Reviewerly/exHarmony huggingface dataset:
Cheating the Machine: E-voting Practices in Kyrgyzstan’s Local Elections

This paper analyses the e-voting experience of the local elections undertaken by Osh city Council in 2016. The process was introduced to ensure fair and democratic elections in Kyrgyzstan after continuous and repeated violent political uprising. The e-system, based on biometrics registration, biometric identification of voters and automated vote counting, was designed to help to avoid the most common election frauds: vote buying, carousel voting and group/family voting. The case study, mainly based on interviews, illustrates the adaptation and modernization of strategies to resist and cheat within the e-voting system. The analysis outlines three widely practised cheating strategies: procedural violations, such as avoiding cross-checking of manual and automated counting and allowing voting without biometrical identification; transformation of bribery into ‘vote auctioning’; and strengthening of kinship-based/regional support and tribal/regional identity under conditions of e-voting.
Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries

Abstract Immediately after a destructive earthquake, the real-time seismological community has a major focus on rapidly estimating the felt area and the extent of ground shaking. This estimate provides critical guidance for government emergency response teams to conduct orderly rescue and recovery operations in the damaged areas. While considerable efforts have been made in this direction, it still remains a realistic challenge for gathering macro-seismic data in a timely, accurate and cost-effective manner. To this end, we introduce a new direction to improve the information acquisition through monitoring the real-time information-seeking behaviors in the search engine queries, which are submitted by tens of millions of users after earthquakes. Specifically, we provide a very efficient, robust and machine-learning-assisted method for mapping the user-reported ground shaking distribution through the large-scale analysis of real-time search queries from a dominant search engine in China. In our approach, each query is regarded as a “crowd sensor” with a certain weight of confidence to proactively report the shaking location and extent. By fitting the epicenters of earthquakes occurred in mainland China from 2014 to 2018 with well-designed machine learning models, we can efficiently learn the realistic weight of confidence for each search query and sketch the felt areas and intensity distributions for most of the earthquakes. Indeed, this approach paves the way for using real-time search engine queries to efficiently map earthquake felt area in the regions with a relatively large population of search engine users.
Classification of Cervix types using Convolution Neural Network (CNN)

The transformation zone is the region in the cervix where precancerous cells develop and later result in cervical cancer. Based on the position of this zone, the cervix is categorized into three classes: Type I, Type II and Type III. Distinguishing these types manually is very challenging for health practitioners. Therefore, it is important to automate the classification of the cervix types using Convolution Neural Network. Various research work has used common CNN architecture such as ResNet, Vgg, Inception which are time-consuming and require a large memory computation. However, this is not suitable for mobile deployment in low-resource regions. Thus, in this work, SqueezeNet a smaller CNN architecture is used and the accuracy obtained is equivalent to AlexNet (a much larger network). It requires a low computation memory and it is suitable for deployment on mobile devices. lt is believed that deploying a cervix type classification system on a mobile device will aid screening by health practitioners and reduce the misdiagnosis of patients.
Enhanced Two-Phase Virtual Machine Placement Scheme for Cloud Computing Datacenters

Over the years, Cloud Computing has offered many benefits such as providing services to end-users on demand. However, infrastructure as service providers is faced with the challenge of handling many end-users' requests for virtual resources. In this regard, one of the key challenges of resource allocation is Virtual Machine Placement (VMP) problem. However, the dynamicity and uncertainty of Cloud platform and the unpredictable nature of the end-users' requests have rendered the VMP problem more interesting. Recently, a two-phase Virtual Machine Placement scheme, combining the benefits of both online (dynamic) and offline (static) formulations were presented. The proposed scheme is based on a prediction-based triggering method used to determine when to trigger the VMP reconfiguration phase. However, the existing method leads to a less accurate prediction outcome, therefore, results in less optimal solution from the reconfiguration phase. This work proposes an enhanced two-phase Virtual Machine Placement strategy based on novel triggering method. The proposed triggering method considers Damped trend exponential smoothing method. An experimental evaluation is performed against the previous approach, considering 160 scenarios. The experimental results show that the proposed work achieved a minimum cost function when compared with the benchmark approach.
Skill Gaps in Management Information Systems Alumni

The aim of this paper is to epitomize on the outcomes of the skill gap questionnaire of Management Information Systems (MIS) alumni from Ahlia University, Bahrain. This study evaluated the importance of the job skills and the skill gaps which are associated with 58 technical and non-technical (soft) skills. These skills were assembled into five classifications in relevance with prior literature. The outcomes of this study can help instructors in the MIS programs of the higher education institutions to improve the MIS curricula, hence the programs, as per the current needs of MIS graduates.
An Acoustic Model of Civil Aviation's Radiotelephony Communication

