Reviewerly/exHarmony huggingface dataset:
News analysis for the detection of cyber security issues in digital healthcare: A text mining approach to uncover actors, attack methods and technologies for cyber defense

Objectives: This research reviews the possibilities of text mining in the area of cybercrime in digital healthcare showing how advanced information retrieval and natural language processing can be used to get insights. The aim is to mine news data to find out what is reported about digital healthcare, what security-related critical events happened, and what actors, attack methods, and technologies play a role there. Methods: Different projects already apply text mining successfully in the cyber domain. However, none of these are specifically tailored to threats in the digital healthcare sector or uses an as big data foundation for analysis. To achieve that goal, different text mining methodologies like fact extraction, semantic fields as well as statistical methods like frequency, correlation and trend calculations were used. The news data for the analysis was provided by the DocCenter from the National Defense Academy (DocCenter/NDA) of the Austrian Armed Forces. About 300,000 news articles were processed and analyzed. Additionally, the open source GDELT dataset was investigated. Results & Conclusion: The data points out that cyber threats are present in digital health technologies and cyberattacks are more and more threatening to organizations, governments, and every person them self. Not only hacker groups, firms, and governments are involved in these attacks, also terroristic organizations use cyberwarfare. That, together with the amount of technology in digital healthcare like pacemakers, IoT, wearables but also the importance of healthcare as critical infrastructure and the dependence on electronic health records makes our society vulnerable.
Geometric Total Variation for Image Vectorization, Zooming and Pixel Art Depixelizing

We propose an original method for vectorizing an image or zooming it at an arbitrary scale. The core of our method relies on the resolution of a geometric variational model and therefore offers theoretic guarantees. More precisely, it associates a total variation energy to every valid triangulation of the image pixels. Its minimization induces a triangulation that reflects image gradients. We then exploit this triangulation to precisely locate discontinuities, which can then simply be vectorized or zoomed. This new approach works on arbitrary images without any learning phase. It is particularly appealing for processing images with low quantization like pixel art and can be used for depixelizing such images. The method can be evaluated with an online demonstrator, where users can reproduce results presented here or upload their own images.
An Algorithmic Approach for Real Time People Counting with Moving Background

The research paper implements the concept of real-time people counting. Real time people counting is a relevant source of information in various applications like security or people management, such as in shopping malls, footfall or fleet management in public transport, congestion management and tourist flow estimation. Today, a major use of people counting is done there where we have a cluttered audience and hence we have to make the management of any kind. The proposed algorithm does real time people counting using mean shift with deep learning data sets. The camera used in the system takes the video sequence as an input to the algorithm. The various steps of the algorithm then detect whether the object present in the video is a human or not. After the object is identified as a human, the counting algorithm is then applied to find the number of people in the video and display the counted result.
A Semi-supervised Knowledge Assessment Paradigm Based on T-CNN Algorithm for the Industrial Massage System

With the popularity of the Industrial Internet and Information Technology, most data resources in the industrial production process could be exploited and utilized completely. But there are some fragmented knowledges, like product data, project documentation and multimedia materials, that need to be collected and organized. For instance, Industrial Massage System (IMS) is a platform for operating field professionals to exchange and share their experiences and knowledge containing significant potential value. In order to effectively utilize the fragmented information, a semi-supervised knowledge assessment paradigm is proposed for this system using Tri-training method based on Convolutional Neural Network (T-CNN). The method initially trains three CNN classifiers employing a small number of labeled examples, and a large pool of unlabeled examples are then assigned pseudo-labels under certain conditions. In further training, these classifiers are fine-tuned through original labeled examples and pseudo-examples. Specifically, in the iteration of the tri-training, a pseudo-example for a classifier is obtained from the reliable hypothesis if the other two classifiers have the same predictions on the labeling. Compared to previous training method, it does not require enormous original labeled data to initialize the model, nor does it need to depend immoderately on expert's domain to label examples. And there are also no demands of the instance space to be described with sufficient and redundant views, like previous co-training style algorithms. Experiments tested on two benchmarks indicate that the algorithm can effectively exploit unlabeled data to enhance the learning performance and achieves better classification capability.
Acoustic model topology optimization for large vocabulary speech recognition

