Reviewerly/exHarmony huggingface dataset:
Privacy-Aware Adversarial Network in Human Mobility Prediction

As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).
Development of inferiority-compensation scale among high school students

An increasing number of high school students are inflicted by different degrees of mental disorders in learning, such as moodiness, learning difficulties, test anxiety, difficulty coping with frustration, etc., which are one of the factors leading to the inferiority of students. In the present study, the initial scale of inferiority compensation for high school students was developed through literature searching, expert evaluation, interviews, and an open scale. 1187 high school students were tested in different periods, after deleting an invalid 83 scales, including 461 copies of valid scale of exploratory factor analysis in the first stage and 643 copies of valid scale of confirmatory factor analysis in the second stage. The results showed that the inferiority compensation scale for high school students consisted of two sub-scale: self-compensation and others-compensation, ach two were composed of five dimensions including academic performance, physical fitness, social communication, appearance, and self-esteem. Confirmatory factor analysis showed that the total scale and the two sub-scale all had good structural validity (RMSEA≤0.08; CFI&IFI ≥ 0.9), and the combined reliability and values (such as the correlation coefficient of each dimension) of the two sub-scale were within the ideal range. With good reliability and validity (Cronbacα&KMO ≥ 0.90), and meeting the requirements of psychometrics, the scale can be used in the relevant research and practice of inferiority compensation for high school students.
Feature Extraction and Classification of Email Spam Detection Using IMTF-IDF+Skip-Thought Vectors

Spam is a major concern in present emails, and there are several reasons for sending spam emails. The two most common ones are advertising and fraud. If supported by suitable preprocessing approaches, the detection algorithm for spam email or spam classifier will function effectively (removal of noise, removal of stop words, stemming, lemmatization, term frequency). Spam that combines both text and image components is referred to as hybrid spam. Compared to spam emails with images and text, it is more unsafe and complex. To distinguish spam or ham, we must use an effective and smart approach in order to have a strong representation of emails and improve classification performance. In this paper, we propose a multi-modal architecture relying on a feature model (MMA-FM) that concatenates two embedding vectors. The text and image sections of the similar emails were separated using a hybrid model (IMTF-IDF+Skip-thoughts) and the convolutional neural network (CNN) as a feature extraction technique. The extracted features are concatenated and given to Naïve Bayes (NB) and Support Vector Machine (SVM) models to classify hybrid email as either spam or ham. In this paper we used two hybrid datasets: Enron, Dredze, and TREC 2007, which are publicly accessible corpora. Our results show that the SVM model provides an accuracy of 99.16%, which is higher when compared to the Naïve Bayes method.
Inclusive and Relative Analysis of Nature Inspired Optimization Algorithms in Reference to Sworn Intelligence

This article provides many nature-inspired algorithms and discusses their characteristics as well as the methods by which they are implemented. This study is an effort to determine the optimal searching algorithm in terms of the number of search agents in a given population. With a dataset and an increasing population size, the parameter of execution time for searching algorithms is examined by the researchers. This comparative research aims to give a more comprehensive knowledge of Nature inspired optimization algorithms (NIOAs) by providing a larger viewpoint and deliberate illumination. This paper provides the pseudocodes of the selected algorithms.
Steganography and Computer Forensics - the art of hiding information: a systematic review

This paper focuses on the study of steganoprahy as an anti-forensic method. The purpose of steganography techniques is to hide information from individuals unrelated to its content. Through a systematic review, the objectives of this article are: (I) explore and investigate the importance of steganography in computer forensics; (II) understand and analyze the methodology used to hide information in a file and (III) understand and analyze the methodology used to extract the information. The results summarize the previous research on this topics and we conclude the article with a warning to develpop more studies regarding this thematic as well as and make the security forces aware of this type of digital evidence and, in the same way that the methodology is being developed, the stegananalysis must also seek, at least, to reach the same technological level
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP). Yet, usually, the privacy of the training data comes at the cost of the resulting ML models' utility. One reason for this is that DP uses one uniform privacy budget epsilon for all training data points, which has to align with the strictest privacy requirement encountered among all data holders. In practice, different data holders have different privacy requirements and data points of data holders with lower requirements can contribute more information to the training process of the ML models. To account for this need, we propose two novel methods based on the Private Aggregation of Teacher Ensembles (PATE) framework to support the training of ML models with individualized privacy guarantees. We formally describe the methods, provide a theoretical analysis of their privacy bounds, and experimentally evaluate their effect on the final model's utility using the MNIST, SVHN, and Adult income datasets. Our empirical results show that the individualized privacy methods yield ML models of higher accuracy than the non-individualized baseline. Thereby, we improve the privacy-utility trade-off in scenarios in which different data holders consent to contribute their sensitive data at different individual privacy levels.
A preliminary example of utilizing AI text generation to support academic writing “Humans befriending their creations some notes on the human-AI relationship”