Civil Aviation's Radiotelephony Communication (CARC) in China involves Chinese and English. Due to the particular grammatical structure and pronunciation of CARC, the universal cross-lingual acoustic model isn't applicable. For the purpose of achieving the Chinese-English speech recognition of CARC, this paper proposes a cross-lingual acoustic model using a shared-hidden-layer Convolution Depth Neural Network (CDNN) with hidden Markov model. It introduces the Convolution Neural Network (CNN) to overcome the diversity of speech signals. In order to shorten the training and decoding time, the Low Frame Rate (LFR) is added in the feature extraction stage. The experimental results show that the acoustic model based on CDNN is better applied to the domain of CARC. The introduction of CNN can further improve recognition performance. Adding the LFR effectively reduces the training time and the word error rate.
Identifying Interesting Moments in Controllers Work Video via Dimensionality Reduction

We explore use of machine learning in automating the discovery of meaningful time intervals in video data. We combine Convolutional Neural Networks and Principal Component Analysis in order to zoom-in on interesting moments in hours-long videos of air traffic controllers work. Experimental results for air traffic control tower at Stockholm Bromma airport confirm feasibility of our approach. The method may be consequently used to single out workload-influencing factors, incident investigation and other post-operational analysis of controllers performance.
SAR-GMTI for Slow Moving Target Based on Neural Network

As an important applications of synthetic aperture radar (SAR), slow moving target detection is causing more concern from people. Commonly, with the increase of computer computational efficiency and utilization of GPU, convolutional neural network has been becoming an efficient approach for target detection and classification. Here we propose a method using Faster R-CNN to detect the slow moving target in SAR images. When using existing datasets to detect moving targets, the detection accuracy is low due to the small defocusing of slow moving targets. So we use the bidirectional imaging mode to create the dataset. By increasing the displacement, it is more conducive to detect slow moving targets. At the same time, neural network provides a feasible way for target detection in this mode. In order to close to the reality echo, we use FEKO to simulate the target echo and use measured ground data to generate the ground echo. Deep learning combined with the forward and backward beams can detect slow moving target more effectively. The simulation results validate the effectiveness of the proposed method.
Key-Dependent S-box Scheme for Enhancing the Security of Block Ciphers

The Advanced Encryption Standard (AES) is the current de facto standard block cipher for extremely confidential information. It is used by many large agencies, and popular for its computational efficiency in both software and hardware. However, the standard (Rijndael) S-box used of AES is a static and fixed matrix. Thus, a backdoor can be built into the cipher to exploit this feature. In this paper, we argue for a very simple key dependent S-box scheme which generates a dynamic S-box for each round of encryption. Our scheme uses some bits of the primary key (or expanded key) to directly manipulate the standard S-box in such a way that its content is changed but its cryptographic properties are preserved. We show using experimental tests that our proposed scheme strengthens the cipher against certain attacks, at the expense of a relatively modest one-time computational procedure during the set-up phase.
Can burrows-Wheeler transform be replaced in chain code compression?

Burrows-Wheeler Transform, followed by Move-To-Front Transform, are often used transformation techniques in data compression. They may reduce the information entropy of the input sequence, which becomes more compressible in this way. This paper suggests an alternative, a Prediction-based Move-To-Front Transform, which may replace the aforementioned transformations. According to the context, consisting of a few already seen symbols, the Prediction-based Move-To-Front Transform selects an appropriate ordered domain of symbols to achieve a better match with the currently transforming symbol. Freeman chain code in four and eight directions, Three-Orthogonal chain code, and Vertex Chain Code were used for experiments. We confirmed that the proposed approach, when using an appropriate length of context, reduces the information entropy to a similar extent as the Burrows-Wheeler Transform followed by the Move-To-Front Transform on chain code data. Both approaches led to a very similar compression efficiency on 32 testing shapes when an arithmetic coder was used in the final stage. The proposed approach turned out to be more efficient when longer chain code sequences were used, obtained by merging all the testing chain codes of the same type.
Credit Card Fraud Detection based on CSat-Related AdaBoost