Acoustic model topology selection work in constructing large vocabulary speech recognition systems is being done empirically or heuristically. In this paper, we propose two improved algorithms, which are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) respectively, on the basis of our previously proposed algorithms to select and optimize model topologies for small or medium vocabulary speech recognition systems. Our improved algorithms attain the goal of optimizing acoustic model topologies for large vocabulary speech recognition systems mainly through modifying the encoding schemes of our previously proposed algorithms. Experiments on the dialogue corpus of Inner Mongolia University show that, compared with the conventional acoustic model topology selection method, our newly proposed algorithms are able to bring much higher recognition performance for large vocabulary speech recognition systems by optimizing their acoustic model topologies.
Fuzzy C-Means Clustering Using Asymmetric Loss Function

In this work, a fuzzy clustering algorithm is proposed based on the asymmetric loss function instead of the usual symmetric dissimilarities.Linear Exponential (LINEX) loss function is a commonly used asymmetric loss function, which is considered in this paper.We prove that the negative likelihood of an extreme value distribution is equal to LINEX loss function and clarify some of its advantages.Using such a loss function, the so-called LINEX Fuzzy C-Means algorithm is introduced.The introduced clustering method is compared with its crisp version and Fuzzy C-Means algorithms through a few real datasets as well as some simulated datasets.
PRIVACY PRESERVING IN DATA MINING

The collection and analysis of data are continuously growing due to the continuous introduction of data from various sources. This huge information has led to the advent of Big Data, Data Science and various other concepts. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. Data mining produces a large amount of data that needs to be analyzed in order to extract useful information from it and gain knowledge. This data is vulnerable to data hackers and rough employees to take advantage of the situation and misuse the data. The privacy preservation is an important concern in data mining as secrecy of sensitive information must be maintained while sharing the data among different un-trusted parties. Privacy preserving data mining (PPDM) protects the privacy of sensitive data without losing the usability of the data. Various techniques have been introduced under PPDM to achieve this goal. This study describes about various techniques of privacy preserving in data mining. It also analyzes their advantages and limitations and comes up with a conclusion that a single technique does not exceed all the parameters such as performance, data utility, level of uncertainty, resistance to data mining algorithms and complexity.
Bilingual Speech Recognition based on Deep Neural Networks and Directed Acyclic Word Graphs

This study explores the use of Bilingual speech recognition system for Indian languages along with Kaldi toolkit and Directed Acyclic Word Graphs as an innovative idea. When using a Large Vocabulary Continuous Speech Recognition (LVCSR), the crucial task is to establish a relationship between sub-word acoustic units across the particular languages. It forms the core for building automatic speech recognition system for multiple languages. Deep neural networks were employed with 2 hidden layers for acoustic modeling to create a Bidirectional Long Short-Term Memory Networks (BLSTM) model. For bilingual speech recognition, standard Mel-Frequency Cepstral Coefficients (MFCC) generated on audio along with Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) were used to align the reference text. The final language models implemented were statistically pruned trigram models. The study aimed at building a refined Telugu-English bilingual speech recognition system by using a Directed Acyclic Word Graph (DAWG) to map phonetically similar words in both the languages.
Rethinking Temporal Object Detection from Robotic Perspectives

Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available.
PENELITIAN TINDAKAN KELAS PEMBELAJARAN BAHASA INGGRIS

Research in English learning is a scientific activity that aims at investigating the rules that work in the process of English learning. Research activities in English learning cover four steps: observing, describing, analyzing, and explaining. The way each step is done depends on the nature of the data and the objective of the research. Classroom Action Research (CAR) for English Learning aims at developing a certain instructional strategy to solve practical instructional problems in English classrooms. Each English learner is basically able to learn English provided that he or she is given the appropriate help as each learner has his or her own style and strategy of learning. CAR for English Learning aims at discovering learning-teaching strategies that match learners’ style and strategies in learning English. CAR is done in several cyles each of which is repeated in the following cycle if the result is not satisfactory yet with the better revised lesson plan. Each cycle begins with lesson planning, implementing the plan, observing the implementation, and reflecing or evaluating the process and the result of the implementation. The result of the reflection determines the following cycle.
Mixed Particle Swarm Optimization Algorithm with Multistage Disturbances

In order to solve the problem that the particle swarm optimization algorithm is easy to fall into the local optimal value, a hybrid particle swarm optimization algorithm with multi-level perturbation is proposed. The algorithm combines the advantages of two classical improved particle swarm optimization algorithms, namely the standard with inertial parameters. Based on the particle swarm optimization algorithm and the particle swarm optimization algorithm with shrinkage factor, a multi-level perturbation mechanism is introduced. When the particle position is updated, the first-order perturbation is introduced to enhance the traversal ability of the particle to the solution space. In the case of “local optimal”, a second-order perturbation is introduced, which causes the optimization process to continue, thus getting rid of the local optimal value. Six test functions are used - Sphere function, Ackley function, Rastrigin function, Styblinski-Tang function, Duadric Function and Rosenbrock function to the proposed. The hybrid particle swarm optimization algorithm is used for simulation and comparison verification. The simulation results show that the proposed hybrid particle swarm optimization algorithm is better than the other two classic improvements in the simulation of the test function. Particle swarm optimization algorithm; in addition, when dealing with multimodal functions, the algorithm is not easily limited by local optimal values.
Book Review: Beyond Reality: Augmented, Virtual, and Mixed Reality in the Library

