Question: Evolutionary shifts in the connection between grammar and vocabulary.

This paper explores a computational perspective on a segment of the lexical acquisition process encountered by children, focusing specifically on the acquisition of word-to-meaning associations. Initially, this task is framed as a formal mathematical challenge. Subsequently, an algorithm is introduced and implemented to address this challenge, with its operation exemplified through a small-scale illustration. This algorithm offers a concrete interpretation of the conceptual frameworks of cross-situational learning and the principle of contrast, as applied to words within an utterance. Notably, it is capable of effectively learning a lexicon with homonyms, even when faced with noisy multi-word input, in environments with referential ambiguity, and without relying on language-specific prior knowledge. Through computational simulations, the robustness of this algorithm is showcased, alongside its potential to tackle lexical acquisition challenges of a scale similar to those encountered by children. These simulations are conducted under stringent, worst-case assumptions regarding the nature and volume of available data, demonstrating the algorithm's capability to operate under such conditions.

In this paper, we discuss the related information theoretical association measures of mutual information and pointwise mutual information, in the context of collocation extraction. We introduce normalized variants of these measures in order to make them more easily interpretable and at the same time less sensitive to occurrence frequency. We also provide a small empirical study to give more insight into the behaviour of these new measures in a collocation extraction setup.

The comprehension of visually presented sentences produces brain activation that increases with the linguistic complexity of the sentence. The volume of neural tissue activated (number of voxels) during sentence comprehension was measured with echo-planar functional magnetic resonance imaging. The modulation of the volume of activation by sentence complexity was observed in a network of four areas: the classical left-hemisphere language areas (the left laterosuperior temporal cortex, or Wernicke's area, and the left inferior frontal gyrus, or Broca's area) and their homologous right-hemisphere areas, although the right areas had much smaller volumes of activation than did the left areas. These findings generally indicate that the amount of neural activity that a given cognitive process engenders is dependent on the computational demand that the task imposes.

This paper presents a computational study of part of the lexical-acquisition task faced by children, namely the acquisition of word-to-meaning mappings. It first approximates this task as a formal mathematical problem. It then presents an implemented algorithm for solving this problem, illustrating its operation on a small example. This algorithm offers one precise interpretation of the intuitive notions of cross-situational learning and the principle of contrast applied between words in an utterance. It robustly learns a homonymous lexicon despite noisy multi-word input, in the presence of referential uncertainty, with no prior knowledge that is specific to the language being learned. Computational simulations demonstrate the robustness of this algorithm and illustrate how algorithms based on cross-situational learning and the principle of contrast might be able to solve lexical-acquisition problems of the size faced by children, under weak, worst-case assumptions about the type and quantity of data available.

We describe a series o,f five statistical models o,f the translation process and give algorithms,for estimating the parameters o,f these models given a set o,f pairs o,f sentences that are translations o,f one another. We define a concept o,f word-by-word alignment between such pairs o,f sentences. For any given pair of such sentences each o,f our models assigns a probability to each of the possible word-by-word alignments. We give an algorithm for seeking the most probable o,f these alignments. Although the algorithm is suboptimal, the alignment thus obtained accounts well for the word-by-word relationships in the pair o,f sentences. We have a great deal o,f data in French and English from the proceedings o,f the Canadian Parliament. Accordingly, we have restricted our work to these two languages; but we,feel that because our algorithms have minimal linguistic content they would work well on other pairs o,f languages. We also ,feel, again because of the minimal linguistic content o,f our algorithms, that it is reasonable to argue that word-by-word alignments are inherent in any sufficiently large bilingual corpus.

Two sets of items can share the same underlying conceptual structure, while appearing unrelated at a surface level. Humans excel at recognizing and using alignments between such underlying structures in many domains of cognition, most notably in analogical reasoning. Here we show that structural alignment reveals how different people's neural representations of word meaning are preserved across different languages, such that patterns of brain activation can be used to translate words from one language to another. Groups of Chinese and English speakers underwent fMRI scanning while reading words in their respective native languages. Simply by aligning structures representing the two groups' neural semantic spaces, we successfully infer all seven Chinese–English word translations. Beyond language translation, conceptual structural alignment underlies many aspects of high-level cognition, and this work opens the door to deriving many such alignments directly from neural representational content.

A critical clinical issue is the identification of a clinical marker, a linguistic form or principle that can be shown to be characteristic of children with Specific Language Impairment (SLI). In this paper we evaluate, as candidate clinical markers, a set of morphemes that mark Tense. In English, this includes -s third person singular, -ed regular past, BE, and DO. According to the Extended Optional Infinitive Account (EOI) of Rice, Wexler, and Cleave (1995), this set of morphemes is likely to appear optionally in the grammars of children with SLI at a rate lower than the optionality evident in younger controls. Three groups of preschool children participated: 37 children with SLI, and two control groups, one of 40 MLU-equivalent children and another of 45 age-equivalent children. Three kinds of evidence support the conclusion that a set of morphemes that marks Tense can be considered a clinical marker: (a) low levels of accuracy for the target morphemes for the SLI group relative to either of the two control groups; (b) affectedness for the set of morphemes defined by the linguistic function of Tense, but not for morphemes unrelated to Tense; and (c) a bimodal distribution for Tense-marking morphemes relative to age peers, in which the typical children are at essentially adult levels of the grammar, whereas children in the SLI group were at low (i.e., non-adultlike) levels of performance. The clinical symptoms are evident in omissions of surface forms. Errors of subject-verb agreement and syntactic misuses are rare, showing that, as predicted, children in an EOI stage who are likely to mark Tense optionally at the same time know a great deal about the grammatical properties of finiteness and agreement in the adult grammar. The findings are discussed in terms of alternative accounts of the grammatical limitations of children with SLI and implications for clinical identification.

