title: A Survey on Software Clone Detection Research

abstract: Code duplication or copying a code fragment and then reuse by pasting with or without any modiflcations is a well known code smell in software maintenance. Several studies show that about 5% to 20% of a software systems can contain duplicated code, which is basically the results of copying existing code fragments and using then by pasting with or without minor modiflcations. One of the major shortcomings of such duplicated fragments is that if a bug is detected in a code fragment, all the other fragments similar to it should be investigated to check the possible existence of the same bug in the similar fragments. Refactoring of the duplicated code is another prime issue in software maintenance although several studies claim that refactoring of certain clones are not desirable and there is a risk of removing them. However, it is also widely agreed that clones should at least be detected. In this paper, we survey the state of the art in clone detection research. First, we describe the clone terms commonly used in the literature along with their corresponding mappings to the commonly used clone types. Second, we provide a review of the existing clone taxonomies, detection approaches and experimental evaluations of clone detection tools. Applications of clone detection research to other domains of software engineering and in the same time how other domain can assist clone detection research have also been pointed out. Finally, this paper concludes by pointing out several open problems related to clone detection research.


title: On the bias of BFS

abstract: Breadth First Search (BFS) and other graph traversal techniques are widely used for measuring large unknown graphs, such as online social networks. It has been empirically observed that an incomplete BFS is biased toward high degree nodes. In contrast to more studied sampling techniques, such as random walks, the precise bias of BFS has not been characterized to date. In this paper, we quantify the degree bias of BFS sampling. In particular, we calculate the node degree distribution expected to be observed by BFS as a function of the fraction of covered nodes, in a random graph $RG(p_k)$ with a given degree distribution $p_k$. Furthermore, we also show that, for $RG(p_k)$, all commonly used graph traversal techniques (BFS, DFS, Forest Fire, and Snowball Sampling) lead to the same bias, and we show how to correct for this bias. To give a broader perspective, we compare this class of exploration techniques to random walks that are well-studied and easier to analyze. Next, we study by simulation the effect of graph properties not captured directly by our model. We find that the bias gets amplified in graphs with strong positive assortativity. Finally, we demonstrate the above results by sampling the Facebook social network, and we provide some practical guidelines for graph sampling in practice.


title: Time-aware structured query suggestion

abstract: Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.


title: Oows: A method to develop web applications from web-oriented conceptual models

abstract: This work presents an OO software production method that provides conceptual modelling extensions (in terms of models and abstraction primitives) to facilitate the web application specification and development process. It also discusses a strategy to automatically go from the problem space (conceptual models) to the solution space (final software product) in an automatic way, by implementing a set of mappings between conceptual primitives and their corresponding software representations. To include a practical view of the approach, the work describes a real case-study of a University Department Web System that has been developed with this method.


title: Business and Social Networks in International Trade

abstract: The first two main sections survey the roles of transnational networks in alleviating problems of contract enforcement and providing information about trading opportunities, respectively. The next section covers how domestic networks influence international trade through their impact on domestic market structure. Two overarching questions unify these sections: how do networks affect efficiency, and will networks grow or shrink in importance for international trade over time. The last main sections develop research agendas for two less studied areas: the role of intermediaries who can connect foreign agents to domestic networks and the ability of transnational production networks to facilitate technology transfer.


title: Current-induced spin–orbit torques

abstract: The ability to reverse the magnetization of nanomagnets by current injection has attracted increased attention ever since the spin-transfer torque mechanism was predicted in 1996. In this paper, we...


title: Self-Efficacy Perspective on Achievement Behavior.

abstract: This article examines the idea that perceived self‐efficacy is an important variable in understanding achievement behavior. Self‐efficacy refers to personal judgments of one's capability to organize and implement behaviors in specific situations. Students gain information about their level of self‐efficacy from self‐performances, vicarious experiences, verbal persuasion, and physiological indices. In forming efficacy judgments, people take into account factors such as perceived ability, task difficulty, effort expenditure, performance aids, and outcome patterns. Even when students acquire efficacy information from self‐performances, efficacy judgments are not mere reflections of those performances because educational practices differ in the type of information they convey about students’ capabilities. Some experimental tests of these ideas are summarized along with their educational implications. The self‐efficacy framework is compared with locus of control, attribution, and self‐worth theories of achieve...


title: Understanding the Value of Integrated RFID Systems: A Case Study from Apparel Retail

abstract: AbstractThis contribution is concerned with the business value of Radio Frequency Identification (RFID) technology in retail. We present a case study of an RFID project at Galeria Kaufhof, a subsid...


title: Task unrelated thought: The role of distributed processing

abstract: Task unrelated thought (TUT) refers to thought directed away from the current situation; for example, a day dream. Encapsulated models of cognition propose that qualitative changes in consciousness, i.e., the production of TUT, can be explained in terms of changes in the quantity of resources deployed for task completion. In contrast, distributed models of cognition emphasize the importance of holistic processes in the generation and maintenance of task focus and are consistent with the effects of higher order variables such as schemata. Three experiments were conducted on healthy participants using a categorical stimulus organization to contrast distributed and encapsulated views of cognition. Experiments 1 and 2 showed that despite the increased difficulty of the alphabetical verbal fluency task fewer TUTs were produced during category fluency condition. Experiment 3 replicated the categorical suppression of TUT while encoding and recalling information in a memory task. The results of these three experiments support the predictions derived from models of cognition emphasizing the importance of stimulus organization in the generation and maintenance of task focus and have potential importance for the scientific evaluation of cognition.


title: Self-Regulated Learning: Beliefs, Techniques, and Illusions

abstract: Knowing how to manage one's own learning has become increasingly important in recent years, as both the need and the opportunities for individuals to learn on their own outside of formal classroom settings have grown. During that same period, however, research on learning, memory, and metacognitive processes has provided evidence that people often have a faulty mental model of how they learn and remember, making them prone to both misassessing and mismanaging their own learning. After a discussion of what learners need to understand in order to become effective stewards of their own learning, we first review research on what people believe about how they learn and then review research on how people's ongoing assessments of their own learning are influenced by current performance and the subjective sense of fluency. We conclude with a discussion of societal assumptions and attitudes that can be counterproductive in terms of individuals becoming maximally effective learners.


title: The relative effects of positive interdependence and group processing on student achievement and attitude in online cooperative learning

abstract: This study investigated the effects of positive interdependence and group processing on student achievement and attitude in online learning. Students in three university courses received initial instruction about teamwork skills and cooperative learning and were randomly assigned to one of three treatment groups in each course. The ''positive interdependence'' and the ''group processing'' groups received subsequent associated skills training. The ''no structure'' control group received no additional training. Following the treatment, the ''positive interdependence'' groups had significantly higher achievement than the ''group processing'' or the ''no structure'' groups. There was no significant difference among any of the three groups on student attitude.


title: Turing Test as a Defining Feature of AI-Completeness

abstract: The paper contributes to the development of the theory of AI-Completeness by formalizing the notion of AI-Complete, C-Complete and AI-Hard problems. The intended goal is to provide a classification of problems in the field of Artificial General Intelligence. We prove Turing Test to be an instance of an AI-Complete problem and further show certain AI problems to be AI-Complete or AI-Hard via polynomial time reductions. Finally, the paper suggests some directions for future work on the theory of AI-Completeness.


title: HbA1c-Based Score Model for Predicting Death Risk in Patients with Hepatocellular Carcinoma and Type 2 Diabetes Mellitus

abstract: Aim. To establish a new score model to predict risk of death in patients with hepatocellular carcinoma and type 2 diabetes mellitus. Methods. This was a retrospective study of 147 patients with hepatocellular carcinoma and type 2 diabetes mellitus who came to Beijing Ditan Hospital between October 2008 and June 2013. Univariate and multivariate logistic regression analysis was performed to obtain the independent factors associated with death risk. A new score model was devised according to these factors. Results. A prediction score model composed of HbA1c, NLR, age, and CTP class was devised, which ranged from 0 to 7. AUROC of the score was 0.853 (P < 0.001, 95% CI: 0.791-0.915). Scores 0-2, 3-4, and 5-7 identified patients as low-, medium-, and high-risk categories. The cumulative survival rate was 93.6%, 83.0%, and 74.5% in the low-risk group in 1, 2, and 3 years, while it was 64.0%, 46.0%, and 26.0% in the medium-risk group, whereas it was 24.0%, 12.0%, and 6.0% in the high-risk group, respectively. The cumulative survival rate was significantly higher in the low-risk group than that in the medium-risk group and high-risk group (P < 0.001). Conclusion. The HbA1c-based score model can be used to predict death risk in patients with hepatocellular carcinoma and type 2 diabetes mellitus.


