Reviewerly/exHarmony huggingface dataset:
Parallel news clustering and topic modeling approaches

Abstract At the current age there is an urgent need in developing massively scalable and efficient tools to Big Data processing. Even the smallest companies nowadays inevitably require more and more resources for data processing routines that could enhance decision making and reliably predict and simulate different scenarios. In the current paper we present our combined work on different massively scalable approaches for the task of clustering and topic modeling of the dataset, collected by crawling Kazakhstan news websites. In particular, we propose Apache Spark parallel solutions to news clustering and topic modeling problems and, additionally, we describe results of implementing document clustering using developed partitioned global address space Mapreduce system. In our work we describe our experience in solving these problems and investigate the efficiency and scalability of the proposed solutions.
Analysing search engine optimization techniques for a full stack web application

Abstract In this paper, we firstly discuss the step by step process to build a fully functioning full stack app and the technologies used while doing so. Following that, a research on various search engine optimization techniques is done for the deployed site of the app. Upon completion of the project and research, we arrived at the algorithm which showed the best results within a given time frame. It was concluded that Google page rank algorithm works the best in this case and also yielded positive results. With that we improved the ranking of our MERN stack application. We also implemented other common techniques for the search engine optimization of a site and have shown results for the same. The factor which significantly improved the ranking of our site was found to be generating more traffic. As the site traffic increased, the cPR score of our site also went up, which was also visible from the increase in the number of clicks.
An Automatic Violence Detection Technique Using 3D Convolutional Neural Network

In the field of machine learning, the deep learning technique plays a very important role as it is useful in various real-life domains. As various crimes and misdeeds are occurring in various public places because of lack of proper monitoring, a number of methods have been proposed for detecting violence from videos. Automatic violence detection has gained increased research importance in case of video surveillance. However, they suffer from various limitations and most of the times it depends on special criteria. In this perspective, this paper proposes an effective violence detection method from videos using 3D convolutional neural network. The proposed methodology uses machine learning and deep learning techniques for improving the accuracy. Comprehensive performance analyses have proven that the proposed method achieves high performance in case of detecting violence from videos. The experimental results also prove that the proposed technique outperforms various other existing methods for detecting violence from videos.
FLUX

With the influx of Web 3.0 the focus in Big Data Analytics has shifted towards modelling highly interconnected data and analysing relationships between them. Graph databases befit the requirements of Big Data Analytics yet organizations still depend on relational databases. A major roadblock in the industry wide adoption of graph databases is that a standard query language is still in its inception stage hence withholding interoperability between the two technologies. In this research we propose a tool FLUX for translating relational database queries to graph database queries.
Video Quality Enhancement Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients in MPEG I-frames

Recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG/MPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a MPEG video decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCT coefficients received from a low-quality I-frames bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same frames with enhanced quality. In experiments with a video dataset, our best model was able to improve from frames with quantized DCT coefficients corresponding to a Quality Factor (QF) of 10 to enhanced quality frames with QF slightly near to 20.
Engineering Federated Learning Systems: A Literature Review

With the increasing attention on Machine Learning applications, more and more companies are involved in implementing AI components into their software products in order to improve the service quality. With the rapid growth of distributed edge devices, Federated Learning has been introduced as a distributed learning technique, which enables model training in a large decentralized network without exchanging collected edge data. The method can not only preserve sensitive user data privacy but also save a large amount of data transmission bandwidth and the budget cost of computation equipment. In this paper, we provide a state-of-the-art overview of the empirical results reported in the existing literature regarding Federated Learning. According to the problems they expressed and solved, we then categorize those deployments into different application domains, identify their challenges and then propose six open research questions.
Time-Series Measurement of Parked Domain Names

Domain parking is a monetization mechanism for displaying online advertisements in unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.
Reasoning about strategic voting in modal logic quickly becomes undecidable