We investigate the identification and, to some extent, the classification of collocational word groups that consist of an adjectival modifier (A), an accusative object noun (N), and a verb (V) by means of parsing a newspaper corpus with a lexicalized probabilistic grammar.1 Data triples are extracted from the resulting Viterbi parses and subsequently evaluated by means of a statistical association measure. The extraction results are then compared to predefined descriptive classes of ANV-triples. We also use a decision tree algorithm to classify part of the data obtained, on the basis of a small set of manually classified examples. Much of the candidate data is lexicographically relevant: the triples include idiomatic combinations (e.g. (sich) eine goldene Nase verdienen, ‘get rich’, lit. ‘earn oneself a golden nose’), combinations of N+V and N+A collocations (e.g. (eine) klare Absage erteilen, ‘refuse resolutely’, lit. ‘give a clear refusal’), next to cases where N+V or N+A collocations are found, in combination with other (not necessarily collocational) context partners. To extract such data from text corpora, a grammar is needed that captures verb+object relations: simple pattern matching on part-of-speech shapes is not sufficient. Statistical tools then allow to order the data in a way useful for subsequent manual selection by lexicographers. 1This work has been carried out in the context of the Transferbereich 32: Automatische Exzerption, a DFG-funded project aiming at the creation of support tools for the corpus-based updating of printed dictionaries in lexicography, carried out in cooperation with the publishers Langenscheidt KG and Duden BIFAB AG.

The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.

The pronunciation of a word can vary widely, and many factors are known to affect this variation. This paper focuses on the role of predictability on word duration. Previous research has suggested that more frequent words are shorter, as are words which are more predictable from neighboring words. This research has tended to focus only on extremely high frequency function words; previous research on content words has not been able to examine natural speech and control for key confounding factors like accent and rate of speech. We examined 1401 content words from the Switchboard corpus and studied the role of word frequency, conditional and joint probabilities with neighboring words and a measure of word semantic association, Latent Semantic Analysis. Using multiple regression to control for confounding factors, we show that two predictability variables principally affect a word’s duration—word frequency, and the conditional probability of a word given the following word. Other predictability variables do not make an additional significant contribution. We discuss the implications for pronunciation modeling.

During communication, speakers and listeners need the mechanisms of executive control to organize thoughts and actions along internal goals. Speakers may use executive functions to select the right word over competing alternatives to refer to the concept in mind. Listeners may use executive functions to coordinate the outputs of multiple linguistic processes to reach a coherent interpretation of what others say. Bilinguals may use executive functions to control which language is to use or to switch from one language to another. The control mechanisms recruited in language processing may be similar to those recruited in perception and attention, supported by a network of frontal, parietal and sub-cortical brain structures. Here we review existing evidences regarding the involvement of domain-general executive control in language processing. We will explain how executive functions are employed to control interference in comprehension and production, within and across languages.


Question: Literary Fingerprinting: A Novel Technique for Visual Analysis of Literature

This paper delves into the origins and evolution of association rule mining, a pivotal concept in data mining. Initially introduced by Agrawal, Imielinski, and Swami in their seminal work [AIS93], the field has since expanded significantly. The Apriori algorithm, a cornerstone technique for frequent itemset mining, was detailed by Agrawal and Srikant in [AS94b]. Independently, Mannila, Tiovonen, and Verkamo [MTV94] devised a variant of this algorithm, utilizing a comparable pruning heuristic. These parallel developments culminated in a collaborative publication [AMS96], where Agrawal, Mannila, Srikant, Toivonen, and Verkamo combined their insights to advance the field. A subsequent method for deriving association rules from frequent itemsets was outlined by Agrawal and Srikant in [AS94a], further enriching the data mining toolkit. This historical overview highlights the collaborative and iterative nature of scientific progress in data mining, underscoring the importance of foundational works and subsequent refinements in shaping the field.

We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.

Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.

Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.

The ACL Anthology Network (AAN)1 is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.

Character recognition is one of the most interesting and challenging research areas in the field of Image processing. English character recognition has been extensively studied in the last half century. Nowadays different methodologies are in widespread use for character recognition. Document verification, digital library, reading bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance.

A document image is composed of a variety of physical entities or regions such as text blocks, lines, words, figures, tables, and background. We could also assign functional or logical labels such as sentences, titles, captions, author names, and addresses to some of these regions. The process of document structure and layout analysis tries to decompose a given document image into its component regions and understand their functional roles and relationships. The processing is carried out in multiple steps, such as preprocessing, page decomposition, structure understanding, etc. We will look into each of these steps in detail in the following sections. Document images are often generated from physical documents by digitization using scanners or digital cameras. Many documents, such as newspapers, magazines and brochures, contain very complex layout due to the placement of figures, titles, and captions, complex backgrounds, artistic text formatting, etc. (see Figure 1). A human reader uses a variety of additional cues such as context, conventions and information about language/script, along with a complex reasoning process to decipher the contents of a document. Automatic analysis of an arbitrary document with complex layout is an extremely difficult task and is beyond the capabilities of the state-of-the-art document structure and layout analysis systems. This is interesting since documents are designed to be effective and clear to human interpretation unlike natural images.

