Reviewerly/exHarmony huggingface dataset:
A Proposed Generalized Eigenfaces System for Face Recognition Based on One Training Image

This paper discusses the results of a study that aimed to develop an eigenface technique known as (PC) 2A that collect the image of the original face with its vertical and horizontal projections. The basic components of the image were analyzed in the image enrichment section. An evaluation of the proposed method demonstrates that it costs less than the standard eigenface technique. Moreover, the experimental results show that a front-end database that has a gray level for each person has one training image; thus, in terms of accuracy, it was possible to get a 3-5% result for the proposed (PC)2A, which is higher than the precision of the standard eigenface technique. The main objective of this paper is to demonstrate the weaknesses and strengthens of the facial recognition approach as an identifier known as eigenfaces. This aim was achieved by using the principal components analysis algorithm based on the images of previously stored training data. The outcomes show the strength of the proposed technique, in which it was possible to obtain accuracy results of up to 96%, which in turn provides support for developing the technique proposed in this paper in the future because this work is of great importance in the field of biological treatments, the need for which has significantly increased over the last 5 years.
Developer-Proof Prevention of SQL Injections

SQL queries, when written by unskilled or hurried developers for web applications, are prone to SQL injections. We propose an alternative approach for querying SQL databases that does not suffer from this flaw. Our approach is based on abstract syntax trees, lets developers build dynamic queries easily, and is easier to set up than an ORM tool. We provide a proof-of-concept in Java, but our approach can be extended to other languages.
Freedom of expression in the digital public sphere : Strategies for bridging information and accountability gaps in algorithmic content moderation

A substantial portion of contemporary public discourse and social interaction is conducted over online social media platforms, such as Facebook, YouTube, Reddit, and TikTok. Accordingly, these platforms form a core component of the digital public sphere which, although subject to private ownership, constitute a digital infrastructural resource that is open to members of the public. As private entities, platforms can set their own rules for participation, in the form of terms of service, community standards, and other guidelines. The content moderation systems deployed by such platforms to ensure that content posted on the platform complies with these terms, conditions, and standards have the potential to influence and shape public discourse by mediating what members of the public are able to see, hear, and say online. Over time, these rules may have a norm-setting effect, shaping the conduct and expectations of users about what acceptable discourse looks like. Thus, the design and implementation of content moderation systems have a powerful impact on the freedom of expression of users and their access to dialogic interaction on the platform. With great power comes great responsibility: the increasing trend towards the adoption of algorithmic content moderation systems that have a questionable track record as regards their ability to safeguard freedom of expression gives rise to urgent concerns on the need to ensure that content moderation is regulated in a manner that safeguards and fosters robust public discourse in the online sphere.
SISTEM PREDIKSI TINGKAT KRIMINALITAS MENGGUNAKAN METODE TRIPLE EXPONENTIAL SMOOTHING: STUDI KASUS PADA POLRES KABUPATEN PROBOLINGGO

Criminality is all forms of action and conduct that violate the law as well social and religious norms that are detrimental to the society both economically and psychologically. The emergence of verious crime forms with new dimension recently shows that the criminality is always developing. In the effort to reduce the increase of the crime rate, a crime action prediction system is needed to anticipate the future. In this research a crime rate prediction system based in the types of criminal action using Triple Exponential Smoothing (TES) method has been developed, and the prediction accuracy of the system is measured by using Mean Absolute Percentage Error (MAPE) method with a case study on Resort Police of Kabupaten Probolinggo, East Jawa. From the conducted test, obtained results MAPE score of theft by weighting (Curat) type is 7% or Very Accurate prediction criterion. MAPE score for crime action type Others, motor vehicle theft (Curanmor), Destruction, and Fraud are 9.8%, 14.4%, 13.6%, and 16%, or Good prediction criterion. For crime action type Beatings, violent theft (Curas), severe persecution (Anirat), Murder, and Thief each one has MAPE score 23.2%, 31.2%, 21.2%, 33.2%, and 50%, or Reasonable prediction criterion, while MAPE score for Animal Theft is 92% or Inaccurate prediction criterion. In general, the criminality rate prediction system using TES method that has been developed for the case study on Resort Police of Kabupaten Probolinggo obtains MAPE score 28,3% or Reasonable prediction criterion.
English spoken stress recognition based on natural language processing and endpoint detection algorithm

