Reviewerly/exHarmony huggingface dataset:
Relapse Detection in Patients with Psychotic Disorders Using Unsupervised Learning on Smartwatch Signals

Relapse detection is a crucial component of mental disorders treatment and management. In this paper, we present our solution for the ICASSP Signal Processing Grand Challenge e-Prevention track 2 Relapse Detection. We propose an unsupervised learning approach to detect relapse in patients with mental health disorders using anomaly detection with an autoencoder. To this end, we extract features from sleep, physical activity, and physiological data recordings. We train an autoencoder exclusively on non-relapse data. To detect relapses, we calculate the reconstruction error of the autoencoder and use it as an anomaly score. Our team, Emotion, ranked second in the e-prevention challenge with a ROC-AUC score of 0.6 and a PR-AUC score of 0.63 on the testing dataset.
Using deep learning algorithms for finding a lost gamma source in a room with unpredictable and different obstacles

This paper proposes a robust algorithm using two convolutional neural networks (CNN) to find a lost gamma source in a room despite various unpredictable obstacles. This algorithm is an improved and complementary method to our previous research, which does not limit the use of the algorithm to a specific geometry, and the presence of obstacles does not reduce the performance and inability of the method. In our previous research, an algorithm including a CNN was used to estimate the location of the lost gamma source in known geometry. In this paper, by adding another CNN to the previous algorithm, the algorithm can be used in environments with unknown geometry. The environmental simulations and data generation are performed using the Geant4 simulation program. The method has been tested in a designed scenario to check its effectiveness. The results show that by using these two CNNs, an agent can intelligently find the gamma source place in a room with multiple obstacles without human intervention with 95 percent accuracy.
How to Push the Fastest Model 50x Faster: Streaming Non-Autoregressive Speech Synthesis on Resouce-Limited Devices

Minimizing the latency of the text-to-speech system on end-user resource-limited devices is one of the top demands of voice-based human-machine interaction applications. In this paper, the FastStreamSpeech model is proposed combining the advantages of the advanced approaches in neural-based speech synthesis. The proposed method includes (1) a specific streaming inference architecture for the non-autoregressive acoustic and vocoder models and (2) an attention masking mechanism in the training phase. The experimental evaluations of the proposed model on budget mobile CPU show that this model can significantly reduce the system latency by 5 to 50 times compared with the original model while maintaining the high quality of output speech.
Joint Neural Representation for Multiple Light Fields

Neural implicit representations have appeared as a promising technique for representing a variety of signals, among which light fields. These representations offer several advantages over traditional grid-based representations, such as independence to the signal resolution. Some work has been done to find good initial representations for a given type of signal, usually via meta-learning approaches. However, exploiting the features shared between different scenes remains an understudied problem. We provide a step towards this end by presenting a method for sharing the representation between thousands of light fields, splitting the representation between a part that is shared between all light fields and a part which varies individually from one light field to another. We show that this joint representation possesses good interpolation properties, and allows for a more light-weight storage of a whole database of light fields, exhibiting a ten-fold reduction in the size of the representation when compared to using a separate representation for each light field.
Investigating the Effectiveness of Using Blockchain Technology for Secure and Transparent Voting Systems

The use of blockchain technology for secure and transparent voting systems has been a topic of increasing interest in recent years. This paper aims to investigate the potential benefits and challenges of utilizing blockchain technology for voting and to examine the effectiveness of blockchain-based voting systems. Through a comprehensive literature review, this paper analyses the advantages of using blockchain technology in voting systems, such as security, transparency, and accountability. Additionally, the challenges associated with blockchain technology, such as scalability and coding errors, are discussed. The paper concludes that blockchain technology has the potential to revolutionize voting systems by providing a secure, transparent, and auditable platform for elections. However, further research and development are required to address the challenges and explore new applications for blockchain technology in the context of electronic voting.
Research on The Application of Video Recommendation Algorithm Based on DeepFM

This paper studies the application of video recommendation algorithm based on DeepFM, studies the specific application of DeepFM algorithm in the field of video recommendation, and analyzes its performance. In this paper, the MovieLens video dataset is selected for experiments, and a simple video recommendation system is implemented by using UserCF, ItemCF and DeepFM. After recalling and sorting the dataset, the conclusion is drawn. The DeepFM model has superior performance in the sorting stage, and the loss is reduced by 0.55 % compared with the use of only FM model.
Psycho-educational support and development of pedagogical skills of teachers in higher education institutions with special learning conditions

