Reviewerly/exHarmony huggingface dataset:
Integrating Uni-messe and FIWARE for Low-Code Development of Complex Context-Aware Applications

A smart system with context-aware technology can dynamically coordinate heterogeneous distributed services according to various contexts in the physical world and cyberspace. Two technologies, context management and rule-based system, are important elements in developing context-aware applications, and these implementation technologies can reduce the manpower required for service development. FIWARE, an open-source context management platform, and Uni-messe, a rule-based event routing hub, are examples of previous research. This study explores an integrated architecture that connects Uni-messe and FIWARE. We first propose the overall structure of the integrated architecture. The coordination and behavior of the five modules that constitute the architecture are discussed. Finally, a demonstration service based on the proposed architecture is constructed and implemented, and the work processes required for development are discussed. By linking Uni-messe and FIWARE, the development of context-aware applications can be carried out efficiently.
A Visual Attention Guidance Approach for Minimally Invasive VR Surgery Training

Minimally invasive surgery, which has attracted much attention in recent years, requires high levels of skills and expertise. It has been found that expert surgeons use more peripheral vision than novices. In our laboratory, we have proposed visual and audio instruction approaches using text or audio message presentation. In this paper, we implemented these approaches into our laparoscopic VR surgery simulator, and preformed evaluation experiments.
Fast Text Based Classification of News Snippets for Telecom Assurance

The quality of Telecom companies’ mobile service can be seriously compromised by the occurrence of different types of events, whether they are expected or not. The goal of this work is to automatically identify online news that report such events. Three possible topics are searched for: “fire”; “meteorologic” and “public gatherings”. Remaining news’ topics should be ignored. Each category was specifically chosen by its relevance towards the most known network providers’ problems. The data is highly unbalanced. We compare different lightweight models for text classification using information collected from several Portuguese online newspapers: Support-Vector Machines (SVM), Fuzzy Fingerprints and K-Nearest Neighbours (KNN). More complex deep models, such as Bert or RoBerta, are dismissed due to the requirement of a fast response. The proposed models predict the categories based entirely on the title and the short news’ snippets that are freely available. Preliminary results indicate F1-scores above 0.78 for each of the three topics.
Online Machine Learning for Auto-Scaling Processing Services in the Edge Computing Environment

The evolution of edge computing devices has enabled machine intelligence techniques to process data close to its producers (the sensors) and end-users. Although edge devices are usually resource-constrained, the distribution of processing services among several nodes enables a processing capacity near that found in cloud environments. However, the edge computing environment is highly dynamic, impacting the availability of nodes in the distributed system. In addition, the processing workload for each node can change constantly. Thus, the scaling of processing services needs to be rapidly adjusted, avoiding bottlenecks or wasted resources while meeting the applicationsâ€™ QoS requirements. This paper presents an auto-scaling subsystem for container-based processing services using online machine learning. The auto-scaling follows the MAPE-K control loop to dynamically adjust the number of containers in response to workload changes. We designed the approach for scenarios where the number of processing requests is unknown beforehand. We developed a hybrid auto-scaling mechanism that behaves reactively while a prediction online machine learning model is continuously trained. When the prediction model reaches a desirable performance, the auto-scaling acts proactively, using predictions to anticipate scaling actions. An experimental evaluation has demonstrated the feasibility of the architecture. Our solution achieved fewer service level agreement (SLA) violations and scaling operations to meet demand than purely reactive and no scaling approaches using an actual application workload. Also, our solution wasted fewer resources compared to the other techniques.
Administrative Communication and Its Impact on Improving the Efficiency of Decision Support Systems in Palestinian Higher Education Institutions

