Reviewerly/exHarmony huggingface dataset:
Discriminant analysis predicted undisciplined policemen

In the wider spectrum of Taiwanese public service spheres, the herculean services and dedication of its committed Police personnel have long been recognized, respected, and admired. However, regrettably, question marks concerning their conduct, discipline, and abuse of power have surfaced on intermittent occasions. A classic example that lingers in the public memory is the bribing of Taiwanese video game companies to some unscrupulous elements of the police department, in the closing decades of the 20th century that triggered public outrage and called for scrutiny concerning serious lapses in the discipline and conduct of Police personnel. This research paper endeavors to understand, analyze and address some of those issues based on empirical data on the police personnel of certain specific work zones/areas taking into account holistically both the sentenced police officers vis-à-vis the law-abiding police officers. This module looks into and sieves through available data for seven critical variables, including their degree of variation through the Identification and Analysis Method to develop a Predictive Model on Police Ethics and the important factors that affect Police Ethics. Concretely based on the integrated research, it is proposed that this Predictive Model has good applicability as well as accurate predictive ability in addressing the core issues that affect Police Ethics. It is hoped that through this Early Warning Predictive Model-all the stakeholders that are Policy and Decision-makers, Regulatory Police Agencies but more importantly the Police personnel themselves would effectively address the criticality of the issues that affect the Police Ethics so as to undertake competent and effective measures to erase/lessen the menace and provide an early rehabilitative care/assistance to build a strong, constructive and visionary Taiwanese Police Force to meet the challenges of 21st century and beyond.
Multi point monitoring method of prestressed tendon based on distributed sensors

Abstract This paper presents a multi-point strain monitoring method based on distributed sensors, which can monitor the multi-point stress of prestressed tendons in concrete. Firstly, the special sensor protection device is installed in the designated position of the component. Then, the sensor is placed in the device and attached to the monitored prestressing tendon. After concrete pouring, the device can form a closed space around the sensor to ensure that the sensor can freely expand and move in a small range when the prestressed tendon is tensioned. The proposed method is applied to the monitoring of the construction period and initial operation period of a large-span spatial structure, and the research results show that the method proposed in this paper can well realize the multi-point monitoring of the prestressed tendon strain in the concrete, and effectively solve the engineering problems; through the analysis of the long-term monitoring data, we can accurately grasp the influence of each construction condition on the structure stress, and clarify the actual stress state and long-term change trend of the structure. The obtained monitoring data play a positive role in guiding the construction of prestressed structure, and the construction safety can be objectively evaluated by analyzing the monitoring data.
A Randomized Montgomery Powering Ladder Exponentiation for Side-Channel Attack Resilient RSA and Leakage Assessment

This paper presents a randomized Montgomery Powering Ladder Modular Exponentiation (RMPLME) scheme for side channel attacks (SCA) resistant Rivest-Shamir-Adleman (RSA) and its leakage resilience analysis. This method randomizes the computation time of square-and-multiply operations for each exponent bit of the Montgomery Powering Ladder (MPL) based RSA exponentiation using various radices (Radix – 2, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) based Montgomery Modular multipliers (MMM) randomly. The randomized computations of RMPLME generates non-uniform timing channels information and power traces thus protecting against SCA. In this work, we have developed and implemented a) an unmasked right-to-left Montgomery Modular Exponentiation (R-L MME), b) MPL exponentiation and c) the proposed RMPLME schemes for RSA decryption. All the three realizations have been assessed for side channel leakage using Welch’s t-test and analyzed for secured realizations based on degree of side channel information leakage. RMPLME scheme shows the least side-channel leakage and resilient against SPA, DPA, C-Safe Error, CPA and Timing Attacks.
Automated Meeting Scheduler Using Chatbot

The main motive behind building the cross-platform application is to provide a fast and convenient way to schedule meetings with the help of an automated chatbot. The application also provides a platform to have a pre-discussion before the meeting in the chatroom which is created once a meeting is approved by its members and can send documents related to the meeting stored on their devices or use the inbuilt OCR scanner of the application.
F-EvoRecSys: Fuzzy Inference meets Evolutionary Approach for Personalized Well-being Recommendations