Abstract In this paper, we show that modal logics for reasoning about social choice quickly become undecidable. In particular, we study modal logics that can be used to reason about situations involving both actual and claimed preferences in the context of a social choice function and argue that reasoning on this level often occurs in social choice. We formally define a particular logic, interpreted in such situations, that can express the properties involved in the Gibbard–Satterthwaite theorem. We then, however, demonstrate that any modal logic interpreted in such situations having a certain natural expressive power, in particular a modality quantifying over all possible claimed preferences, becomes undecidable when there are enough agents in the system. We also discuss a decidable special case and provide a complete axiomatization of fragment of the language.
Impact of Similarity Measures in K-means Clustering Method used in Movie Recommender Systems

Abstract One of the most challenging tasks in today’s era is to deliver personalized information to the user or group of users according to their preferences. The decision making process is used as a tool by the recommender systems to suggests various products and items. The goal of recommender system is to deliver germane information to the user based on their likings. In this research work, we study and compare the effect of similarity measures used in k-means clustering in movie recommender systems. Our proposed method used the sampling, PCA and k-means clustering to recommend movie from the MovieLens dataset. In the whole process, some similarity measures are used in k-means clustering such as Euclidean, Minkowski, Mahalanobis, Cosine similarity and Pearson correlation. The aim of our work is to study the effect of similar measures in movie recommender systems in terms of standard deviation (SD), mean absolute error (MAE), root mean square error (RMSE), t-value, Dunn Matrix, average similarity and computational time using publicly available MovieLens dataset. The results achieved from the experiments indicates that Cosine similarity is best technique in movie recommender system in terms of accuracy, efficiency and processing speed and also able to get MAE of 0.65, which is best between all similarity measures.
A Light Weight Multi-Head SSD Model For ADAS Applications

Moving objects detection is considered as one of the prime safety indicators in the Advanced Driver Assistance System (ADAS). For implementing on resource-limited embedded platforms and still yield sufficient frame rate and quality, the paper proposes a lightweight multi-head single shot detector (SSD) model that strengthens the moving object detection significantly. The paper also introduces focal loss method to deal with imbalance problem of detecting pedestrians and bikes in training datasets (vehicles, bikes, and pedestrians). Lastly, the proposed lightweight network can be deployed on low-power embedded devices to achieve real-time processing performance (512x256) yielding 30fps.
Anonymizing Location Information in Unstructured Text Using Knowledge Graph

There is a growing need to anonymize data as new businesses are increasingly utilizing vast amount of unstructured text. Also, unstructured text have a risk of personal location estimation by considering location information. Nevertheless, existing generalizations do not take into location information and therefore cannot robustly handle this attack. In this study, we proposed anonymizing location information in unstructured text using knowledge graph newly constructed from an actual geographic information system. Our method has the advantages of anonymization, taking into account actual geographic information, handling abbreviations and spelling inconsistencies, and allowing for dynamic graph updates. The results of the evaluation experiments show that anonymization is more robust than existing methods against location estimation attacks without compromising its usefulness as a dataset. Also, we found that the names of organizations and places with a high probability of occurrence in unstructured text are more likely to lead to personal identification.
Polysemy Needs Attention: Short-Text Topic Discovery With Global and Multi-Sense Information

The topic model has been widely applied to various research domains such as information retrieval, data mining, and so on. It can discover topics of texts in an unsupervised way. In the early years, most researches mainly focused on long texts. With the emergence of the Internet, the number of short texts is growing rapidly. Most existing schemes to solve the sparsity problems of short texts, are mainly based on data aggregation or model improvements. Among them, the Biterm Topic Model is one of the most representative models. It proposed a new way to model topics based on document-level word pairs and has shown creativity and effectiveness. However, this strategy ignores those semantically similar and rarely co-occurrent word pairs. What's more, most researches ignore the multi-sense phenomenon in natural languages. In this paper, we utilize multi-sense word vectors to extract similar word pairs from the whole corpus by considering multiple senses. Based on this idea, we introduce a novel short-text topic model, which disambiguates multiple senses of words and generates more reasonable global biterms. Experimental results on two open-source English datasets have shown superiority to state-of-the-art topic models.
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam

