nils-herrmann/clean_scirepeval huggingface dataset:
An object detection method for describing soccer games from video

We propose a novel object detection and tracking method in order to detect and track objects necessary to describe contents of a soccer game. On the contrary to intensity oriented conventional object detection methods, the proposed method refers to color rarity and local edge property, and integrally evaluate them by a fuzzy function to achieve better detection quality. These image features were chosen considering the characteristics of soccer video images, that most non-object regions are roughly single colored (green) and most objects tend to have locally strong edges. We also propose a simple object tracking method, that could track objects with occlusion with other objects using a color based template matching. The result of an evaluation experiment applied to actual soccer video showed very high detection rate in detecting player regions without occlusion, and promising ability for regions with occlusion.
Moodbile: A Framework to Integrate m-Learning Applications with the LMS

Learning Management Systems (LMS) have become widespread among most centres for education and training. Being a mature technology, LMS have left the vanguard of innovation. Educational usages of the Web 2.0, Personal Learning Environments, Game-based Learning and particularly the introduction of mobile phones and tablets in education are happening outside the boundaries of the LMS. This paper proposes a way to integrate mobile devices and educational applications with the LMS through webservices; introducing the Moodbile project that provides an extension of Moodle 2.0 webservices for mobile integration and two mobile clients ready to use on real courses.
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability

Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.
Comparative analysis of single and three-phase dual active bridge bidirectional DC-DC converters

Many modern power conversion systems require a bi-directional energy transfer capability as a central part of their system operation. Preferably, such systems should use a single high efficiency power electronic conversion system to reduce size, weight and cost. For higher power bi-directional conversion, the common topology proposed is a dual active bridge structure, where two DC-AC converters are coupled back-to-back through an AC inductor/transformer. Either single-phase or three-phase converters have been proposed for such a system, but to date no clear-cut basis for selecting between these two alternatives has been established. This paper presents a comparative evaluation of single and three-phase back-to-back converters for use in a bi-directional power transfer application. The paper considers these two alternatives from the perspective of operating performance and losses, and provides a basis for selecting between the two topologies in any particular operating context.
Dynamic Social Network Analysis of a Dark Network: Identifying Significant Facilitators

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.
Tajik-Farsi Persian Transliteration Using Statistical Machine Translation

Tajik Persian is a dialect of Persian spoken primarily in Tajikistan and written with a modified Cyrillic alphabet. Iranian Persian, or Farsi, as it is natively called, is the lingua franca of Iran and is written with the Persian alphabet, a modified Arabic script. Although the spoken versions of Tajik and Farsi are mutually intelligible to educated speakers of both languages, the difference between the writing systems constitutes a barrier to text compatibility between the two languages. This paper presents a system to transliterate text between these two different Persian dialects that use incompatible writing systems. The system also serves as a mechanism to facilitate sharing of computational linguistic resources between the two languages. This is relevant because of the disparity in resources for Tajik versus Farsi.
Machinery equipment early fault detection using Artificial Neural Network based Autoencoder

Machinery equipment early fault detection is still in an open challenge. The objective of this paper is to introduce a parametric method Artificial Neural Network based Autoencoder implemented to perform early fault detection of a machinery equipment. The performance of this method is then compared to one of the industry state of the art nonparametric methods called Similarity Based Modeling. The comparison is done by analyzing the implementation result on both artificial and real case dataset. Root Mean Square Error (RMSE) is applied to measure the performance. Based on the result of the research, both of these methods are effective to do pattern recognition and able to identify data anomaly or in this case is fault identification.
Gabor-based kernel PCA with fractional power polynomial models for face recognition

This paper presents a novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.
AppsPlayground: automatic security analysis of smartphone applications

Today's smartphone application markets host an ever increasing number of applications. The sheer number of applications makes their review a daunting task. We propose AppsPlayground for Android, a framework that automates the analysis smartphone applications. AppsPlayground integrates multiple components comprising different detection and automatic exploration techniques for this purpose. We evaluated the system using multiple large scale and small scale experiments involving real benign and malicious applications. Our evaluation shows that AppsPlayground is quite effective at automatically detecting privacy leaks and malicious functionality in applications.
Text Mining for Biology And Biomedicine

Text mining is defined by Hearst (1999) as the automatic discovery of new, previously unknown, information from unstructured textual data. This is often seen as comprising of three major tasks: information retrieval (gathering relevant documents), information extraction (extracting information of interest from these documents), and data mining (discovering new associations among the extracted pieces of information). Most researchers in the natural language processing (NLP) community are familiar with work on information extraction and its subtasks such as noun phrase chunking, named entity recognition, and anaphora resolution, typically applied to newswire articles. The explosive growth of biomedical literature has prompted increasing interest in applying such techniques to biomedical text in order to address the information overload faced by domain experts. This is reflected by the proliferation of articles reviewing this work (Reviews 2006), which typically appear in bioinformatics journals and target experts in biosciences as their primary audience. Text Mining for Biology and Biomedicine provides an overview of the fundamental approaches to biomedical NLP in more depth than is typically offered in a review article. The book consists of an introductory chapter written by the editors and nine chapters that each discuss a different subarea of biomedical NLP. Each chapter is authored by researcher(s) with significant contributions to the overviewed subarea. In the introductory chapter, Sophia Ananiadou and John McNaught adhere to the definition of text mining by Hearst (p. 1), although their interpretation lays more emphasis on the unstructured nature of the input textual data than on the potential novelty of the output information. Those “interested in organizing, searching, discovering, or communicating biological knowledge” (p. 2) are the targeted readers of the book. The book aims to provide them with an “extensive summarization and discussion of the research literature in text mining and reported systems, geared towards informing and educating rather than oriented towards other text mining experts” (p. 3). Information retrieval is placed outside the scope of the book, which focuses on performing familiar tasks such as named entity recognition (NER) and information extraction (IE) on biomedical text, but also extensively discusses problems that are less studied in general NLP but are of particular importance in this area, such as the exploitation of domain-specific knowledge sources, the construction of terminologies, how to deal with abbreviations, and so on. An outline of the main aims and challenges in biomedical NLP is followed by an overview of how these issues are discussed in each chapter. Chapter 2, “Levels of natural language processing for text mining” by Udo Hahn and Joachim Wermter, first explains how each level of linguistic analysis (i.e., morphology, syntax, and semantics) is associated with distinct NLP components. Then a reference architecture for text mining that combines these components with each other
Antimicrobial potential of 27 plants consumed by chimpanzees (Pan troglodytes verus Blumenbach) in Ivory Coast