Research Objective: The aim of the research is to identify administrative communication and its impact on improving the efficiency of decision support systems in universities. Society and Sample of Research: The research was applied to Islamic University- Gaza, and the sample of the academic research consists of administrative position s at Islamic University- Gaza, totaling 87 employees. Research Methodology: A descriptive and analytical approach was used and secondary data was obtained through a questionnaire that was distributed to the research community using the comprehensive enumeration method. The questionnaire was distributed to all members of the study population, and 65 questionnaires were retrieved with a recovery rate of 79.3%. Research Results: The research concluded with a set of results, the most important of which are:1. The results confirmed the existence of a positive statistically significant relationship between administrative communication and improving the efficiency of decision support systems at Islamic University- Gaza.2. The results showed a statistically significant effect of administrative communication in improving the efficiency of decision support systems at Islamic University- Gaza.3. The results confirmed that there are statistically significant differences between the averages of the respondents' answers about administrative communication and improving the efficiency of support systems at Islamic University- Gaza that are attributed to the job title.4. The results showed that there are no statistically significant differences between the average of the respondents' answers about administrative communication and improving the efficiency of support systems at Islamic University- Gaza decision, due to (age group, academic degree, and years of service). Research Recommendations: The research reached a set of recommendations, the most important of which are:1. The importance of universities reformulating procedures, the nature of work and the organizational structure in these universities in a way that improves the application of administrative communication, as well as working to reduce centralization, avoiding duplication, red tape and repeated oversight, in a way that contributes to improving the level of performance and achieving goals, and implementing strategic and operational plans.2. The necessity of adopting the principle of integration in the work of information technology and moving away from duplication, and continuing administrative communication and feedback to reveal any developments, and building an information technology platform that provides an opportunity for universities to use it as an infrastructure in administrative communication.3. The need for higher management of universities to pay attention to decision support systems, and to make intensive and continuous efforts to support and enhance efforts aimed at enriching and enhancing the role of decision support systems in universities.
Lightcnn-Consistent Makeup Transfer on Generative Adversarial Networks⋆

Some researchers have proposed end-to-end makeup transfer with problems such as blurred local makeup details, excessive network resource consumption, and difficulty in collecting paired datasets. To solve the above problems, LightCNN-ConsistentGAN based on paired data is proposed for instance makeup migration. Firstly, a pre-trained LightCNN model is used as the perceptual loss. The LightCNN model is rich in face feature information by training on face datasets, and as the perceptual loss can make the network better focus on face information. Secondly training data alignment not only allows the model to extract only shape texture related features of the five senses, but also reduces the interference of background information on local feature extraction. Finally, paired datasets exist that allow supervised learning to train the model. In this paper, we use both paired and unpaired datasets for testing, and the experimental results show that the proposed method not only simplifies the training process compared with the original BeautyGAN, but also produces visually pleasing makeup faces and accurate migration effects. The SSIM metrics scores for evaluating the makeup application and removal effects are 0.881 and 0.880, respectively, which are improved by 0.01 compared to BeautyGAN, and the FID scores are 39.71 and 37.31, too.Some researchers have proposed end-to-end makeup transfer with problems such as blurred local makeup details, excessive network resource consumption, and difficulty in collecting paired datasets. To solve the above problems, LightCNN-ConsistentGAN based on paired data is proposed for instance makeup migration. Firstly, a pre-trained LightCNN model is used as the perceptual loss. The LightCNN model is rich in face feature information by training on face datasets, and as the perceptual loss can make the network better focus on face information. Secondly training data alignment not only allows the model to extract only shape texture related features of the five senses, but also reduces the interference of background information on local feature extraction. Finally, paired datasets exist that allow supervised learning to train the model. In this paper, we use both paired and unpaired datasets for testing, and the experimental results show that the proposed method not only simplifies the training process compared with the original BeautyGAN, but also produces visually pleasing makeup faces and accurate migration effects. The SSIM metrics scores for evaluating the makeup application and removal effects are 0.881 and 0.880, respectively, which are improved by 0.01 compared to BeautyGAN, and the FID scores are 39.71 and 37.31, too.
The Dynamic System of Sports Simulation Movement with Computer Virtual Reality Technology

Computer virtual reality technology can dynamically simulate the real world, and the dynamic environment created can respond to the user’s verbal commands, posture, etc. in real time, building a real-time interactive relationship between the virtual environment and the user. At present, computer virtual reality technology has been successfully applied in many sports, including golf, skiing, basketball, weightlifting, trampoline, gymnastics, diving, etc. This paper mainly combines the overview of computer virtual reality technology, analyzes the sports simulation sports system based on virtual reality technology, and takes aerobics project as an example to study the dynamic system of sports simulation sports under computer virtual reality technology.
How Visually Impaired People Use Information Media and E-Commerce in Japan

Although existing information technology has several challenges in terms of low levels of accessibility for visually impaired people, it can become a promising tool that encourages them to actively participate in society with appropriate technical development. In this study, a questionnaire-based investigation was conducted to clarify the current state of the information media and functions of accessibility support utilized by visually impaired people. The number of respondents who do not shop online was small. Especially in the blind group, more than 70% stated that they shop online always or often. We also investigated whether visually impaired people sell items, including offline, and the media they use or why they do not sell items. While the data demonstrate that those with blindness can use brick-and-mortar stores where they can sell face-to-face, directly to the store, they imply that those with blindness are apprehensive about selling or listing using ICT devices. The data also indicate that both those with blindness and with low vision would consider selling or listing their items for sale if selling/listing procedures and operations were easier. This suggests that technical support or a solution within a system is required.
Deep Learning Based Real-Time Face Tracking System in Multi-Camera

