Reviewerly/exHarmony huggingface dataset:
Siamese-Discriminative Person Re-identification

Person re-identification is a well-known technique in deep learning to identify a person over images captured from different non-overlapping camera views. For re-identification, a technique must be able to differentiate and match photographs of individuals independent of image variations. Significant changes in viewpoint, illumination, resolution, occlusion, color, pose, and camera angle contribute to these variations. Recently, many algorithms have been proposed for the person re-identification task, which can handle these variations. However, these algorithms do not perform well if the training and testing data differ. This issue is addressed in this work. This work includes the development of a discriminative network for re-identification tasks. The proposed network is a combination of a classifier-based feature extractor and a siamese network. It produces a score between 1 and − 1, depending on the similarity and dissimilarity. This score is used to find the similarity between the probe and gallery images. It was trained on the Market-1501 dataset and tested on MOT17, Duke-MTMC, and the MSMT17 dataset. The proposed network has shown a 28.62% gain in overall accuracy on Duke-MTMC and an increase of 200% on MSMT17. It attained an 80.7% accuracy on the MOT17 data, despite the fact that it had not been specifically trained on that data. Which shows that this network can work well even though it was trained on a different set of data.
Securing a SaaS Application on AWS Cloud

In the last few years, we have seen tremendous growth in cloud adoption and migration, especially the new emerging companies and startups in multiple domains who are embracing cloud technologies to avoid the on-premises costs of maintaining the servers. As organizations grow and invest in digital transformation every year, the cloud is becoming an ever more crucial part of the organization and getting integrated with multiple core services. It is highly recommended that they look at their cloud security components and make them secure and robust to avoid a cyber-attack. Year after year IT world has been witnessing a series of news headlines and data leaks that occurred because of cloud architecture misconfigurations. In this article, the authors demonstrated a few secured architectures on Amazon Web Services (AWS), which is one of the top cloud service providers in the world. This paper’s target is to educate and set up a guideline for a secured architecture baseline on AWS cloud adoption for new or existing customers to review their architecture and encourage them to deploy the security components on AWS. This paper provides a brief overview of the various architectures proposed and implemented that can act as a solution for handling the various issues related to cloud computing, especially cloud security.
Globalizing Pre-trained Local BERT Embedding of Text for Sentiment Analysis

Sentiment analysis (SA) model calculates the opinion of the text after representing text as a learned feature vector. Vector representation generated from the model is crucial for sentiment analysis. Modern transformer models, including generative pre-training (GPT) and Bidirectional Encoder Representations from Transformers (BERT), dynamically learn text representation. However, the sentiment orientation from a global perspective is ignored. On the other hand, several models such as Convolutional Neural Network (CNN) is also utilized to learn text representation. With pre-trained sentiment-aware static word embedding as an input, model captures the global representation of text. But the local dynamic representations are disregarded. So in this paper, the suggested model combines the local dynamic representation with the sentiment oriented global text representation to produce accurate text representation. It acquires global representation by refined pre-trained word embedding with a CNN and dynamic local representations by utilizing a pre-trained BERT transformer. The combined global and local representation from CNN and pre-trained BERT presents the text more precisely from sentiment point of view. The result signifies that the proposed model identified sentiment with greater accuracy than the pre-trained transformer.
Health Data, Social Media, and Privacy Awareness: A Pilot Study

Despite several disputes and the overall perceived lack of privacy on social media platforms among the general population, they are now widely used around the globe and have become commonplace. Vast volumes of data in various formats are being posted on these platforms. Over the past decade, the widespread adoption and proliferation of online medical forums and social platforms illustrates the variety of information being shared. Usually, when dealing with sensitive medical information, to ensure that all ethical and legal criteria are met, processing and maintaining calls for a high quality of security and privacy safeguards. We were curious if medical information on such socially available platforms goes through the same quality of privacy safeguards. Our literature study identified a significant lapse in the privacy attitude of users who post medical information online. In this paper, we report on the responses from our survey about how medical information and its privacy on social media is perceived. We look at the drivers and impediments to personal health information being shared online, focusing on privacy-related issues. We then use our findings to aid in developing a workflow for a tool that provides access to a general social media user with state-of-the-art anonymizing and privacy-protecting techniques.
Structured Teaching Using Drone Simulators for Students' Confidence in Real Flight

