMI certified companies. In conclusion, the experimental results show the effectiveness of using this integrated approach compared to other existing approaches. Keywords—Agile software development; Scrum; TMMI; Software Testing; CMMI

Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all testtakers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.

This paper discusses some aspects of testing random and pseudorandom number generators. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications (NIST Special Publication) is presented. This paper also demonstrates the software implementation of the test suite with output protocols and presents experiences from testing some random and pseudorandom generators.

Programming foundations is not an easy subject to be taught — many students have difficulties understanding the abstract concepts of programming and have a wrong view about the programming activity. In order to address these problems, experiences have suggested the integrated teaching of programming concepts and software testing in introductory CS courses. Shortly, the idea is that testing can contribute to enhance the students' capabilities of understanding and analysis. However, such perspective requires tools to provide an adequate feedback to evaluate the students' performance concerning programming and testing activities. In this paper we describe ProgTest — a web-based tool for the submission and automatic evaluation of practical programming assignments based on testing activities. Results from a preliminary validation of ProgTest are also presented. Such results provide evidences on the practical use of ProgTest as a supporting mechanism for the integrated teaching of programming foundations and software testing.

Editor: Peter Anderson Rochester Institute of Technology 1 Lomb Memorial Dr. Rochester, NY 14623 pga@cs.rit.edu Each technique receives similar coverage, including a description of its new and prerequisite vocabulary, its model, the bug assumptlons that underlie its effectiveness, typical applications, examples (almost all based on standard IRS tax forms), caveats and limitations, automation and tools for the technique, a self-evaluation quiz, and exercises. Common threads in the presentation show the role

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.

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.

We present Cicero, a mixed-initiative application for prototyping two-dimensional sprite-based games across different genres such as shooters, puzzles, and action games. Cicero provides a host of features which can offer assistance in different stages of the game development process. Noteworthy features include AI agents for gameplay simulation, a game mechanics recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system that allows searching for particular interaction patterns. In order to evaluate the efficacy and usefulness of the different features of Cicero, we conducted a user study in which we compared how users perform in game debugging tasks with different kinds of assistance.

ÐPairwise testing is a specification-based testing criterion, which requires that for each pair of input parameters of a system, every combination of valid values of these two parameters be covered by at least one test case. In this paper, we propose a new test generation strategy for pairwise testing.

Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NPhard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.


Question: Using deep residual networks for foreground segmentation in surveillance videos to detect anomalies.

This paper presents DeepAnomaly, a novel approach that integrates background subtraction techniques with deep learning to identify obstacles and anomalies within agricultural fields, particularly effective in surveillance scenarios. Convolutional neural networks (CNNs) have become prevalent in autonomous vehicle technology for obstacle detection. However, these systems struggle with distant or heavily occluded objects and lack the capability to detect novel object types or atypical situations. Given the uniform visual characteristics of agricultural landscapes and the distinct appearance of anomalies such as people, animals, and other foreign objects, DeepAnomaly leverages this homogeneity to perform anomaly detection. We showcase DeepAnomaly's superior performance as a rapid, state-of-the-art detector for anomalies that are far, heavily obscured, or previously unseen. DeepAnomaly is benchmarked against leading obstacle detection algorithms, including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a specific human detection scenario, DeepAnomaly outperforms RCNN, identifying humans at greater distances (45-90 meters) than RCNN can manage. While RCNN performs comparably at shorter ranges (0-30 meters), DeepAnomaly boasts significantly fewer model parameters and a much faster processing time per image (182 ms vs. 25 ms, a 7.28 times speedup). Unlike many CNN-based methods, DeepAnomaly's high accuracy, low computational requirements, and minimal memory usage make it highly suitable for real-time systems operating on embedded GPUs (Graphics Processing Units).

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified endto-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time).

Semantic object segmentation (SOS) is a challenging task in computer vision that aims to detect and segment all pixels of the objects within predefined semantic categories. In image-based SOS, many supervised models have been proposed and achieved impressive performances due to the rapid advances of well-annotated training images and machine learning theories. However, in video-based SOS it is often difficult to directly train a supervised model since most videos are weakly annotated by tags. To handle such tagged videos, this paper proposes a novel approach that adopts a segmentation-by-detection framework. In this framework, object detection and segment proposals are first generated using the models pre-trained on still images, which provide useful cues to roughly localize the semantic objects. Based on these proposals, we propose an efficient algorithm to initialize object tracks by solving a joint assignment problem. As such tracks provide rough spatiotemporal configurations of the semantic objects, a voting-based refinement algorithm is further proposed to improve their spatiotemporal consistency. Extensive experiments demonstrate that the proposed framework can robustly and effectively segment semantic objects in tagged videos, even when the image-based object detectors provide inaccurate proposals. On various public benchmarks, the proposed approach obtains substantial improvements over the state-of-the-arts.

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “small metallic threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).

Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.

Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 32×32 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.

Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.

We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms. 2005 Elsevier B.V. All rights reserved.

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF [9] and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT [33]). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG [6]) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.

Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.

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.


Question: Using deep residual networks for foreground segmentation in surveillance videos for anomaly detection.

In this study, we delve into the mechanisms underlying deep residual networks [1], a class of highly sophisticated architectures known for their superior accuracy and favorable convergence properties. Our analysis focuses on the propagation dynamics within the residual building blocks, revealing that forward and backward signals can be seamlessly transmitted from one block to another, provided identity mappings are employed as skip connections and post-addition activation is utilized. A series of experimental evaluations underscores the significance of these identity mappings. Inspired by these findings, we introduce an enhanced residual unit that facilitates easier training and enhances generalization capabilities. We showcase enhanced outcomes using a 1001-layer ResNet on CIFAR-10 (attaining a 4.62% error rate) and CIFAR-100, as well as a 200-layer ResNet on ImageNet. For reproducibility and further experimentation, the source code is accessible at: https://github.com/KaimingHe/resnet-1k-layers.