In the field of Financial Technology, machine learning provides important support for decision-making through the effective use of data. Credit card fraud detection technology is a good example, but it still faces two challenges: the unbalanced data sets and cost-sensitive characteristics. In this paper, we proposed an enhanced CSat (Customer Satisfaction)-related AdaBoost. Based on the traditional AdaBoost, we consider the expected loss of the impact of customer satisfaction and re-adjust the weight of different categories in the cost adjustment function of the basic classifier. Considering the serious consequences of fraud transactions, we also implemented a metric related to the Total Profit of Classification (TPC) to evaluate performance. The results show that the CSat-related AdaBoost performed better in F1-score and AUC score compared to the traditional AdaBoost and some mainstream models, the reliability and interpretability of TPC as an evaluation metric is also demonstrated in our paper.
A Subjectivity Detection-Based Approach to Sentiment Analysis

With the rise of Web 2.0 where loads of complex data are generated every day, effective subjectivity classification has become a difficult task in these days. Subjectivity classification refers to classifying information into subjective (expressing feelings) or objective (expressing facts). In this paper, we use Yelp reviews dataset. Our aim is to prove that a dataset with the objective sentences removed from each review gives better results than the dataset containing both subjective and objective sentences. To achieve this, we have used two approaches, each divided into two phases. The first phase of both the approaches is mainly the subjectivity classification phase where we filter out the objective sentences and keep the subjective sentences in the reviews, thus creating a new dataset with purely subjective reviews. The second phase of the first approach uses CountVectorizer which creates word vectors, and we fit the model to the classifiers. The second phase of first approach is repeated for both the datasets, and we get better results for the newly created dataset which contains purely subjective reviews. The second phase of the second approach uses Word2Vec, an implementation of neural network which creates distributed word vectors. We fit this Word2Vec model to the classifier, and we analyze the results. Again, the newly created dataset gives better results after we repeat this phase of the second approach for both the datasets.
Application Research of Apriori Algorithm Based on Matrix Multiplication in Children's Drug Interaction

The Apriori algorithm of association rules requires multiple scans of the transaction database to obtain frequent item sets, which results in the generation of a large number of candidate sets and affects the operation efficiency of the algorithm. In this paper, improved Apriori algorithm based on matrix multiplication is applied to the study of the interaction among children's anti-infective drugs, antipyretic-analgesic and anti-inflammatory drugs, and drugs for digestive system, so as to explore the rule of the combination of three kinds of drugs, and provide theoretical guidance and reference for children's clinical rational drug use.
Design information system of registration and scheduling information laboratory of information systems and the decision of Bandung Islamic University

Abstract The development of information technology in the field of industry is very important and it takes on the world of work. Information systems are used as a tool for taking a decision in a forefront. In this case the problems often encountered in the laboratory is still using conventional spring system by means of recording and storing the archives using the Microsoft Excel application, it takes a lot of time at the time of input and determination student teaching schedule so that the required report has delay constraint information giving rise to the proceedings and time inefficient. Approach in the development of an information system using the method Framework for the Application of Systems Thinking (FAST), Model Driven Development with strategy. The FAST method using a tool used for analyzing and designing the system. The tools which are Use Case Diagrams for communication between entities, Entity Relationship Diagram for the relationships between entities, and Data Flow diagrams for data flow between entities and processes. Stages FAST method used in the study that is up to the stage of building and testing. On the functional needs of the system that was designed as a system that can apply practical lab course schedule and choose easily and can do data reports results of students who follow the teaching. After that is done the analysis of decision is to determine and select the best candidate solutions, namely the candidates by using the MySQL database. In doing this the system design using the MySQL database with the XAMPP tool in designing a database information system registration and scheduling and Sublime Text display in designing information systems. With this system he designed, is expected to assist the laboratory in managing student data to making the student reports quickly and precisely.
Recommendation of Research Collaborator Based on Semantic Link Network

Recommending appropriate collaborators to researchers can promote their research. In many cases, however, it is difficult for researchers to find proper collaborators from large number candidates. This paper proposes a scientific collaborator recommendation approach based on the semantic link networks, where nodes are authors, papers and interests indicated by keywords, and semantic links are write links, cite links, and contain links between these semantic nodes. Five semantic paths on the semantic link networks are proposed for deriving future collaboration between authors. Experiments on three datasets of scientific journal papers show that our method achieves good performance in predicting future collaborators. Comparing the combinations of five semantic paths reaches the following results: (1) co-author relationship, keyword information, and citation relationship play an important role in finding appropriate collaborators; and, (2) combining all the five semantic paths can get the best results on collaborator recommendation task.
Taking a Dive Into Google’s Chrome Cookie Ban

