Reviewerly/exHarmony huggingface dataset:
Human only Made Life Signs Detection for Disaster Survivor Rescue

No one can tell or predict when and where a natural disaster such as an earthquake will occur and the damages it will cause. Or an overflow of a large amount of water beyond its normal limit (water flood) shallowing city as we used to watch on news TV after the passage of a strong typhoon causing heavy rain. This paper proposes a Human only Made Life Signs Detection for Disaster Survivors Rescue when a disaster occur particularly during a water flood. We use the matrix-based pairs of opposing pixels positioned directly around the observed point that belongs to the edge of the life sign target. First one dimension matrix for bitmap memorization values of the RGB components of pixel is used. Next these values of the RGB components of pixel color are copied from bitmap to matrix. The number of bytes in a row is rounded up to the nearest number divisible by four. To validate the proposed new rescue system, we have chosen items that can be found basically in any kitchen specially food and fruits such as, cabbage, orange sweet potato, banana and so on., combined them in any way we want them to look like, take picture then processing the image. The result seems promising but more improvement needs to be done before it integration into the drone.
Optimization of target alignment in inertial confinement fusion facilities based on redundant measurements

Target alignment technology is one of the most essential technologies in inertial confinement fusion facilities, and is directly related to the results of high-energy physics experiments. In this study, we examine the error in the target alignment scheme, classify the error based on the source and performance, minimize the error through redundant measurement methods (target fusion and sensor fusion), and improve the precision of the target alignment scheme. By integrating these methods, the alignment error of the target is considerably improved and the position uncertainty is reduced.
A Lightweight Deep Learning Model for MOT

Multiple object tracking (MOT) is a high complexity computer vision task, it has to detect multiple target objects in frames and extract their features for matching. Through deep learning techniques, MOT can be solved much easier while getting more accurate results, however it is still hard to be adopted for real-time applications because of the high computational cost. In this research, we applied pruning method on JDE, which is a well-known MOT model structure, and proposed a lightweight JDE model that obtained acceptable accuracy with lower computational cost. Evaluate our proposed model through real-world surveillance video, the result has shown that comparing to original JDE, detection accuracy had decreased about 15.65% in average, although computational efficiency had increased about 71.54% in average, which proved that the proposed model is possible to be used for real-time applications.
A Novel Distributed Cubature Kalman Filter and Its Application to Target Tracking

This paper studies the problem of distributed state estimation of nonlinear systems. Due to the increase of state components or dimensions, the unscented Kalman filtering algorithm cant solve this large-scale system with higher accuracy. We propose the cubature Kalman filtering strategy to deal with this case in microsystem. For wireless sensor networks, we extend the cubature Kalman filter to the distributed form which can greatly reduce the computation burden of single node. Compared with the previous distributed algorithms, this paper can achieve the exact consensus via the maximum consensus technique. Through the simulation, the maximum consensus cubature Kalman filter with higher estimation accuracy compare to the centralized algorithm and the average consensus cubature Kalman filter. Because of its higher estimation accuracy, the centralized algorithm can be replaced in the state estimation problem.
Performance Improvement of Breast Cancer Diagnosis based on Mammogram Images using Feature Extraction and Classification Methods

Breast cancer is the most common cause of death in the elderly women category. In 2018, there were almost 0.63 million cases of death found in 2.09 million new cases of breast cancer. Early detection is a key to lower the rate of death caused by breast cancer. One of the most typical methods for early detection is utilizing digital image processing on mammogram images. The detection process has three main steps, namely preprocessing, feature extraction, and classification. Feature extraction plays a significant role in providing an accurate detection system. In this study, we evaluated three different methods of feature extraction. They are feature extraction based on texture, gray level co-occurrence matrix (GLCM), and morphology. In the classification step, we compared the performance of K-Nearest Neighbor, Naive Bayes, and Support Vector Machine methods. We used a mammogram dataset from the Mammographic Image Analysis Society (MIAS) consisting of 100 images labeled as normal and 51 images specified as abnormal. The experimental results show that combining all extracted features and using Naive Bayes classifier obtained the highest accuracy of 98.68%. The results suggest that early detection of breast cancer can be performed accurately using appropriate detection methods.
Analysis of a software based hardware composer adaptation

