nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

This article presents a gesture recognition/adaptation system for human--computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human--machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well as or better than nonadapting offline template-based methods.
Gossip: Automatically Identifying Malicious Domains from Mailing List Discussions

Domain names play a critical role in cybercrime, because they identify hosts that serve malicious content (such as malware, Trojan binaries, or malicious scripts), operate as command-and-control servers, or carry out some other role in the malicious network infrastructure. To defend against Internet attacks and scams, operators widely use blacklisting to detect and block malicious domain names and IP addresses. Existing blacklists are typically generated by crawling suspicious domains, manually or automatically analyzing malware, and collecting information from honeypots and intrusion detection systems. Unfortunately, such blacklists are difficult to maintain and are often slow to respond to new attacks. Security experts set up and join mailing lists to discuss and share intelligence information, which provides a better chance to identify emerging malicious activities. In this paper, we design Gossip, a novel approach to automatically detect malicious domains based on the analysis of discussions in technical mailing lists (particularly on security-related topics) by using natural language processing and machine learning techniques. We identify a set of effective features extracted from email threads, users participating in the discussions, and content keywords, to infer malicious domains from mailing lists, without the need to actually crawl the suspect websites. Our result shows that Gossip achieves high detection accuracy. Moreover, the detection from our system is often days or weeks earlier than existing public blacklists.
Chapter 17 Introducing Machine Learning Concepts with WEKA

This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information.
JSAT: Java Statistical Analysis Tool, a Library for Machine Learning

Java Statistical Analysis Tool (JSAT) is a Machine Learning library written in pure Java. It works to fill a void in the Java ecosystem for a general purpose library that is relatively high performance and flexible, which is not adequately fulfilled by Weka (Hall et al., 2009) and Java-ML (Abeel et al., 2009). Almost all of the algorithms are independently implemented using an Object-Oriented framework.
Face Translation between Images and Videos using Identity-aware CycleGAN

This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exist two major technical challenges: 1) designing a robust translation model between static images and dynamic videos, and 2) preserving facial identity during image-video translation. To address such two problems, we generalize the state-of-the-art image-to-image translation network (Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image translation context by exploiting a image-video translation model and an identity preservation model. In particular, we apply the state-of-theart Wasserstein GAN technique to the setting of image-video translation for better convergence, and we meanwhile introduce a face verificator to ensure the identity. Experiments on standard image/video face datasets demonstrate the effectiveness of the proposed model in both terms of qualitative and quantitative evaluations.
Design and Development of a Medical Big Data Processing System Based on Hadoop

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics – Volume, Variety, Velocity and Value (the 4 Vs) – that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations.
Efficient Disfluency Detection with Transition-based Parsing

Automatic speech recognition (ASR) outputs often contain various disfluencies. It is necessary to remove these disfluencies before processing downstream tasks. In this paper, an efficient disfluency detection approach based on right-to-left transitionbased parsing is proposed, which can efficiently identify disfluencies and keep ASR outputs grammatical. Our method exploits a global view to capture long-range dependencies for disfluency detection by integrating a rich set of syntactic and disfluency features with linear complexity. The experimental results show that our method outperforms state-of-the-art work and achieves a 85.1% f-score on the commonly used English Switchboard test set. We also apply our method to in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.
Motivation for Achievement in Mathematics : Findings , Generalizations , and Criticisms of the Research

In this review we examine recent research in the area of motivation in mathematics education and discuss findings from research perspectives in this domain. We note consistencies across research perspectives that suggest a set of generalizable conclusions about the contextual factors, cognitive processes, and benefits of interventions that affect students’ and teachers’ motivational attitudes. Criticisms are leveled concerning the lack of theoretical guidance driving the conduct and interpretation of the majority of studies in the field. Few researchers have attempted to extend current theories of motivation in ways that are consistent with the current research on learning and classroom discourse. In particular, researchers interested in studying motivation in the content domain of school mathematics need to examine the relationship that exists between mathematics as a socially constructed field and students’ desire to achieve.
Global Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partial Shading Conditions

The power–voltage characteristic of photovoltaic (PV) arrays displays multiple local maximum power points when all the modules do not receive uniform solar irradiance, i.e., under partial shading conditions (PSCs). Conventional maximum power point tracking (MPPT) methods are shown to be effective under uniform solar irradiance conditions. However, they may fail to track the global peak under PSCs. This paper proposes a new method for MPPT of PV arrays under both PSCs and uniform conditions. By analyzing the solar irradiance pattern and using the popular Hill Climbing method, the proposed method tracks all local maximum power points. The performance of the proposed method is evaluated through simulations in MATLAB/SIMULINK environment. Besides, the accuracy of the proposed method is proved using experimental results.
MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning

