Query: Two-Phase Malicious Web Page Detection Scheme Using Misuse and Anomaly Detection

Relevant passages:
Detection and analysis of drive-by-download attacks and malicious JavaScript code <=SEP=> JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code.
 This paper presents a novel approach to the detection and analysis of malicious JavaScript code. Our approach combines anomaly detection with emulation to automatically identify malicious JavaScript code and to support its analysis. We developed a system that uses a number of features and machine-learning techniques to establish the characteristics of normal JavaScript code. Then, during detection, the system is able to identify anomalous JavaScript code by emulating its behavior and comparing it to the established profiles. In addition to identifying malicious code, the system is able to support the analysis of obfuscated code and to generate detection signatures for signature-based systems. The system has been made publicly available and has been used by thousands of analysts.

Beyond blacklists: learning to detect malicious web sites from suspicious URLs <=SEP=> Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.

Anomaly Based Web Phishing Page Detection <=SEP=> Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in Web pages, in particular, the discrepancy between a Web site's identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the Web site. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate

Effective analysis, characterization, and detection of malicious web pages <=SEP=> The steady evolution of the Web has paved the way for miscreants to take advantage of vulnerabilities to embed malicious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other malicious targets, or cease control of victim's system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complexity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolution of web page artifacts and (3) exibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evolutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introducing novel features to capture fine-grained snapshot of malicious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work.

ADAM: Detecting Intrusions by Data Mining <=SEP=> Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM ( Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection. Keywords—Intrusion Detection, Data Mining, Association Rules, Classifiers.

Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders <=SEP=> Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.

A shared " passengers & goods " city logistics system <=SEP=> Many strategic planning models have been developed to help decision making in city logistics. Such models do not take into account, or very few, the flow of passengers because the considered unit does not have the same nature (a person is active and a good is passive). However, it seems fundamental to gather the goods and the passengers in one model when their respective transports interact with each other. In this context, we suggest assessing a shared passengers & goods city logistics system where the spare capacity of public transport is used to distribute goods toward the city core. We model the problem as a vehicle routing problem with transfers and give a mathematical formulation. Then we propose an Adaptive Large Neighborhood Search (ALNS) to solve it. This approach is evaluated on data sets generated following a field study in the city of La Rochelle in France.

Speaker Identification using Mel Frequency Cepstral Coefficient and BPNN <=SEP=> Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. BPNN is used for identification of speaker after training the feature set from MFCC. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency. Information from speech recognition can be used in various ways in state-of-the-art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text-dependent speaker modeling techniques when the words spoken are not given. Furthermore, it has been shown that the choice of words and phones itself can be a useful indicator of speaker identity. Also, recognizer output enables higher-level features, in particular those related to prosodic properties of speech. Keywords— Speaker identification, BPNN, MFCC, speech processing, feature extraction, speech signal

Language support for fast and reliable message-based communication in singularity OS <=SEP=> Message-based communication offers the potential benefits of providing stronger specification and cleaner separation between components. Compared with shared-memory interactions, message passing has the potential disadvantages of more expensive data exchange (no direct sharing) and more complicated programming.In this paper we report on the language, verification, and run-time system features that make messages practical as the sole means of communication between processes in the Singularity operating system. We show that using advanced programming language and verification techniques, it is possible to provide and enforce strong system-wide invariants that enable efficient communication and low-overhead software-based process isolation. Furthermore, specifications on communication channels help in detecting programmer mistakes early---namely at compile-time---thereby reducing the difficulty of the message-based programming model.The paper describes our communication invariants, the language and verification features that support them, as well as implementation details of the infrastructure. A number of benchmarks show the competitiveness of this approach.

Timeline generation with social attention <=SEP=> Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.

Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software <=SEP=> The use of automatic static analysis has been a software engineering best practice for decades. However, we still do not know a lot about its use in real-world software projects: How prevalent is the use of Automated Static Analysis Tools (ASATs) such as FindBugs and JSHint? How do developers use these tools, and how does their use evolve over time? We research these questions in two studies on nine different ASATs for Java, JavaScript, Ruby, and Python with a population of 122 and 168,214 open-source projects. To compare warnings across the ASATs, we introduce the General Defect Classification (GDC) and provide a grounded-theory-derived mapping of 1,825 ASAT-specific warnings to 16 top-level GDC classes. Our results show that ASAT use is widespread, but not ubiquitous, and that projects typically do not enforce a strict policy on ASAT use. Most ASAT configurations deviate slightly from the default, but hardly any introduce new custom analyses. Only a very small set of default ASAT analyses is widely changed. Finally, most ASAT configurations, once introduced, never change. If they do, the changes are small and have a tendency to occur within one day of the configuration's initial introduction.