Many citizens nowadays cope with busy and dynamic lifestyles. Adopting or maintaining a healthy lifestyle to prevent chronic diseases or mental disorders is a core societal challenge. The current global pandemic has evidenced more than ever before the critical importance of engaging citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Recommender Systems for personalized health and well-being have lately become a research trend, particularly for food and physical activity recommendation. This paper presents F-EvoRecSys: an extension of an evolutionary algorithm-driven approach for "healthy bundle" well-being recommendations that incorporates a fuzzy inference engine aimed at improving physical activity recommendations based on users’ exercising habits. An experimental study demonstrates how this can lead to more diversified recommendations. The paper also discusses challenges and future directions for personalized well-being recommender systems under different perspectives.
One-Class Classification with Noise-Based Data Augmentation for Industrial Anomaly Detection

Deep learning based approaches have shown promising results for anomaly detection. One of them is the one-class classification, a typical method treated as an unsupervised learning model. In this problem, a number of unlabeled samples are given. The model will learn a description of the unlabeled samples. The description is then used to detect an unusual sample and treat it as an anomaly object. It, however, should be better to learn an anomaly detector if we can have some labeled samples which include both normal and anomalous, to leverage the description of unlabeled samples. Learning with these labeled samples for anomaly detection is also known as the semi-supervised method. In this paper, we present an improvement of the deep SAD, a semi-supervised model, for anomaly detection in industrial systems. We propose to use synthetic anomalies which can be generated using noises. Two noise models are used, including the confetti and polygon noises, to augment the anomalies for training the model. Experiments will be conducted for the standard dataset MVTec-AD to show that our model outperforms related baseline models, especially with a small dataset available.
Analysis of English text genre classification based on dependency types

The present study aims to explore whether dependency type can be used as a distinctive text vector for classifying English genres. Three classification methods, namely principal component analysis, hierarchical clustering, and random forest were employed to investigate the clustering effect. Results show that dependency type is an effective measure in distinguishing text genres, especially between spoken genre and written genre.
Experimental Comparison of Machine Learning Techniques for Analysing the Facial Expression

Emoticons are miniature pictures that are customarily used in internet community Communications in the 21st century. The fusion of textual and imagery contained in the same message develops today's modern way of conversation. In spite of being universally utilized in online media, Emoticons basic interpretation has received very little observation from a “Natural Language Processing” point of view. In this paper, we investigate the relation between facial expressions and emoticons, studying the novel task of predicting which emojis are evoked by the user's facial expressions. We experimented with variants of word embedding techniques, and train various models based on MNBs and LSTMs in this task respectively. The experimental results show that our model can predict reasonable emoticons from emotions.
Consensus kernel subspace clustering based on coefficient discriminant information

At present, multiple kernel subspace clustering technology has been extensively used in various fields of machine learning and computer vision. However, the traditional multiple kernel subspace clustering method still has two problems. On the one hand, the combination of kernel functions is uncertain. Choosing inappropriate kernel functions from different sources or data with different characteristics will make the results inaccurate. On the other hand, data sources are extensive and subspace division is not accurate. In this paper, we unify coefficient discriminant information and multiple kernel subspace clustering into one process. Firstly, we combine the consensus Hilbert space to propose a consensus kernel method. This method reconstructs a set of different kernel matrices into kernel matrices more in line with the data characteristics, which can effectively solve the uncertainty of the combination of kernel functions. Secondly, the diagonal structure of blocks is constructed by subspace clustering, which can effectively solve the problems of different data sources. In the process, we propose a coefficient discriminant information which can constrain the data in the coefficient space to group as many of the same types of data as possible, and separate different types of data as much as possible. Finally, we propose a new iterative optimization method based on the Alternating Direction Multiplier Method (ADMM) to solve the final optimization goal. Our experiment is conducted on twenty different data sets. According to the final experimental results, our proposed algorithm has higher ACC, NMI and ARI than the latest clustering methods.
Object Tracking Algorithm with Two-way Parallel Fully-convolutional Siamese Networks

Fully-convolutional Siamese Networks (SiamFC) tracker usually achieves good performance in real-time visual tracking. Its accuracy may suffer from the complex background in some tracking scenarios. However, in other situations including target occlusion, deformation, and so on, the background information can help infer the location of the target. To improve the overall performance of SiamFC under multiple scenarios, a new algorithm with two-way parallel architecture is proposed. One branch uses the traditional SiamFC, and the other employs a modified SiamFC which can suppress the background information causing interference with the target in the template. The final matching results are selected by using the average location error (ALE) as an evaluation metric to describe the confidence level of the matching results, so the robustness of the algorithm can be enhanced under various object tracking scenarios. Testing experiments are performed on the OTB dataset as well as the VOT2017 dataset. The experimental results show that the proposed algorithm can achieve better performance when compared to the original SiamFC tracker.
Applying Faulty Statement Category Frequency to Localize Faults for Student Programs