Procedure, raw materials, and detailed notes about the process of making the article with AI support.
STUDY OF CHRONOLOGICAL ORDER IN INTERSECTING PRINTED AND PEN STROKES WITH THE HELP OF CHROMATICITY DIAGRAM

Writing cross stroke examination is one of the difficult and challenging problem in forensic document examination. In this work, we tried to find sequence of order in crossing printed and pen strokes. The study mainly deals with application of chromaticity diagram generated by Video Spectral Comparator (VSC)-6000. Chromaticity co-ordinates are generated from three different points of first, second and cross strokes. When two strokes cross each other, then surface on point of intersection corresponds to second stroke. So, we have started this work with the assumption that chromaticity co-ordinate (x,y) of the crossing stroke should be similar to that of second stroke. We perform our experiment in both homogeneous and heterogeneous crossing strokes. We repeat the same experiment five times by preparing different samples each time to establish more valid conclusion but we get positive and conclusive results only in the case of heterogeneous crossing strokes where printed stroke is above the pen strokes.
An Adaptive Quasi-Hyperbolic Momentum Method Based on AdaGrad+ Strategy

The adaptive momentum method is a research hotspot in the field of optimization in recent years. At present, most adaptive momentum methods use AdaGrad-type strategy combined with Heavy-Ball or Nesterov accelerated gradient momentum(NAG). However, the AdaGrad-type strategy does not perform well in constrained optimization, and the Heavy-Ball and NAG momentum cannot reduce the gradient variance and bias at the same time. To solve the above problems, researchers propose AdaGrad+ and quasi-hyperbolic momentum (QHM). In this paper, a QHM momentum method based on AdaGrad+ will be proposed, combining the AdaGrad+ strategy of adjusting the step size adaptively with the QHM method, giving it the advantages of both AdaGrad+ and QHM The iterative formula of the method is given and briefly analyzed in this paper. The feasibility of the method is verified by general convex optimization experiments. In addition, we also conduct deep neural network training experiments to confirm that the method has good performance in practical applications.
Modeling of arsenic dynamics in groundwater of &amp;#1072; river floodplain contaminated with mine tailings: Ogosta River case, NW Bulgaria

This study aims to reveal the arsenic dynamics in groundwater of &amp;#1072; river floodplain contaminated with mine tailings under temperate climate conditions and natural river hydrodynamics. Arsenic concentrations were monitored in the primary morphological units of the floodplain in the upper stretch of the Ogosta River in NW Bulgaria. Iron, lead-silver, and gold mining heavily affected the river valley in the second half of the 20th century. We used groundwater monitoring data from 21 piezometers for the period 2016-2020. Based on the geochemical and geomorphological conditions in the valley, the piezometers were grouped into three clusters. Regression models were developed for each cluster and representative piezometers to predict arsenic concentrations. In the active floodplain, seasonal fluctuations in arsenic concentrations followed the river and groundwater regime. In this part of the valley floor, we determined two periods of elevated arsenic concentrations during the spring and autumn/winter seasons that coincide with high river water stages. Arsenic content in the groundwater of the higher floodplain was less dependent on the water level fluctuations but followed changes in redox potential, electrical conductivity, and water temperature. The obtained results showed the elaborated models as valuable tools for studying arsenic dynamics in alluvial aquifers of contaminated river floodplains. The suggested models could be coupled with groundwater monitoring systems to monitor arsenic concentrations and identify periods of the year with levels below and above threshold values.
Lightweight Stepless Super-Resolution of Remote Sensing Images via Saliency-Aware Dynamic Routing Strategy

Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of sub-image patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good trade-off between performance and complexity. The code is available at \url{https://github.com/hanlinwu/SalDRN}.
Question and criterion method for assessing the quality of the organization’s digital educational environment

It is of interest to create a technological, accessible and convenient method for evaluating multi-component and multi-dimensional educational systems, with the maximum share of automation and intellectualization of all related events. The purpose of the study is to substantiate the question and criterion method for assessing the quality of the digital educational environment of an organization based on mathematical methods of the theory of clustering and pattern recognition (using the example of monitoring the development of the digital educational environment of additional education practices for children). Methodology. The quality of educational systems or resources can be assessed using their inherent criteria, presented in the form of an information vector. The current values of these indicators determine the rating of a given system in a set of similar systems, and the dynamics of their changes over time shows the degree of development of each criterion sign. Monitoring sheets describing each system with an information vector represent a plurality of objects that can be clustered into certain classes. From a mathematical point of view, it is convenient to divide such systems into classes using a mining algorithm, and to take the metric of city blocks as a measure of the similarity of objects. Results. Monitoring of the organization's digital educational environment is carried out according to the functional characteristics of the environment based on the assessment of the organization's official website. According to the pyramid method, a question tree was developed on the functional components of the digital educational environment of additional education practices for children, according to which an information vector of the environment was formed, the values of which were determined by experts. Assessment of sites is carried out according to expert estimates in the automated system of competitive procedures “ASCO.” Conclusion. The proposed method allows monitoring the digital educational environment of an organization using mathematical methods of clustering theory and pattern recognition.
فعالية استخدام وسائل نص القصة لترقية مهارة القراءة