Designing an automatic solver for math word problems has been considered as a crucial step towards general AI, with the ability of natural language understanding and logical inference. The state-of-the-art performance was achieved by enumerating all the possible expressions from the quantities in the text and customizing a scoring function to identify the one with the maximum probability. However, it incurs exponential search space with the number of quantities and beam search has to be applied to trade accuracy for efficiency. In this paper, we make the first attempt of applying deep reinforcement learning to solve arithmetic word problems. The motivation is that deep Q-network has witnessed success in solving various problems with big search space and achieves promising performance in terms of both accuracy and running time. To fit the math problem scenario, we propose our MathDQN that is customized from the general deep reinforcement learning framework. Technically, we design the states, actions, reward function, together with a feed-forward neural network as the deep Q-network. Extensive experimental results validate our superiority over state-ofthe-art methods. Our MathDQN yields remarkable improvement on most of datasets and boosts the average precision among all the benchmark datasets by 15%.
ConVis: A Visual Text Analytic System for Exploring Blog Conversations

Today it is quite common for people to exchange hundreds of comments in online conversations (e.g., blogs). Often, it can be very difficult to analyze and gain insights from such long conversations. To address this problem, we present a visual text analytic system that tightly integrates interactive visualization with novel text mining and summarization techniques to fulfill information needs of users in exploring conversations. At first, we perform a user requirement analysis for the domain of blog conversations to derive a set of design principles. Following these principles, we present an interface that visualizes a combination of various metadata and textual analysis results, supporting the user to interactively explore the blog conversations. We conclude with an informal user evaluation, which provides anecdotal evidence about the effectiveness of our system and directions for further design.
Early fire and smoke detection based on colour features and motion analysis

This work addresses the issue of fire and smoke detection in a scene within a video surveillance framework. Detection of fire and smoke pixels is at first achieved by means of a motion detection algorithm. In addition, separation of smoke and fire pixels using colour information (within appropriate spaces, specifically chosen in order to enhance specific chromatic features) is performed. In parallel, a pixel selection based on the dynamics of the area is carried out in order to reduce false detection. The output of the three parallel algorithms are eventually fused by means of a MLP.
The dendritic cell algorithm

Artificial immune systems are a collection of algorithms inspired by the human immune system. Over the past 15 years, extensive research has been performed regarding the application of artificial immune systems to computer security. However, existing immune-inspired techniques have not performed as well as expected when applied to the detection of intruders in computer systems. In this thesis the development of the Dendritic Cell Algorithm is described. This is a novel immune-inspired algorithm based on the function of the dendritic cells of the human immune system. In nature, dendritic cells function as natural anomaly detection agents, instructing the immune system to respond if stress or damage is detected. Dendritic cells are a crucial cell in the detection and combination of ‘signals’ which provide the immune system with a sense of context. The Dendritic Cell Algorithm is based on an abstract model of dendritic cell behaviour, with the abstraction process performed in close collaboration with immunologists. This algorithm consists of components based on the key properties of dendritic cell behaviour, which involves data fusion and correlation components. In this algorithm, four categories of input signal are used. The resultant algorithm is formally described in this thesis and is validated on a standard machine learning dataset. The validation process shows that the Dendritic Cell Algorithm can be applied to static datasets and suggests that the algorithm is suitable for the analysis of time-dependent data. Further analysis and evaluation of the Dendritic Cell Algorithm is performed. This is assessed through the algorithm’s application to the detection of anomalous port scans. The results of this investigation show that the Dendritic Cell Algorithm can be applied to detection problems in real-time. This analysis also shows that detection with this algorithm produces high rates of false positives and high rates of true positives, in addition to being robust against modification to system parameters. The limitations of the Dendritic Cell Algorithm are also evaluated and presented, including loss of sensitivity and the generation of false positives under certain circumstances. It is shown that the Dendritic Cell Algorithm can perform well as an anomaly detection algorithm and can be applied to real-world, realtime data.
Independent comparison of popular DPI tools for traffic classification

Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classification. However, the actual performance of DPI is still unclear to the research community, since the lack of public datasets prevent the comparison and reproducibility of their results. This paper presents a comprehensive comparison of 6 well-known DPI tools, which are commonly used in the traffic classification literature. Our study includes 2 commercial products (PACE and NBAR) and 4 open-source tools (OpenDPI, L7-filter, nDPI, and Libprotoident). We studied their performance in various scenarios (including packet and flow truncation) and at different classification levels (application protocol, application and web service). We carefully built a labeled dataset with more than 750 K flows, which contains traffic from popular applications. We used the Volunteer-Based System (VBS), developed at Aalborg University, to guarantee the correct labeling of the dataset. We released this dataset, including full packet payloads, to the research community. We believe this dataset could become a common benchmark for the comparison and validation of network traffic classifiers. Our results present PACE, a commercial tool, as the most accurate solution. Surprisingly, we find that some open-source tools, such as nDPI and Libprotoident, also achieve very high accuracy.
8.8 iRazor: 3-transistor current-based error detection and correction in an ARM Cortex-R4 processor