Face detection and tracking have become popular in recent years. It has critical importance in security, defense, and robotic uses encountered in daily life. For this purpose, many decision support systems or expert systems using artificial intelligence and machine learning have been developed. Thanks to the developments in the field of deep learning and hardware many effective and reliable face track- ing systems realized. But there are still very few real-time scalable end-to-end systems. Also, the realization of this system on multiple cameras is a real challenge, too. In this study, a real-time deep learning-based face tracking system working on multiple cameras was developed. In the realized sys- tem, SCRFD model is used for face recognition, ArcFace model is used for face recognition, and an updated DeepSORT algorithm is used for more stable face tracking. In addition, Apache Kafka stream processing system and Socket.IO bidirectional communication library were used to process multicam- era data in real-time and scalable. In the proposed system, when an image is entered into the system, it can be displayed on the web page after approximately 127 ms.
A Robust Image Denoising Method With Multiview Texture-Aware Convolutional Neural Networks

Image denoising is an issue of intensive research in the image processing community. As the wave of deep learning advances, image denoising with convolutional neural networks has recently made drastic progress, however, they usually can not recover atypical details from noisy images. Motivated by the above, we propose a multiview texture-aware convolutional neural network named MVTANet, which comprises primary denoising network and multiview texture-aware modular. Proposed multiview texture-aware modular owns two variants, i.e., main and secondary texture-aware modular. First, the denoised image is obtained through the primary denoising network. Then, the denoised image and clean image are input into multiview texture-aware modular, respectively, and we obtain two sets of intermediate features and calculate the corresponding perceptual loss. This perceptual loss is designed to generate auxiliary supervision for tiny detail recovery. By setting different initial parameters and parameter freezing technology, this modular can be further focused on restoring atypical details. Extensive experiments demonstrate that the proposed MVTANet is superior to the state-of-the-art denoising methods.
Manifold Discovery for High-Dimensional Data Using Deep Method

It is a challenge for manifold discovery from the data in the high-dimensional space, since the data in the high-dimensional space is sparsely distributed, which hardly provides rich information for manifold discovery so as to be possible to obtain deformed manifolds. To address this issue, this paper designed a deep model based on Brenier theorem for manifold discovery. Since Brenier theorem can find the optimal transportation mass distance between the reconstructed data distribution and the original data distribution, the manifold discovered from in the reconstructed data distribution can be as close to the original manifold as possible. Results not only show that the proposed method wins over competing methods both the precision of manifold discovery and resistance to data sparsity, but also show those non-linear architectures with deep paradigms outperform those architectures without deep paradigms in terms of manifold discovery. We find that the loss function derived by Brenier theorem can help that models minimize the error between the reconstructed manifold and the original manifold. Moreover, the manner constraining neurons with norm-2 is better than that of with norm-1 in both easing data sparsity and improving the precision of for manifold discovery.
Short-Term PV Plant Power Production Forecasting and Hyperparameters Grid Search for LSTM and MLP Models

In recent years, the use of green energy resources has known a considerable growth due to many factors such as improved performance and reliability, which lead to more investments in these technologies, especially photovoltaic. Photovoltaic technology represents an alternative to none environmentally friendly sources such as fossil fuels and coal-fired electricity in today’s world because it is abundant, clean, and inexhaustible. Although the generated PV power is clean, however, this power is highly unconnected and unstable. To overcome this issue, the use of deep learning methods helps predict the day ahead power production of PV plants. This work grid search five hyperparameters of two deep learning models LSTM (Long Short-Term Memory Network), and MLP (Multi- layer Perceptron) for forecasting power production. The performance of the used methods was demonstrated with a case study using an actual dataset collected from Rabat, Morocco. The values of the three performance evaluation indicators, RMSE, MAE, and MAPE, show that the MLP model exhibits roughly superior performance in forecasting accuracy, stability, and speed.
Calib-StyleSpeech: A Zero-Shot Approach in Voice Cloning of High Adaptive Text to Speech System with Imbalanced Dataset