QUALITY PARAMETERS OF HIGHER EDUCATION OFFICERS OF THE NATIONAL POLICE IN THE CONDITIONS OF IMPERATIVE HUMAN-SCALE VALUES Chapter 10 proves that the knowledge and consideration of subjective, i.e. internal mental and psycho-pedagogical personal characteristics is of particular importance in the process of professional training of the future police officer. From such positions, the essence and features of psycho-pedagogical support and development of pedagogical skills of teachers of higher education institutions with special learning conditions are specified. An analysis of the development of methodological principles and methodological specifics of psycho-pedagogical support and the development of pedagogical skills of teachers of higher education institutions with special learning conditions was carried out; the main directions of implementation of psycho-pedagogical support of teachers are determined, with the coverage of theoretical provisions, methods and psycho-pedagogical technologies; on the example of practical-research work, the specifics of the implementation of a psycho-pedagogical workshop for teachers of higher education institutions with special learning conditions are highlighted; the importance and prospects for the introduction of a system of psycho-pedagogical support and the development of pedagogical skills of teachers of higher education institutions with special conditions of learning based on a synergistic approach are determined. ----------------------------------------------------------------------------------------------------------------- How to Cite: Semenova, A., Kostiuk, A. (2023). Psycho-educational support and development of pedagogical skills of teachers in higher education institutions with special learning conditions. Quality parameters of higher education officers of the National Police in the conditions of imperative human-scale values. Kharkiv: PC TECHNOLOGY CENTER, 175–192. doi: https://doi.org/10.15587/978-617-7319-67-1.ch10 ------------------------------------------------------------------------------------------------------------------ Indexing:
Challenges in Data Extraction from Graphical Labels in the Commercial Products

Text detection from images is an important and challenging computer vision task. It has a wide variety of applications in reading sign-boards in autonomous navigation, document image analysis, as well as in assisting visually disabled population. This article presents the need for text extraction of various sizes and fancy typefaces to recognize the text in graphical labels of daily consumables. A dataset of graphical labels of daily consumables and medicines is collected and annotated for further detection using Yolo-v5. We compare the performance of the existing optical character recognition (OCR) system on synthetic datasets and real-world datasets, emphasizing the significance of small text detection from images. It summarizes the limitations in text recognition in the real-world scenario with examples and plausible solutions. We focus on enhancing the OCR for small text extraction on these pictorial labels so that the visually impaired shopper never misses out on critical information.
Evaluation of Energy Efficiency in Photovoltaic Panels with Solar Tracker Based on Diffuse Control

The objective of this work was to evaluate the efficiency of the energy produced by photovoltaic (PV) panels with solar tracker based on fuzzy control versus a fixed position PV panel with azimuth: −23.45° and elevation: 12.39°, in the city of Pampas-Tayacaja Huancavelica Peru at 3660 meters above sea level with a south latitude: 12. 39522° and west longitude: 74.87266°. An experimental prototype of a solar tracker was implemented, with an algorithm embedded in a PIC 18F4550 microcontroller; this microcontroller sends pulses to the longitudinal positioning motor driver and latitudinal positioning motor driver. The first motor, powered with 12 Vdc and current consumption of 1.5 A, produces the movement from east to west and vice versa; the second motor, with the same electrical characteristics as the first one, generates movements from north to south and vice versa. The data acquired from the Light Dependent Resistive (LDR) sensors were sent by the microcontroller to the fuzzy logic controller implemented with the LabVIEW fuzzy system designer tool via USB communication. When evaluated, the efficiency of the PV panel with proposed fuzzy control, based tracker was higher in the clear or sunny day reaching 25.28%; and while in the cloudy day it was observed that the efficiency obtained is lower 8.97%, in both cases with respect to the fixed position PV panel. Therefore, it is concluded that the energy efficiency of the PV panel with the proposed solar tracker is always higher than that of the fixed position PV panel.
Smart SKIVVY an Autonomous Robot Using IoT

