Reviewerly/exHarmony huggingface dataset:
Information Technologies Implementation to Ensure the Project-Based Distance Learning

The article considers the peculiarities of Information Technologies implementation in the distance learning process on the project-based approach. Studies have confirmed that, in addition to technical and information issues, distance education is complicated by the level of training of specialists, which affects the level of the necessary knowledge required for effective realisation of educational projects. Insufficient level of mastery of project-based methodological tools for distance learning, lack of willingness to implement IT innovations for educational content systematisation, and the educational space organisation are often the reasons that don't let Higher Education Institutions (HEI) expand e-learning profitability. In order to increase the level of compliance of the educational process with the needs of students and the educational services market, it is necessary to constantly modernize the learning process. For this, it is necessary to constantly test new educational formats, gamify the educational process, in particular, implement project-based distance learning by implementing modern information technologies.
Forecasting of SPP Generation at Different Stages of its Existence Using the Example of the 2.3 MW Plant in the Kharkiv Region of Ukraine

The results of studies of the generation of a solar power plant (SPP) with a capacity of 2.336 MW, which is located in the Kharkiv region of Ukraine, are presented. The results of the forecast generation, which were determined using the PHOTOVOLTAIC GEOGRAPHICAL INFORMATION SYSTEM (PV-GIS) program. Analysis of the forecast and actual generation data showed that the actual annual deviation from the forecast ranged from +5.47% to −5.24%, which is slightly higher than the annual generation variability of 4.87% determined by PV-GIS. The economic performance of a solar power plant is affected by the accuracy of forecasting hourly generation a day in advance. In order to improve the accuracy of forecasting the hourly generation of the SPP, an analysis of data from the guaranteed buyer, who buys all the generated electricity, and the plant's SmartLogger1000 monitoring system was performed. According to the results of the analysis, it was found that there is a difference in the data of the guaranteed buyer and the monitoring system, which arose as a result of the non-synchronization of the SmartLogger1000 monitoring system with the guaranteed buyer. The analysis of the hourly forecasts of the guaranteed buyer and the forecasting service showed that the sum of the hourly deviations of the forecast of the SPP service from the actual generation for December 2022 was 71.4%, and the sum of the hourly deviations of the forecast of the guaranteed buyer was 117.9%. An increase in the accuracy of forecasting was achieved due to the accumulation of statistical data and the study of factors influencing the generation of SPP.
Graph Based Approach for Predicates Identification from Textual Data: A Case Study

Correct Identification of predicates like nouns, verbs and prepositions is a subtask of answer extraction in Machine Reading Comprehension system. With the help of NLP Tools and Stanford parser, the methodology is proposed for extraction of noun phrases, verbs and preposition. It is based on the concept of semantic relations associated with noun phrases, verbs and prepositions. It has been observed that the proposed methodology works with different genre of domains. This is implemented with different Reading Comprehension datasets. The proposed case study indicate that the methodology is generalized and can be applied to different datasets. The knowledgebase generated with predicates identification can be used in various application which are based on information extraction. It ensures the concept of generalization and transfer domain. This work highlights the problems identified in predicates identification when applied to different datasets. It also suggests the solutions to overcome some of the problems, which indicates the future research.
Realtime Risk Monitoring System using Convolutional Neural Network for COVID19 Pandemic

The COVID19 pandemic has significantly changed the lifestyle of billions of people across the globe. It has greatly affected almost all sectors of business, industry and public life. As per the WHO's guidelines, wearing a face mask has become the new compulsory and precautionary measures for everyone. Currently, all the public and private service providers will expect their stakeholders to wear face mask in an appropriate way to avail any services. Therefore, detection of face mask at public places is a crucial task to help the society to overcome current pandemic. This paper presents a unique approach to not only detect face mask but also calculate the risk of getting infected by COVID-19 using machine learning algorithms. The proposed model detects the various faces present in an input video, identifies if it has a mask present or not. If the mask is not detected, the model calculates the risk of human being getting infected based on their age. Finally, the model generates the output and provides analysis based on the real time data it has processed. As a real-time surveillance system, the model can also classify a face when a person is moving in the live video. The proposed method attained a highest accuracy of 99.57 % against standard datasets under study. The authors experimented and explored various Convolutional Neural Network models like DenseNet, MobileNet_V2, Inception_V3 and YOLO_V4 find the best model, detecting the presence of masks accurately without causing over-fitting.
Features of Implementing The Competency-Based Approach to Teaching Engineering Students in The Context of Industry 4.0