As part of the democratization of information, libraries are expanding their offerings beyond knowledge-based resources into the realm of virtual reality. In Beyond Reality, Varnum produces nine unique chapters on augmented, virtual, and mixed realities (AR/VR/MR). Six of the chapters are university case studies, two are from public libraries, and the final chapter is from an intellectual property attorney. The selected case studies offer a variety of geographic locations and sizes, which will help match libraries considering pursuing AR/VR/MR programs.
Secure Sparse Representations in L0 Norm Minimization and Its Application to EtC Systems

In this paper, we propose a method to estimate secure sparse representations in LO norm minimization and its application to Encryption-then-Compression (EtC) systems. The proposed scheme provides a practical Orthogonal Matching Pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove, theoretically, that the proposal has exactly the same estimation performance as the unencrypted variant of the OMP algorithm. We demonstrate the security strength of the proposed secure sparse representations. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of the observed signals.
Correction to: DeepGRU: Deep Gesture Recognition Utility

The given name and family name of an author were not tagged correctly in the originally published article. The author’s given name is “Joseph J.” and his family name is “LaViola.” This was corrected.
OBJECT COUNTING PADA DATA VIDEO

&lt;p&gt;&lt;em&gt;One object counting implementation is counting the number of road users from video data sources obtained from CCTV streaming. Video processing on CCTV is usually done on the server side by sending video data. If the need is only to determine the density of traffic, then the method is considered too expensive to be implemented because of the cost of internet connection and bandwidth that must be spent. The solution is to use a small computing device that can process the video first, and the calculation results are sent to the server regularly. In this study, a comparison between the Tensorflow Object Counting learning algorithm and the MOG2 Background Subtractor image processing algorithm with the aim to determine the accuracy of the calculation. The result is known that better accuracy is given by the MOG2 Background Subtractor technique and also the process is carried out using only a small percentage of the amount of memory and processor compared to the Tensorflow Object Counting technique. MOG2 Background Substractor technique is expected to be used on devices that have small data sources&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;/em&gt;&lt;em&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/em&gt;&lt;em&gt;: &lt;/em&gt;&lt;em&gt;Object Counting, Tensorflow&lt;/em&gt;&lt;em&gt;, MOG2 Background Substractor&lt;/em&gt;&lt;/p&gt;&lt;p&gt;Salah satu implementasi object counting adalah menghitung jumlah pengguna jalan dari sumber data video yang didapat dari streaming CCTV. Pemprosesan video pada CCTV biasanya dilakukan disisi server dengan mengirimkan data video. Jika keperluannya hanya untuk mengetahui kepadatan lalu lintas, maka cara tersebut dinilai terlalu mahal untuk diimplementasikan karena biaya koneksi internet dan bandwidth yang harus dikeluarkan. Pemecahannya adalah menggunakan perangkat komputasi kecil yang dapat memproses video tersebut terlebih dahulu, dan hasil perhitungannya dikirimkan ke server secara berkala. Pada penelitian ini dilakukan perbandingan antara algoritma pembelajaran Tensorflow Object Counting dan algoritma image processing MOG2 Background Substractor dengan tujuan untuk mengetahui akurasi penghitungan. Hasilnya diketahui akurasi yang lebih baik diberikan oleh teknik MOG2 Background Substractor dan juga proses yang dilakukan hanya menggunakan prosentase jumlah memori dan prosessor yang kecil dibandingkan teknik Tensorflow Object Counting. Sehingga teknik MOG2 Background Substractor ini diharapkan dapat digunakan pada perangkat yang memiliki sumber data kecil. &lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Kata kunci&lt;/strong&gt; : Object Counting, Tensorflow, MOG2 Background Substractor.&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/p&gt;
Optimization Of Specialists Training Structure By Educational Programs Structuring