In this paper, we tackle the paraphrase detection task. We present a novel recursive autoencoder architecture that learns representations of phrases in an unsupervised way. Using these representations, we are able to extract features for classification algorithms that allow us to outperform many results from previous works.

Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually. Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness. Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included. Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.


Question: A novel technique called literature fingerprinting for visual literary analysis.

This paper introduces an innovative method for authorship attribution that moves beyond the traditional Bag-Of-Words model by incorporating linguistic and stylistic features. We explore various document representations that capture grammatical structures and writing styles to enhance the accuracy of authorship identification. Additionally, we outline two strategies for integrating these representations: combination vectors and ensemble-based meta-classification. Our empirical results confirm the effectiveness of our approach, demonstrating that the inclusion of linguistic and stylistic information significantly improves the performance of authorship attribution systems.

**Question**: The Role of Virtual Reality in Enhancing Language Learning

**Documents**: Virtual Reality for Language Learning: A Review of the Literature
The purpose of this paper is to review the literature on the use of virtual reality (VR) in language learning. We examine how VR can be used to create immersive environments for language learners, and how this can potentially improve their language skills. We also discuss the challenges and limitations of using VR in language learning, and suggest areas for future research.

**Similar Documents**: This article provides a comprehensive review of the existing literature on the application of virtual reality (VR) in enhancing language learning experiences. We explore the ways in which VR can create immersive contexts that facilitate language acquisition, potentially leading to improved language proficiency. We also address the obstacles and constraints associated with the integration of VR in language education, and propose directions for future studies in this field.

Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain “fractal-like” and “self-similar” behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectors; these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized “basis” for text data sets.

Today, the characterization of the technological age is done by the digital images spread. They are the most common form of conveying information whether through internet, newspapers, magazines, or scientific journals. They are used as a strong proof of various crimes and as evidence used for various purposes. The modification, capturing or creating of the image has become easier and available with the emergence of means of image editing and processing tools. One of the most important and popular types of image forgery is a copy-move forgery in which an image part is copied and then pasted into the same image that has the intention of hiding something important or showing a false scene. Because the important properties of the copied parts come from the same image, such as brightness, noise, and texture which will be compatible with the entire image that makes more difficult for experts for the detection and distinguishing the alteration. Usually, the detecting copy move forgery conventional techniques suffer severely from the time-consuming problem. The evaluation of the improved method had been done using (150) images that were selected from two different datasets, “CoMoFoD” and “MICC-F2000”. Experimental results show that the improved method can accurately and quickly reveal the doubled regions of a tampered image. In addition, greatly reducing the processing time in comparison to the Khan algorithm, and the accuracy is kept at the same level. Owing to the availability and technological advancement of the image editing sophisticated tools, there is an increase in the loss of authentication in digital images. Thus, this led us to the proposal of different detection techniques that checks whether the digital images are forged or authentic. The specific type of forgery technique is copy move forgery in which widely used research topic is detection under digital image forensics. In this thesis, an enhancement of copy move image forgery classification is done by implementing hybrid features with classification algorithms like SIFT with SVM and EM algorithm and ORB with SVM and EM.The technique works by applying Firstly the DCT on an image and then on a resultant image, SIFT is obtained after applying DCT. A supervised learning method is proposed for classifying a copy-move image forgery of TIFF, JPEG, and BMP. The process starts with reducing the color of the photos. Achieve the accuracy more than 90%.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.

We present a new edition of the Google Books Ngram Corpus, which describes how often words and phrases were used over a period of five centuries, in eight languages; it reflects 6% of all books ever published. This new edition introduces syntactic annotations: words are tagged with their part-of-speech, and headmodifier relationships are recorded. The annotations are produced automatically with statistical models that are specifically adapted to historical text. The corpus will facilitate the study of linguistic trends, especially those related to the evolution of syntax.

We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.

Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.

text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques of text categorization and classification is recently developed. Some of them are supervised and some of them unsupervised manner of document arrangement. This presented paper discusses different method of text categorization and cluster analysis for text documents. In addition of that a new text mining technique is proposed for future implementation. Keywords— text mining, classification, cluster analysis, survey

Several standard text-categorization techniques were applied to the problem of automated essay grading. Bayesian independence classifiers and knearest-neighbor classifiers were trained to assign scores to manually-graded essays. These scores were combined with several other summary text measures using linear regression. The classifiers and regression equations were then applied to a new set of essays. The classifiers worked very well. The agreement between the automated grader and the final manual grade was as good as the agreement between human graders.

Author profiling task aims to identify different traits of an author by analyzing his/her written text. This study presents a Stylometry-based approach for detection of author traits (gender and age) for cross-genre author profiles. In our proposed approach, we used different types of stylistic features including 7 lexical features, 16 syntactic features, 26 character-based features and 6 vocabulary richness (total 56 stylistic features). On the training corpus, the proposed approach obtained promising results with an accuracy of 0.787 for gender, 0.983 for age and 0.780 for both (jointly detecting age and gender). On the test corpus, proposed system gave an accuracy of 0.576 for gender, 0.371 for age and 0.256 for both.