Nowadays, there are more and more researches on the application of natural language processing technology in computer-aided language system, which can provide a good assistant role for foreign language learners. However, in the research of computer-aided language system, there are still some deficiencies in the recognition of English spoken stress nodes, which cannot be well recognized. Based on this, this paper proposes a method of English spoken accent recognition based on natural language processing and endpoint detection algorithm, which aims to promote the accuracy of accent recognition in the computer-aided language system and improve the performance of the computer-aided language system. In order to avoid the interference of background noise, this paper proposes a short-term time-frequency endpoint detection algorithm which can accurately judge the beginning and end of speech in complex environment. Then, on the basis of traditional speech feature extraction and fractal dimension theory, a nonlinear fractal dimension speech feature is extracted. Finally, RankNet is used to process the extracted features to realize the recognition of English spoken stress nodes. In the simulation analysis, the application effect of the short-term time-frequency endpoint detection algorithm proposed in this paper in the complex background noise and the effect of non-linear fractal dimension speech features on the recognition of English spoken stress nodes are verified. Finally, the performance and good application effect of the method designed in this paper are illustrated.
Robust Slide Cartography in Colon Cancer Histology

Robustness against variations in color and resolution of digitized whole-slide images (WSIs) is an essential requirement for any computer-aided analysis in digital pathology. One common approach to encounter a lack of heterogeneity in the training data is data augmentation. We investigate the impact of different augmentation techniques for whole-slide cartography in colon cancer histology using a newly created multi-scanner database of 39 slides each digitized with six different scanners. A state of the art convolutional neural network (CNN) is trained to differentiate seven tissue classes. Applying a model trained on one scanner to WSIs acquired with a different scanner results in a significant decrease in classification accuracy. Our results show that the impact of resolution variations is less than of color variations: the accuracy of the baseline model trained without any augmentation at all is 73% for WSIs with similar color but different resolution against 35% for WSIs with similar resolution but color deviations. The grayscale model shows comparatively robust results and evades the problem of color variation. A combination of multiple color augmentations methods lead to a significant overall improvement (between 33 and 54 percentage points). Moreover, fine-tuning a pre-trained network using a small amount of annotated data from new scanners benefits the performance for these particular scanners, but this effect does not generalize to other unseen scanners.
PENGEMBANGAN MEDIA PEMBELAJARAN BUKU CERITA BERGAMBAR BERBASIS KEMAMPUAN MEMBACA PADA PEMBELAJARAN TEMATIK

This research on the development of learning media for picture book learning has the objective of improving reading skills in grade 1 elementary school students, knowing the level of product validity tested by learning material experts and learning media experts. The validity of this product is carried out by the validator and the practitioner. The type in this research is Research and Development (R&amp;D). The data collection technique in this study was carried out by going through the preliminary study stage. At this stage it is carried out through three stages, namely literature study, field survey, and preparation of product drafts. The data analysis technique in this study uses descriptive techniques. The results of this study found that the level of validity of the product development of picture story book learning media in improving the reading ability of grade 1 elementary school students according to material experts was getting a score of 40 with a percentage of 83% which is in the very high category and according to learning media experts getting a score of 70 with a percentage of 97% who are at very high scores
A Cyber Sensor Model for Cyber-Physical-Social Systems

Engineering sustainable cyber-physical-social systems demand a transdisciplinary approach. Within an arbitrary domain, many systems, including those of the physical and cyber categories, may already be in-situ; however, heterogeneity permeates such systems, for example, differing protocols, data formats, among others. Heterogeneity is not a deliberate feature of an arbitrary system; rather, it is the cumulative result of pragmatic decisions that were made during design and is driven by many different factors, some of which may not be technological. Nonetheless, heterogeneity represents a critical obstacle for system designers as they seek to harness and integrate diverse system elements to deliver innovative services. This obstacle is acutely manifested in cyber-physical-social systems when collecting and fusing data for evidence-based decision-making; social and human-derived data exacerbate the problem. This paper proposes a programming model for fusing information sources in cyber-physical-social systems. The efficacy of the model is validated via a usability analysis.
Support Vector Machine with Information Gain Based Classification for Credit Card Fraud Detection System