There has been a booming demand for integrating Convolutional Neural Networks (CNNs) powered functionalities into Internet-of-Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, more extensive applications of such IoT systems are still limited by two challenges. First, some applications, especially medicine-and wearable-related ones, impose stringent requirements on the camera form factor. Second, powerful CNNs often require considerable storage and energy cost, whereas IoT devices often suffer from limited resources. PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck. Existing compression techniques, which can potentially tackle the second challenge, are far from realizing the full potential in storage and energy reduction, because they mostly focus on the CNN algorithm itself. To this end, this work proposes SACoD, a Sensor Algorithm Co-Design framework to develop more efficient CNN-powered PhlatCam. In particular, the mask coded in the Phlat-Cam sensor and the backend CNN model are jointly optimized in terms of both model parameters and architectures via differential neural architecture search. Extensive experiments including both simulation and physical measurement on manufactured masks show that the proposed SACoD framework achieves aggressive model compression and energy savings while maintaining or even boosting the task accuracy, when benchmarking over two state-of-the-art (SOTA) designs with six datasets across four different vision tasks including classification, segmentation, image translation, and face recognition. Our codes are available at: https://github.com/RICE-EIC/SACoD.
Security Analysis of Paxos Mechanism Design Based on Game Theory

At present, a large number of token-less blockchain system use Paxos algorithm to reach a consensus. Based on game theory, this paper finds that when Paxos algorithm runs, each node in system may produce a kind of selfish behavior. The selfish behavior will lead to a game, which is called the computing resource dilemma game in this paper. By analyzing the payoff matrix and establishing the utility function model, it is found that Pareto optimal Nash equilibrium in the game needs to meet certain security conditions. Otherwise, each node will choose an uncooperative strategy, which will eventually lead to the system stop working. By solving the perfect Nash equilibrium solution of the subgame in the game, the security conditions for achieving the purpose(each node adheres to the cooperative strategy) of Paxos mechanism design are obtained. And the effectiveness of the security conditions is proved by simulation experiments.
Quantum Algorithms for Solving Linear Regression Equation

Abstract Linear regression is one of the most important and common analytical methods in mathematical statistics. The letter studies a general model of linear regression problem based on least squares method, and investigates the impact of quantum algorithms on the time complexity of solving linear regression problem when quantum algorithms can be implemented on quantum computers. With the help of the general model of linear regression problem in terms of least squares method, we propose a novel simplified quantum scheme for solving linear regression equation based on the sparsity-independent quantum singular value estimation algorithm. For a linear regression equation with dimension n , our scheme can reduce the time complexity from O ( Nn 2 ) to <?CDATA $O(\sqrt{N}\mathrm{log}N\,)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> <mml:mi>log</mml:mi> <mml:mi>N</mml:mi> <mml:mspace width="0.25em" /> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> when both the condition number κ of related matrices and the reciprocal of precision ∈ are small in size O (play log N ), where m is the dimension of the input u and N is the number of samples.
Information Technologies for Philological Education in the Digital Age

The training of specialists in the Humanities cannot fail to undergo transformation in modern civilizational conditions. This applies to updating the content of academic disciplines and pedagogical technologies, which should contribute to the development of basic competencies, skills, abilities and knowledge of future philologists. The purpose of this research is to improve the didactic system of teaching Humanities students in response to the high requirements and standards imposed by the state and society. Design and methodology: information technologies for data processing and presentation, online educational resources and services, interactive learning, project method. The article reflects the results of using information technologies in combination with interactive, group and project methods of organizing the educational process in Magnitogorsk Institute for the Humanities. Students learn to create projects in the field of modern Russian language and Russian culture, students get integrated knowledge about information services, master technologies for searching, processing, presenting and scientific evaluation of information, which is confirmed by their publications: dictionaries, scientific articles, and research methods.
Mitosis detection in biomedical images