Based on an analysis of economic development trends, the importance of the industrial sector of the economy has been identified. It has been argued that the further development of industries in the context of technological change in the formation of Industry 4.0 will have an impact on the labour market. The labour market, in turn, will impose updated requirements on engineering specialists in terms of a set of professional competences. Employers' requirements for the knowledge and skills of future employees are also changing. Therefore, the implementation of a competency-based approach in the training of engineering students is becoming more relevant in today's digitalised society. In this regard, the study proposes the use of distance education as a relevant tool for implementing the competency-based approach in Industry 4.0. The authors pointed out the features and advantages of distance education in gaining knowledge and developing professional competences for future engineers. In addition, the paper presents a list of key competences for engineering students in the context of shaping Industry 4.0.
A Portable System for Bidirectional Sign Language Translation for Deaf and Dumb

Sign language is a type of interaction used by individuals who are deaf or hard of hearing to interact with others. They have their very own syntax and vocabulary and are entirely natural. Although there are similarities between different sign languages, they are not global and are usually not mutually comprehensible. Because sign language is not a common language, figuring out what signs mean takes a lot of time and work. As a result, hearing-impaired people are rarely communicated with, isolating them from the rest of society. A mechanism for turning signs to text and text to signs is necessary to overcome this issue and ensure smooth communication. The existing systems produce good results when it comes to sign translation, yet there are occasional misclassifications. This can be enhanced even more by detecting and classifying the signs that are being misclassified. Our system provides the communication interface that performs the bidirectional sign language conversion and it uses Convolutional neural networks as the classification model which gave the training accuracy of 94.86%.
Multiplicative Partially Homomorphic CRT Secret Sharing : (Preliminary Version)

A new CRT-based positive (non-zero) secret-sharing scheme with perfect information-theoretic (PIT) security and multiplicative homomorphism is presented. The scheme is designed to support the evaluation of multiplications of non-zero secrets of multiplicative groups.Our CRT-based scheme is partially homomorphic, supporting homomorphic multiplications. Nevertheless, it has the potential to be regarded as fully homomorphic for practical scenarios, such as bounded-sized multi-cloud databases.
Deep Packet Inspection at Scale: Search Optimization Through Locality-Sensitive Hashing

Deep packet inspection is a primary tool for security specialists, surveillance analysts, and network engineers to lawfully intercept and analyze network traffic. In order to process this data or select streams of interest from the large amount of data flowing in today’s internet, solutions must be capable of identifying network traffic as quickly and accurately as possible. The ever-increasing diversity of data as well as sheer size has rendered the current regular expression matching and filtering solutions ineffective. We propose locality-sensitive hash embedding techniques Alpine and Palm for packet analysis. The fixed size of hashes as well as the adaptability of distance measures is proven to address the network traffic classification problem in our experiments and improves scalability over current state-of-the-art, automata-based search engines. In this paper, we analyze the system’s ability to classify network traffic by many data layer protocols and traffic types with over 99% accuracy. The model is also proven effective in areas where the regular expressions are inapplicable, such as traffic profiling. Finally, we provide real benchmarks of the system’s ability to scale to large signature and hash sets with much improved performance, demonstrating real-world applicability and generalizability of locality-sensitive hashing to deep packet inspection technology.
SureRepute: Reputation System for Crowdsourced Location Witnesses

Location is an important attribute for many mobile applications but it needs to be verified. For example, a user of a tourism application that gives out rewards can falsify his location to pretend that he has visited many attractions and thus receive benefits without deserving them. To counter these attacks, the system asks users to prove their location through witnesses, i.e., other devices that happen to be at the location at the same time and that can be partially trusted. However, for this approach to be effective, it is important to keep track of the witness behavior over time. Many crowdsourcing applications, like Waze, build up reputations for their users, and rely on user co-location and redundant inputs for data verification.In this work, we present SureRepute, a reputation system capable of withstanding reputation attacks while still maintaining user privacy. The results show that the system is able to protect itself and its configuration is flexible, allowing different trade-offs between security and usability, as required in real-world applications. The experiments show how the reputation system can be easily integrated into existing applications without producing a significant overhead in response times.
Conversion of Indian Sign Language to Speech by Using Deep Neural Network