Level educational system is one of the most promising means of conscious control of educational reforms. The main idea is to partition the content disciplines being integrated into elementary components and to form training thesaurus (structured sets of elementary components and connections between them). Then comes the overlap of discipline thesauri, distinguishing of areas of their mutual "overlap" and synthesizing. Improving of the efficiency and training quality is achieved by the use of a branched structure (structuring) of each studied course and the curriculum as a whole. Within the professional discipline engineering activity involves the allocation of some "knowledge core" around which various "applications" are implemented. This procedure is multi-stage and multi-tired. If students are taught to use this technique, then from the point of view of the systems theory, it is possible to move from a closed system in which goals are set from the outside, to an open system with active elements, in which goals are formed within the system on the basis of self-movement, and to intensify cognitive activity. The technique considered in this paper can serve as a basis for the creation of active training systems, when the learner evaluates the cost-quality ratio for the multi-link trajectory of training. It is obvious that for its implementation it is necessary to train students the basics of optimization.
Soccer Playing Robot Goal Scoring Algorithm Using Fuzzy Petri nets

To facilitate the creation of a robotic soccer team, a robust kicking strategy and algorithm must be developed. Through the use of Fuzzy Petri nets, a strategy was made and developed into an algorithm to produce a 95% success rate. Image processing and recognition was used to implement this algorithm onto NAO robots.
Comparative Study on Mass loss by the Sun and Energy Available for Utilization between two Tropical Stations in Nigeria

The electromagnetic radiation emitted from the Sun is called solar radiation. Almost all life on Earth evolved with the Sun as a major influence. The rising and setting Sun defined the daily cycle we still respond to biologically. This study investigates theyearly, monthly and daily variation of mass loss by the Sun for two locations; Gusau and Calabar situated across the Sahelianand Coastal climatic zones of Nigeria using daily, monthly and yearly global solar radiation meteorological data obtained fromthe National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 – June 2005). Theenergy available for utilization based on the mass loss by the Sun for the two locations was also investigated. The fluctuations inthe results revealed that the mass loss by the Sun varies significantly from year to year, month to month and from day to day; thus, indicating that it is site dependent and strongly depends on the global solar radiation and solar activities in each of the locations. The highest yearly, monthly and daily mass losses by the Sun for Gusau are 6.3155 1017 kg y -1 in 1985, 5.5691 1016 kg m -1 in April and 2.3482 1015 kg d -1 on April 21, 1985 respectively and the lowest yearly, monthly and daily mass losses by the Sun are 5.6423 1017 kg y -1 in 1999, 4.2740 1016 kg m -1 in December and 1.3031 1015 kg d -1 on December 25, 1999 respectively. The highest yearly, monthly and daily mass losses by the Sun for Calabar are 4.6558 1017 kg y -1 in 1984, 4.6451 1016kg m -1 in February and 2.0252 1015 kg d -1 on February16, 1984 respectively and the lowest yearly, monthly and daily mass losses by the Sun are3.6806 1017 kg y-1 in 1983, 2.5346 1016 kg m-1 in August and 1.9546 1014 kg d-1 on August7, 1983 respectively. The results indicated that the solar energy available for utilization for Gusau are greater than that of Calabar and this is a reflection of the abundant amount of global solar radiation received on a horizontal surface for Gusau as compared to Calabar.
Toward an Educational Dystopia? Liquid Evil, tina, and Post-academic University

This chapter was written in the spring of 2016 and discussed during one of our last meetings as a group with a drive to produce our book ‘on academia’. Leonidas Donskis received comments to this chapter, but did not manage to digest these. We left the chapter as it was, with some minor editorial changes. Actually, thinking about educational dystopia is our common project, exactly to help raise discussion about what can be termed the post-academic university. Towards an Educational Dystopia? is thus a root piece for this book.
Authenticity Determination System Based on Image Matching Using Local Features

In this paper, we propose a system to determine authenticity using image matching. This system uses feature point matching to detect objects. We compare the corresponding points between images using local features and image size to improve the detection accuracy. Consequently, the detection accuracy improved by magnifying the target images to the size of the template image when the KAZE feature was used.
Semantic Web Services Discovery Approach Based on MARDS

Web services are a way for businesses to offer their services over the Web.The concept of semantic Web services, is the fruit of the convergence of Web services field with the Semantic Web, indeed its main objective is to make web services more accessible to the machine by automating the various tasks of their life cycle such as discovery, selection, invocation and composition.In this work, we study the problem of semantic discovery of services by exposing a state of the art on the discovery approaches achieved, and proposing a method that is based on Multi-Agent Reactive Decisional Systems (MARDS) and ontologies.The use of a multi-agent system ensures great flexibility in our proposed approach.