In the past ten years or more, robots have shown to be of immense relevance by lowering the labour of humans, especially in the industrial sector. Robots are machines with human-like inclinations and capacities that can perform tasks based on their programming. In our project, it functions as a subordinate based on the needs of the user in a straightforward manner to comprehend this utilizing a variety of inputs. The trolly distributes goods from one person to another while decreasing human workload. It is clever and recognizes obstructions in its path by itself, adjusting its course accordingly to arrive at the desired location. An Arduino, a raspberry pi, a motor driver, and dc motors are some of the components utilized in this project to make the task easy to perform. Reducing the use of humans to simple jobs is the key objective.
A COMPARISON OF RECENT SUPERVISED AND UNSUPERVISED LEARNING TECHNIQUES FOR DETECTING FAKE REVIEWS

This comparative research article provides an in-depth examination of modern supervised and unsupervised learning strategies for detecting bogus reviews.Detecting bogus reviews on e-commerce sites has become increasingly critical as the quantity of them grows.In this paper, we compare the performance of various machine learning algorithms for supervised learning, including Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB), and Gradient Boosting (GB), as well as deep learning techniques like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).We also investigate the usefulness of unsupervised learning methods such as k-means clustering and Principal Component Analysis (PCA).For supervised learning, our findings indicate that deep learning approaches outperform classical machine learning algorithms.Additionally, unsupervised learning methods such as k-means clustering and PCA are excellent in detecting bogus reviews.The findings of this work can help in the selection of appropriate algorithms for fake review identification, as well as contribute to future research in this field.
Human Activity Recognition in a Realistic and Multiview Environment Based on Two-Dimensional Convolutional Neural Network

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.
Fault Simulation and Characteristic Analysis of Photovoltaic Array

With the continuous development of photovoltaic power generation technology, the fault diagnosis technology of photovoltaic system is of great significance to realize the cost reduction and efficiency increase of the system. As the core component of the system, the reliability of photovoltaic array is the key of the whole system. Because the I-V curve of photovoltaic array reflects rich operation information, it can be used to extract and diagnose fault features, and is widely used in the field of photovoltaic array fault diagnosis. In this paper, the fault array is simulated, and the fault information in the output characteristic curve is analyzed and extracted.
Oversampling-based node imbalance classification method in graph

Graph neural networks have been widely used in many fields. Most studies are used to solve the node classification task, but most of the time the distribution of the data is unbalanced, which affects the classification accuracy of the model. In this paper, we balance the data distribution in the graph by generating new samples in the embedded space and introducing random variables to control the spatial distance between the new samples and the target samples. We also propose a framework to solve the unbalanced node classification task in the graph, and the experimental results show that the method is effective.
Aprogressive Image Dehazing Framework with inter and Intra Contrastive Learning

Image dehazing, aims to estimate latent haze-free images from hazy images, suffering from a lot of lost information. Existing contrastive learning methods tend to utilize hazefree images as positive samples without consideration of negative samples. Even if negative samples are employed, the connection between patches within an image is always ignored. In addition, it is hard to train end-to-end dehazing networks due to the enormous gap between hazy images and corresponding clear images. In this paper, we propose a novel progressive image dehazing framework with inter and intra contrastive learning to solve the above problems. Specifically, the Inter and Intra Contrastive Learning (IICL) is proposed, in which the brightest and darkest patches within the same image are considered for contrastive learning. Furthermore, a progressive image dehazing framework consisting of an efficient Pre-restore Module (PRM) and an Alternative Restored Module (ARM) is proposed to facilitate the end-to-end model training. It is noted that our framework can be a complement to existing image dehazing methods. Extensive experiments on the dehazing benchmark demonstrate that our framework benefits various dehazing models which surpass previous state-of-the-art image dehazing methods.
Amharic ATS - A Comparison Between Graph Based and Statistical Based Approach using Rouge Metric and Human Evaluation

This paper presents a study into existing Automatic Text Summarization models applied on the Amharic languagea widely used and spoken language in Ethiopia. With more than 40 million speakers across the world, including the diaspora, it is of significance to have a mechanism where large Amharic texts can be condensed into understandable and short paragraphs. The models that have been implemented and used previously have shown great results and promise, especially the TextRank algorithm, which has been studied in this paper, along with TF-IDF and Cosine Similarity algorithms. Our paper mainly concentrated on the evaluation aspect of summarized Amharic texts with human summaries, which are likely to have more depth and context. The study compares and contrasts human generated summaries with machine generated ones, on the same text. The evaluation comprised of human evaluation and the Rouge Metrics. The results in both cases signified TextRank, a graph-based approach, to lead to optimal summaries.
A Systematic Review of the Application of Machine Learning in CpG island (CGI) Detection and Methylation Prediction

