Reviewerly/exHarmony huggingface dataset:
Spatial-Temporal Information-Based Littering Action Detection in Natural Environment

A significant environmental problem in Vietnam and around the world is litter and garbage left on the street. The illegal dumping of garbage can have negative consequences on the environment and the quality of human life, as can all forms of pollution, in addition to having a large financial impact on communities. Thus, in order to reduce the impact, we require an automatic litter detecting method. In this study, we propose a new method for spotting illegal trash disposal in surveillance footage captured by real-world cameras. The illegal littering is recognized by a deep neural network. An ordered series of frames makes up a video. The order of the frames carries the temporal information, and each frame contains spatial information. To model both of these features, convolutional layers are used for spatial processing, and instead of using recurrent layers for obtaining temporal information, we take advantage of transformer encoder for encoding sequential data by evaluating each element in the sequence's relevance. Besides, CNNs is also used to extract important features from images, hence lowering input size without compromising performance. This leads to reduce computational requirements for the transformer. Through testing on actual recordings of various dumping operations, we show that the proposed strategy is effective. Specifically, the validation outcomes from the testing data's prediction reveal an accurate value of 92.5%, and this solution can be implemented in a real-time monitoring system.
An Efficient Cryptographic Accelerators for IoT System Based on Elliptic Curve Digital Signature

Given the importance of security requirements in today's Internet of Things (IoT) landscape, this study focuses on enhancing the security of IoT systems through encryption algorithms implemented on the SoC-FPGA platform. Specifically, this paper presents the development of a hardware system that generates public keys and performs digital signature generation and verification using the Elliptic Curve Digital Signature Algorithm (ECDSA) based on the SECP256K1 curve. The ECDSA encryption hardware functions as a co-processor and demonstrates a maximum operating frequency of 30 MHz. In terms of performance, the ECDSA IP achieves efficient processing speeds, taking approximately 17 ms to generate a public key and produce a digital signature and nearly 30 ms to verify the digital signature. These results showcase a well-balanced design that enables a trade-off between speed, area, and power dissipation in the proposed system.
An AIoT Device for Raising Awareness About Trash Classification at Source

Waste segregation is a critical issue for environmental protection and sustainable growth. In Vietnam, public awareness and action on waste separation at source remain limited, highlighting the importance of engaging individuals, particularly students, in transforming waste disposal practices. Modern technologies, including the Internet of Things (IoT) and Artificial Intelligence (AI), have revolutionized various aspects of our lives and offer promising solutions to raise public awareness on this issue. This paper proposes an IoT device named BEG (BACHKHOA Eco-friendly Guide) integrating AI-based Computer Vision technology to classify waste via a camera. Unlike existing smart trash cans, which classify and dispose of the trash automatically, our device provides information about the waste type to guide users on proper disposal, thus reinforcing awareness of garbage classification at source. We also introduce the BEGNet, a Convolutional Neural Network (CNN) employing RegNetY120 as its backbone, which demonstrates superior performance in accuracy compared to other approaches on both the Trashnet dataset and our custom dataset - BKTrashImage. The proposed BEG device will improve knowledge about waste segregation, reduce improperly disposed waste, and foster a thriving circular economy.
Research and development of data compression methods for technical monitoring systems

Objective . The aim of the study is to develop methods for data compression in relation to monitoring systems. Method . The studies were carried out mainly experimentally, by processing data generated by the information-measuring system of the Altai State Technical University in the process of meteorological observations, temperature control and control of the consumption of such energy resources as hot and cold water, heat supply. However, when it was proposed to change their structure and presentation form to compact the stored data, the degree of compression was found theoretically. Result . The results of studies conducted with the participation of the author of the publication in the field of data processing, transmission and storage are presented. Methods for streaming data compression “on the fly” as it arrives, both lossy and lossless, as well as methods based on optimizing the structure of databases designed to store information collected during monitoring, are considered. The main attention is paid to the consideration of data compression created in the process of temperature monitoring, since it is the control and monitoring of temperature processes that is most common in various technical process control systems, heat metering systems both on the consumer side (housing and communal services) and on the side of the heat supply organization. Conclusion . In the vast majority of cases, lossless compression allows you to compress the information received from the monitoring system by more than 10 times, and lossy compression is much stronger, and without losing the pragmatic value of the stored information.
Improve Medicine Prescribing Performance Using Recommendation Systems