Online Judge (OJ) system has been widely used in the teaching of programming courses. When students use the OJ system to submit programs, OJ can only provide simple feedback on whether the program is correct or not. However, students with little programming experience are difficult to find and fix even simple bugs. Although a lot of approaches were proposed to localize faults in open-source and industrial software programs, whether these techniques can achieve satisfactory results in student programs has not been thoroughly investigated. In this paper, we propose a Faulty Statement category Frequency-based Fault Localization (FSFFL) method to locate bugs in student programs. Specifically, motivated by the observation that the faulty statement categorization for the same OJ question is always similar, we employ the history information of student programs and divide the corresponding code statements into seven categories, and then we calculate the faulty frequency for each category. Finally, we use the frequency information to give weight to the suspiciousness values of statements calculated by traditional Spectrum-Based Fault Localization (SBFL) methods. To verify the effectiveness of our proposed approach, we conduct empirical studies on 2,411 faulty programs from the OJ platform. Experimental results show that our approach with the Dstar formula can improve the fault localization accuracy by 18.73&#x0025; on average compared with the traditional SBFL methods in terms of EXAM metric.
On the terminological standard draft “SIBID. Library collection. Terms and definitions”

The author examines the draft of standard new version (GOST R 7.0.94–2015 “SIBID. Library document acquisitions. Terms and definitions”. The need for ordering terms in the area of collection development and collection studies. Owing to the terminology’s expanding onto collection studies, the standard title has changed from “Library collections. Terms and definitions”. The standard focuses on terms and definitions within the subject area of “library collection studies, library collections”. The vectors of standard drafting are discussed: outdated standardized definitions are edited; processes structure and contents are changed according to the modern theory and practice related to library collections; concept list for library collection characteristics is expanded; new concepts to describe library activities in digital environment are introduced. The draft standard comprises 170 terms and the alphabet index. The author describes Rosstandardregulated approaches toward national standard development [Rosstandard – Federal Agency on Technical Regulating and Metrology]. The new version key provisions and definitions can be used for references and will support further development of new regulations, methodological documents and instructions for library activities, and library collection studies.
Breast tumor ultrasound image classification method based on the fusion of deep network features and texture morphological features

The automatic classification of benign and malignant tumors based on ultrasound images has important application value in improving the efficiency and accuracy of clinical diagnosis of the breast tumors. Aiming at the problem of low sensitivity of traditional methods in clinical auxiliary diagnosis applications, an automatic classification method of benign and malignant tumors in breast ultrasound images based on the fusion of global deep network features and texture features is proposed. The experiment uses a total of 791 breast ultrasound image data sets (benign: 373, malignant: 418). Through training the proposed method model, and performing a five-fold cross-validation experiment, an accuracy rate of 87.90% is obtained, and the AUC value is 0.9401. Experimental results show that the proposed method has a better classification effect compared with similar methods, and can effectively improve the efficiency and accuracy of benign and malignant breast tumor diagnosis.
Callenges for teaching Georgian language Phonetics in high schools

Significant and important details should be taken into account when planning a Phonetics training course. As you know, the Morphology and Syntax of the Georgian language are taught in small doses at school, and thus, these subjects are somewhat familiar to the entrants. But we can not say the same about "Phonetics". This scientific discipline is a completely new fruit for first-year students of the Philology contingent, which puts lecturers in front of great responsibilities and serious difficulties. Thus, when working out a Phonetics lecture course, we must carefully select the issues that we will provide to students seeking new knowledge, not in terms of learning and mastery, but in terms of assessment and cognition. We hope that the model we have developed will give the freshman the opportunity to think rationally, to choose and to formulate his own position, even to identify/develop a new theory/point of view, and to create his scientific skills.
Wavelet Transform Based Compression of Raw SAR Data