Question: Literary Fingerprinting: A Novel Technique for Visual Analysis of Literary Works

This paper explores a novel technique for identifying significant patterns within vast collections of discovered association rules, a concept first introduced by Agrawal, Imielinski, and Swami. Association rules are formulated as "for 90% of the records in the dataset, if the record contains the values in set W, then it also contains value 1 in column B". While there are efficient algorithms to uncover these rules from extensive data repositories, the sheer volume of rules generated can pose a significant challenge for users trying to sift through and identify interesting insights. To address this issue, we propose a straightforward yet powerful framework using rule templates. This approach enables users to succinctly define the characteristics of rules that are of particular interest. Furthermore, we showcase the effectiveness of our method through visualizations of rule sets, demonstrating how a visual analytics tool can seamlessly integrate with rule templates to facilitate the discovery of meaningful patterns. This combination not only simplifies the process of analyzing large rule sets but also enhances the user's ability to discern patterns that might otherwise be obscured by the sheer volume of data.

This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches.

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals. An item such as clothing is a visual attribute because it can usually be used to describe people. The method proposed in this article aims to identify people based on the visual information derived from their attire. Deep learning is used to train the computer to classify images based on clothing content. We first demonstrate clothing classification using a large scale dataset, where the proposed model performs relatively poorly. Then, we use clothing classification on a dataset containing popular logos and famous brand images. The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling 70% of the test images correctly.

Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social network of the authors of the documents. So far these problems have been viewed as separate problems and considered independently from each other. In this paper we argue that these two problems are in fact inter-dependent and should be addressed together. We develop a Bayesian hierarchical approach that performs topic modeling and author community discovery in one unified framework. The effectiveness of our model is demonstrated on two blog data sets in different domains and one research paper citation data from CiteSeer.

Handwriting Analysis or Graphology is a scientific method of identifying, evaluating and understanding personality through the strokes and patterns revealed by handwriting. Handwriting reveals the true personality including emotional outlay, fears, honesty, defenses and many others. Professional handwriting examiners called graphologist often identify the writer with a piece of handwriting. Accuracy of handwriting analysis depends on how skilled the analyst is. Although human intervention in handwriting analysis has been effective, it is costly and prone to fatigue. Hence the proposed methodology focuses on developing a tool for behavioral analysis which can predict the personality traits automatically with the aid of a computer without the human intervention. In this paper a method has been proposed to predict the personality of a person from the baseline, the pen pressure, the letter‘t’, the lower loop of letter ‘y’ and the slant of the writing as found in an individual’s handwriting. These parameters are the inputs to a Rule-Base which outputs the personality trait of the writer.

Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACLAnthology Reference Corpus; consisting of over 1.1M “not_cite” and 85K “cite” sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore sectionwise difficulty of the task and analyze the performance of our best model on a published article. ACM Reference Format: Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan. 2018. Citation Worthiness of Sentences in Scientific Reports. In SIGIR ’18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8–12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3209978.3210162

Indian epics have not been analyzed computationally to the extent that Greek epics have. In this paper, we show how interesting insights can be derived from the ancient epic Mahabharata by applying a variety of analytical techniques based on a combination of natural language processing, sentiment/emotion analysis and social network analysis methods. One of our key findings is the pattern of significant changes in the overall sentiment of the epic story across its eighteen chapters and the corresponding characterization of the primary protagonists in terms of their sentiments, emotions, centrality and leadership attributes in the epic saga.

We presentCiteSeer : an autonomous citation indexing system which indexes academic literature in electronic format (e.g. Postscript files on the Web). CiteSeer understands how to parse citations, identify citations to the same paper in d ifferent formats, and identify the context of citations in the body of articles. CiteSeer provides most of the advantages o f traditional (manually constructed) citation indexes (e.g . the ISI citation indexes), including: literature retrieval by following citation links (e.g. by providing a list of papers tha cite a given paper), the evaluation and ranking of papers, au thors, journals, etc. based on the number of citations, and t he identification of research trends. CiteSeer has many advantages over traditional citation indexes, including the abi lity to create more up-to-date databases which are not limited to a preselected set of journals or restricted by journal public ation delays, completely autonomous operation with a correspond ing reduction in cost, and powerful interactive browsing of the literature using the context of citations. Given a parti cular paper of interest, CiteSeer can display the context of ho w the paper is cited in subsequent publications. This context may contain a brief summary of the paper, another author’s response to the paper, or subsequent work which builds upon the original article. CiteSeer allows the location of paper s by keyword search or by citation links. Papers related to a give n paper can be located using common citation information or word vector similarity. CiteSeer will soon be available for public use.

Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.

A document image is composed of a variety of physical entities or regions such as text blocks, lines, words, figures, tables, and background. We could also assign functional or logical labels such as sentences, titles, captions, author names, and addresses to some of these regions. The process of document structure and layout analysis tries to decompose a given document image into its component regions and understand their functional roles and relationships. The processing is carried out in multiple steps, such as preprocessing, page decomposition, structure understanding, etc. We will look into each of these steps in detail in the following sections. Document images are often generated from physical documents by digitization using scanners or digital cameras. Many documents, such as newspapers, magazines and brochures, contain very complex layout due to the placement of figures, titles, and captions, complex backgrounds, artistic text formatting, etc. (see Figure 1). A human reader uses a variety of additional cues such as context, conventions and information about language/script, along with a complex reasoning process to decipher the contents of a document. Automatic analysis of an arbitrary document with complex layout is an extremely difficult task and is beyond the capabilities of the state-of-the-art document structure and layout analysis systems. This is interesting since documents are designed to be effective and clear to human interpretation unlike natural images.