The Android operating system uses the hardware composer module to write visual outputs to buffers that will be shown on the screen. The module is device-specific, meaning that it is up to the device vendors to implement the functionality. The hardware composer is usually implemented to support hardware rendering, which provides a performance boost, but it makes the implementation dependent on graphics drivers. These dependencies limit the number of devices that can use certain implementations because they may lack the access to required drivers, or the drivers may have differences in the case of open source ones. Software rendering can be used to eliminate these problems and grant portability at the expense of performance. Hardware composers that use software rendering are convenient for Android systems running inside a virtual environment such as a Linux container, because they grant them independence from the host hardware. This paper will analyze the modifications made to an open source, hardware dependent implementation of a hardware composer to use software rendering, and thus be able to run on any platform that can support a Wayland graphics environment.
Improving efficiency in training on sewing ao dai with supporting tools including new assembling process and instructional videos

In reality, practice teaching shows that the number of students completing the Ao Dai products does not meet the requirements on time and quality with a high rate up to 60%. One of the main reasons is that the currently sewing process still has many limitations, especially in large groups, most students do not observe clearly and carefully the instructor's sample manipulation. This article presents research results on building supporting tools including a new sewing process and instructional videos to improve Ao Dai sewing efficiency. Experimental teaching results showed that the percentage of students who completed products on time and requirements in terms of quality increased by more than 40%. Most students satisfied with the quality of the supporting tools which help to not only improve their knowledge but also practice sewing skills.
RM3: A Risk Management Framework For IT Project Success

Risk Management (RM) is critical for projects’ success as it can predict undesirable events that may occur. Nevertheless, RM is lacking in industry, especially for projects in the IT sector. For this purpose, our study introduces RM3, a new framework for analyzing and measuring risks to compensate for known and unknown factors affecting the path to the projects’ success. RM3 comprises three primary processes: project complexity analysis, risk analysis, and risk monitoring and control. Based on a robust action-research study, we demonstrate RM3’s feasibility and effectiveness through an actual project within an IT company.
A Novel Class-wise Forgetting Detector in Continual Learning

Deep learning model suffers from catastrophic forgetting when learning continuously from stream data. Existing strategies for continual learning suppose the forgetting always happens when learning a new task and only deals with the previous task's global forgetting. This study introduces a novel active forgetting detector based on a windowing technique that monitors the model's forgetting rate for each encountered class label. When the model experiences the forgetting issue, we adapt the forgetting classes by using a proposed replay from experience method called online triplet rehearsal. We conduct comprehensive experiments on four vision datasets to demonstrate that the proposed approach performs significantly better than three state-of-the-art continual learning methods.
Bio-Inspired Optimization to Improve Neural Identifiers for Discrete-Time Nonlinear Systems

This work aims to apply metaheuristic optimization, offered by bio-inspired algorithms, to enhance the behavior of neural identifiers for unknown nonlinear systems in discrete-time whose model, for this purpose, is assumed unknown. Due to this fact, a new and efficient training algorithm based on the UKF approach is used in combination with the bio-inspired algorithms that are used in order to tune the covariance matrices for the training algorithm. Simulations on a two degree of freedom robot arm are performed obtaining encourage results.
ABOUT THE MIDDLE VOLGA PERIOD IN THE LIFE OF AN OUTSTANDING BIBLIOGRAPHER AND WRITER A.S. POLYAKOV

In August 2021, the cultural community of St. Petersburg celebrated the 265th anniversary of St. Petersburg State Theater Library, which is known worldwide as the richest repository of materials about the theater and for the theater. It has been collecting its unique fund, including a number of rare and valuable collections, since the XVIII century. The author’s research attention is focused on the facts of the Middle Volga period in A.S. Polyakov’s biography – the legend of one of the country’s oldest libraries. After the Bolsheviks came to power in Russia in 1918–1923, Alexander Sergeevich Polyakov, working as the head of this book depository, largely determined its modern multifunctional appearance. During this difficult period in the life of the state and the society, with great enthusiasm the talented writer edited several different print media, the most famous of which was one of the first theatrical magazines of the Soviet Russia, “The Biryuch of Petrograd State Theaters”. Alexander Polyakov was not a native of St. Petersburg. His small homeland is the Middle Volga region. The author explores the childhood and youth years of the future bibliophile and writer, which he spent in Simbirsk and Kazan governorates. The researcher tells that Alexander was not only born and brought up in the Volga region, but also studied at Simbirsk Classical Gymnasium and the Imperial Kazan University. The article gives some facts about an active participation of this native of Simbirsk in the socio-political life of the Middle Volga region which deserve special attention. For example, in 1901–1904 A.S. Polyakov played a major role in the creation of Kazan and Simbirsk organizations of the Party of Socialist Revolutionaries (SRs). In 1905, he was one of the most famous participants of the First Russian Revolution in the region. So, being under the tacit supervision of the police, Polyakov at that time constantly spoke at meetings and rallies in the town of Simbirsk and some uyezds of Simbirsk province, actively participated in agitation events of social revolutionaries of various kinds. In 1906, a young native of the Volga region moved to an illegal position, and then secretly left the Volga region. In 1907, the future outstanding bibliographer was detained by the police in St. Petersburg and sent into administrative exile. The article also provides previously little-known facts of his personal life. At this, the author tells about Polyakov’s parents and other family members. The specialist paid special attention to his wife, Elizaveta Polyakova (Dubova), a native of the Middle Volga region, who was a St. Petersburg student and a member of the Socialist Revolutionary Party. The young revolutionary was repeatedly brought to administrative responsibility and after the defeat of the revolution of 1905–1907 was exiled to the Eastern Siberia.
GPU Overdrive Fault Attacks on Neural Networks