Background: CpG island (CGI) detection and methylation prediction play important roles in studying the complex mechanisms of CGIs involved in genome regulation. In recent years, machine learning (ML) has been gradually applied to CGI detection and CGI methylation prediction algorithms in order to improve the accuracy of traditional methods. However, there are a few systematic reviews on the application of ML in CGI detection and CGI methylation prediction. Therefore, this systematic review aims to provide an overview of the application of ML in CGI detection and methylation prediction. Method: The review was carried out using the PRISMA guideline. The search strategy was applied to articles published on PubMed from 2000 to July 10, 2022. Two independent researchers screened the articles based on the retrieval strategies and identified a total of 54 articles. After that, we developed quality assessment questions to assess study quality and obtained 46 articles that met the eligibility criteria. Based on these articles, we first summarized the applications of ML methods in CGI detection and methylation prediction, and then identified the strengths and limitations of these studies. Result and Discussion: Finally, we have discussed the challenges and future research directions. Conclusion: This systematic review will contribute to the selection of algorithms and the future development of more efficient algorithms for CGI detection and methylation prediction.
Nghiên cứu quá trình sinh địa hóa trong hệ thống “thảm thực vật – vật rơi rụng – đất” tại rừng nhiệt đới thuộc Vườn Quốc gia Bidoup-Núi Bà

The study of geochemical processes is an urgent task for understanding the functioning of the little-studied system "vegetation-litter-soil" and the biosphere as a whole. In mountainous areas, the processes of soil formation acquire unique features and are complicated by the processes of redistribution of matter. This work introduces the results of studies of geochemical processes in 2018-2022 on the territory of the Bidoup-Nui Ba National Park (Central Vietnam) during the wet and dry seasons. The influence of the relief on the formation of plant mortmass and the change in the ash content of fallen branches and leaves is considered. The results of studies of vertical and horizontal migration of chemical elements in soils of mid-mountain primary tropical monsoon forests are presented. Regularities of geochemical migration of chemical elements within all types of elementary geochemical landscapes, from autonomous to accumulative, are revealed. The existence in soils of several geochemical barriers characteristic of zheltozems has been revealed. The study made it possible to clarify the elements that are most actively involved in biogeochemical migration and accumulation in different seasons of the year, to assess the biogeochemical mobility of elements and the intensity of the processes of their biological accumulation by bamboo organs growing in different landscape conditions in the wet and dry seasons of the year. The research results make it possible to identify the features and patterns of changes in the content of trace elements in the "vegetation-litter-soil-soil" system, their biological accumulation and biogeochemical mobility during the wet and dry seasons in the Bidoup-Nui Ba National Park. The paper also presents the results of a study of soil samples for the presence of heavy metals, taken in the Bidoup-Nui Ba National Park. In the course of this study, native Cd contamination was detected in concentrations typical for objects subjected to technogenic pollution.
Continuing Use Behavior Analysis of MSMEs on the Mosan Digital Platform Using the UTAUT2 Model

This study looks at the adaptation of digital platforms as part of the support for Mercy Corps Timor-Leste's Covid-19 Recovery for MSME Program' which targets MSMEs run by women to increase the ease of transactions, through collaboration with an e-wallet platform named MOSAN.This study aims to provide an overview of the technology used by empirically analyzing the factors that influence the user behavior of MSMEs as MOSAN users.This study uses a quantitative method with a simplified UTAUT2 model or theory according to the phenomenon under study.The result of this research empirically and descriptively shows the factors influencing MSME use behavior finally showing that price value and effort expectancy as independent variables which highly influenced the use behavior positively and significantly towards continuing use behavior of the MOSAN platform, including the moderate variable of income generated and digital business literacy.In contrast, other independent variables in this research model have not shown a significant effect on the user behavior variable to achieve continuing use behavior, but this research shows the improvement of digital business literacy in 150 MSMEs because of the intervention of the covid-19 recovery program for MSMEs that also influencing continuing use behavior of MSMEs.E-wallet is a new concept of digital platform in Timor-Leste that still needs system improvements and more development on all the factors that affect its use, such as good governance, infrastructure, capabilities of human resources including MSMEs themselves in adapting to the uncertain situation of the future business environment.