14 January 2020: After months of speculation, Google finally announced its plan to phase out third-party cookies on Chrome within the next two years. As Chrome boasts a worldwide market share in excess of 64%, the news sent shockwaves in the online advertising industry, whose foundations are largely built on web cookies. Concerns have been expressed that the banning of third-party cookies from Chrome could further strengthen Google and Facebook because of their large addressable first-party audience at scale. As to Google’s proposed “Privacy Sandbox” initiative, which it claims will “fundamentally enhance privacy on the web”, no one really knows how it will be implemented in practice and the industry seems to be confused. This uncertainty could hurt investment in an area that has already lost its attractiveness to investors. Against this background, this short paper examines Chrome’s policy change and its wider implications on the online advertising industry. Yet in order to appreciate the importance of the announced change we need first to get back to the basics and understand what cookies are and how they are used in online advertising. This is all the more the case considering that, while many people talk about “cookies”, few seem to have a solid understanding of what they are, how they are created, and for which purpose.
Differential Power Analysis of the Picnic Signature Scheme

This work introduces the first differential side-channel analysis of the Picnic Signature Scheme, an alternate candidate in the ongoing competition for post-quantum cryptography by the National Institute of Standards and Technology (NIST). We present a successful side-channel analysis of the underlying multiparty implementation of the LowMC block cipher (MPC-LowMC) and show how side-channel information can be used to recover the entire secret key by exploiting two different parts of the algorithm. LowMC key recovery then allows to forge signatures for the calling Picnic post-quantum signature scheme. We target the NIST reference implementation executed on a FRDM-K66F development board. Key recovery succeeds with fewer than 1000 LowMC traces, which can be obtained from fewer than 30 observed Picnic signatures.
Robust and Real-Time Obstacle Region Detection Based on Depth Feature for Vehicle Detection

Obstacle region detection has been one of the most important tasks for detecting and classifying preceding vehicles moving along the road. Traditional U-V disparity-based obstacle detection methods consider only single disparity pixels to extract obstacle regions. They might fail to produce accurate obstacle regions when noise is encountered in the original disparity map. Recently, local patterns have been successfully applied to handle noise in many systems, such as texture classification and vehicle detection. Therefore, this paper introduces a new approach to compute U and V disparity using maximum local density encoding and ternary pattern features. Our method produced better performance than the existing U-V disparity methods in terms of detection rate and accuracy. In addition, we also evaluated the performance of the proposed method with the Faster RCNN-based object detection. Experimental results show that our method improved the detection rate of Faster RCNN by 1.65% on the KITTI dataset, and by 2.28% on the CCD dataset. In addition, the proposed method also improved the running time of Faster RCNN by 47% on the KITTI dataset.
The problems of implementation of inclusive education in Ukraine: generalization the experience of empirical sociological researches experience

The article is devoted to current problems of the strategy of inclusion in the education system. The authors attempt to analyze documents regulating legal relations in the field of inclusive education, the accessibility of both educational and medical institutions, the readiness of the teaching community to work with children with special educational needs. The empirical basis formed by an array of empirical data collected by conducting a traditional analysis of documents, interviewing parents and teachers. The analysis result of conducted research allows us to conclude that informants are fully aware difficulties of inclusive education today. They are associated with the lack of architectural accessibility of the majority of school buildings, proper professional training of teachers, experience in communicating with children with various types and severity of diseases, and lack of readiness of society adequately to perceive the idea of integration.
Symmetric Galois groups under specialization

Given an irreducible bivariate polynomial f (t, x) ∈ ℚ[t, x], what groups H appear as the Galois group of f (t0, x) for infinitely many t0 ∈ ℚ? How often does a group H as above appear as the Galois group of f(t0, x), t0 ∈ ℚ? We give an answer for f of large x-degree with alternating or symmetric Galois group over ℚ(t). This is done by determining the low genus subcovers of coverings $$\tilde X \to \mathbb{P}_\mathbb{C}^1$$ with alternating or symmetric monodromy groups.
Research on Radar Information Processing System Based on Open Architecture

Aiming at the problems of unconformity, poor versatility and difficult maintenance of existing radar information processing systems, this paper proposes a new scheme for airborne radar information processing system based on open architecture. Supported by OpenVPX bus and SRIO switching technology, the high-speed parallel processing of radar signals is realized by spot digital boards integrated with high-performance DSP and FPGA. The single-channel communication bandwidth reaches 10Gbps, and the communication bandwidth of adjacent modules reaches 80Gbps. The test results show that it only takes 12.8s to complete 16K*16K complex image processing. The spot standard board facilitates function reconstruction and maintenance support, meeting the design requirements.