In this article, we propose Calib-StyleSpeech, a novel end-to-end Text-to-Speech (TTS) system, which achieves a Zero-shot learning approach in cloning a new voice without using the meta-learning method. Our model uses a block to extract the style from the reference mel-spectrogram, embedding this style vector into the acoustic model to condition the hidden features. And then, training it with a multi-speaker dataset that still contains noise and resonant utterances. We also extract a content vector from this mel-spectrogram simultaneously with the style vector. This vector will imitate the hidden vector extracted from the phoneme sequence by the encoder of the acoustic model. The extracted content vector takes responsibility on calibrate the style vector by the Mutual Information (MI) constraint in order to eliminate the dependency between content and style representation space. Calib-StyleSpeech can synthesize a high fidelity voice with only 36 utterances (about 3 min) in training data. Furthermore, the experimental results prove that model can learn to speak in a new voice with only one small reference record (about 5 s) of the target voice without any fine-tune stage and get a competitive similarity score with another state-of-the-art while maintaining naturalness and intelligence.
Sculpting in Augmented Reality

Sculpting is a complex craft that relies on the synergy of multiple factors. The makers’ dexterity, material properties and environmental conditions are fundamental in determining a successful sculpting process. Although advanced software solutions emulate this craft through sophisticated interactions and design options, craftsmen and digital makers criticize these interfaces’ lack of physical engagement. This study explores augmented reality technology to define an additional method for sculpting processes that links the physical craft with methodologies from 3D modelling software. The research output consists of an augmented reality app - SculptAR - that applies concepts from multimodal interactivity studies to implement gesture and voice recognition methods. The aim is to integrate these inputs and define augmented sculpting as an engaging and interactive experience that merges physical and digital skills. Furthermore, this research wants to highlight the emergence of possibilities for developing interactive experiences that include various human senses.
Time, Memory and Accuracy Tradeoffs in Side-Channel Trace Profiling

Template attacks are one of the most powerful classes of side-channel attacks. Template attacks begin with an offline step, in which the side-channel traces are profiled, and decoders are created for each side-channel leak. In this paper, we analyze the compression step of the trace profiling process. This compression step, which is a central part of the decoder's training process, is used to reduce the amount of time, memory consumption, and data required to successfully perform the attack; various practical methods have been proposed for this step, including one which uses an efficient means both for selecting the points of interest (POI) in the power trace and for preprocessing noisy data. We investigate ways to improve the efficiency of the attack by implementing several compression methods which select the most informative power consumption samples from power traces. We develop a unique dedicated evaluation system to compare the performance of various decoders with different compression methods on real-world power traces. Our findings indicate that our proposed decoder for side-channel traces outperforms the current state of art in terms of speed, resource consumption, and accuracy. We also demonstrate our decoder's effectiveness under resource-constrained conditions, and show that it achieves over 70% accuracy even if there are fewer than 1,000 traces in the profiling phase.
Adversarial attack application analytics in machine learning

Machine learning is one of the most widely studied and applied technologies, but it is itself vulnerable to attack and its algorithms have the risk of privacy leakage. In this article, through the experts currently popular speech recognition scene, reveals how to build the antagonism against data, make its differences with the source data is subtle, so much so that humans can’t through sensory recognition, and machine learning model can accept and the classification of making the wrong decision, at the same time made attack, finally prospects the study model to research the development and application of security and privacy protection.
An IoT-based Surveillance Method

Crimes have long been a nerve-racking ordeal to pursue a remedy, not just for the government but also for the whole community. It is undeniably true that there is a dramatic increase in crime rate among many nations. This rise stems from many factors. There appears to be some evidence that administrative lock-ups do not minimize crime rates. The atmosphere plays a significant role in shaping a criminal. There are so many slums where criminals live today. It is recorded that a child whose parent is guilty is more likely to be involved in social tragedies as a result of living among gamblers, drug dealers, or even murderers, to name only a few. It would seem, besides the family, that schools would take responsibility for sharpening an individual. Numerous juvenile criminals are supposed to be a bully when they are students. The inattention from teachers and parents, particularly during the student's adolescence, makes the struggling students more vulnerable to hardened criminals. To summarize, the increase in crime rate could be attributed to a number of factors including the low extreme penalties and vice enclosures. To reduce the number of crime rates in India and provide evidence to the police, we came up with a solution for which we created an Internet of Things (IoT)-based surveillance app. As the use of mobile applications is increasing at a rapid rate in India, we have designed this application so as to reduce the crime rate and provide safety measures and awareness among the society of people who are thinking of committing a crime.