Sign language is the most used non-verbal communication method. It is mostly used by persons who have hearing and speaking disabilities. In this world, normal people find it difficult to interpret sign language, so it is very difficult to communicate with mute and deaf people. Mute and deaf people aren't able to express their thoughts to the world. To interpret the meaning of sign language an interpreter is needed. To eliminate the need of an interpreter a system is proposed using neural network. The proposed system can easily interpret the meaning of human hand sign to convert it into relevant speech. Our system has been implemented by using a deep learning technique, Convolution Neural Network (CNN). The hand images are pre-processed using canny edge detection technique for better accuracy. After pre-processing, the classification and prediction steps are applied. The speech synthesizer is used to convert text into speech using Google text to speech API. The proposed system achieves accuracy of up to 98%.
Prototype of Covid Tracer Management Application

As the world battles against the COVID-19 pandemic, a significant part of the world is trying to ease lockdowns while rapidly identifying individuals who may have been infected. Yet, such contact tracing is commonly a relentless and slow cycle that depends on face-to-face meetings, which is also dangerous. The government has come up with a contact tracing application, but such solutions are deemed by the public as too intrusive and non-beneficial, especially when it is obvious that this pandemic will be with us for a long time. As such, by integrating a reward-based approach to contact tracing, it would increase the level of awareness and interest in actively using such solutions among the public. The aim of this application is to increase the number of users using the contact tracing application. The usage of this mobile application would be beneficial to the users, which would increase the number of users of this application. The function of a contact tracing application would be more effective with the presence of a rewards system as more people would use the application to check-in at the places they go. The system is developed using Android Studio and uses Google Firebase as the database.
Design of Advanced Encryption Standard Algorithm

With the AES algorithm is used in almost all the digital communication for transmitting the data. Many of the researchers have designed the AES algorithm for ensuring more secured communication between the end users. In this research work an approach is made to design the AES algorithm in hardware by using the VHDL language by using 128 bits. The use of encryption and decryption process will be faster for any of the application as compared to the other 128 bits algorithm. The design also shows the significant improvement in the parameters like time period, faster output, etc
Examining Deep Learning Methods For The Detection Of Sepsis

Sepsis is a fatal syndrome that develops when the body reacts to an infection or virus, thereby, inducing inflammation which might result in organ dysfunction. Detecting sepsis at an earlier stage is critical, as a delay in antibiotic treatment has been reported to result in an increase in mortality rate, with a 7.6% increase in fatalities for individuals with critical sepsis condition every few hours the antibiotic administration is delayed. Hence, the earlier sepsis is detected and quicker the treatment is administered, the better the outcome for the patient and a reduced cost would have to be borne by the healthcare system. At present, there is no particular test to detect sepsis. Detection is usually done by keeping track of body temperature, heart rate etc. and conducting appropriate tests when organ failure occurs. This is not a reliable method and hence the need for better ways of detection arises. With the evolution of digital technology, Machine Learning (ML) has the ability to revolutionize medicine. Deep learning, a subset of ML, has gained immense popularity in recent times due to the ample quantity of data generated in various fields. Around 2.5 quintillion bytes of data are generated each day, according to statistics. As a result, the expansion of the world's economy is increasingly being attributed to big data. This paper presents a comparison of the classification report, accuracy and runtime of Neural Network, LSTM, and CNN on the public dataset proposed by PhysioNet Challenge 2019. The paper aims to study the effectiveness of different deep learning methods on clinical data. Data preprocessing involves handling missing values by imputation of mean, median and mode. SMOTE (Synthetic Minority Oversampling Technique) is an oversampling method that has been incorporated to manage imbalanced data by replicating minority class entries. The paper explains the advantages of CNN (Convolutional Neural Networks) that is capable of effective feature extraction and LSTM (Long short-term memory) that can assist in time series analysis and prediction. This will help estimate the theoretical resources required for performing the computation. This will provide a foundation for developing ensemble methods using these models and help understand its intricacies, advantages and disadvantages.
A General-Purpose CNN Accelerator Based on Improved Systolic Array for FPGAs