title: A Semi-smooth Newton Method for Regularized State-constrained Optimal Control of the Navier-Stokes Equations

abstract: In this paper, we study semi-smooth Newton methods for the numerical solution of regularized pointwise state-constrained optimal control problems governed by the Navier-Stokes equations. After deriving an appropriate optimality system for the original problem, a class of Moreau-Yosida regularized problems is introduced and the convergence of their solutions to the original optimal one is proved. For each regularized problem a semi-smooth Newton method is applied and its local superlinear convergence verified. Finally, selected numerical results illustrate the behavior of the method and a comparison between the max-min and the Fischer-Burmeister as complementarity functionals is carried out.


title: Construction of Chinese Idiom Knowledge-base and Its Applications

abstract: Idioms are not only interesting but also distinctive in a language for its continuity and metaphorical meaning in its context. This paper introduces the construction of a Chinese idiom knowledge base by the Institute of Computational Linguistics at Peking University and describes an experiment that aims at the automatic emotion classification of Chinese idioms. In the process, we expect to know more about how the constituents in a fossilized composition like an idiom function so as to affect its semantic or grammatical properties. As an important Chinese language resource, our idiom knowledge base will play a major role in applications such as linguistic research, teaching Chinese as a foreign language and even as a tool for preserving this non-material Chinese cultural and historical heritage.


title: ManPy: An Open-Source Layer of DES Manufacturing Objects Implemented in SimPy

abstract: This paper presents a new Discrete Event Simulation (DES) engine focused in the modelling of manufacturing systems. This work is part of an FP7 project titled "Decision support in Real-time for Efficient Agile Manufacturing" (DREAM) which started in October of 2012. As a first step of our work a review of open-source simulation tools was carried out and the results indicated that SimPy could provide a basis for developing a simulation application development platform. Using SimPy we created a layer of basic manufacturing objects, which we named Many (Manufacturing in Python). Many was tested in several model topologies for its accuracy, speed and expandability. In parallel to this work a Graphical User Interface (GUI) is being developed separately, but having in mind that the two modules will cooperate. Results are demonstrated and insight as to how this work will be expanded to a modular, semantic free open simulation application development platform is presented.


title: DOI 10.1007/s11036-007-0017-1 Pervasive Healthcare and Wireless Health Monitoring

abstract: With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, the wireless infrastructure can support many current and emerging healthcare applications. This could fulfill the vision of "Pervasive Healthcare" or healthcare to anyone, anytime, and anywhere by removing locational, time and other restraints while increasing both the coverage and the quality. In this paper, we present applications and requirements of pervasive healthcare, wireless networking solutions and several important research problems. The pervasive healthcare applications include pervasive health monitoring, intelligent emergency management system, pervasive health-care data access, and ubiquitous mobile telemedicine. One major application in pervasive healthcare, termed comprehensive health monitoring is presented in significant details using wireless networking solutions of wireless LANs, ad hoc wireless networks, and, cellular/GSM/3G infrastructure-oriented networks. Many interesting challenges of comprehensive wireless health monitoring, including context-awareness, reliability, and, autonomous and adaptable operation are also presented along with several high-level solutions. Several interesting research problems have been identified and presented for future research.


title: A multiview 3D modeling system based on stereo vision techniques

abstract: This paper introduces a stereo vision system to automatically generate 3D models of real objects. 3D model generation is based on the merging of multiview range images obtained from a digital stereo camera. Stereo images obtained from the camera are rectified, and a correlation-based stereo matching technique reconstructs range images from them. A turntable stage is also employed to obtain multiple range images of the objects. To register range images into a common coordinate system automatically, we introduce and calibrate a turntable coordinate system with respect to the camera coordinate system. After the registration of multiview range images, a 3D model is reconstructed using a volumetric integration technique. Error analysis on turntable calibration and 3D model reconstruction shows the accuracy of our 3D modeling system.


title: On Feature Reduction using Deep Learning for Trend Prediction in Finance

abstract: One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning tools is effective in price trend prediction. The focus has been put mainly on Restricted Boltzmann Machines (RBM) and on output obtained by them. Few attention has been payed to Auto-Encoders (AE) as an alternative means to perform a feature reduction. In this paper we investigate the application of both RBM and AE in more general terms, attempting to outline how architectural and input space characteristics can affect the quality of prediction.


title: A Programmable Galois Field Processor for the Internet of Things

abstract: This paper investigates the feasibility of a unified processor architecture to enable error coding flexibility and secure communication in low power Internet of Things (IoT) wireless networks. Error coding flexibility for wireless communication allows IoT applications to exploit the large tradeoff space in data rate, link distance and energy-efficiency. As a solution, we present a light-weight Galois Field (GF) processor to enable energy-efficient block coding and symmetric/asymmetric cryptography kernel processing for a wide range of GF sizes (2m, m = 2, 3, ..., 233) and arbitrary irreducible polynomials. Program directed connections among primitive GF arithmetic units enable dynamically configured parallelism to efficiently perform either four-way SIMD 5- to 8-bit GF operations, including multiplicative inverse, or a wide bit-width (e.g., 32-bit) GF product in a single cycle. To illustrate our ideas, we synthesized our GF processor in a 28nm technology. Compared to a baseline software implementation optimized for a general purpose ARM M0+ processor, our processor exhibits a 5-20 x speedup for a range of error correction codes and symmetric/asymmetric cryptography applications. Additionally, our proposed GF processor consumes 431μW at 0.9V and 100MHz, and achieves 35.5pJ/b energy efficiency while executing AES operations at 12.2Mbps. We achieve this within an area of 0.01mm2.


title: DeepMem: Learning Graph Neural Network Models for Fast and Robust Memory Forensic Analysis

abstract: Kernel data structure detection is an important task in memory forensics that aims at identifying semantically important kernel data structures from raw memory dumps. It is primarily used to collect evidence of malicious or criminal behaviors. Existing approaches have several limitations: 1) list-traversal approaches are vulnerable to DKOM attacks, 2) robust signature-based approaches are not scalable or efficient, because it needs to search the entire memory snapshot for one kind of objects using one signature, and 3) both list-traversal and signature-based approaches all heavily rely on domain knowledge of operating system. Based on the limitations, we propose DeepMem, a graph-based deep learning approach to automatically generate abstract representations for kernel objects, with which we could recognize the objects from raw memory dumps in a fast and robust way. Specifically, we implement 1) a novel memory graph model that reconstructs the content and topology information of memory dumps, 2) a graph neural network architecture to embed the nodes in the memory graph, and 3) an object detection method that cross-validates the evidence collected from different parts of objects. Experiments show that DeepMem achieves high precision and recall rate in identify kernel objects from raw memory dumps. Also, the detection strategy is fast and scalable by using the intermediate memory graph representation. Moreover, DeepMem is robust against attack scenarios, like pool tag manipulation and DKOM process hiding.


title: The Effects of Transformational and Change Leadership on Employees' Commitment to a Change: A Multilevel Study

abstract: The effects of transformational leadership on the outcomes of specific change initiatives are not well understood. Conversely, organizational change studies have examined leader behaviors during specific change implementations yet have failed to link these to broader leadership theories. In this study, the authors investigate the relationship between transformational and change leadership and followers’ commitment to a particular change initiative as a function of the personal impact of the changes. Transformational leadership was found to be more strongly related to followers’ change commitment than change-specific leadership practices, especially when the change had significant personal impact. For leaders who were not viewed as transformational, good change-management practices were found to be associated with higher levels of change commitment.


title: Digitizing Offline Shopping Behavior Towards Mobile Marketing.