Microrobot Design Using Fiber Reinforced Composites <=SEP=> Mobile microrobots with characteristic dimensions on the order of 1cm are difficult to design using either MEMS (microelectromechanical systems) technology or precision machining. This is due to the challenges associated with constructing the high strength links and high-speed, low-loss joints with micron scale features required for such systems. Here we present an entirely new framework for creating microrobots which makes novel use of composite materials. This framework includes a new fabrication process termed Smart Composite Microstructures (SCM) for integrating rigid links and large angle flexure joints through a laser micromachining and lamination process. We also present solutions to actuation and integrated wiring issues at this scale using SCM. Along with simple design rules that are customized for this process, our new complete microrobotic framework is a cheaper, quicker, and altogether superior method for creating microrobots that we hope will become the paradigm for robots at this scale.

Mr. DLib's Living Lab for Scholarly Recommendations <=SEP=> We introduce the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab ́s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%.

A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems <=SEP=> This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardi-ness. In the stochastic model, we further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches.

Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping <=SEP=> We introduce an encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks. Like connectionist temporal classification (CTC) models, RNA defines a probability distribution over target label sequences including blank labels corresponding to each time step in input. The probability of a label sequence is calculated by marginalizing over all possible blank label positions. Unlike CTC, RNA does not make a conditional independence assumption for label predictions; it uses the predicted label at time t−1 as an additional input to the recurrent model when predicting the label at time t. We apply this model to end-to-end speech recognition. RNA is capable of streaming recognition since the decoder does not employ attention mechanism. The model is trained on transcribed acoustic data to predict graphemes and no external language and pronunciation models are used for decoding. We employ an approximate dynamic programming method to optimize negative log likelihood, and a sampling-based sequence discriminative training technique to fine-tune the model to minimize expected word error rate. We show that the model achieves competitive accuracy without using an external language model nor doing beam search decoding.

Effective Bug Triage Based on Historical Bug-Fix Information <=SEP=> For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.

How Important is Weight Symmetry in Backpropagation? <=SEP=> Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem” [1] is thought to be one of the main reasons of BP’s biological implausibility. Using 15 different classification datasets, we systematically study to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.’s demonstration [2] but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter—the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) [3] and/or a “Batch Manhattan” (BM) update rule. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. ar X iv :1 51 0. 05 06 7v 3 [ cs .L G ] 2 D ec 2 01 5

Smarter universities: A vision for the fast changing digital era <=SEP=> In this paper we analyze the current situation of education in universities, with particular reference to the European scenario. Specifically, we observe that recent evolutions, such as pervasive networking and other enabling technologies, have been dramatically changing human life, knowledge acquisition, and the way works are performed and people learn. In this societal change, universities must maintain their leading role. Historically, they set trends primarily in education but now they are called to drive the change in other aspects too, such as management, safety, and environment protection. The availability of newer and newer technology reflects on how the relevant processes should be performed in the current fast changing digital era. This leads to the adoption of a variety of smart solutions in university environments to enhance the quality of life and to improve the performances of both teachers and students. Nevertheless, we argue that being smart is not enough for a modern university. In fact, universities should better become smarter. By “smarter university” we mean a place where knowledge is shared between employees, teachers, students, and all stakeholders in a seamless way. In this paper we propose, and discuss a smarter university model, derived from the one designed for the development of

Impact of dietary fiber intake on glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese patients with type 2 diabetes mellitus: the Fukuoka Diabetes Registry <=SEP=> BACKGROUND
Dietary fiber is beneficial for the treatment of type 2 diabetes mellitus, although it is consumed differently in ethnic foods around the world. We investigated the association between dietary fiber intake and obesity, glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese type 2 diabetic patients.


METHODS
A total of 4,399 patients were assessed for dietary fiber intake using a brief self-administered diet history questionnaire. The associations between dietary fiber intake and various cardiovascular risk factors were investigated cross-sectionally.


RESULTS
Body mass index, fasting plasma glucose, HbA1c, triglyceride and high-sensitivity C-reactive protein negatively associated with dietary fiber intake after adjusting for age, sex, duration of diabetes, current smoking, current drinking, total energy intake, fat intake, saturated fatty acid intake, leisure-time physical activity and use of oral hypoglycemic agents or insulin. The homeostasis model assessment insulin sensitivity and HDL cholesterol positively associated with dietary fiber intake. Dietary fiber intake was associated with reduced prevalence of abdominal obesity, hypertension and metabolic syndrome after multivariate adjustments including obesity. Furthermore, dietary fiber intake was associated with lower prevalence of albuminuria, low estimated glomerular filtration rate and chronic kidney disease after multivariate adjustments including protein intake. Additional adjustments for obesity, hypertension or metabolic syndrome did not change these associations.


CONCLUSION
We demonstrated that increased dietary fiber intake was associated with better glycemic control and more favorable cardiovascular disease risk factors including chronic kidney disease in Japanese type 2 diabetic patients. Diabetic patients should be encouraged to consume more dietary fiber in daily life.

Concept Decompositions for Large Sparse Text Data Using Clustering <=SEP=> 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.