In recent years, convolutional neural networks have been increasingly used for processing image tasks, such as target recognition, image enhancement, and other areas. In the embedded and IoT fields, the small size and low-power application characteristics make it impractical to deploy high-performance computing devices on hardware platforms in this field. In this paper, a general neural network accelerator is designed that can be scaled to any size, which can be adapted to hardware platforms in different application areas including data centers and IoT according to power consumption and arithmetic power requirements. The accelerator is based on the improved systolic array, which improves the utilization and throughput of data and reduces the system power consumption. This paper uses int8 data type to match embedded and IoT low-power requirements. In this paper, the accelerator is deployed on the XC7Z020 hardware platform, compared with CPU and GPU platforms, and evaluate the performance of handwritten digit recognition neural network based on the Minist dataset. The experimental results show that the energy consumption ratio of the design in this paper is 10 times better compared to current advanced CPUs and GPUs, and more than 300 times better compared to current CPUs in the embedded domain.
Optimized MobileNetV2 Based on Model Pruning for Image Classification

Due to the large memory requirement and a large amount of computation, traditional deep learning networks cannot run on mobile devices as well as embedded devices. In this paper, we propose a new mobile architecture combining MobileNetV2 and pruning, which further decreases the Flops and number of parameters. The performance of MobileNetV2 has been widely demonstrated, and pruning operation can not only allow further model compression but also prevent overfitting. We have done ablation experiments at CIIP Tire Data for different pruning combinations. In addition, we introduced a global hyperparameter to effectively weigh the accuracy and precision. Experiments show that the accuracy of 98.3 % is maintained under the premise that the model size is only 804.5 KB, showing better performance than the baseline method.
Rate Controllable Learned Image Compression Based on RFL Model

In this paper, we propose a rate controllable image compression framework, Rate Controllable Variational Autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the state-of-the-art rate controllable Image compression networks.
Flocking Birds of a Feather Together: Dual-step GAN Distillation via Realer-Fake Samples

Recent Generative Adversarial Networks (GAN) models deliver visually authentic images and find their applications in various applications. Due to their heavy size, however, it remains cumbersome to deploy state-of-the-art GAN on the edge side, such as mobile terminals. In this paper, we propose a practical and very effective approach for distilling GAN models, so as to produce competent student models that come with smaller sizes. Unlike other convolutional neural network models whose final output spans a small or reasonably-sized space, based on which knowledge distillation is carried out, GAN produces images as output that span an enormous space, making the distillation much more challenging. To this end, we propose an effective dual-step distillation approach tailored for GAN. Instead of directly enforcing the student output distribution to imitate that of the teacher, we partition the generated images into two sets, the “realer” one and the “fake” one, based on the image quality, and sequentially minimize the discrepancy first between fake images of the student and the teacher, and then between realer ones. This seemingly simple strategy turns out highly effective in terms of exploring output space structure and further the knowledge transfer. Experimental results have shown that the proposed method can be easily generalized and demonstrate the learned portable generator produces competitive or even better results both in quality and quantity.
No-reference Stereoscopic Image Quality Assessment Based on Parallel Multi-scale Perception

With the rapid development of 3D technologies, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods are in great demand. In this paper, we propose a parallel multi-scale feature extraction convolution neural network (CNN) model combined with novel binocular feature interaction consistent with human visual system (HVS). In order to simulate the characteristics of HVS sensing multi-scale information at the same time, parallel multi-scale feature extraction module (PMSFM) followed by compensation information is proposed. And modified convolutional block attention module (MCBAM) with less computational complexity is designed to generate visual attention maps for the multi-scale features extracted by the PMSFM. In addition, we employ cross-stacked strategy for multi-level binocular fusion maps and binocular disparity maps to simulate the hierarchical perception characteristics of HVS. Experimental results show that our method is superior to the state-of-the-art metrics and achieves an excellent performance.
Carbon Efficient Placement of Data Center Locations

The demand for computing is projected to grow rapidly over the next decade. At the same time, technology companies have pledged to reduce carbon emissions associated with the electricity generation needed to meet this computing demand. To achieve this goal, data centers need to be sited at locations with access to low carbon electricity while continuing to meet transmission limits within the electric grid. In this paper, we present a model that determines the optimal placement of future data centers that accounts for the underlying power system dynamics. This model utilizes a bilevel program where the lowerlevel problem calculates the electricity dispatch of the entire electric power network by solving a DC OPF and the upperlevel problem identifies data center locations that minimize the carbon emissions of this dispatch. We test this model under a variety of operating conditions and show that to minimize carbon emissions from new data center loads, it is not enough to place new data centers near renewable generation sources. Instead one needs to consider grid operations and limits.