**Question**: A Comparative Study on Feature Selection Techniques for Text Classification

**Documents**: A Comparative Study of Feature Selection Techniques for Text Classification
In this paper, we present a comparative study of feature selection techniques for text classification. We compare four different feature selection techniques: chi-square, mutual information, information gain, and document frequency. We conduct experiments on three different datasets: Reuters-21578, 20 Newsgroups, and WebACE. We use the Naive Bayes classifier for classification. The results show that the chi-square feature selection technique outperforms the other techniques in terms of accuracy on the Reuters-21578 dataset. On the 20 Newsgroups dataset, the information gain feature selection technique performs the best. On the WebACE dataset, the document frequency feature selection technique performs the best. We also analyze the effect of the number of features selected on the classification accuracy. The results show that the classification accuracy increases as the number of features selected increases, but the increase in accuracy becomes smaller as the number of features selected increases.

**Similar Documents**: This paper offers a comprehensive comparison of various feature selection techniques for the purpose of text classification. We evaluate and contrast four prominent methods: chi-square, mutual information, information gain, and document frequency. Our experimental analysis is conducted across three distinct datasets: Reuters-21578, 20 Newsgroups, and WebACE, using the Naive Bayes classifier for classification tasks. The findings reveal that on the Reuters-21578

Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our article is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, for example, captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation in both 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer. We demonstrate how the results of our method can be used for considerably improving the speed and quality of image-based 3D reconstruction algorithms, and we compare our results to state-of-the-art segmentation and multiview stereo methods.

Recently the use of video surveillance systems is widely increasing. Different places are equipped by camera surveillances such as hospitals, schools, airports, museums and military places in order to ensure the safety and security of the persons and their property. Therefore it becomes significant to guarantee the proper working of these systems. Intelligent video surveillance systems equipped by sophisticated digital camera can analyze video information's and automatically detect doubtful actions. The camera tampering detection algorithms may indicate that accidental or suspicious activities have occurred and that causes the abnormality works of the video surveillance. It uses several techniques based on image processing and computer vision. In this paper, comparative study of performance of four algorithms that can detect abnormal disturbance for video surveillance is presented also a new proposed method is exposed.

We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.

We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report 79.0% accuracy on the Kinetics dataset without using any pre-training, largely surpassing the previous best results of this kind. On AVA action detection we achieve a new stateof-the-art of 28.3 mAP. Code will be made publicly available.

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.

Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects’ motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-Fish.

Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies.

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.


Question: Using deep residual networks for foreground segmentation in surveillance videos for anomaly detection.

This paper introduces SegNet, a deep convolutional encoder-decoder architecture specifically designed for semantic pixel-wise segmentation tasks. The SegNet architecture comprises an encoder network, a corresponding decoder network, and a pixel-wise classification layer. The encoder network mirrors the topological structure of the 13 convolutional layers found in the VGG16 network. The decoder's function is to upscale the low-resolution feature maps generated by the encoder to the original input resolution, facilitating pixel-level classification. SegNet's innovation lies in its unique upsampling technique; the decoder utilizes pooling indices obtained during the max-pooling phase of the encoder to perform non-linear upsampling. This approach obviates the need for learning the upsampling process. The resulting sparse feature maps are then convolved with trainable filters to generate dense maps. We evaluate SegNet against prominent architectures such as FCN, DeepLab-LargeFOV, and DeconvNet, highlighting the trade-offs between memory usage and accuracy. SegNet was developed with scene understanding applications in mind, focusing on both memory efficiency and computational speed during inference. It boasts a significantly reduced number of trainable parameters compared to its competitors and can be trained end-to-end using stochastic gradient descent. Quantitative benchmarks on road scenes and SUN RGB-D indoor segmentation tasks demonstrate SegNet's high performance, competitive inference time, and superior memory efficiency. To facilitate further research and application, we provide a Caffe implementation of SegNet and an interactive web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.

We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.

We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report 79.0% accuracy on the Kinetics dataset without using any pre-training, largely surpassing the previous best results of this kind. On AVA action detection we achieve a new stateof-the-art of 28.3 mAP. Code will be made publicly available.

Privacy protection from unwanted video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

Traditional approaches for action detection use trimmed data to learn sophisticated action detector models. Although these methods have achieved great success at detecting human actions, we argue that huge information is discarded when ignoring the process, through which this trimmed data is obtained. In this paper, we propose Action Search, a novel approach that mimics the way people annotate activities in video sequences. Using a Recurrent Neural Network, Action Search can efficiently explore a video and determine the time boundaries during which an action occurs. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently but also accurately find human activities, outperforming stateof-the-art methods.

This paper introduces training of shadow detectors under the large-scale dataset paradigm. This was previously impossible due to the high cost of precise shadow annotation. Instead, we advocate the use of quickly but imperfectly labeled images. Our novel label recovery method automatically corrects a portion of the erroneous annotations such that the trained classifiers perform at state-of-the-art level. We apply our method to improve the accuracy of the labels of a new dataset that is 20 times larger than existing datasets and contains a large variety of scenes and image types. Naturally, such a large dataset is appropriate for training deep learning methods. Thus, we propose a semantic-aware patch level Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information. This means that the detected shadow patches are refined based on image semantics. Our proposed pipeline can be a useful baseline for future advances in shadow detection.

Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.

Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.