Attribution versus persuasion as a means for modifying behavior. <=SEP=> The present research compared the relative effectiveness of an attribution strategy with a persuasion strategy in changing behavior. Study 1 attempted to teach fifth graders not to litter and to clean up after others. An attribution group was repeatedly told that they were neat and tidy people, a persuasion group was repeatedly told that they should be neat and tidy, and a control group received no treatment. Attribution proved considerably more effective in modifying behavior. Study 2 tried to discover whether similar effects would hold for a more central aspect of school performance, math achievement and self-esteem, and whether an attribution of ability would be as effective as an attribution of motivation. Repeatedly attributing to second graders either the ability or the motivation to do well in math proved more effective than comparable persuasion or no-treatment control groups, although a group receiving straight reinforcement for math problem-solving behavior also did well. It is suggested that persuasion often suffers because it involves a negative attribution (a person should be what he is not), while attribution generally gains because it disguises persuasive intent.

Taslihan Virtual Reconstruction - Interactive Digital Story or a Serious Game <=SEP=> During the Ottoman period, Taslihan was the largest accommodation complex in Sarajevo, Bosnia and Herzegovina. Today, only one wall remains as a memento of its existence. In this paper, we compare user appreciation of an interactive digital story about this building and of a serious game about Taslihan to see which application offers more knowledge and immersion while bringing this monument to life in the collective memory of the people.

Mobile apps for science learning: Review of research <=SEP=> This review examined articles on mobile apps for science learning published from 2007 to 2014. A qualitative content analysis was used to investigate the science mobile app research for its mobile app design, underlying theoretical foundations, and students' measured outcomes. This review found that mobile apps for science learning offered a number of similar design features, including technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. Many of the studies cited a specific theoretical foundation, predominantly situated learning theory, and applied this to the design of the mobile learning environment. The most common measured outcome was students' basic scientific knowledge or conceptual understanding. A number of recommendations came out of this review. Future studies need to make use of newer, available technologies; isolate the testing of specific app features; and develop additional strategies around using mobile apps for collaboration. Researchers need to make more explicit connections between the instructional principles and the design features of their mobile learning environment in order to better integrate theory with practice. In addition, this review noted that stronger alignment is needed between the underlying theories and measured outcomes, and more studies are needed to assess students' higher-level cognitive outcomes, cognitive load, and skill-based outcomes such as problem solving. Finally, more research is needed on how science mobile apps can be used with more varied science topics and diverse audiences. © 2015 Elsevier Ltd. All rights reserved.

Characteristics and Expected Returns in Individual Equity Options <=SEP=> I study excess returns from selling individual equity option portfolios that are leverageadjusted monthly and delta-hedged daily. Strikingly, I find that several measures of risk rise by maturity, although expected returns decrease. Based on my analysis, I identify three new factors –level, slope, and value– in option returns, which together explain the cross-sectional variation in expected returns on option portfolios formed on moneyness, maturity and option value (the spread between historical volatility and the Black-Scholes implied volatility). This three-factor model helps explain expected returns on option portfolios formed on a variety of different characteristics that include carry, VRP, volatility momentum, idiosyncratic volatility, illiquidity, etc. While the level premium appears to be a compensation for marketwide volatility and jump shocks, theories of risk-averse financial intermediaries help us to understand the slope and the value premiums.

Survey of fraud detection techniques <=SEP=> Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields. Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of different techniques to detect frauds.

Measuring emotional intelligence with the MSCEIT V2.0. <=SEP=> Does a recently introduced ability scale adequately measure emotional intelligence (EI) skills? Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; J. D. Mayer, P. Salovey, & D. R. Caruso, 2002b), the authors examined (a) whether members of a general standardization sample and emotions experts identified the same test answers as correct, (b) the test's reliability, and (c) the possible factor structures of EI. Twenty-one emotions experts endorsed many of the same answers, as did 2,112 members of the standardization sample, and exhibited superior agreement, particularly when research provides clearer answers to test questions (e.g., emotional perception in faces). The MSCEIT achieved reasonable reliability, and confirmatory factor analysis supported theoretical models of EI. These findings help clarify issues raised in earlier articles published in Emotion.

A complete formalized knowledge representation model for advanced digital forensics timeline analysis <=SEP=> Having a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis. © 2014 Digital Forensics Research Workshop. Published by Elsevier Limited. All rights

Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction <=SEP=> Goal recognition is the task of inferring an agent’s goals given some or all of the agent’s observed actions. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-theshell planners. However, obtaining accurate cost or reward models of an agent and incorporating them into the planning model becomes an issue in real applications. Towards this end, we propose an Inverse Reinforcement Learning (IRL)based opponent behavior modeling method, and apply it in the goal recognition assisted Dynamic Local Network Interdiction (DLNI) problem. We first introduce the overall framework and the DLNI problem domain of our work. After that, an IRL-based human behavior modeling method and Markov Decision Process-based goal recognition are introduced. Experimental results indicate that our learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models.

Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function <=SEP=> Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rank-based in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach.

Closed-Loop Deep Brain Stimulation Is Superior in Ameliorating Parkinsonism <=SEP=> Continuous high-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson's disease (PD) management. However, the mechanisms underlying DBS effects remain enigmatic and are the subject of an ongoing debate. Here, we present and test a closed-loop stimulation strategy for PD in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. Application of pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Furthermore, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. Thus, closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.