abstract: The proliferation of mobile technologies makes it possible for mobile advertisers to go beyond the realtime snapshot of the static location and contextual information about consumers. In this study, we propose a novel mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To evaluate the effectiveness of this strategy, we design a large-scale randomized field experiment in a large shopping mall in Asia based on 83,370 unique user responses for two weeks in 2014. We found the new mobile trajectory-based advertising is significantly more effective for focal advertising store compared to several existing baselines. It is especially effective in attracting highincome consumers. Interestingly, it becomes less effective during the weekend. This indicates closely targeted mobile ads may constrict consumer focus and significantly reduce the impulsive purchase behavior. Our finding suggests marketers should carefully design mobile advertising strategy, depending on different business contexts.


title: Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters

abstract: In this paper we present a new tempo estimation algorithm which uses a bank of resonating comb filters to determine the dominant periodicity of a musical excerpt. Unlike existing (comb filter based) approaches, we do not use handcrafted features derived from the audio signal, but rather let a recurrent neural network learn an intermediate beat-level representation of the signal and use this information as input to the comb filter bank. While most approaches apply complex post-processing to the output of the comb filter bank like tracking multiple time scales, processing different accent bands, modelling metrical relations, categorising the excerpts into slow / fast or any other advanced processing, we achieve state-of-the-art performance on nine of ten datasets by simply reporting the highest resonator’s histogram peak.


title: Orchestration of IT/cloud and networks: From Inter-DC interconnection to SDN/NFV 5G services

abstract: The so-called 5G networks promise to be the foundations for the deployment of advanced services, conceived around the joint allocation and use of heterogeneous resources, including network, computing and storage. Resources are placed on remote locations constrained by the different service requirements, resulting in cloud infrastructures (as pool of resources) that need to be interconnected. The automation of the provisioning of such services relies on a generalized orchestration, defined as to the coherent coordination of heterogeneous systems, applied to common cases such as involving heterogeneous network domains in terms of control or data plane technologies, or cloud and network resources. Although cloud-computing platforms do take into account the need to interconnect remote virtual machine instances, mostly rely on managing L2 overlays over L3 (IP). The integration with transport networks is still not fully achieved, including leveraging the advances in software defined networks and transmission. We start with an overview of network orchestration, considering different models; we extend them to take into account cloud management while mentioning relevant existing initiatives and conclude with the NFV architecture.


title: A Comprehensive Study of Security of Internet-of-Things

abstract: Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT. Moreover, commercialization of IoT has led to public security concerns, including personal privacy issues, threat of cyber attacks, and organized crime. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this survey attempts to provide a comprehensive list of vulnerabilities and countermeasures against them on the edge-side layer of IoT, which consists of three levels: (i) edge nodes, (ii) communication, and (iii) edge computing. To achieve this goal, we first briefly describe three widely-known IoT reference models and define security in the context of IoT. Second, we discuss the possible applications of IoT and potential motivations of the attackers who target this new paradigm. Third, we discuss different attacks and threats. Fourth, we describe possible countermeasures against these attacks. Finally, we introduce two emerging security challenges not yet explained in detail in previous literature.


title: NetShield: massive semantics-based vulnerability signature matching for high-speed networks

abstract: Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed vulnerability signatures (a.k.a data patches) can exactly describe the vulnerability conditions and achieve better accuracy. However, how to efficiently apply vulnerability signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue. This paper presents the first systematic design of vulnerability signature based parsing and matching engine, NetShield, which achieves multi-gigabit throughput while offering much better accuracy. Particularly, we made the following contributions: (i) we proposed a candidate selection algorithm which efficiently matches thousands of vulnerability signatures simultaneously requiring a small amount of memory; (ii) we proposed an automatic lightweight parsing state machine achieving fast protocol parsing. Experimental results show that the core engine of NetShield achieves at least 1.9+Gbps signature matching throughput on a 3.8GHz single-core PC, and can scale-up to at least 11+Gbps under a 8-core machine for 794 HTTP vulnerability signatures.


title: The feasibility study on the combined equipment between micro-SMES and inductive/electronic type fault current limiter

abstract: The concept of the combined equipment between micro-SMES and inductive/electronic type FCL is proposed in this paper. Having the multifunction for a superconducting device, the new equipment can serve as the protective component for a dual power system. The specification of a testing model was determined and the transient performance was analyzed by Matlab software. The results show that the combined equipment is realizable for a dual power system application, where it has the major function of limiting fault current (FCL function) and the minor function of maintaining power fluctuation (SMES function).


title: Machine Learned Sentence Selection Strategies for Query-Biased Summarization

abstract: It has become standard for search engines to augment result lists with document summaries. Each document summary consists of a title, abstract, and a URL. In this work, we focus on the task of selecting relevant sentences for inclusion in the abstract. In particular, we investigate how machine learning-based approaches can effectively be applied to the problem. We analyze and evaluate several learning to rank approaches, such as ranking support vector machines (SVMs), support vector regression (SVR), and gradient boosted decision trees (GBDTs). Our work is the first to evaluate SVR and GBDTs for the sentence selection task. Using standard TREC test collections, we rigorously evaluate various aspects of the sentence selection problem. Our results show that the effectiveness of the machine learning approaches varies across collections with different characteristics. Furthermore, the results show that GBDTs provide a robust and powerful framework for the sentence selection task and significantly outperform SVR and ranking SVMs on several data sets.


title: Tracking Users on the Internet with Behavioral Patterns: Evaluation of Its Practical Feasibility

abstract: Traditionally, service providers, who want to track the activities of Internet users, rely on explicit tracking techniques like HTTP cookies. From a privacy perspective behavior-based tracking is even more dangerous, because it allows service providers to track users passively, i. e., without cookies. In this case multiple sessions of a user are linked by exploiting characteristic patterns mined from network traffic.


title: NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

abstract: Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.


title: IndiGolog: A High-Level Programming Language for Embedded Reasoning Agents

abstract: IndiGolog isaprogramming languagefor autonomousagentsthat sense their environment anddo planning astheyoperate. Insteadof classical planning, it supports high-level program execution. The programmer provides a high-level nondeterministicprograminvolving domain-speci? c actions andteststo perform the agent’s tasks. The IndiGolog interpreterthenreasons aboutthepreconditions andeffectsofthe actionsintheprogramtonda legalterminatingexecution.To support this, the programmer provides a declarative specication of the domain (i.e.,primitive actions,preconditions andeffects, whatis known aboutthe initial state)inthe situation calculus. Theprogrammer can controlthe amountof non-determinism in the program and how muchof it is searched over. The language isrichand supports concurrentprogramming.Programsareexecuted onlinetogether withsensingthe environment and monitoringforevents,thus supporting thedevelopmentofreactiveagents.We discussthe language, itsimplementation, and applicationsthathave beenrealized withit.


Accessory bronchiole-alveolar communications


title: A low-cost multi-target simulator for FMCW radar system calibration and testing

abstract: We present the realization of a novel radar target simulator, based on a modulated-reflector radar system. The simulator, designed for the 24 GHz frequency band, uses low-cost modulated-reflector nodes for each single target and thus is highly scalable. It is demonstrated, that the concept is able to simulate multiple artificial targets, located at user-selectable ranges and even velocities, utilized within a completely static setup. Furthermore, the concept allows to dynamically adjust the radar cross section of each single target. This future-ready system is perfectly suitable for fast and reliable IF-chain calibration of multi-channel radar systems.


title: Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition Based on Hierarchical MRFs

abstract: Markov Random Fields (MRFs) are a popular tool in many computer vision problems and faithfully model a broad range of local dependencies. However, rooted in the Hammersley–Clifford theorem, they face serious difficulties in enforcing the global coherence of the solutions without using too high order cliques that reduce the computational effectiveness of the inference phase. Having this problem in mind, we describe a multi-layered (hierarchical) architecture for MRFs that is based exclusively in pairwise connections and typically produces globally coherent solutions, with 1) one layer working at the local ( pixel ) level, modeling the interactions between adjacent image patches; and 2) a complementary layer working at the object (hypothesis) level pushing toward globally consistent solutions. During optimization, both layers interact into an equilibrium state that not only segments the data, but also classifies it. The proposed MRF architecture is particularly suitable for problems that deal with biological data (e.g., biometrics), where the reasonability of the solutions can be objectively measured. As test case, we considered the problem of hair / facial hair segmentation and labeling, which are soft biometric labels useful for human recognition in-the-wild . We observed performance levels close to the state-of-the-art at a much lower computational cost, both in the segmentation and classification (labeling) tasks.