In this study, we introduce a decision support system for prescribing medication based on the application of association rules and analysis of prescription history data. Additionally, the research proposes techniques for analyzing prescription data, constructing a storage database, selecting rule sets for information extraction, data visualization, and improving the recommendation system's effectiveness. The experimental results on a real data set, which includes 76,028 health insurance prescriptions, 363,697 data lines, 71 prescribing doctors, 521 medications, and 1,129 disease codes according to the ICD (International Statistical Classification of Diseases and Related Health Problems). Experimental results show that the Apriori algorithm gives good results with high accuracy, improves the efficiency and accuracy of drug prescriptions, support for doctors in making more accurate prescribing decisions and reducing patient risk.
Human Intrusion Detection for Security Cameras Using YOLOv8

Human intrusion detection is a critical concern in the field of security, especially for finding illicit entrance at private or restricted places. Unauthorized intrusions occur when attackers gain illicit access to residential or restricted areas and engage in property theft. Security monitoring is essential in various locations, including residential areas and households, particularly during specified periods, typically from 10 pm to 6 am. In this study, we propose an approach for detecting human intrusion with YOLOv8 for security cameras. The model was identified and tracked objects in the predetermined ROI area, and got over 80% accuracy from human identification data. After training the model, the software was employed with several fundamental functions for detection and tracking of unauthorized human intrusions.
<scp>WegFormer</scp>: Transformers for weakly supervised semantic segmentation

Abstract Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), there are still deficiencies of object incompleteness due to the lack of receptive field and insufficient utilization of global context information of CNN. Based on the above observations, we propose a simple and effective method, WegFormer. Specifically, WegFormer captures the global context information with the Vision Transformer (ViT) as the classification network and is equipped with Deep Taylor Decomposition (DTD) principle and Soft Erase (SE) module to generate more integral pseudo labels and smooth further. However, we observe that although the generated pseudo‐labels are more complete, they intrude into the background region, that is, background incompleteness problem. The Efficient Potential Object Mining (EPOM) module we propose solves this problem well. Extensive experiments on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the effectiveness of WegFormer, where superior results of and are obtained on the PASCAL VOC 2012 and MS COCO 2014 validation sets, respectively.
Current Situation and Improvement Path of Digital Literacy of College Teachers Under the Background of Educational Digitization

The advent of the digital age has caused profound changes in the field of education. The digitization of education has become an inevitable trend, and hence the promotion of digital literacy among teachers is an essential requirement for strengthening the digitization of education. This study aimed to construct a digital literacy index system for college teachers, including five dimensions: digital knowledge, digital technology use, digital information management, digital problem-solving literacy and digital security. A questionnaire survey was conducted to understand the current level of digital literacy among college teachers, their current deficiencies were analyzed, and reasonable suggestions for the digital literacy path of college teachers were provided.
Image Recommendation Based on Pre-trained Deep Learning and Similarity Matching

Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recommending appropriate products to them. There are many techniques in recommendation systems which can predict rating scores to recommend next products. In this work, we propose an approach for recommendation based on product images using pre-trained deep learning models and similarity matching. Specifically, in the proposed model, we have utilized the pre-trained deep learning models (e.g., the VGG16) to extract the image features. Then, based on the image features, we compute similarities between the products (e.g. using Cosine similarity). For recommending similar products to the users, we do the same tasks, e.g., extracting feature of the current image, computing its similarity with other images in the database, and generating a list of TOP-N (e.g. TOP-5) most similar products. Experimental results on two public data sets show that the approach can give good recommendations at more than 90% of accuracy.
The Efficiency of Japanese Character Classification by Attention Mechanism

To improve model performance in the Japanese handwritten character classification task. We designed the most miniaturized but also high-performance CNN-based model. In our initial efforts, we designed a Vanilla CNN architecture and used an attention-based classification head (ML Decoder) to improve accuracy. With 3.6 million parameters, this model achieved an accuracy rate of 99.02% and 97.93% on the test set of the 10-class and 49-class, respectively. After that, we create a network named Residual CNN using the Vanilla CNN model combined with the residual connect of the ResNet network to retain information from the previous layers of the model. After changing the Vanilla CNN backbone to Residual CNN, the model included 3.8M parameters and achieved an accuracy rate of 99.08% and 98.11% on the test set of the 10-class and 49-class, respectively. In addition, the Grad-CAM method is used to improve the explanatory power of how the model makes decisions.
On the Approach to the Formation of a System of Remuneration for the Teaching Staff of Universities