Synthetic Aperture Radar has been an emerging technology to cope with the huge amount of data to cover a wide swath and high-resolution. The data collected consists of both range and cross-range samples. Frequency Modulated Continuous Wave(FMCW) radar generates pulses with continuous PRI(Pulse Repetition Interval). To cope with the huge amount of data it is necessary to compress it before transmission to downlink. The quantization of data is done to reduce the bit/sample of data. In the frequency domain, efficient compression can be done using transform methods. To compress the data various techniques have been established. Block Adaptive Quantization combined with various transforms gives efficient compression compared to only BAQ. This paper covers the Wavelet Transform method compression of raw real SAR data. Block Adaptive Quantization of data is done along with DWT. The results have been evaluated with the Traditional BAQ algorithm. Improved Signal to Noise ratio has been obtained using wavelet BAQ.
Implementation of Static Telecytology Applications for Accreditation Purposes According to ISO 15189

Telecytology can be defined as the process of diagnostic cytology performed on digital images. The process of diagnostic cytology performed on digital images can be used for teaching, professional examinations, obtaining expert opinions on severe cases, and routine diagnosis of the entire laboratory workload. Ηowever, little information exists about its possible use for proficiency testing purposes. The existing studies have found a high (90%–95%) concordance between telecytological and glass slide diagnoses, but they refer to a small number of cases. Thus, the purpose of the chapter is to examine the feasibility of developing a proficiency testing scheme for cytology labs wishing to be accredited according to ISO 15189:2012 by using low-cost telemedical applications.
Crossref reports for publishers and scientific publications

The paper deals with the issues of placing metadata of scientific publications in the Crossref bibliographic database. All information contained in the metadata is analyzed by Crossref and displayed in various reports. The reports give the publisher an idea of the completeness and correctness of the bibliographic data presented. The completeness and correctness of metadata directly or indirectly affect the number of views and citations of a publication, and, accordingly, the ratings of scientific publications, authors, and organizations.
Sentiment Analysis of Text with Lossless Mining

Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.
Watermarking of Digital Image Based on Complex Number Theory

The objective of this paperwork is to implement a watermarking algorithm using complex number theory. Here, each pixel of the original image is considered as the real part of each complex number, and the imaginary part corresponds to the pixel of the watermark. The matrix consists of the absolute value of the complex number treated as the watermarked image. The real and imaginary components of each complex number cannot be retrieved without the phase angle, which is treated as the key to a secured watermarked image. The phase angle of each complex number transmitted through a secured channel. At the receiving end, when both the magnitude and phase angle of each pixel estimated properly, then the real and imaginary components of each complex number corresponding to the pixel of original and watermarked images are determined with some accuracy.
Dolphin Identification Method Based on Improved PST

Dolphin identification is an important task on the environmental protection. In previous studies phase stretch transform (PST) edge detection algorithm was a global algorithm that used the phase contour of the complex amplitude image to detect edges. In this paper, the improved PST algorithm is applied to the actual scene for identifying the Chinese white dolphins. The edges of the detected dolphins are selected by frames and can be displayed in the RGB image. The noise is removed to ensure that the pixel value near the edge will not affect the result of edge detection. The MSE and PSNR metric are applied to evaluate the performance of edge detection operators on several real images.
Predicting Stars on Open-Source GitHub Projects

With rising popularity of platforms like GitHub, statistics like the number of stars can help comprehend the popularity of a project. In this paper, we evaluate a dataset of 2000 projects along with multiple GitHub features. The dataset informed using the GHTorrent project along with manual adjustments. Next, we build a smart model to predict the number of stars for a repository based on the features. Our work shows that it is indeed possible to predict the popularity/GitHub stars for a project based on the features. We use algorithms like Random Forest and a log-scaled dataset to achieve an R-square score of 0.88.
Bidirectional LSTM Networks for Abstractive Text Summarization

With the constant increase in the amount of available information, especially textual, there exists a need for an efficient and automated text summarization mechanism. Two main text summarization algorithms have been the focus of numerous research studies: extractive and abstractive summarization. This paper focuses on implementing a variant of abstractive summarization using Long Short-Term Memory (LSTM) networks by modeling the summarization task as a Sequence-to-Sequence (Seq2Seq) problem. The dataset used in the research was Amazon’s database of food and product reviews. The study employs the encoder-decoder architecture, with stacked LSTMs in the encoder phase and an attention layer mechanism. Moreover, the paper experiments with using bidirectional LSTMs instead of the standard unidirectional approach. This, combined with stacking, leads to increased sequence representation and better summarization performance. The trained model was evaluated using BLEU and ROUGE performance metrics, showing acceptable results. Moreover, the produced summaries generally seemed to correspond to original summaries, conveying the intended meaning. In the end, the paper outlines several possible improvements to be attempted in future models.