title: Cyber Incivility @ Work: The New Age of Interpersonal Deviance

abstract: Abstract The current study was designed to extend the interpersonal deviance literature into the online domain by examining the incidence and impact of supervisor cyber incivility and neuroticism on employee outcomes at work. Conservation of Resources (COR) theory was used as the guiding framework because cyber incivility is thought to deplete energetic resources in much the same way that other stressors do, ultimately leading to negative outcomes like burnout. Results indicate that supervisor cyber incivility is positively related to burnout, absenteeism, and turnover intentions. Support was also found for the role of neuroticism as a moderator of the relationship between supervisor cyber incivility and outcomes. In general, the relations between cyber incivility and outcomes were stronger for those individuals reporting higher levels of neuroticism. Results are discussed in terms of COR theory, and possible mechanisms for the role of neuroticism in the stressor-strain relationship are discussed. The cur...


title: Ultra high extinction-ratio and ultra low chirp optical intensity modulation for pure two-tone lightwave signal generation

abstract: Pure two-tone signal generation with spurious suppression ratio of 47 dB was demonstrated by using an optical Mach-Zehnder modulator with precisely balanced operation, where extinction ratio and chirp parameter were respectively 64 dB and 0.0099.


title: A partitioned control scheme for mobile robot path tracking

abstract: An approach to autonomously navigate a full-sized autonomous vehicle that treats vehicle control and obstacle detection separately is considered. The vehicle control that has enabled an autonomous vehicle to travel at speeds up to 35 km/h is discussed. The limitations of existing schemes that restrict their consideration to kinematic models are pointed out, and it is shown that it is possible to obtain an increase in performance through the use of approximate dynamical models that capture first-order effects. The present approach combines such a modeling philosophy with accurate feedback in world coordinates from sensors that have only recently become available. Experimental results of an implementation are presented. >


title: Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing

abstract: In this paper, we develop a new lossy compression framework for hyperspectral images, termed hyperspectral coded aperture (HYCA), which combines the ideas of spectral unmixing and compressive sensing. It takes advantage of two main properties of hyperspectral data, namely the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. In other words, our proposed approach intends to exploit the fact that the high dimensional hyperspectral data lives in a subspace of much lower dimension due to the mixing phenomenon. Our experimental results, conducted with synthetic hyperspectral data, indicate that the proposed approach represents a promising new strategy.


title: Deep Learning in Software Engineering

abstract: Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future.


title: Sorbitol-based osmotic diarrhea: Possible causes and mechanism of prevention investigated in rats

abstract: AIM: To study the possible causes of sorbitol (S)-based diarrhea and its mechanism of reduction by rice gruel (RG) in cecectomized rats. ::: ::: METHODS: S was dissolved either in distilled water or in RG (50 g/L) and ingested as a single oral dose (1.2 g/kg body mass, containing 0.5 g/L phenol red as a recovery marker) by S (control) and S + RG groups (n = 7), respectively. This dose is over the laxative dose for humans. Animals were sacrificed exactly 1 h after dose ingestion, without any access to drinking water. The whole gastro-intestinal tract was divided into seven segments and sampled to analyze the S and marker remaining in its contents. ::: ::: RESULTS: Gastric-emptying and intestinal transit were comparatively slower in the S + RG group. Also, the S absorption index in the 3rd and last quarter of the small intestine (24.85 ± 18.88% vs 0.0 ± 0.0% and 39.09 ± 32.75% vs 0.0 ± 0.0%, respectively, P < 0.05) was significantly higher in the S + RG group than in the control group. The S absorption index and the intestinal fluid volume are inversely related to each other. ::: ::: CONCLUSION: The intestinal mal-absorption of S is the main reason for S-based osmotic diarrhea. Where RG enhanced the absorption of S through passive diffusion, the degree of diarrhea was reduced in cecectomized rats.


Mobile Security Assurance through ArchiMate


title: Lichenoid folliculitis: A unifying concept†

abstract: Skin diseases presenting with keratotic papules, atrophy, cicatricial alopecia and/or "lichenoid" histopathologic changes have been described under at least 30 names. This family of diseases contains 2 subgroups, largely based on clinical features: keratosis pilaris atrophicans (KPA; including keratosis pilaris atrophicans faciei/ulerythema ophryogenes, atrophoderma vermiculatum, and keratosis follicularis spinulosa decalvans); and the lichen planopilaris (LPP) subgroup (including LPP, frontal fibrosing alopecia, Graham-Little-Piccardi-Lassueur Syndrome and fibrosing alopecia in a pattern distribution). An interface dermatitis with lichenoid inflammation is characteristic of the LPP group of disorders, but the literature provides scant information about the histopathology of the KPA group. Our experience has been that the 2 subgroups show a unifying histologic similarity as well as considerable clinical overlap. Because these conditions overlap clinically and histologically, we propose that the term lichenoid folliculitis (LF) be used to refer to this group of diseases, thus simplifying cumbersome nomenclature and highlighting the possibility of shared pathogenesis and treatment options.


title: Learning to Compare: Relation Network for Few-Shot Learning

abstract: We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.


Gamification and Education : A Literature


title: Harvesting Low-Frequency (<5 Hz) Irregular Mechanical Energy: A Possible Killer Application of Triboelectric Nanogenerator

abstract: Electromagnetic generators (EMGs) and triboelectric nanogenerators (TENGs) are the two most powerful approaches for harvesting ambient mechanical energy, but the effectiveness of each depends on the triggering frequency. Here, after systematically comparing the performances of EMGs and TENGs under low-frequency motion ( 10–100 V) and independent of frequency so that most of the generated power can be effectively used to po...


The Abelian sandpile model on an infinite tree


title: Optoelectronic Techniques for Surface Characterization of Fabrics

abstract: In the textile field, fabric surface properties influence the tactile feel, the visual aspect and some mechanical properties. They are strongly linked to manufacturing process, particularly to surface processes. Therefore the study of these fabric surface properties proves of importance. More precisely, the tactile feel is one of the most important garment selling points since one of the first consumer actions is to touch the fabric. Thanks to objective tactile parameters, the manufacturer can design and produce fabrics which please the consumers while respecting functionality of cloth. Moreover, for standard woven or knitted structures or low grammage nonwovens, the surface state can give some information about tensile properties or strain during tensile stress. In order to characterize surface contact devices, such as tribometers1, are often used. They provide some information about friction behaviour, roughness of the surface or some other criteria more or less complex. This type of devices is often designed and used for hard materials. Concerning textile surfaces, because of their softness, these methods can have an influence on measurements in so far as superficial hairiness and even intrinsic structure may be modified. Both of these structural characteristics, hairiness and texture, are predominant in the tactile feel of textile surfaces. Complementary to tribological methods widely used but whose precision may be insufficient, non-contact methods have been developed in order to characterise fabric surface state. These methods are mainly based on optical principles and have been getting a growing interest. In this chapter we will present our contributions to the non-contact characterization of textile surfaces, firstly through the characterization of the state of surface (intrinsic structure and hairiness) and then through the evaluation of some mechanical properties of textile fabrics (strain and tensile properties). Implementations resulting in various apparatuses and some results are given in order to illustrate our purpose.


title: A novel hybrid visual odometry using an RGB-D camera

abstract: RGB-D cameras provide both a color image and a depth image which contains the real depth information about per-pixel. The richness of their data and the development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a novel hybrid visual odometry using an RGB-D camera. Different from the original method, it is a pure visual odometry method without any other information, such as inertial data. The important key is hybrid, which means that the odometry can be executed in two different processes depending on the conditions. It consists of two parts, including a feature-based visual odometry and a direct visual odometry. Details about the algorithm are discussed in the paper. Especially, the switch conditions are described in detail. Beside, we evaluate the continuity and robustness for the system on public dataset. The experiments demonstrate that our system has more stable continuity and better robustness.