The paper attempts to propose an approach to improving the system of remuneration of the teaching staff of universities based on the experience of Russian universities, at a time when the high quality of higher education, at least natural science and engineering, was recognized all over the world, and the experience of US universities. Discussion and criticism of the existing wage system indicate that the system was built without taking into account well-known approaches to organizing and stimulating there. The experience of the higher education system shows that the average level of remuneration of the teaching staff of universities should exceed the average level of remuneration in the country by at least two times. The ratio of the level of remuneration of the most highly qualified teachers — heads of departments, professors, doctors of science, and the least qualified teachers — assistants should be at least 3–3.5 times. The remuneration of novice teachers who do not have academic degrees and titles, who have work experience, should not be lower than the average level of remuneration in the economy. The permanent part of remuneration in the form of salaries should be at least 80-85% of the average wage. The variable component in the form of incentive payments is not a mandatory element of the remuneration system and can be retained in the form of bonus payments based on performance. It is expedient to differentiate salaries according to the amount of scientific and pedagogical experience and additional payments for performing the duties of heads of faculties and their deputies.
PETSAI: Physical Education Teaching Support with Artificial Intelligence

Physical Education Subject (PES) is a challenging problem for conveying IT into education. From the perspective of Artificial Intelligence (AI) and realizing PES's abstractness in knowledge representation, we pay attention to the idea of an intelligent system with a combination breath of computer vision and speech recognition. Inspired by that vision, we propose a support system called PETSAI for teaching the movements of the PES. Our primary idea is to introduce a "audio-visual" framework to compute the scores of the movements. To this end, we leverage human pose estimation (HPE) to detect a student's skeleton for each movement and utilize convolutional neural networks (CNN) for motion and gesture classification. In addition, we also use the power of speech recognition to control the entire framework. Two operations, the speech and image processes, will be performed in parallel. Moreover, we also provide an algorithm to calculate scores (from gestures to movements). Finally, we implement the system on both platforms, including the website and desktop application.
A New Integrated Medical-Image Processing System with High Clinical Applicability for Effective Dataset Preparation in ML-Based Diagnosis

We have developed a new medical-image processing system with high clinical applicability for effective dataset preparation in computer-aided diagnosis (CAD) based on machine learning (ML) techniques. Despite the wide range of application of ML-based CAD, it remains a challenge to apply this technique to clinical diagnosis of specific diseases or evaluation of specific medical practices. This is due to the lack of an effective framework for preparing a sufficient number of datasets. Consequently, there is absence of a guideline or standard procedure for processes such as image acquisition, anonymization, annotation, preprocessing and feature extraction. To address this ongoing issue, we proposed a system that was designed to integrate the special functions such as incremental anonymization, annotation assistance, and hybrid process flow for preprocessing and feature extraction. The incremental anonymization aimed to enable batch processing after image acquisition, reducing the daily workload of medical specialists in their institutions. Cross annotation and error correction were also supported even outside the medical institution by cogeneration of annotation sheets with anonymized images and by its OCR-based data-collection process. A hybrid process flow combining a simple manual operation and complemental automation algorithm was adopted to accelerate preprocessing and feature extraction. The system prototype successfully streamlined the dataset preparation process, including the generation of 3D breast-mesh closures and their associated geometric features. This substantial enhancement in efficiency demonstrates the system's high clinical applicability and its potential to significantly contribute to the field of breast reconstruction evaluation.
Basing Belief on Quasi-Factive Evidence

Topological semantics have proved to be a very fruitful approach in formal epistemology, two noticeable representatives being the interior semantics and topological evidence models. In this paper, we introduce the concept of quasi-factive evidence as a way to account for untruthful evidence in the interior semantics. This allows us to import concepts from topological evidence models, thereby connecting the two frameworks in spite of their apparent disparities. This approach sheds light on the interpretation of belief in the interior semantics, and gives meaning to concepts that used to be essentially technical: the closure-interior semantics can be interpreted as the condition of existence of a quasi-factive justification, while the extremally disconnected spaces are now characterized as those where the available information is always consistent. But our most important result is the equivalence between the interior-closure-interior semantics and what we call the strengthening condition, along with a sound and complete axiomatization. Finally, we build on this strengthening condition to introduce a notion of relative plausibility.
Device Classification via Passive Fingerprints with Clustering Algorithm