This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.


Question: Literary Fingerprinting: A Novel Technique for Visual Analysis of Literature

This paper explores a novel technique, termed Literature Fingerprinting, aimed at enhancing visual literary analysis. Unlike conventional methodologies, Literature Fingerprinting adopts a probabilistic approach to support vector machines (SVMs), aligning it more closely with regularized likelihood methods. This technique not only offers a visual representation of literary texts but also quantifies the likelihood of certain features or patterns appearing within the texts. By comparing Literature Fingerprinting to established regularized likelihood methods, the study reveals its unique capabilities in uncovering nuanced textual characteristics that might be overlooked by traditional analysis techniques. This probabilistic output framework for SVMs in literary analysis allows for a more comprehensive understanding of texts, potentially revolutionizing the way scholars and enthusiasts approach literary studies. Through visual representations and probabilistic outputs, Literature Fingerprinting bridges the gap between qualitative and quantitative analysis, offering a fresh perspective on literary texts that could lead to new insights and interpretations.

Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.

This paper presents a technique for visualizing the interactions between developers in software project evolution. The goal is to produce a visualization that shows more detail than animated software histories, like code_swarm [15], but keeps the same focus on aesthetics and presentation. Our software evolution storylines technique draws inspiration from XKCD's "Movie Narrative Charts" and the aesthetic design of metro maps. We provide the algorithm, design choices, and examine the results of using the storylines technique. Our conclusion is that the it is able to show more details when compared to animated software project history videos. However, it does not scale to the largest projects, such as Eclipse and Mozilla.

The importance of citations is widely recognized by the scientific community. Citations are being used in making a number of vital decisions such as calculating impact factor of journals, calculating impact of a researcher (H-Index), ranking universities and research organizations. Furthermore, citation indexes, along with other criteria, employ citation counts to retrieve and rank relevant research papers. However, citing patterns and in-text citation frequency are not used for such important decisions. The identification of in-text citation from a scientific document is an important problem. However, identification of in-text citation is a tough ask due to the ambiguity between citation tag and content. This research focuses on in-text citation analysis and makes the following specific contributions such as: Provides detailed in-text citation analysis on 16,000 citations of an online journal, reports different pattern of citations-tags and its in-text citations and highlights the problems (mathematical ambiguities, wrong allotments, commonality in content and string variation) in identifying in-text citations from scientific documents. The accurate identification of in-text citations will help information retrieval systems, digital libraries and citation indexes.

When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprisewide search environments. To address these real-time constraints, we developed a new measure of document similarity called Textual Spatial Cosine Similarity, which is able to detect similitude at the semantic level using word placement information contained in the document. We will see in this paper that two degenerate cases exist for this model, which coincide with Cosine Similarity on one side and with a paraphrasing detection model to the other.

In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

Argumentative Zoning (AZ) is an analysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annotation experiments have however so far been restricted to one discipline, computational linguistics (CL). Here, we present a more informative AZ scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to CL. We use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. Our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.

Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook presents a comprehensive discussion of the latest techniques in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world applications.

Nowadays, companies such as Google [1] and Facebook [2] are present on most websites either through advertising services or through social plugins. They follow users around the web collecting information to create unique user profiles. This serious threat to privacy is a major cause for concern, as this reveals that there are no restrictions on the type of information that can be collected, the duration it can be stored, the people who can access it and the purpose of use or misuse. In order to determine if there indeed is any statistically significant evidence that fingerprinting occurs on certain web services, we integrated 2 software namely, FPBlock [6], a prototype browser extension that blocks web-based device fingerprinting, and AdFisher [10], an automated tool that sending out thousands of automated Web browser instances, in such a manner so that the data collected by FP-Block could be fed to AdFisher for analysis.

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.

With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word–emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy–sadness axis, whereas men prefer terms from the fear–trust axis. Finally, we show visualizations that can help people track emotions in their emails.


Question: Literary Fingerprinting: A Novel Technique for Visual Analysis of Literature

This paper presents an innovative method for automatically distinguishing between texts written by male and female authors. Unlike more straightforward tasks such as topic categorization or specific author attribution, discerning an author's gender from their writing presents a more nuanced challenge. However, it is demonstrated that advanced text categorization algorithms can effectively leverage a blend of straightforward lexical and grammatical characteristics to accurately predict the gender of the writer of an unknown formal text with an accuracy rate of roughly 80%. These same methodologies are also capable of identifying whether a given document is a work of fiction or non-fiction with an impressive accuracy of approximately 98%. This showcases the versatility and precision of automated text analysis techniques in handling a variety of categorization tasks.

Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.

This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.

In today’s world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to “Research Articles”. A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured, detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic modelLatent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.

Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel finegrained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women’s Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.

In this work, we describe a supervised cross-lingual methodology for detecting novel and conventionalized metaphors that derives generalized semantic patterns from a collection of metaphor annotations. For this purpose, we model each metaphor annotation as an abstract tuple – (source, target, relation, metaphoricity) – that packages a metaphoricity judgement with a relational grounding of the source and target lexical units in text. From these annotations, we derive a set of semantic patterns using a three-step process. First, we employ several generalized representations of the target using a variety of WordNet information and representative domain terms. Then, we generalize relations using a rule-based, pseudo-semantic role labeling. Finally, we generalize the source by partitioning a semantic hierarchy (defined by the target and the relation) into metaphoric and non-metaphoric regions so as to optimally account for the evidence in the annotated data. Experiments show that by varying the generality of the source, target, and relation representations in our derived patterns, we are able to significantly extend the impact of our annotations, detecting metaphors in a variety of domains at an F-measure of between 0.88 and 0.92 for English, Spanish, Russian, and Farsi. This generalization process both enhances our ability to jointly detect novel and conventionalized metaphors and enables us to transfer the knowledge encoded in metaphoricity annotations to novel languages.

Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.

Individuality of fingerprints can be quantified by computing the probabilistic metrics for measuring the degree of fingerprint individuality. In this paper, we present a novel individuality evaluation approach to estimate the probability of random correspondence (PRC). Three generative models are developed respectively to represent the distribution of fingerprint features: ridge flow, minutiae and minutiae together with ridge points. A mathematical model that computes the PRCs are derived based on the generative models. Three metrics are discussed in this paper: (i) PRC of two samples, (ii) PRC among a random set of n samples (nPRC) and (iii) PRC between a specific sample among n others (specific nPRC). Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the NIST4 database.


Question: Moral development, executive functioning, peak experiences, and brain patterns in professional and amateur classical musicians: Analysis through the lens of a Unified Theory of Performance.

Distinct Brain Morphologies in Musicians versus Non-Musicians: Implications for Skill Acquisition and Development

From an early age, musicians engage in the acquisition of intricate motor and auditory abilities, such as interpreting visual musical notation and simultaneously executing motor responses while monitoring auditory feedback. This rigorous training, which spans from childhood to adulthood, is known to shape the brain's structure. Through a comprehensive voxel-by-voxel morphometric analysis, our study identified variations in gray matter volume within motor, auditory, and visual-spatial regions when comparing professional keyboard players to both amateur musicians and non-musicians. While some of these multi-regional disparities might be attributed to inherent predispositions, we posit that they predominantly reflect structural adaptations in response to prolonged skill refinement and the repetitive practice of these skills. This conjecture is bolstered by the strong correlation we observed between brain structure, musician status, and the intensity of practice, in addition to substantial animal research evidencing structural modifications in response to extended motor training. However, determining the exact balance between innate predispositions and the effects of practice will require further experimental investigations.

Can treating oneself with compassion after making a mistake increase self-improvement motivation? In four experiments, the authors examined the hypothesis that self-compassion motivates people to improve personal weaknesses, moral transgressions, and test performance. Participants in a self-compassion condition, compared to a self-esteem control condition and either no intervention or a positive distraction control condition, expressed greater incremental beliefs about a personal weakness (Experiment 1); reported greater motivation to make amends and avoid repeating a recent moral transgression (Experiment 2); spent more time studying for a difficult test following an initial failure (Experiment 3); exhibited a preference for upward social comparison after reflecting on a personal weakness (Experiment 4); and reported greater motivation to change the weakness (Experiment 4). These findings suggest that, somewhat paradoxically, taking an accepting approach to personal failure may make people more motivated to improve themselves.