title: Survey of weakly supervised semantic segmentation methods

abstract: Weakly supervised image semantic segmentation is quite popular in computer vision and machine learning today. The existing supervised semantic segmentation approaches require lots of pixel-based annotation for training, which are labor- and time-consuming to obtain. By comparison, weakly supervised methods don't need the pixel-level label, and they can do the semantic segmentation work by only using the image-level label. This paper provides a review on weakly supervised semantic segmentation methods. First, the existing methods are described. Then, the main datasets and evaluating indicator are exposed. Subsequently, the results are discussed. Finally, the conclusions are given.


title: Multi-spectral SIFT for scene category recognition

abstract: We use a simple modification to a conventional SLR camera to capture images of several hundred scenes in colour (RGB) and near-infrared (NIR). We show that the addition of near-infrared information leads to significantly improved performance in a scene-recognition task, and that the improvements are greater still when an appropriate 4-dimensional colour representation is used. In particular we propose MSIFT–a multispectral SIFT descriptor that, when combined with a kernel based classifier, exceeds the performance of state-of-the-art scene recognition techniques (e.g., GIST) and their multispectral extensions. We extensively test our algorithms using a new dataset of several hundred RGB-NIR scene images, as well as benchmarking against Torralba's scene categorization dataset.


title: A greedy approach to sparse canonical correlation analysis

abstract: We consider the problem of sparse canonical correlation analysis (CCA), i.e., the search for two linear combinations, one for each multivariate, that yield maximum correlation using a specified number of variables. We propose an efficient numerical approximation based on a direct greedy approach which bounds the correlation at each stage. The method is specifically designed to cope with large data sets and its computational complexity depends only on the sparsity levels. We analyze the algorithm's performance through the tradeoff between correlation and parsimony. The results of numerical simulation suggest that a significant portion of the correlation may be captured using a relatively small number of variables. In addition, we examine the use of sparse CCA as a regularization method when the number of available samples is small compared to the dimensions of the multivariates.


abstract: In this paper, a design of Substrate Integrated Waveguide (SIW) cavity backed slot antenna for polarization diversity application is presented. The proposed design uses a pair of slots placed at right angle with respect to each other at the top metallic plate of the cavity to generate two linear orthogonally polarized radiation pattern at same frequency to implement polarization diversity technique. The antenna interchanges between two separate feed line to excite either of the two orthogonal modes TE120 and TE210 respectively at same frequency and as a result, the proposed antenna exhibits a radiation pattern switching between Vertical (VP) and Horizontal Polarization (HP) at 9.98 GHz. The simulated result of the proposed design shows an isolation better than 20 dB between two input port and a gain of 4.2 dBi at broadside direction.


title: Toward Secure and Dependable Storage Services in Cloud Computing

abstract: Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, such a service is also relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with very lightweight communication and computation cost. The auditing result not only ensures strong cloud storage correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.


title: Novelty-Driven Cooperative Coevolution

abstract: Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents' individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.


title: Usability: Lessons Learned … and Yet to Be Learned

abstract: The philosopher of science J. W. Grove (1989) once wrote, “There is, of course, nothing strange or scandalous about divisions of opinion among scientists. This is a condition for scientific progress” (p. 133). Over the past 30 years, usability, both as a practice and as an emerging science, has had its share of controversies. It has inherited some from its early roots in experimental psychology, measurement, and statistics. Others have emerged as the field of usability has matured and extended into user-centered design and user experience. In many ways, a field of inquiry is shaped by its controversies. This article reviews some of the persistent controversies in the field of usability, starting with their history, then assessing their current status from the perspective of a pragmatic practitioner. Put another way: Over the past three decades, what are some of the key lessons we have learned, and what remains to be learned? Some of the key lessons learned are:• When discussing usability, it is important ...


title: An efficient system for vehicle tracking in multi-camera networks

abstract: The recent deployment of very large-scale camera networks has led to a unique version of the tracking problem whose goal is to detect and track every vehicle within a large urban area. To address this problem we exploit constraints inherent in urban environments (i.e. while there are often many vehicles, they follow relatively consistent paths) to create novel visual processing tools that are highly efficient in detecting cars in a fixed scene and at connecting these detections into partial tracks.We derive extensions to a network flow based probabilistic data association model to connect these tracks between cameras. Our real time system is evaluated on a large set of ground-truthed traffic videos collected by a network of seven cameras in a dense urban scene.


title: Multi-oriented Text Extraction from Information Graphics

abstract: Existing research on analyzing information graphics assume to have a perfect text detection and extraction available. However, text extraction from information graphics is far from solved. To fill this gap, we propose a novel processing pipeline for multi-oriented text extraction from infographics. The pipeline applies a combination of data mining and computer vision techniques to identify text elements, cluster them into text lines, compute their orientation, and uses a state-of-the-art open source OCR engine to perform the text recognition. We evaluate our method on 121 infographics extracted from an open access corpus of scientific publications. The results show that our approach is effective and significantly outperforms a state-of-the-art baseline.


title: PLDA for speaker verification with utterances of arbitrary duration

abstract: The duration of speech segments has traditionally been controlled in the NIST speaker recognition evaluations so that researchers working in this framework have been relieved of the responsibility of dealing with the duration variability that arises in practical applications. The fixed dimensional i-vector representation of speech utterances is ideal for working under such controlled conditions and ignoring the fact that i-vectors extracted from short utterances are less reliable than those extracted from long utterances leads to a very simple formulation of the speaker recognition problem. However a more realistic approach seems to be needed to handle duration variability properly. In this paper, we show how to quantify the uncertainty associated with the i-vector extraction process and propagate it into a PLDA classifier. We evaluated this approach using test sets derived from the NIST 2010 core and extended core conditions by randomly truncating the utterances in the female, telephone speech trials so that the durations of all enrollment and test utterances lay in the range 3-60 seconds and we found that it led to substantial improvements in accuracy. Although the likelihood ratio computation for speaker verification is more computationally expensive than in the standard i-vector/PLDA classifier, it is still quite modest as it reduces to computing the probability density functions of two full covariance Gaussians (irrespective of the number of the number of utterances used to enroll a speaker).


title: Who Benefits from the"Sharing"Economy of Airbnb?

abstract: Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.


Disentangling Variational Autoencoders for Image Classification


title: Generating Domain-Specific Dictionaries Using Bayesian Learning

abstract: This paper aims to operationalize subjective information processing in financial news disclosures. In order to measure news tone, previous research commonly utilizes manually-selected positive and negative word lists, such as the Harvard-IV psychological dictionary. However, such dictionaries may not be suitable for the domain of financial news because positive and negative entries could have different connotations in a financial context. To overcome the problem of words that are selected ex ante, we incorporate several Bayesian variable selection methods to select the relevant positive and negative words from financial news disclosures. These domain-specific dictionaries outperform existing dictionaries in terms of both their explanatory power and predictive performance, resulting in an improvement of up to 93.25 % in the correlation between news sentiment and stock market returns. According to our findings, the interpretation of words strongly depends on the context and managers need to be cautious when framing negative content using positive words.


title: Reviewing South Africa’s malaria elimination strategy (2012–2018): progress, challenges and priorities

abstract: Background ::: With a sustained national malaria incidence of fewer than one case per 1000 population at risk, in 2012 South Africa officially transitioned from controlling malaria to the ambitious goal of eliminating malaria within its borders by 2018. This review assesses the progress made in the 3 years since programme re-orientation while highlighting challenges and suggesting priorities for moving the malaria programme towards elimination.


title: Cortical involvement in the generation of “involuntary” movements in restless legs syndrome

abstract: The pathophysiology of the restless legs syndrome (RLS) is poorly understood. One index symptom is the urge to move, increasing at night. It is largely accepted that movement in RLS is “involuntary” and that the generators are below the level of the neocortex. Here the authors provide evidence that characteristic leg movements in RLS are preceded by preparatory cortical activation that cannot be attributed to sensory processing or to the expectancy of movement.