A structured learning approach through the constructivism approach is a method that helps students build understanding in phases, from easy to difficult. The use of drone simulators in the subject of drone handling is a practical way to build understanding for students in phases. Good student understanding gives them the confidence to operate drones from virtual simulators to real through real drone operators. The study wants to see the relationship between the use of drone simulators and students’ confidence in operating real drones. A questionnaire served as the main research instrument. 35 students participated as purposive samples. This investigation can be divided into three sections; initial knowledge data before entering class, drone simulator data as simulator data, and real flight data collected afterward as final data - using both descriptive analyses such as mean, frequency analysis as well as inferential paired-sample T-Test to find relationships among them all. The findings of the study show that there is a significant difference between the students’ confidence before and after participating in the class on the actual handling of drones. This shows that the handling of dangerous drones in terms of safety needs to start with structured drone simulators to ensure that students can master the knowledge, technical and practical aspects of drones. It is hoped that this structured and phased teaching and management process can be done and developed for other courses as well. The findings of the study indicate that using structured drone simulators positively impacts students’ confidence in operating real drones. The results highlight the importance of starting with simulators to ensure that students can master the necessary knowledge, technical skills, and practical aspects of drone handling. This approach is seen as a safer way to introduce students to handling potentially dangerous drones while building their competence and self-assurance.
Research on Matrix Factorization Recommendation Algorithm Based on Local Differential Privacy

Mobile Edge Computing (MEC) has gained significant attention in enhancing the efficiency of Recommendation systems. However, the trustworthiness of servers poses a challenge as they can potentially compromise user privacy. To address this issue, we propose a framework for matrix factorization-based recommendation using Local Differential Privacy (LDP). Initially, user data is perturbed using Piecewise Mechanism (a kind of LDP algorithm) and published to an edge server. The edge server performs basic computations on the perturbed data, while the cloud server employs matrix factorization to compute latent factors for users and items, which are then sent back to the edge server. Finally, the edge server computes similarity values and generates personalized recommendations for users. Through extensive simulations, our algorithm ensures recommendation accuracy while preserving user privacy. By comparing with the generalized differential privacy mechanism, the Piecewise Mechanism used in this paper has a better recommendation effect, thereby demonstrating its practical utility.
Research on Security Assets Attention Networks for Temporal Knowledge Graph Enhanced Risk Assessment

The rapid development and extensive application of cyberspace have brought numerous opportunities to Internet users. Due to the characteristics of virtual, and open nature, cybersecurity assets are highly susceptible to attacks. Therefore, security asset risk assessment is a challenging task in the field of cyberspace security. We present Security Asset Attention Network (SeAAN), a novel model that achieve risk assessment of asset node to capture temporal knowledge graph structural evolution. Specifically, SeAAN computes risk assessment of asset node through joint attention focus on both structural neighbor and temporal history, which assigns distinct snapshots to facts at various time stamps, capturing dynamic knowledge fluctuations effectively. Extensive experiments demonstrate that SeAAN achieves significant performance on a real-world benchmark dataset for temporal knowledge graph enhanced security asset risk assessment. Moreover, our ablation analysis confirms the efficacy of integrating structural attention and temporal self-attention in a joint manner. Empirical results on real-world datasets demonstrate that our model exhibits more substantial performance enhancements compared to conventional approaches.
Students’ Digital Readiness in Vocational College for Industrial Revolution 4.0

Students often encounter challenges in developing their digital technology skills, primarily due to a lack of proficiency in using computer applications, internet-based apps, and similar tools. The failure of graduates in mastering the digital technology has a negative effect on their ability to get a job related to Industrial Revolution 4.0 (IR4.0). Thus, the objective of this study is to identify students’ digital readiness for IR4.0 in the aspects of attitude, knowledge and technical skills at one of Vocational College in the Southern Zone in Malaysia. A total of 306 respondents were selected using the stratified random sampling. This study is quantitative method and the research instrument consists of a set of questionnaires containing 28 question items divided into Sections A, B, C and D. All the data collected through the questionnaires were analysed descriptively, using the Statistical Package for Social Science Version 20.0 (SPSS), to obtain frequency values, percentages, means and standard deviations. The results of the study reveal that the level of the students’ digital readiness for IR4.0 from the aspects of attitude, knowledge and technical skills is at a satisfactory level. The mean average value obtained for all the research questions is 3.34, and it is at a moderate score level. Therefore, vocational colleges need to pay more attention to students’ digital readiness for IR4.0 from the aspect of attitude, knowledge and technical skills, so that the graduates can meet the needs of the industry now, and propel the nation towards IR4.0.
A Multiview Approach to Tracking People in Crowded Scenes Using Fusion Feature Correlation