Personality is strongly influenced by motivation systems that organise responses to rewards and punishments and that drive approach and avoidance behavior. Neuropsychological research has identified: (a) two avoidance systems, one related to pure active avoidance and escape, and one to passive avoidance and behavioral inhibition produced by goal-conflict; and (b) two approach systems, one related to the actions of reward seeking and one to experience and behavior related to pleasure on receiving reward. These systems mediate fluid moment-by-moment reactions to changing stimuli, with relatively stable person-specific sensitivities to these stimuli manifested in personality traits. We review what is known about these motivational traits, integrating the theory-driven approach based on animal learning paradigms with the empirical tradition of the Big Five personality model. People differ from one another, and this fact is obvious to everyone. It is common to talk about people’s personalities using lexical terms to describe their characteristic ways of thinking, feeling and behaving (e.g., ‘bold’, ‘lazy’, ‘intelligent’), and we use these descriptors to infer people’s intentions and likely future behavior. Personality psychologists have long analyzed the ratings of large numbers of trait descriptive adjectives to produce the most widely used taxonomy of personality: the Big Five, which includes the dimensions of Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience ⁄ Intellect (John, Naumann, & Soto, 2008). These Big Five traits also emerge from existing personality questionnaires that were not designed specifically to measure them (e.g., Markon, Krueger, & Watson, 2005), suggesting it is a good candidate for a consensual model in personality psychology. What the Big Five model does not immediately offer, however, is an explanation for the causal sources of personality traits. Why do people think, feel, and act in the ways that they do? People react to situations, of course; but different people react differently to the same situation, suggesting that they have different behavioral propensities. In order to answer this why question, we must discover what drives people’s actions and reactions. Inferring motivation from observed personality has been something of a dark art in psychology. However, one promising approach to this question is based on the biology of motivational control systems, studied by psychologists for over a century in non-human animals, and for somewhat less time in humans. This approach operates on the premise that stable individual differences in behavior (personality traits) must be due to relatively stable individual differences in the operation of brain systems that produce (state) behavior from moment-tomoment. From this perspective, each of our many traits reflects the operations of a set of brain systems that has evolved to respond to a different class of functional requirements (Denissen & Penke, 2008; McNaughton, 1989; Nettle, 2006; Pickering & Gray, 1999). Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 a 2013 Blackwell Publishing Ltd In what follows, we focus on those motivational processes and personality traits most closely aligned with biological research on reactions to reward and punishment and associated approach and avoidance behavior. This focus is warranted both by the importance of these phenomena for motivation and by the existence of extensive research on them. Our aim is to offer an introduction for researchers wishing to explore the role of motivation in personality from the perspective of these underlying psychobiological systems. Only after a description of what is known about the operation of these systems do we branch out to consider the personality traits associated with them. Our major assumption is that most fundamental personality traits have a motivational core; and we aim to show that the descriptive personality research tradition, which produced the Big Five, can be integrated with the experimental research tradition that has focused on the sensitivities of basic motivation systems. In this review, we focus on systems related to approach and avoidance primarily at the level of explanation that Gray (1975) labeled ‘the conceptual nervous system’, which is based on analysis of behavior as well as neurobiology and attempts to describe important psychological processes without specifying their exact instantiation in the nervous system – this approach has afforded a detailed analysis of reactions to classes of motivationally significant stimuli and can be used to derive predictions concerning the functions of the real nervous system (e.g., in fMRI studies). Rather than going into extensive detail regarding the biological basis of the systems, we focus primarily on their functions, discussing biological evidence only when it is necessary for addressing some functional question. Approach-Avoidance Theories of Motivation and Their Relation to Personality The most important classes of motivational stimuli can be grouped into ‘rewards’ and ‘punishments’. Animals can be seen as cybernetic systems with attractors and repulsors (positive and negative goals) that have evolved to promote survival and reproduction (Carver & Scheier, 1998; DeYoung, 2010d). Without a tendency to approach beneficial stimuli (e.g., food, drink, and sexual mates) and to avoid aversive stimuli (e.g., predators and poisons) a species would not survive. ‘Reward’ and ‘punishment’ may seem straightforward concepts, but they hide some nonobvious complexities. For the classical behaviorist, rewards increase the frequency of the behavior leading to them, whereas punishments decrease the frequency of behavior leading to them. That is, a ‘reward’ is something a person will work to obtain; and a ‘punishment’ is something a person will work to avoid. But the behaviorist definition of ‘reward’ also includes a different class of stimuli, namely the termination or omission of expected punishment. The effect on behavior and emotion of the ‘hope’ of achieving a reward is similar to that of anticipated ‘relief’ through avoiding a punishment. Similarly, although a ‘punishment’ can be described as something people will work to avoid or escape from (or which they will attack defensively), the omission of an expected reward is experienced as punishing; an effect known as frustrative nonreward. Thus, ‘fear’ has important similarities with ‘frustration’. (For further discussion of this literature, see Corr & McNaughton, 2012.) These complexities can be understood straightforwardly from the cybernetic perspective, in which rewards are any stimuli that indicate progress toward or attainment of a goal, whereas punishments are any stimuli that disrupt progress toward a goal. However, in any experimental situation, it is necessary to confirm that the subject perceives stimuli as actually rewarding and punishing, as there are likely to be significant individual differences in how people react to the same stimuli (for further discussion of this point, see Corr, forthcoming). Motivation and Personality 159 a 2013 Blackwell Publishing Ltd Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 Current approach-avoidance theories trace their origins to early researchers who posited that two motivation ⁄emotion processes underlie behavior (e.g., Konorski, 1967; Mowrer, 1960; Schneirla, 1959), one related to reward (approach behavior and positive emotions), and the other to punishment (avoidance behavior and negative emotions). Neuroscience measures, including pharmacological manipulation, assessment of neural activity, and neuroanatomical studies, have been used to investigate the neuropsychological systems that underlie reactions to these classes of stimuli, providing confirmation of the hypothesis that distinct systems underlie rewardand punishment-related motivation (Gray & McNaughton, 2000). This animal-based work migrated into personality psychology in the 1970s via Jeffrey A. Gray (e.g., 1970, 1972a,b, 1975, 1977), whose Reinforcement Sensitivity theory (RST) argued that the major traits of personality reflect long-term stabilities in systems that mediate reactions to different classes of reinforcing stimuli, generating emotion and shaping (‘motivating’) approach and avoidance behavior. The leap from understanding motivational systems to understanding personality traits requires the postulate that relatively stable individual differences exist in the operations of these brain-behavioral systems. A personality trait can be defined as a probabilistic constant in equations that predict the frequency and intensity with which individuals exhibit various motivational states, as well as the behavioral, emotional, and cognitive states that accompany these motivational states (DeYoung, 2010c; Fleeson, 2001; Fleeson & Gallagher, 2009). Note that this assumes exposure of the population to a normal range of situations. If situations are limited to prevent exposure to some trait-relevant class of situations, then individual differences in that trait may not be manifest. A neuropsychological approach to personality aims to understand both the biological systems that are responsible for the states associated with any given trait and the parameters of those systems that cause them to differ across individuals. The systems themselves will be present in every intact human brain, but the values of their parameters will vary from person to person. Thus, for example, all people have brain systems that respond to punishing stimuli, but in different individuals these systems respond differently to a given stimulus. It is the typical level of response of such a system in any given individual, averaged across different situations, that is associated with that individual’s score on the personality trait in question. This is not to imply that an individual wil