title: Syntax Parsing: Implementation Using Grammar-Rules for English Language

abstract: Syntactic parsing deals with syntactic structure of a sentence. The word 'syntax' refers to the grammatical arrangement of words in a sentence and their relationship with each other. The objective of syntactic analysis is to find syntactic structure of a sentence which is usually depicted as a tree. Identifying the syntactic structure is useful in determining the meaning of a sentence. Natural language processing is an arena of computer science and linguistics, concerned with the dealings amongst computers and human languages. It processes the data through lexical analysis, Syntax analysis, Semantic analysis, Discourse processing, Pragmatic analysis. This paper gives various parsing methods. The algorithm in this paper splits the English sentences into parts using POS tagger, It identifies the type of sentence (Facts, active, passive etc.) and then parses these sentences using grammar rules of Natural language. The algorithm has been tested on real sentences of English and it accomplished an accuracy of 81%.


title: Equivalence between LINE and Matrix Factorization

abstract: LINE [1], as an efficient network embedding method, has shown its effectiveness in dealing with large-scale undirected, directed, and/or weighted networks. Particularly, it proposes to preserve both the local structure (represented by First-order Proximity) and global structure (represented by Second-order Proximity) of the network. In this study, we prove that LINE with these two proximities (LINE(1st) and LINE(2nd)) are actually factoring two different matrices separately. Specifically, LINE(1st) is factoring a matrix M (1), whose entries are the doubled Pointwise Mutual Information (PMI) of vertex pairs in undirected networks, shifted by a constant. LINE(2nd) is factoring a matrix M (2), whose entries are the PMI of vertex and context pairs in directed networks, shifted by a constant. We hope this finding would provide a basis for further extensions and generalizations of LINE.


The moral ambiguity of advertising : spreading sexism under the mask of humor


title: Grid of the future

abstract: Many believe the electric power system is undergoing a profound change driven by a number of needs. There's the need for environmental compliance and energy conservation. We need better grid reliability while dealing with an aging infrastructure. And we need improved operational effi ciencies and customer service. The changes that are happening are particularly signifi cant for the electricity distribution grid, where "blind" and manual operations, along with the electromechanical components, will need to be transformed into a "smart grid." This transformation will be necessary to meet environmental targets, to accommodate a greater emphasis on demand response (DR), and to support plug-in hybrid electric vehicles (PHEVs) as well as distributed generation and storage capabilities. It is safe to say that these needs and changes present the power industry with the biggest challenge it has ever faced. On one hand, the transition to a smart grid has to be evolutionary to keep the lights on; on the other hand, the issues surrounding the smart grid are signifi cant enough to demand major changes in power systems operating philosophy.


title: Categorize the Quality of Cotton Seeds Based on the Different Germination of the Cotton Using Machine Knowledge Approach

abstract: A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.


title: Experience in the design and development of a game based on head-tracking input

abstract: Tracking technologies, such as eye and head-tracking, provide novel techniques for interacting with video games. For instance, players can shoot with their eyes in a first person shooter using gaze-based input. Head-tracking systems allow players to look around a virtual cockpit by simply moving their head. However, tracking systems are typically based on expensive specialized equipment. The prohibitive costs of such systems have motivated the creation of low-cost head-tracking solutions using simple web cameras and infrared light detection. In this paper, we describe our experience developing a simple shooting game which incorporates such low-cost head-tracking technology.


title: Robust rank aggregation for gene list integration and meta-analysis

abstract: MOTIVATION ::: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. ::: ::: ::: RESULTS ::: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings. ::: ::: ::: AVAILABILITY ::: All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/.


title: Large-area silicon drift detectors for new applications in nuclear medicine imaging

abstract: In this paper, we have investigated the performance of a large-area (1 cm/sup 2/) square-shaped silicon drift detector (SDD) operating as an X-ray detector in direct conversion mode, as well as a photodetector in combination with a single crystal CsI:Tl scintillator (10 mm /spl times/ 10 mm /spl times/ 7 mm). An external FET, along with a low-noise charge sensitive preamplifier, was used for testing the prototype detector in the temperature range of -50 to +26/spl deg/C. In direct conversion mode, the SDD exhibited an energy resolution of 195 eV FWHM (at 5.9 keV, -23.5/spl deg/C, 4 /spl mu/s shaping time) and 170 eV FWHM (at 5.9 keV, -50/spl deg/C, 8 /spl mu/s shaping time). Pulse height spectra and signal rise times were recorded at different X-ray interaction locations across the SDD surface with the aid of a moveable collimator. Depending on collimator position, we measured signal rise times between 100 and 750 ns. After coupling the CsI-scintillator directly to the SDD surface, we obtained an excellent energy resolution of 7.0% FWHM with an uncollimated /sup 57/Co source (122 keV, -10/spl deg/C, 12 /spl mu/s shaping time). At -23.5/spl deg/C, we measured a signal rise time of approximately 25 /spl mu/s for this scintillator-SDD unit. Using a pulsed LED as excitation source, we were able to evaluate the electron drift characteristics of the tested SDD and to identify the temporal behavior of the CsI:Tl scintillator to be the dominant component of this long rise time. A short analysis regarding the different SDD signal rise times with and without scintillator will be presented.


title: Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions

abstract: Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our proposed solution represents the treatment response curves using linear time-invariant dynamical systems---this provides a flexible means for modeling response over time to highly variable dose curves. Moreover, for multivariate data, the proposed method: uncovers shared structure in treatment response and the baseline across multiple markers; and, flexibly models challenging correlation structure both across and within signals over time. For this, we build upon the framework of multiple-output Gaussian Processes. On simulated and a challenging clinical dataset, we show significant gains in accuracy over state-of-the-art models.


Towards Effective Log Summarization


More on Regularization and (Generalized) Ridge Operators


title: Gaussian Process Random Fields

abstract: Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and parallelizeable approximation to the full GP marginal likelihood, enabling latent variable modeling and hyperparameter selection on large datasets. We demonstrate its effectiveness on synthetic spatial data as well as a real-world application to seismic event location.


title: An application of recurrent neural networks to discriminative keyword spotting

abstract: The goal of keyword spotting is to detect the presence of specific spoken words in unconstrained speech. The majority of keyword spotting systems are based on generative hidden Markov models and lack discriminative capabilities. However, discriminative keyword spotting systems are currently based on frame-level posterior probabilities of sub-word units. This paper presents a discriminative keyword spotting system based on recurrent neural networks only, that uses information from long time spans to estimate word-level posterior probabilities. In a keyword spotting task on a large database of unconstrained speech the system achieved a keyword spotting accuracy of 84.5%


title: Points of Interest and Visual Dictionaries for Automatic Retinal Lesion Detection

abstract: In this paper, we present an algorithm to detect the presence of diabetic retinopathy (DR)-related lesions from fundus images based on a common analytical approach that is capable of identifying both red and bright lesions without requiring specific pre- or postprocessing. Our solution constructs a visual word dictionary representing points of interest (PoIs) located within regions marked by specialists that contain lesions associated with DR and classifies the fundus images based on the presence or absence of these PoIs as normal or DR-related pathology. The novelty of our approach is in locating DR lesions in the optic fundus images using visual words that combines feature information contained within the images in a framework easily extendible to different types of retinal lesions or pathologies and builds a specific projection space for each class of interest (e.g., white lesions such as exudates or normal regions) instead of a common dictionary for all classes. The visual words dictionary was applied to classifying bright and red lesions with classical cross validation and cross dataset validation to indicate the robustness of this approach. We obtained an area under the curve (AUC) of 95.3% for white lesion detection and an AUC of 93.3% for red lesion detection using fivefold cross validation and our own data consisting of 687 images of normal retinae, 245 images with bright lesions, 191 with red lesions, and 109 with signs of both bright and red lesions. For cross dataset analysis, the visual dictionary also achieves compelling results using our images as the training set and the RetiDB and Messidor images as test sets. In this case, the image classification resulted in an AUC of 88.1% when classifying the RetiDB dataset and in an AUC of 89.3% when classifying the Messidor dataset, both cases for bright lesion detection. The results indicate the potential for training with different acquisition images under different setup conditions with a high accuracy of referral based on the presence of either red or bright lesions or both. The robustness of the visual dictionary against image quality (blurring), resolution, and retinal background, makes it a strong candidate for DR screening of large, diverse communities with varying cameras and settings and levels of expertise for image capture.