Due to their genetic proximity, chimpanzees share with human several diseases including bacterial, fungal and viral infections, such as candidiasis, acquired immune deficiency syndrome (AIDS), Ebola virus disease. However, in its natural environment, chimpanzees are tolerant to several pathogens including simian immunodeficiency virus (SIV), virus related to human immunodeficiency virus (HIV) that contribute to the emergence of opportunistic diseases such as microbial infections.



Twenty seven species of plants consumed by chimpanzees were evaluated for their antimicrobial potential against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, Candida tropicalis and Candida glabrata using the agar diffusion technique and micro-dilution in 96-well plates. In total 132 extracts (33 dichloromethane, 33 methanol, 33 ethyl acetate and 33 aqueous) were tested.



The results showed that 24 extracts (18 %) showed activity against bacteria and 6 extracts (5 %) were active against yeasts. The minimal inhibitory concentrations (MICs) values of active extracts ranged between 23 and 750 μg/ml for bacteria and between 188 and 1500 μg/ml for yeasts.



Tristemma coronatum was the most promising on the studied microorganisms followed by Beilschmiedia mannii. The extracts of the two plants indicated by chimpanzees have potential for antimicrobial use in human.
Clustering Sequences with Hidden Markov Models

This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs) . The problem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more difficult problem of determining the number of clusters K, from the data, is investigated. Experimental results indicate that the proposed techniques are useful for revealing hidden cluster structure in data sets of sequences.
ECG Signal Processing Using Digital Signal Processing Techniques

This work describes the implementation of wavelet-based de noising algorithm on electrocardiogram (ECG) signal and detection of important parameter such as heart rate, amplitude, timings of the ECG, etc. The algorithm is implemented in DSP based starter kit (DSK) with a twoelectrode ECG preamplifier. The signal from the ECG preamplifier is acquired through the Codec input of the DSP starter kit. The acquired data is subjected to signal processing techniques such as removal of power line frequencies and high frequency component removal using wavelet-denoising technique. ECG component analysis such as QRS peak detection, heart rate calculation, etc is performed using nonlinear filter technique called first order derivative and moving average filter. The performance of the algorithm is studied in the DSP environment as well as MATLAB environment for comparison. The results of this study reveal the potentiality of the DSP system for routine clinical use.
ProbLog: A Probabilistic Prolog and Its Application in Link Discovery

A general framework for minimisation-based belief change is presented. A problem instance is made up of an undirected graph, where a formula is associated with each vertex. For example, vertices may represent spatial locations, points in time, or some other notion of locality. Information is shared between vertices via a process of minimisation over the graph. We give equivalent semantic and syntactic characterisations of this minimisation. We also show that this approach is general enough to capture existing minimisation-based approaches to belief merging, belief revision, and (temporal) extrapolation operators. While we focus on a set-theoretic notion of minimisation, we also consider other approaches, such as cardinality-based and prioritybased minimisation.
A Matrix Decomposition based Webshell Detection Method

WebShell is a web based network backdoor. With the help of WebShells, the hacker can take any control of the web services illegally. The current method of detecting WebShells is just matching the eigenvalues or detecting the produced flow or services, which is hard to find new kinds of WebShells. To solve these problems, this paper analyzes the different features of a page and proposes a novel matrix decomposition based WebShell detection algorithm. The algorithm is a supervised machine learning algorithm. By analyzing and learning features of known existing and non-existing WebShell pages, the algorithm can make predictions on the unknown pages. The experimental results show that, compared with traditional detection methods, this algorithm spends less time, has higher accuracy and recall rate, and can detect new kinds of WebShells with a certain probability, overcoming the shortcomings of the traditional feature matching based method, improving the accuracy and recalling rate of WebShell detection.
Learning to aggregate vertical results into web search results

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.
The reflective practitioner: How professionals think in action

What do you do to start reading the reflective practitioner how professionals think in action arena ? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this the reflective practitioner how professionals think in action arena .
A Unified Local and Global Model for Discourse Coherence

We present a model for discourse coherence which combines the local entitybased approach of (Barzilay and Lapata, 2005) and the HMM-based content model of (Barzilay and Lee, 2004). Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt (Barzilay and Lapata, 2005) by relaxing independence assumptions so that it is effective when estimated generatively. Our model performs the ordering task competitively with (Soricut and Marcu, 2006), and significantly better than either of the models it is based on.
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning

In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machinelearning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively. This research shows that this method of feature extraction holds some promise for the classification of various pairs of motor movements, which can be used in a BCI context to mentally control a computer or machine. EEG; BCI; ICA; MRCP; ERD/ERS; machine learning; NN; SVM