The somatic marker hypothesis proposes that both the amygdala and the orbitofrontal cortex are parts of a neural circuit critical for judgment and decision-making. Although both structures couple exteroceptive sensory information with interoceptive information concerning somatic/emotional states, they do so at different levels, thus making different contributions to the process. We define "primary inducers" as stimuli that unconditionally, or through learning (e.g., conditioning and semantic knowledge), can (perceptually or subliminally) produce states that are pleasurable or aversive. Encountering a fear object (e.g., a snake), a stimulus predictive of a snake, or semantic information such as winning or losing a large sum of money are all examples of primary inducers. "Secondary inducers" are entities generated by the recall of a personal or hypothetical emotional event or perceiving a primary inducer that generates "thoughts" and "memories" about the inducer, all of which, when they are brought to memory, elicit a somatic state. The episodic memory of encountering a snake, losing a large sum of money, imagining the gain of a large sum of money, or hearing or looking at primary inducers that bring to memory "thoughts" pertaining to an emotional event are all examples of secondary inducers. We present evidence in support of the hypothesis that the amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers. The ventromedial cortex is a critical substrate in the neural system necessary for the triggering of somatic states from secondary inducers. The amygdala system is a priori a necessary step for the normal development of the orbitofrontal system for triggering somatic states from secondary inducers. However, once this orbitofrontal system is developed, the induction of somatic states by secondary inducers via the orbitofrontal system is less dependent on the amygdala system. Perhaps the amygdala is equivalent to the hippocampus with regard to emotions, that is, necessary for acquiring new emotional attributes (anterograde emotions), but not for retrieving old emotional attributes (retrograde emotions). Given the numerous lesion and functional neuroimaging studies illustrating the involvement of the amygdala in complex cognitive and behavioral functions, including "social cognition," we suggest that this involvement is a manifestation of a more fundamental function mediated by the amygdala, which is to couple stimuli/entities with their emotional attributes, that is, the processing of somatic states from primary inducers.

OBJECTIVE
Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being.

METHOD
Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants.

RESULTS
We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes.

CONCLUSION
These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.

The peak-end rule is a psychological heuristic observing that people's retrospective assessment of an experience is strongly influenced by the intensity of the peak and final moments of that experience. We examine how aspects of game player experience are influenced by peak-end manipulations to the sequence of events in games that are otherwise objectively identical. A first experiment examines players' retrospective assessments of two games (a pattern matching game based on Bejeweled and a point-and-click reaction game) when the sequence of difficulty is manipulated to induce positive, negative and neutral peak-end effects. A second experiment examines assessments of a shootout game in which the balance between challenge and skill is similarly manipulated. Results across the games show that recollection of challenge was strongly influenced by peak-end effects; however, results for fun, enjoyment, and preference to repeat were varied -- sometimes significantly in favour of the hypothesized effects, sometimes insignificant, but never against the hypothesis.

Musical emotions, such as happiness and sadness, have been investigated using instrumental music devoid of linguistic content. However, pop and rock, the most common musical genres, utilize lyrics for conveying emotions. Using participants' self-selected musical excerpts, we studied their behavior and brain responses to elucidate how lyrics interact with musical emotion processing, as reflected by emotion recognition and activation of limbic areas involved in affective experience. We extracted samples from subjects' selections of sad and happy pieces and sorted them according to the presence of lyrics. Acoustic feature analysis showed that music with lyrics differed from music without lyrics in spectral centroid, a feature related to perceptual brightness, whereas sad music with lyrics did not diverge from happy music without lyrics, indicating the role of other factors in emotion classification. Behavioral ratings revealed that happy music without lyrics induced stronger positive emotions than happy music with lyrics. We also acquired functional magnetic resonance imaging data while subjects performed affective tasks regarding the music. First, using ecological and acoustically variable stimuli, we broadened previous findings about the brain processing of musical emotions and of songs versus instrumental music. Additionally, contrasts between sad music with versus without lyrics recruited the parahippocampal gyrus, the amygdala, the claustrum, the putamen, the precentral gyrus, the medial and inferior frontal gyri (including Broca's area), and the auditory cortex, while the reverse contrast produced no activations. Happy music without lyrics activated structures of the limbic system and the right pars opercularis of the inferior frontal gyrus, whereas auditory regions alone responded to happy music with lyrics. These findings point to the role of acoustic cues for the experience of happiness in music and to the importance of lyrics for sad musical emotions.

The author acknowledge support from National Institute of Health Research Grants MH55687 and AG15685. Marc Howard is now at the Department of Psychology, Boston University, 64 Cunningham Street, Boston, MA 02215. Address correspondence and reprint requests to Michael J. Kahana, Volen Center for Complex Systems, Brandeis University, MS 013, Waltham, MA 02254. E-mail: kahana@brandeis.edu. Marc W. Howard and Michael J. Kahana

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.

OBJECTIVE
This study investigated the relationships between specific performance components, eye-hand coordination, visuomotor integration, in-hand manipulation, and handwriting skill.

METHOD
A sample of 48 typical first grade students were identified as good and poor handwriters by their teachers. Each child completed the Motor Accuracy Test; the Developmental Test of Visual-Motor Integration (VMI); two tests of in-hand manipulation, including a rotation and a translation task; and the Minnesota Handwriting Test (MHT).

RESULTS
All test scores for the subjects with good handwriting were significantly higher than those of the subjects with poor handwriting. Each performance component test was significantly correlated to MHT scores. Translation, VMI, and rotation scores were significant predictors of MHT scores, accounting for almost 73% of variance. A discriminant analysis using the performance components correctly classified 98% of the students as good or poor handwriters.

CONCLUSION
In-hand manipulation has significant association to handwriting skill.

A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.