title: Tibetan Text Clustering Based on Machine Learning

abstract: Tibetan information processing technology has been obtained some achievements. But it falls behind Chinese and English information processing. It still needs to be paid more attention. Text clustering has the potential to accelerate the development of Tibetan information processing. In this paper, we propose an approach of Tibetan text clustering based on machine learning. Firstly, the approach is to execute Tibetan word segmentation with Tibetan texts. Then feature selection and text representation are conducted. Finally, K-means and DBSCAN are adopted to deal with the text clustering. The experimental results present that DBSCAN has better performance for Tibetan text clustering. Text clustering systems are designed based on proposed approach. The study is meaningful for the Tibetan text classification, information retrieval as well as construction of high-quality Tibetan corpus.


title: Deconvolution methods for 3-D fluorescence microscopy images

abstract: This paper presents an overview of various deconvolution techniques of 3D fluorescence microscopy images. It describes the subject of image deconvolution for 3D fluorescence microscopy images and provides an overview of the distortion issues in different areas. The paper presents a brief schematic description of fluorescence microscope systems and provides a summary of the microscope point-spread function (PSF), which often creates the most severe distortion in the acquired 3D image. Finally, it discusses the ongoing research work in the area and provides a brief review of performance measures of 3D deconvolution microscopy techniques. It also provides a summary of the numerical results using simulated data and presents the results obtained from the real data.


title: Accelerating HMMER on GPUs by implementing hybrid data and task parallelism

abstract: Many biologically motivated problems are expressed as dynamic programming recurrences and are difficult to parallelize due to the intrinsic data dependencies in their algorithms. Therefore their solutions have been sped up using task level parallelism only. Emerging platforms such as GPUs are appealing parallel architectures for high-performance; at the same time they are a motivation to rethink the algorithms associated with these problems, to extract finer-grained parallelism such as data parallelism. In this paper, we consider the hmmersearch program as a representative of these problems and we re-design its computational algorithm to extract data parallelism for a more efficient execution on emerging platforms, despite the fact that hmmersearch has data dependencies. Our approach outperforms other existing methods when searching a very large database of unsorted sequences on GPUs.


title: Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors

abstract: This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers’ movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.


title: The Sushi peptides: structural characterization and mode of action against Gram-negative bacteria

abstract: The compositional difference in microbial and human cell membranes allows antimicrobial peptides to preferentially bind microbes. Peptides which specifically target lipopolysaccharide (LPS) and palmitoyl-oleoyl-phosphatidylglycerol (POPG) are efficient antibiotics. From the core LPS-binding region of Factor C, two 34-mer Sushi peptides, S1 and S3, were derived. S1 functions as a monomer, while S3 is active as a dimer. Both S1 and S3 display detergent-like properties in disrupting LPS aggregates, with specificity for POPG resulting from electrostatic and hydrophobic forces between the peptides and the bacterial lipids. During interaction with POPG, the S1 transitioned from a random coil to an alpha-helix, while S3 resumed a mixture of alpha-helix and beta-sheet structures. The unsaturated nature of POPG confers fluidity and enhances insertion of the peptides into the lipid bilayer, causing maximal disruption of the bacterial membrane. These parameters should be considered in designing and developing new generations of peptide antibiotics with LPS-neutralizing capability.


title: Software-Defined Networking: State of the Art and Research Challenges

abstract: Network usage and demands are growing at a rapid pace, while the network administrators are facing difficulties in tracking the frequent users’ access of the network. Consequently, managing the infrastructure supporting these demands has become a complicated and time-consuming task. Networks are also in a flux state, they are not only expanding but require reconfigurations to meet the business needs. Software defined networking (SDN) and network function virtualization (NFV) technologies have emerged as promising solutions that change the cost profile and agility of internet protocol (IP) networks. Conceptually, SDN separates the network control logic from its underlying hardware, enabling network administrators to exert more control over network functioning and providing a unified global view of the network. However, SDN and NFV can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges.


title: Im2Avatar: Colorful 3D Reconstruction from a Single Image

abstract: Existing works on single-image 3D reconstruction mainly focus on shape recovery. In this work, we study a new problem, that is, simultaneously recovering 3D shape and surface color from a single image, namely "colorful 3D reconstruction". This problem is both challenging and intriguing because the ability to infer textured 3D model from a single image is at the core of visual understanding. Here, we propose an end-to-end trainable framework, Colorful Voxel Network (CVN), to tackle this problem. Conditioned on a single 2D input, CVN learns to decompose shape and surface color information of a 3D object into a 3D shape branch and a surface color branch, respectively. Specifically, for the shape recovery, we generate a shape volume with the state of its voxels indicating occupancy. For the surface color recovery, we combine the strength of appearance hallucination and geometric projection by concurrently learning a regressed color volume and a 2D-to-3D flow volume, which are then fused into a blended color volume. The final textured 3D model is obtained by sampling color from the blended color volume at the positions of occupied voxels in the shape volume. To handle the severe sparse volume representations, a novel loss function, Mean Squared False Cross-Entropy Loss (MSFCEL), is designed. Extensive experiments demonstrate that our approach achieves significant improvement over baselines, and shows great generalization across diverse object categories and arbitrary viewpoints.


title: Distributed practice in verbal recall tasks: A review and quantitative synthesis

abstract: The authors performed a meta-analysis of the distributed practice effect to illuminate the effects of temporal variables that have been neglected in previous reviews. This review found 839 assessments of distributed practice in 317 experiments located in 184 articles. Effects of spacing (consecutive massed presentations vs. spaced learning episodes) and lag (less spaced vs. more spaced learning episodes) were examined, as were expanding interstudy interval (ISI) effects. Analyses suggest that ISI and retention interval operate jointly to affect final-test retention; specifically, the ISI producing maximal retention increased as retention interval increased. Areas needing future research and theoretical implications are discussed.


title: Neuromuscular Training Improves Performance and Lower-Extremity Biomechanics in Female Athletes

abstract: Myer, G.D., K.R. Ford, J.P. Palumbo, and T.E. Hew- ett. Neuromuscular training improves performance and lower- extremity biomechanics in female athletes. J. Strength Cond. Res. 19(1):51-60. 2005.—The purpose of this study was to ex- amine the effects of a comprehensive neuromuscular training program on measures of performance and lower-extremity move- ment biomechanics in female athletes. The hypothesis was that significant improvements in measures of performance would be demonstrated concomitant with improved biomechanical mea- sures related to anterior cruciate ligament injury risk. Forty-one female basketball, soccer, and volleyball players (age, 15.3 6 0.9 years; weight, 64.8 6 9.96 kg; height, 171.2 6 7.21 cm) under- went 6 weeks of training that included 4 main components (ply- ometric and movement, core strengthening and balance, resis- tance training, and speed training). Twelve age-, height-, and weight-matched controls underwent the same testing protocol twice 6 weeks apart. Trained athletes demonstrated increased predicted 1 repetition maximum squat (92%) and bench press (20%). Right and left single-leg hop distance increased 10.39 cm and 8.53 cm, respectively, and vertical jump also increased from 39.9 6 0.9 cm to 43.2 6 1.1 cm with training. Speed in a 9.1-m sprint improved from 1.80 6 0.02 seconds to 1.73 6 0.01 seconds. Pre- and posttest 3-dimensional motion analysis demonstrated increased knee flexion-extension range of motion during the landing phase of a vertical jump (right, 71.9 6 1.48 to 76.9 6 1.48; left, 71.3 6 1.58 to 77.3 6 1.48). Training decreased knee valgus (28%) and varus (38%) torques. Control subjects did not demonstrate significant alterations during the 6-week interval. The results of this study support the hypothesis that the com- bination of multiple-injury prevention-training components into a comprehensive program improves measures of performance and movement biomechanics.