Mitosis is an important stage in the life cycle of cells. The analysis of mitosis can provide important information for the study of cell behaviors during cell culture. In addition to being used to analyze the proliferation of cells, mitosis detection can be used to improve the accuracy of cell-tracking results. In this chapter, we will give a detailed description of the mitosis detection problem, and then introduce some advanced research in mitosis detection areas in recent years. In Section 1 of this chapter, we introduce the mitosis process during the cell life cycle and then give a definition of the mitosis detection problem from the perspective of computer vision. In Section 2, we introduce some common microscopy imaging methods for mitosis detection. Various microscopy imaging modalities can lead to different techniques used for mitosis detection. It is essential to have some understanding of these microscopy imaging methods. Based on these techniques, mitosis can be detected from a single static image or a sequence of dynamic images. Computer vision and machine-learning techniques used to deal with both situations are further described in Section 3, and then we divide the mitosis detection methods into four categories: traditional tracking-based methods, tracking-free methods on static images, hybrid methods with short-term tracking and modeling of the probability graph model, and finally, the recently proposed deep learning-based methods. For mitosis detection on static microscopy images, we focus the description of some popular image feature extraction methods, including the application of some classic features such as GIST and SIFT. We further introduce the recent research findings for the dynamic modeling of the mitosis process. In some applications, this dynamic information can provide important information that helps to improve the accuracy of mitosis event detection. The dynamic information can be extracted with cell-tracking methods or spatial-temporal features, modeled with a probability graph model or an RNN model. We also introduce the application of deep learning methods to combine the spatial appearance feature and the temporal dynamic for mitosis detection tasks. Finally, in the last section, we summarize these existing methods and discuss the potential directions for the mitosis detection problem in future research.
Combining Objective Key Results, Net Promoter Score and Social Return of Investment to Measure Project Impact

This paper presents an alternative approach to measure impact, combining indexes that are already stablish to elicit objectives (Objective Key Results, known as OKRs) achievement, user satisfaction (Net Promoter Score; or NPS) and return of investment (social return of investment; SROI). Calculation plots the design in a 9-matrix grid, each one representing a level of impact and pointing ways of improvement. Besides presenting details of what we are calling Conceição Moura Impact Index (CMII), we also share the results of the first pilot; an experiment conducted in a five-year-old project called iCANamy, and more specifically its program for teachers.
A face recognition algorithm based on CNN with ELBP and DCGAN

Convolutional neural network (CNN) have been widely used in face recognition. The illumination and the size of the data set affect the accuracy of face recognition based on CNN. In order to improve the accuracy of face recognition based on CNN, an improved face recognition algorithm based on CNN with extend local binary pattern (ELBP) and deep convolutional generative adversarial network (DCGAN) is proposed. ELBP uses a circular operator which can be of any size, and the coverage area can be adjusted arbitrarily. It has gray and rotation invariance, and has good robustness to illumination. In real scenes, it is more difficult to obtain face photos of a person with different lighting and different scenes. In order to solve face recognition in the case of small data sets, this paper uses DCGAN to generate new face pictures based on the original pictures. By expanding the data set, the accuracy of face recognition is improved. Experiments have proved that the proposed method in this paper has higher accuracy in face recognition than traditional methods.
Realtime monitoring of Bitcoin prices on several Cryptocurrency markets using Web API, Telegram Bot, MySQL Database, and PHP-Cronjob

Various ways have been done by researchers from all over the world to predict the price of Bitcoin which is the first digital commodity based on blockchain. Various forecasting and computational techniques have been widely applied and continue to be refined to find a prediction package that has the closest accuracy. The purpose of this research is to test hundreds of API data shared by hundreds of Cryptocurrency Markets and as a result we have succeeded in extracting the latest bitcoin price data from 20 Crypto Markets via shared APIs. This data will then be stored in a MySQL database on hosting automatically when a condition is reached. This data can then be mined continuously using Cronjob, and to facilitate the reading of data that has been collected, we use a telegram bot as well as using a web-based application. This step is expected to help in efforts to monitor bitcoin price movements and predict future price movements.