The problems faced by the students are that many teachers use traditional teaching aids, and this causes the ability of the students to comprehend the reading skill to be weak and they cannot understand the read texts. As for the objectives of the research, to know the process of teaching the skill of reading before applying the means of the text of the story in the private Islamic middle school, Nour Al-Rashad Al-Aziziya, and to know the extent of the effectiveness of using the means of the text of the story to improve the skill of reading. The research method used by the researcher was the procedural approach. To collect data, the researcher observed and provided the pre and posttest, and the researcher continued the two cycles. As for the results of the research, the process of teaching and learning the skill of reading was in agreement with the steps of applying the means of the story text and its steps. The teacher chooses the subject of the educational materials such as the reading texts, who may cut it by breaking the papers as the total number of students, and the use of the means of the text of the story in the process of teaching and learning the skill of reading is effective based on The result of the average for students from the second round obtains the criteria for the results of the completion limit, which means 86 in the percentage of 100% for the excellent.
Assessment of ESP students’ learning outcomes in a digital learning environment

Problem statement. The description of a pedagogic research in the field of formative assessment theory and practice is presented. The goal of the study is to define pedagogic and methodological conditions for effective evaluation of ESP students’ learning outcomes in a digital learning environment. Methodology. On the basis of key theoretical provisions of formative assessment and interdisciplinary approaches LSPCLIL, a complete set of criteria-based ESP evaluation tasks and assessment schemes were developed for Business English course delivered in the groups of BMSTU masters students majoring in economics. The approach to grading that correlates with BMSTU point-rating system was substantiated. The comparative analysis of modern platforms allowed to select the domestic service pruff.me, which was used as a ground for a digital assessment space (DAS), a part of BMSTU digital learning environment needed for implementing formative assessment of ESP learning outcomes. Results. The developed evaluation materials integrated in the DAS were tested by ESP students in a distant format. Conclusion. The research showed that creating DAS as an essential component of a university digital learning environment can contribute to effective assessment of ESP students’ competence-based learning outcomes provided that underlying methodology is taken into account and formative assessment technology is implemented in full volume.
Trends and features of the informatization of higher education modern stage

Problem statement. Nowadays, people are increasingly talking not about the progressive development of higher education in the context of the use of digital technologies, but about its more drastic digital transformation. The problem of identifying and systematizing patterns characterizing the development of universities in the context of the mass introduction of new digital technologies, as well as changes in external influences on student training systems, is urgent. The solution of this problem is significant for determining further scientific and pedagogical research, as well as ways to develop teacher training systems. The purpose of the study is to identify on the basis of domestic and foreign analytical data (OECD, UNESCO and others) directions, characteristics, problems and prospects of informatization of higher education. Methodology. The analysis of scientific publications on the development of didactics and the use of modern teaching tools in universities is based on the study of the species composition and specifics of existing digital resources, the use of mathematical methods for processing numerical data and technologies for their visualization. Results. It is shown that modern informatization of higher education is characterized by the spread of online courses, collections of digital resources, a decrease in the number of computer equipment in universities, the penetration of technologies of the new industrial revolution and many other factors. The development of a fundamental component of higher education that is invariant with respect to the development of technologies, the preparation of students for the use of promising technologies in professional activities, the pooling of resources into a single digital educational environment are significant. Conclusion. Research should be continued to ensure and evaluate the quality of all types of learning tools, to identify theoretical and practical approaches to the integration and unification of disparate information systems. There is a need for a scientifically based substantive and methodological update of the systems of training and retraining of all specialists working in the conditions of digital transformation of higher education.
Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models

&lt;p&gt;Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD), an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise&lt;/p&gt;
Object Recognition in Soccer Sports Videos

Object recognition plays a vital role in many applications in computer vision. In sports video recognition an action is a major part, in that object recognition is a key requirement. In the video, many challenges are faced which includes fast motion, occluded objects, different sizes of objects, difficult illumination, and continuous change in the background in identifying the object is a major task. The proposed system’s main aim is to deliver a summary of the existing system’s object detection approaches that belongs to CNN and debug their performance on soccer sport video and their training videos and match with the input soccer video. The performance of object recognition is discussed in various situations.