Graphics processing units (GPUs) are commonly used to accelerate training and inference of deep neural networks (DNNs). Modern cloud nodes are shared by multiple users to execute workloads concurrently. However, the reliability and security of sharing the heterogeneous CPU-GPU have not been carefully evaluated. In this paper, we thoroughly characterize fault injections and propagation in a victim convolutional neural network (CNN) on a GPU, and analyze the controllability of the attack. We successfully launch an end-to-end misclassification attack during CNN inferences with careful timing control.
Magnification Dependent Histopathology Breast Cancer Image Binary Classification Using Different Levels Of EfficientNet

Breast cancer kills millions of women annually around the world; early diagnosis of this perilous disease can result in efficient treatment. Computer Aided Detection systems by using advanced screening tools can automatically help the physician all over the world to detect the cancer. In our approach we are dealing with histopathological Images and state-of-art deep learning network named EfficientNet, it has been applied to BreakHis images to classify them into benign or malignant. For breast cancer classification in histopathological images using deep learning we applied eight different stages of EfficientNet that has different amounts of parameters on four diverse resolutions from our image dataset. This pre-trained Network with ImageNet weights represents high accuracy in training as well as in testing part. The best result among 32 findings in our proposed work gives the accuracy of 94.41 % for training, 99.28% while predictingand 99%Sensitivity, Recall and F1-score.
Multi-Iterative Superpixel Segmentation based on Local Brightness and Darkness Information

Super-pixel segmentation algorithms are widely used in the preprocessing steps for computer vision applications. A crucial aspect of Super-pixel segmentation is preserving structure boundaries. However, in many images with complex structures, the contrast between foreground and background are very low, which becomes a challenge for Super-pixel segmentation. In this paper, we introduce a new method to evaluate local brightness and darkness for gray images. Then, we use local brightness and darkness information to enhance the weak structure boundary for Super-pixel segmentation. Furthermore, we also introduce a Super-pixel merging method for SLIC to eliminate numbers of Super-pixel blocks, especially nearby the boundaries between different objects. The experimental results show the proposed algorithm makes Super-pixels adhere to object boundaries better and improve the over-segmentation.
Keys as Secret Messages: Provably Secure and Efficiency-balanced Steganography on Blockchain

To improve efficiency of stegosystem on blockchain and balance the time consumption of Encode and Decode operations, we propose a new blockchain-based steganography scheme, called Keys as Secret Messages (KASM), where a codebook of mappings between bitstrings and public keys can be pre-calculated by both sides with some secret parameters pre-negotiated before covert communication. By applying properties of elliptic curves and pseudorandom number generators, we realize key derivation of codebook item, and we construct the stegosystem with provable security under chosen hiddentext attack. By comparing KASM with Blockchain Covert Channel (BLOCCE) and testing on Bitcoin protocol, we conclude that our proposed stegosystem encodes hiddentexts faster than BLOCCE does and can decode stegotexts in highly acceptable time. The balanced time consumption of Encode and Decode operations of KASM make it applicable in the scene of duplex communication. At the same time, KASM does not leak sender’s private keys, so sender’s digital currencies can be protected.
Analogy among Different Conventional Watermarking Transforms and Noise Removing Filters against Various Attacks