In the credit card industry, fraud is one of the major issues to handle as sometimes the genuine credit card customers may get misclassified as fraudulent and vice-versa. Several detection systems have been developed but the complexity of these systems along with accuracy and precision limits its usefulness in fraud detection applications. In this paper, a new methodology Support Vector Machine with Information Gain (SVMIG) to improve the accuracy of identifying the fraudulent transactions with high true positive rate for the detection of frauds in credit card is proposed. In SVMIG, the min-max normalization is used to normalize the attributes and the feature set of the attributes are reduced by using information gain based attribute selection. Further, the Apriori algorithm is used to select the frequent attribute set and to reduce the candidate’s itemset size while detecting fraud. The experimental results suggest that the proposed algorithm achieves 94.102% higher accuracy on the standard dataset compared to the existing Bayesian and random forest based approaches for a large sample size in dealing with legal and fraudulent transactions
D-(DP)2SGD: Decentralized Parallel SGD with Differential Privacy in Dynamic Networks

Decentralized machine learning has been playing an essential role in improving training efficiency. It has been applied in many real-world scenarios, such as edge computing and IoT. However, in fact, networks are dynamic, and there is a risk of information leaking during the communication process. To address this problem, we propose a decentralized parallel stochastic gradient descent algorithm (D-(DP)2SGD) with differential privacy in dynamic networks. With rigorous analysis, we show that D-(DP)2SGD converges with a rate of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mn>1</mn> <mo>/</mo> <msqrt> <mrow> <mi>K</mi> <mi>n</mi> </mrow> </msqrt> </mrow> </mfenced> </math> while satisfying <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>ε</mi> </math> -DP, which achieves almost the same convergence rate as previous works without privacy concern. To the best of our knowledge, our algorithm is the first known decentralized parallel SGD algorithm that can implement in dynamic networks and take privacy-preserving into consideration.
SOFT SKILLS DEVELOPMENT IN POSTGRADUATE STUDENTS DURING FOREIGN LANGUAGE TRAINING COURSE

Universities integrate into the global academic community, providing highly skilled staff for educational institutions and businesses. Soft skills development in postgraduate students is one of the major challenges that universities face nowadays. The employability of young researchers, their pursuit of an academic career and meeting the requirements of non-academic employers largely depend on their professional development. Soft skills are skills that contribute to professional development, are applicable in different settings and environments and help choose a career path.There is a set of particular soft skills needed for postgraduate students and young researchers which can be developed through the course of English as a foreign language. They are communication skills listed below: • to speak effectively in any meeting relevant to the research project; • to convey complex information to various audiences; • to coherently organize material for presentations and reports, • to facilitate discussions, speak English fluently and coherently; • to write conference abstracts, journal manuscripts, research papers in English.The presented study considers the ways to develop communication soft skills in postgraduate students via foreign language training course. The objectives of the study are to describe soft skills development as a trend in modern education; to review the main ideas on modernization of postgraduate training course and the improvement of didactic methods; to substantiate the importance of presentation skills and research writing for postgraduate students. The conclusions contribute to better understanding of the ways to improve and modernize the foreign language course content regarding communication soft skills development to meet current demands and expectations in educational and academic environments.
An image encryption algorithm with a plaintext‐related quantisation scheme

This paper describes an image encryption algorithm that utilises a plaintext-related quantisation scheme. Various plaintext-related approaches from other algorithms are presented and their properties are briefly discussed. Main advantage of the proposed solution is the achievement of a similar behaviour like that of more complex approaches with a plaintext-related technique used in a rather simple step such as quantisation. This design should result in a favourable computational complexity of the whole algorithm. The properties of the proposal are evaluated by a number of commonly used numerical parameters. Also, the statistical properties of a pseudo-random sequence that is quantised according to the plain image pixel intensities are investigated by tests from NIST 800-22 suite. Obtained results are compared to values reported in related works and they imply that the proposed solution produces encrypted images with comparable statistical properties but authors' design is faster and more efficient.
Psychological and pedagogical problems of digitalization of Russian education