Most of the current tracking methods for multi-target pedestrian tracking are unable to solve the problem where the tracking targets are blocked and reappears after disappearing in the camera perspectives, which brings great challenges to its practical application. To tackle this problem in dense crowds, we propose a multi-target pedestrian tracking method based on fusion feature correlation under multi-vision: Updating the pedestrian feature pool based on GMM to reduce the feature pollution; Then dynamically calculating the similarity threshold of target features based on K-means algorithm; Use the idea of voting to match pedestrian features and determine the addition and reappearance of pedestrians. The results on open dataset Shelf show that our method improve the accuracy and success rate of tracking under the condition of occlusion and reappearance after disappearance.
Methods of theaching geography in profile 10th grades

Introduction. The study of geography in modern conditions is very important for the development of youth and the nation as a whole. There is a growing need in the society to study geographical science at a level higher than the standard one. At present, there is an opportunity to study geography in senior classes in depth at the profile level. The purpose of this article is to highlight the methods of teaching geography for students of specialized 10th grade. Main material. The authors consider the method of teaching profile level geography for 10th grade students. The article analyzes curriculum, the main tasks and nature of geography study, singles out directions of practical activity in different kinds of work, as well as the teacher’s work in different employment conditions. Practical activities for the 10th grades of the profile level have a fairly large workload in hours, so most work should be done independently to demonstrate the results and knowledge to the teacher and the class. During the study, the authors made a list of requirements for the study of geography, including approaches, types, technologies, methods of teaching students. The article analyzes practical works on the course of profile geography in the 10th grade and the main forms of conducting lessons during the study. Conclusions. The curriculum in geography is aimed at developing students in various fields. Teachers need to adapt quickly to the latest conditions in teaching geography and help students learn about the world. The curriculum of the profile level for the 10th grade contains a rather large workload for geography teachers and students, so it is aimed at effective study of the course and a large amount of work done. The basic requirements for the study of geography, levels of mental activity, types of learning and different methods of cognitive activity in teaching geography were analyzed in the course of study. During the analysis of two practical works, the authors have singled out basic knowledge and skills that students consolidate and develop during the task. Each student develops research skills and the ability to achieve the expected result while completing the task.
Leveraging the Digital Tracing Alert in Virus Fight: The Impact of COVID-19 Cell Broadcast on Population Movement

Digital tracing alerts (DTAs) have emerged as effective means to share information with agility in responding to disaster outbreaks. Governments are able to instantaneously coordinate the available information to provide information related to the disaster and promote preventive actions. However, despite the opportunities granted by these innovative technologies in managing disasters, privacy concerns can arise in regard to how much of individuals’ private information should be collected and disclosed. With these considerations, we examine the extent to which instant digital tracing alerts and the information included in the alerts affect people’s actions toward disaster management in the context of South Korea. Our results show that collecting and disclosing detailed private information is unnecessary and may instead diminish the effects of DTAs. The effect of digital alerts being more pronounced among young and male individuals and in business-centric areas. Furthermore, because the effectiveness of DTAs decreases with the cumulative number of DTAs received, governments should send alerts that include more urgent information that is directly related to the risk posed by a disaster. Our results provide policymakers and law enforcement with novel insights into whether and how the usage of information technology can facilitate disaster management and to what extent they should collect and expose private information to effectively safeguards public health and safety during a crisis. The fast and comprehensive implementation of DTAs in South Korea in response to the global outbreak offers other countries learning opportunities with respect to successful collaboration among parties involved in the development and design of DTA-related infrastructure and education. We emphasize that collaboration among central policymakers, local/municipal districts, telecommunications companies, and healthcare centers is essential to establishing an innovative IT-driven disaster management infrastructure and mechanisms that help inform citizens in taking desired actions in an emergency or disastrous events.
Multi-scale Fusion-Based Object Detection Network for Advance Driver Assistance Systems