It is well known that technology scaling has led to increasing process/voltage/temperature/aging margins that substantially degrade performance and power in modern processors and SoCs. One approach to address these large timing margins is the use of specialized registers on critical paths that perform error detection and correction (EDAC) [1-5]. While promising, the previously proposed implementations have been limited in several ways. Most notably, they often incur large overheads beyond conventional register designs (e.g., 8-to-44 additional transistors per register). This becomes an obstacle for commercial designs and, hence, there have been no reported implementations of EDAC approaches within substantial commercial processors. Finally, the performance gain from EDAC approaches has not been thoroughly quantified in relation to competing, lower overhead approaches such as frequency binning and canary circuits/critical path monitors .
Adaptive Edge Detection for Real-Time Video Processing using FPGAs

Real-time video and image processing is used in a wide variety of applications from video surveillance and traffic management to medical imaging applications. These operations typically require very high computation power. Standard definition NTSC video is digitized at 720x480 or full D1 resolution at 30 frames per second, which results in a 31MHz pixel rate. With multiple adaptive convolution stages to detect or eliminate different features within the image, the filtering operation receives input data at a rate of over 1 giga samples per second. Coupled with new high-resolution standards and multi-channel environments, processing requirements can be even higher. Achieving this level of processing power using programmable DSP requires multiple processors. A single FPGA with an embedded soft processor can deliver the requisite level of computing power more cost-effectively, while simplifying board complexity.
Migrating Monolithic Mobile Application to Microservice Architecture: An Experiment Report

The microservice architecture (MSA) is an emerging cloud software system, which provides fine-grained, self-contained service components (microservices) used in the construction of complex software systems. DevOps techniques are commonly used to automate the process of development and operation through continuous integration and continuous deployment. Monitoring software systems created by DevOps, makes it possible for MSA to obtain the feedback necessary to improve the system quickly and easily. Nonetheless, systematic, SDLC-driven methods (SDLC: software development life cycle) are lacking to facilitate the migration of software systems from a traditional monolithic architecture to MSA. Therefore, this paper proposes a migration process based on SDLC, including all of the methods and tools required during design, development, and implementation. The mobile application, EasyLearn, was used as an illustrative example to demonstrate the efficacy of the proposed migration process. We believe that this paper could provide valuable references for other development teams seeking to facilitate the migration of existing applications to MSA.
Design and Development of a Medical Parallel Robot for Cardiopulmonary Resuscitation

The concept of a medical parallel robot applicable to chest compression in the process of cardiopulmonary resuscitation (CPR) is proposed in this paper. According to the requirement of CPR action, a three-prismatic-universal-universal (3-PUU) translational parallel manipulator (TPM) is designed and developed for such applications, and a detailed analysis has been performed for the 3-PUU TPM involving the issues of kinematics, dynamics, and control. In view of the physical constraints imposed by mechanical joints, both the robot-reachable workspace and the maximum inscribed cylinder-usable workspace are determined. Moreover, the singularity analysis is carried out via the screw theory, and the robot architecture is optimized to obtain a large well-conditioning usable workspace. Based on the principle of virtual work with a simplifying hypothesis adopted, the dynamic model is established, and dynamic control utilizing computed torque method is implemented. At last, the experimental results made for the prototype illustrate the performance of the control algorithm well. This research will lay a good foundation for the development of a medical robot to assist in CPR operation.
Applications of ontologies in requirements engineering: a systematic review of the literature

There is an increase use of ontology-driven approaches to support requirements engineering (RE) activities, such as elicitation, analysis, specification, validation and management of requirements. However, the RE community still lacks a comprehensive understanding of how ontologies are used in RE process. Thus, the main objective of this work is to investigate and better understand how ontologies support RE as well as identify to what extent they have been applied to this field. In order to meet our goal, we conducted a systematic literature review (SLR) to identify the primary studies on the use of ontologies in RE, following a predefined review protocol. We then identified the main RE phases addressed, the requirements modelling styles that have been used in conjunction with ontologies, the types of requirements that have been supported by the use of ontologies and the ontology languages that have been adopted. We also examined the types of contributions reported and looked for evidences of the benefits of ontology-driven RE. In summary, the main findings of this work are: (1) there are empirical evidences of the benefits of using ontologies in RE activities both in industry and academy, specially for reducing ambiguity, inconsistency and incompleteness of requirements; (2) the majority of studies only partially address the RE process; (3) there is a great diversity of RE modelling styles supported by ontologies; (4) most studies addressed only functional requirements; (5) several studies describe the use/development of tools to support different types of ontology-driven RE approaches; (6) about half of the studies followed W3C recommendations on ontology-related languages; and (7) a great variety of RE ontologies were identified; nevertheless, none of them has been broadly adopted by the community. Finally, we conclude this work by showing several promising research opportunities that are quite important and interesting but underexplored in current research and practice.