In browser fingerprinting, device identification becomes increasingly difficult as the number of accesses increases because the number of combinations to be predicted increases exponentially. This paper proposes a method that employs a clustering algorithm to minimize the number of prediction pairs. The proposed method also evaluates whether two access logs were sourced from the same device and groups the results using the union-find algorithm for each device. The proposed method was applied to the access logs of an actual website, and experiments were conducted to investigate the impact of the pair reduction process. To investigate the impact of the reduction, the proposed method and the proposed method excluding the reduction were applied to a dataset of about 100,000 access logs. As a result, the processing time of the proposed method was reduced to about 1% compared to the proposed method without the reduction, and the adjusted Rand index improved by about 0.30. The experimental results demonstrate the feasibility of rapidly grouping devices, even when the target data contains unlearned variables or patterns.
Your money or your data: Avatar embodiment options in the identity economy

In the physical world, choices about self-representation are tied to the body. However, avatar embodiment offers users many more options. These options are often constrained or promoted according to the economic models of the platforms that support different virtual worlds. Still, work on user motivations for avatar embodiment has generally not accounted for these constraints. To help explain users' interest (or lack of interest) in immersive technology, we discuss the mismatch between platform intentions and avatar affordances. We describe how user and platform motivations intersect in the ‘embodied identity economy’, a model in which users either ‘pay’ for access to embodied experiences with data from their physical identity or fund economy with cash payments. We present a framework of avatar embodiment using two dimensions: consistency versus discrepancy with the user’s physical identity, and experiential versus identity-based self-presence. We describe three ways in which avatars can be consistent with the user’s physical body: through appearance, through behavior, and the extent to which avatar data is linked with the user’s identity in the physical world. We relate this concept to recent discussions of a proposed ‘metaverse’ as a hub for life online.
Two-Way Linear Probing Revisited

Linear probing continues to be one of the best practical hashing algorithms due to its good average performance, efficiency, and simplicity of implementation. However, the worst-case performance of linear probing seems to degrade with high load factors due to a primary-clustering tendency of one collision to cause more nearby collisions. It is known that the maximum cluster size produced by linear probing, and hence the length of the longest probe sequence needed to insert or search for a key in a hash table of size n, is Ω(logn), in probability. In this article, we introduce linear probing hashing schemes that employ two linear probe sequences to find empty cells for the keys. Our results show that two-way linear probing is a promising alternative to linear probing for hash tables. We show that two-way linear probing has an asymptotically almost surely O(loglogn) maximum cluster size when the load factor is constant. Matching lower bounds on the maximum cluster size produced by any two-way linear probing algorithm are obtained as well. Our analysis is based on a novel approach that uses the multiple-choice paradigm and witness trees.
Detection of Crowd concentrations with YOLO V3

Crowd detection using street cameras has attracted a lot of research in recent years. In this paper, we propose a simple, fast, and effective method using YOLOv3 model for crowd detection. Using image frames extracted from surveillance video, pedestrian objects are detected, counted and a warning signal is sent out when a crowd occurs. The obtained results on test data extracted from 2 data sets STCrowd, SmartCity, and our self-collected dataset confirm the feasibility of the proposed method.
Topic based document modeling for information filtering

Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant documents are major parts of the systems. Various approaches have been proposed for modelling the first component. In this study, we utilized a topic-modelling technique, Latent Dirichlet Topic Modelling, to model user’s interest for IFs. In particular, an extended model of it to represent user’s interest named Latent Dirichlet Topic Modelling with high Frequency Occurrences, shorted as LDA_HF, was proposed with the intention to enhance retrieving performance of IFs. The new model was then compared to the existing methods in modelling user’s interest such as BM25, pLSA, and LDA_IF over the big benchmark datasets, RCV1 and R8. The results of extensive experiments showed that the new proposed model outperformed all the state-of-the-art baseline models in user modelling such as BM25, pLSA and LDA_IF according to 4 major measurement metrics including Top20, B/P, MAP, and F1. Hence, the model LDA_HF promises one of the reliable methods of enhancing performance of IFs.