An autonomous vehicle (AV) needs a precise understanding of its surroundings to function well. AV technology that employs computer vision techniques transforms visual input into data that permits autonomous mobility. A significant part of this system is object detection, which has been the subject of several studies, the majority of which seem to employ 2D detection techniques. Automatic driving systems ensure a pleasant, safe, and effective ride (ADSs). However, fatalities in automobiles with ADS are happening more often. If the intensity of state-of-the-art is not increased, the maximum capacity of ADSs cannot be attained; hence, an important factor is to focus on model efficiency. In computer vision, network efficiency has grown in importance. In this article, we extensively analyze the neural network architectural design options for object identification and put forth a number of significant efficiency-boosting enhancements. The first method we suggest is a weighted bidirectional feature pyramid network (BiFPN) that enables quick as well as simple multi-scale feature composition. The second approach suggested by us is an integrated scaling approach, which scales the pixel density, depth, and width of all backbone, feature model, and box/class prediction networks simultaneously. This paper focuses on building brand-new object detection methodology depending on these improvements and Network backbones that significantly beat former models over a varied range of resource restrictions. Proposed methodology is 2×–4× times faster on GPU and model size is 4×–9× smaller compared to previous algorithms. With a 0.5 IOU achieves an Accuracy of over 50% compared to stage 2 detection methods also focusing on making it much faster and more reliable.
An Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation

With ever-growing audience and reach, social media has become a platform for propaganda, fake news and rumour. As compared to textual content, images are much more effective in communicating a message and compounding any intended emotional effect on the audience. Hence, the focus of this work is the emotion in the visual content of the disinformation campaigns. As the visuals in social media need not always carry a human face, the emotion conveyed by social media visuals is contextual emotion instead of emotion on isolated human faces. The work described in this paper makes two hypothesis, i.e. contextual emotion conveyed by social media visuals enhance the intended message and such contextual emotion in each type of disinformation carry some distinctive traits. To carry out this investigation, a visual image dataset has been manually curated for the three types of disinformation. A contextual emotion detection model trained on a public dataset has been used on this curated dataset along with interpretability techniques to carry out the investigation described in this paper. This manually curated dataset has been open sourced to aid in any future research.
Vectorization of Python Programs Using Recursive LSTM Autoencoders

To assess students’ performance in a coding class, educators must depend on manual analysis techniques due to a few problems in applying data analytic methods directly to computer programs. Computer programs are processed in the form of Abstract Syntax Trees, whereas machine learning or analytical algorithms require a linear vector as an input. A form of variational autoencoding network is developed to transform the source program’s syntax tree into a linear embedding vector. It is then trained on many Python programs written by novice programmers. Now, this model is assessed on various datasets including real-world programs submitted by students in a classroom-based environment. The encoding vectors obtained might be used for educational data analysis, tracing students’ learning paths, and to find outliers. The conversion of the resulting vectors back into respective syntax trees is performed using the decoding network and these trees may be used to provide hints for students.
Stress-Strain State Assessment of Building Subfoundations Having Flexible Constructive Scheme in Irregular Soil Stratification

Introduction. One of the main problems in the operation of buildings and structures is possibility of their damage due to non-uniform deformation of subfoundation soil caused by the various natural and technogenic factors. To establish the possible character and degree of damage of the above-surface structures, it is necessary to calculate the value of subfoun- dation relative differential settlement at the level beneath the foundation bottom. The subfoundation settlement is calculated within the compressible soil thickness using the specific gravity values and soil deformation modulus. The increment of settlement is induced by the increase of vertical stresses in the massif and increase of soil deformability upon growth of moisture. The purpose of the study is to establish the dependence of the magnitude of the relative difference in settlement on the deformation properties of soils with uneven occurrence of layers in order to predict the nature of damage to above-ground structures. Materials and Methods . An example of subfoundation deformation calculations at the bottom level of the free-standing foundations of a building having flexible constructive scheme has been studied. A model of stratification of two heterogeneous geotechnical elements with a 1:4 boundary slope and differing physical and mechanical properties has been used. It has been assumed that one of the geotechnical elements has natural properties enabling the building foundations to rest on it without prior site preparation. Another geotechnical element has significantly worse properties. The calculations on determining the settlement have been made in accordance with the layer-wise summation methodology. Results . The regularity in alteration of the predicted settlement value depending on the ratio of firm and soft soils thickness in the irregular stratification has been established. It has been proven that the difference between the specific gravity values of the heterogeneous beds within 10 % does not affect the depth of compressible soil thickness if the calculation of settlement is made using the layer-wise summation methodology. Discussion and conclusions. The calculation results analysis of the subfoundation settlement of the equidistant column foundations was carried out, the engineering solution was proposed to ensure reliability per limit state groups.