title: Preventing Information Leakage from Encoded Data in Lattice Based Cryptography

abstract: Lattice based Cryptography is an important sector which is ensuring cloud data security in present world. It provides a stronger belief of security in a way that the average-case of certain problem is akin to the worst-case of those problems. There are strong indications that these problems will remain safe under the availability of quantum computers, unlike the widely used issues like integer-factorization and discrete logarithm upon which most of the typical cryptosystems relies. In this paper, we tend to discuss the security dimension of Lattice based cryptography whose power lies within the hardness of lattice problems. Goldreich-Goldwasser-Halevi (GGH) public-key cryptosystem is an exemplar of lattice-based cryptosystems. Its security depends on the hardness of lattice issues. GGH is easy to understand and is widely used due to its straightforward data encoding and decoding procedures. Phong Nguyen, in his paper showed that there's a significant flaw within the style of the GGH scheme as ciphertext leaks information on the plaintext. Due to this flaw the practical usage of GGH cryptosystem is limiting to some extent. So as to enhance the safety and usefulness of the GGH cryptosystem, in this paper we proposed an improvised GGH encryption and decryption functions which prevented information leakage. We have implemented a package in MATLAB for the improvement of GGH cryptosystem. In our work we proposed some methods to improve GGH algorithm and make it more secure and information leakage resistant.


title: Implementing the PPM Data Compression Scheme

abstract: The prediction by partial matching (PPM) data compression algorithm developed by J. Cleary and I. Witten (1984) is capable of very high compression rates, encoding English text in as little as 2.2 b/character. It is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a variant is described that encodes and decodes at over 4 kB/s on a small workstation and operates within a few hundred kilobytes of data space, but still obtains compression of about 2.4 b/character for English text. >


Origami Diagrams and 3 D Animation from Flat-Foldable Crease Patterns Sequences


title: Near-optimal fully-dynamic graph connectivity

abstract: In this paper we present near-optimal bounds for fullydynamic graph connectivity which is the most basic nontrivial fully-dynamic graph problem. Connectivity queries are supported in O(log n/log log log n) time while the updates are supported in O(log n(log log n) 3) expected amortized time. The previous best update time was O((log n)2). Our new bound is only doubly-logarithmic factors from a general cell probe lower bound of f2(log n~ log log n). Our algorithm runs on a pointer machine, and uses only standard AC ° instructions. In our developments we make some comparatively trivial observations improving some deterministic bounds. The space bound of the previous O((log n) ~) connectivity algorithm is improved from O(m + n log n) to O(m). The previous time complexity of fully-dynamic 2-edge and biconnectivity is improved from O((log n) 4) to O((log n) 3 log log n).


title: Cross-Device Search

abstract: Ownership and use of multiple devices such as desktop computers, smartphones, and tablets is increasing rapidly. Search is popular and people often perform search tasks that span device boundaries. Understanding how these devices are used and how people transition between them during information seeking is essential in developing search support for a multi-device world. In this paper, we study search across devices and propose models to predict aspects of cross-device search transitions. We characterize multi-device search across four device types, including aspects of search behavior on each device (e.g., topics of interest) and characteristics of device transitions. Building on the characterization, we learn models to predict various aspects of cross-device search, including the next device used for search. This enables many applications. For example, accurately forecasting the device used for the next query lets search engines proactively retrieve device-appropriate content (e.g., short documents for smartphones), while knowledge of the current device combined with device-specific topical interest models may assist in better query-sense disambiguation. %to help the searcher once they transition to the target device.


title: Data Management in Machine Learning: Challenges, Techniques, and Systems

abstract: Large-scale data analytics using statistical machine learning (ML), popularly called advanced analytics, underpins many modern data-driven applications. The data management community has been working for over a decade on tackling data management-related challenges that arise in ML workloads, and has built several systems for advanced analytics. This tutorial provides a comprehensive review of such systems and analyzes key data management challenges and techniques. We focus on three complementary lines of work: (1) integrating ML algorithms and languages with existing data systems such as RDBMSs, (2) adapting data management-inspired techniques such as query optimization, partitioning, and compression to new systems that target ML workloads, and (3) combining data management and ML ideas to build systems that improve ML lifecycle-related tasks. Finally, we identify key open data management challenges for future research in this important area.


title: Decreased Bone Mineral Density in Adults Born with Very Low Birth Weight: A Cohort Study

abstract: BACKGROUND ::: Very-low-birth-weight (VLBW, <1,500 g) infants have compromised bone mass accrual during childhood, but it is unclear whether this results in subnormal peak bone mass and increased risk of impaired skeletal health in adulthood. We hypothesized that VLBW is associated with reduced bone mineral density (BMD) in adulthood. ::: ::: ::: METHODS AND FINDINGS ::: The Helsinki Study of Very Low Birth Weight Adults is a multidisciplinary cohort study representative of all VLBW births within the larger Helsinki area from 1978 to 1985. This study evaluated skeletal health in 144 such participants (all born preterm, mean gestational age 29.3 wk, birth weight 1,127 g, birth weight Z score 1.3), and in 139 comparison participants born at term, matched for sex, age, and birth hospital. BMD was measured by dual energy X-ray absorptiometry at age 18.5 to 27.1 y. Adults born with VLBW had, in comparison to participants born at term, a 0.51-unit (95% confidence interval [CI] 0.28-0.75) lower lumbar spine Z score and a 0.56-unit (95% CI 0.34-0.78) lower femoral neck Z score for areal BMD. These differences remained statistically significant after adjustment for the VLBW adults' shorter height and lower self-reported exercise intensity. ::: ::: ::: CONCLUSIONS ::: Young adults born with VLBW, when studied close to the age of peak bone mass, have significantly lower BMD than do their term-born peers. This suggests that compromised childhood bone mass accrual in preterm VLBW children translates into increased risk for osteoporosis in adulthood, warranting vigilance in osteoporosis prevention.


title: www.elsevier.com/locate/geb Trust and trustworthiness across different age groups

abstract: We examine the degree of trust and trustworthiness in an experimental trust game with 662 participants from six different age groups, ranging from 8-year-olds to retired persons. Although both trust and trustworthiness have been identified as fundamental pillars for efficient economic interactions, economic research has devoted little attention to measuring their strength in different age groups. In our experiment subjects interact with members of the same age group. We find that trust increases almost linearly from early childhood to early adulthood, but stays rather constant within different adult age groups. Trustworthiness prevails in all age groups.


Action Recognition via Nonparametric Incremental Learning


title: Feature Selection and Design of Intrusion Detection System Based on k-Means and Triangle Area Support Vector Machine

abstract: Nowadays, challenged by malicious use of network and intentional attacks on personal computer system, intrusion detection system has become an indispensible and infrastructural mechanism for securing critical resource and information. Most current intrusion detection systems focus on hybrid supervised and unsupervised machine learning technologies. The related work has demonstrated that they can get superior performance than applying single machine learning algorithm in detection model. Besides, with the scrutiny of related works, feature selecting and representing techniques are also essential in pursuit of high efficiency and effectiveness. Performance of specified attack type detection should also be improved and evaluated. In this paper, we incorporate information gain (IG) method for selecting more discriminative features and triangle area based support vector machine (TASVM) by combining k-means clustering algorithm and SVM classifier to detect attacks. Our system achieves accuracy of 99.83%, detection rate of 99.88% and false alarm rate of 2.99% on the 10% of KDD CUP 1999 evaluation data set. We also achieve a better detection performance for specific attack types concerning precision and recall.


title: Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs

abstract: The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to construct feature sets. To fight against malware variants and zero-day malware, we introduce graph similarity metrics to uncover homogeneous application behaviors while tolerating minor implementation differences. We implement a prototype system, DroidSIFT, in 23 thousand lines of Java code. We evaluate our system using 2200 malware samples and 13500 benign samples. Experiments show that our signature detection can correctly label 93\% of malware instances; our anomaly detector is capable of detecting zero-day malware with a low false negative rate (2\%) and an acceptable false positive rate (5.15\%) for a vetting purpose.


title: Is 2+2=4? Meta-analyses of brain areas needed for numbers and calculations

abstract: Abstract Most of us use numbers daily for counting, estimating quantities or formal mathematics, yet despite their importance our understanding of the brain correlates of these processes is still evolving. A neurofunctional model of mental arithmetic, proposed more than a decade ago, stimulated a substantial body of research in this area. Using quantitative meta-analyses of fMRI studies we identified brain regions concordant among studies that used number and calculation tasks. These tasks elicited activity in a set of common regions such as the inferior parietal lobule; however, the regions in which they differed were most notable, such as distinct areas of prefrontal cortices for specific arithmetic operations. Given the current knowledge, we propose an updated topographical brain atlas of mental arithmetic with improved interpretative power.