E-learning, its technologies, programs and devices used in the learning process bring important changes to traditionally existing models of education. They set the transformation of the framework of education: prohibitions and restrictions, prescriptions and recommendations. However, new technologies and devices, introducing new technological and communication capabilities, one way or another, but inherit many features of the old, and even more so, do not cancel these - old technologies, devices and their individual features. Purpose of the study: understanding the problems of the development of Russian education in the context of its digitalization, the role of digitalization of education in the development of industry and science. The research method is a systematic theoretical analysis of the psychological and pedagogical aspects of digitalization of Russian education. Research results. There is an urgent need to critically assess current technological (including digital) developments and teaching methods (big data, algorithms, smart learning), related pedagogical and anthropological (human) concepts, and theoretical problems facing educational research and modern education theory. The task of education in Russia can be set as the task of restoring the best from what was lost and enriching with new contents and forms, including the development of a methodology for attracting digital resources into the educational process. It is also important to solve not only the problems of social accessibility, but also the problems that block a person's access to education at the level of his own prohibitions on development and individuation inherent in mass culture.
Implementation of face-to-face online learning system based on audio video, presentation and chat using the Moodle e-learning platform

Currently, the implementation of teaching and learning at SMP Negeri 1 Binjai Kwala Begumit was done in the classroom alternately. However, with the current condition of pandemic covid-19, the learning process no longer carried out fully in schools. The school has not been using information technology in the form of e-learning applications in the teaching and learning process. The school has difficulty in recording the existing teaching and learning process: assignments, exams, assessments, and other activities. Therefore the use of e-learning applications is now very much needed. With existing school facilities, such as internet facilities and the ICT teachers, training in developing and implementing e-learning for teaching staff become the best alternative so that learning process can be done properly.
VODAN BR: a gestão de dados no enfrentamento da pandemia coronavírus

The world is experiencing a pandemic caused by the variant of the Coronavirus named SARS-CoV-2, causing the infectious disease COVID-19. Global actions are being carried out to combat this virus, including the Virus Outbreak Data Network (VODAN), which aims to establish a federated data infrastructure aligned with the FAIR principles and which supports the data collect from medical records of patients infected with high contagion. FIOCRUZ, which coordinates the GO FAIR Brazil Health network, took over the VODAN BR network in partnership with UNIRIO and UFRJ, with the participation of the hospitals Gaffrée and Guinle, Municipal São José and Israelita Albert Einstein. This article aims to present the national infrastructure aligned with the guidelines and the international network, for collecting and managing data from patient records, of these hospitals, infected with COVID-19, to establish an information model that supports the confrontation of this and other possible pandemics.
#PraCegoVer: A Large Dataset for Image Captioning in Portuguese

Automatically describing images using natural sentences is essential to visually impaired people’s inclusion on the Internet. This problem is known as Image Captioning. There are many datasets in the literature, but most contain only English captions, whereas datasets with captions described in other languages are scarce. We introduce the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese. In contrast to popular datasets, #PraCegoVer has only one reference per image, and both mean and variance of reference sentence length are significantly high, which makes our dataset challenging due to its linguistic aspect. We carry a detailed analysis to find the main classes and topics in our data. We compare #PraCegoVer to MS COCO dataset in terms of sentence length and word frequency. We hope that #PraCegoVer dataset encourages more works addressing the automatic generation of descriptions in Portuguese.
Benchmarking performance of RaySGD and Horovod for big data applications

With the advent of big data, training deep learning models quickly has gained prime significance. The faster a model is trained, the more relevant are its predictions in a given context. Deep learning is used for non structured data such as images, videos, sounds, text corpus, all of which represent a huge volume of data and also use complex models. Training these workloads can often take days or even weeks, because of various factors such as size of data, complexity of model, network and the underlying hardware infrastructure. The recognized divide and conquer solution to expedite the training process is to distribute either data or the model. Alas, the challenges of a distributed training setup are well known - creating and maintaining a cluster, enabling data or model parallelism along with uninterrupted communication across the cluster nodes.In this paper, we focus on two lightweight libraries for distributed deep learning, RaySGD and Horovod, which aim to alleviate these challenges by providing support for seamless parallellization. We conduct an in-depth benchmarking exercise to evaluate the performance of both libraries for training time(latency) incurred. Our experiments are conducted on a combination of various parameters such as hardware setup (CPU or GPU based), standard and manually coded models, real world and synthetic datasets. We also vary batch sizes of large workloads and number of worker nodes in a distributed setting. The insights obtained from our experiments act as guidelines for data scientists, facilitating the decision making process when conducting distributed training of big data applications on RaySGD or Horovod.