Nowadays improving lossless data hiding methods is on demand. For this demand, here in this paper the conventional transforms-Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) have been implemented to do watermark on various host images. To find out which transform method is more reliable and lossless, the performance parameters-Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) are analyzed here. To compare the robustness among the methods, the watermarked images have been attacked by various noises. Different noise removing filters have been run over the noisy images to find out an appropriate filter for a corresponding noise.
The K-Core Decomposition Algorithm Under the Framework of GraphBLAS

A graph’s k-core is the unique largest induced subgraph where all nodes’ degrees are greater than or equal to k. The k-core decomposition algorithm is to find the coreness value for each node in a graph, which is the maximum value of k all the k-core containing this node. K-core is one of the most commonly used references to evaluate the node importance in various scientific disciplines. The widely used classical k-core decomposition algorithm has O(n+m) complexity [1]. However, it is not suitable for parallelization. In this paper, we propose an algebraic k-core decomposition algorithm that is O(k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> n+m) in computational complexity and can be efficiently parallelized on GPU under the GraphBLAS framework [2]. We can efficiently parallelize and calculate the coreness value for graphs with billions of edges. On a 14-core CPU server and a large scale sparse datasets, our algebraic algorithm outperforms the state-of-the-art ParK [3] and PKC [4] algorithms. The algebraic algorithm, in particular, achieves up to 4× acceleration in CPU, whereas our parallel GPU implementation on several large scale graphs achieves up to 6× acceleration over our CPU version.
Success Factors &amp; Challenges for Mobile Collaborative Learning Implementation in Higher Education

In the last decade, the mobile device has expanded exponentially, and 95 percent of the world's population now lives in a region served by a mobile cellular network. Collaboration in learning can provide opportunities for skill practice and the development of new knowledge and partnerships both within and outside of the classroom and in environments where there are no classes or other gathering places, or collaborative events. However, there is an inefficient use of mobile learning mobility opportunities, a lack of interaction and direct engagement, a lack of experience, and a lack of awareness about the university's effectiveness and applicability. In this article, we offer a comprehensive literature review of how mobile collaborative learning in higher education works, emphasizing the challenges and success factors that have been identified. We conducted a comprehensive study of the literature on mobile collaborative learning. Our keyword search found 621 papers from the years 2016 to 2021. We excluded studies and removed the duplicates into 37 papers. The majority of the papers included in this study were from Q1 journals (48,5%). We found 62 success factors that were grouped into 5 categories. Design, interactivity, usability, user perception, ease of use, satisfaction, usefulness, communication, and interaction are the most salient success factors. While challenges grouped into 11 categories including motivation, satisfaction, UI/UX design, usability, framework, integration, sentiments, security, algorithm, multimedia and critical thinking skill.
The Formation of Method for Evaluation of Integral Parameters of the Patient’s Condition Monitoring, Forecasting of Consolidated Data

The integrated indicator is proposed, that connects quantitative and qualitative medical parameters. Its properties are investigated. It is shown that the upper boundary of its estimation depends on the number of parameters that are generalized, and the lower does not depend. Modeling of monitoring of condition parameters at the remote recovery of post-stroke patients is carried out, data compression coefficients are determined.
To the question of text slides design

In the context of the COVID-19 pandemic, all educational institutions were forced to switch to a remote work format. At the same time, some webinars and video lectures did not attract the proper attention of specialists, and the assimilation of the material was at a low level. Usually, it is not taken into account that in order to increase the efficiency of perception of information, both auditory and visual perceptions are necessary. A multimedia presentation is a modern, effective way of presenting information (combining a reference, methodological or didactic development, etc.), made using specialized computer programs, in which all modern multimedia capabilities are involved: graphics, animation, texts, tables, photos, video and audio materials. Despite the fact that the guidelines for the preparation and presentation of slides can be found freely available on the Internet and in many printed publications, they often constitute a body of information that is largely ignored by the user (reader). This publication provides recommendations and their rationale for creating text slides for multimedia presentations.
Computer Based Evaluation of Speech Voicing for Training English Pronunciation

This paper presents new developed tool for automatic analysis of voiced and unvoiced parts of speech. In the test speech, voiced frames, unvoiced frames as well as silent frames are detected and compared with reference speech. Statistical evaluation in the form of histograms and percentage similarity then serves to assess the correct pronunciation. The user can observe a gradual trend in improving pronunciation either in one learning session or throughout the course. This tool was implemented in a MATLAB-based Graphical User Interface and can be used to check the pronunciation of both individual words and long utterances.