Reviewerly/exHarmony huggingface dataset:
Digital Forensic Investigation Framework for Dashcam

Dashboard cameras (dashcams) are becoming the most reliable eyewitness on the streets. Those small installed devices inside vehicles record everything in front of the car during its journey. These devices are becoming popular and usually record incidents or scary scenes on the way. Thus, those scenes become digital evidences that need to be handled by law enforcement agencies. When dashcam owners have digital evidence on their devices, they post it online on their social media accounts, and sometimes they mention the traffic police handle. This means sharing this digital evidence with the public, which affects the digital evidence on many levels. There are a few standards from the National Institute of Standards and Technology (NIST), INTERPOL, and others on collecting digital evidence from incident scenes by digital forensics first responders. In contrast, there are no frameworks or guidelines for delivering digital evidence online. In this paper, we improved the first phase of the NIST standard for digital investigators to receive dashcam evidence online from drivers through a website designed to prove our concept. In addition, the new framework considers the integrity and availability of the data and its metadata during the digital evidence lifecycle.
A Performance Analysis of Different MongoDB Consistency Levels

MongoDB DBMS is a NoSQL database management system that is known for its good performance in clusters of computers. In addition, the MongoDB has distinct levels of consistency that can be defined at the moment that a database operation is performed. The work described in this paper aims at investigating the MongoDB‘s performance using different levels of consistency. More specifically, the throughput and runtime performance of MongoDB operations are evaluated based on some workloads provided by the YCSB+T benchmark, which is an extension of the YCSB benchmark. The performance tests were performed according to two of the consistency level settings offered by MongoDB, w:1, j:true and readConcern (rc):local and w:majority, j:true and rc:local. The conclusion reached is that better performance is achieved when using the w:1, j:true and readConcern:local configuration. As a result, if there is a need for higher performance and data consistency is not an essential requirement, a write concern configuration that writes only to the primary node is a good solution.
A Two-Step Approach to Boost Neural Network Generalizability in Predicting Defective Software

With society’s digitalization, the ever-growing dependence on software increased the negative impact of poor software quality. That impact was estimated at $2.41 trillion to the US economy in 2022. In searching for better tools for supporting quality assurance efforts, such as software testing, many studies have demonstrated the use of Machine Learning (ML) classifiers to predict defective software modules. They could be used as tools to focus test efforts on the potentially defective modules, enhancing the results achieved with limited resources. However, the practical applicability of many of those studies is arguable because of (1) the misuse of their training datasets; (2) the improper metrics used to measure those classifiers’ performance; (3) the use of data from only a system or project; and (4) the use of data from only a computer programing language. When those factors are not considered, the experiments’ results are biased towards a very high accuracy, leading to improper conclusions related to the generalizability of classifiers to practical uses. This study sheds light on those issues and points out promising results by proposing and testing the cross-project and cross-language generalizability of a novel 2-step approach for artificial neural networks (ANN) using a large dataset of 17,147 software modules from 12 projects with distinct programming languages (C, C++, and Java). The results demonstrated that the proposed approach could deal with an imbalanced dataset and outperform a similar ANN trained with the conventional approach. Moreover, the proposed approach was able to improve by 277% the number of defective modules found with the same software test effort.
ХИМИЯ ПӘНІН ОҚЫТУДА ИНТЕРАКТИВТІ ӘДІСТЕР МЕН ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНУ

The article addresses issues related to the definition of new values in order to improve the quality of modern chemistry and education, the personal development of students, the problems of teaching strategies that awaken students ' interest and motivation in the Natural Sciences. Calling the attention of students to education, science with New available information that meets the requirements of the time, fresh data in the field of Education. The focus is on interactive learning using information and communication technologies, using the discipline in practice. Therefore, our goal is to compile an interactive course in Chemistry based on the peculiarities of interactive learning related to information and communication technologies and more interesting presentations of the discipline. The beginning of our work consisted in a review of initial research devoted to the development of theoretical foundations for the use of information and communication technologies. Next, we present some developed methodological options that demonstrate the possibility of using interactive methods. At the second stage, in accordance with various interactive chemistry curricula and analysis of chemistry curricula in Turkey and Kazakhstan, we have developed interactive educational material with interesting built-in content for general secondary schools. In the next step, all the prepared materials were tested, and the methods used in teaching chemistry came into one form.
БЕЙОРГАНИКАЛЫҚ ХИМИЯНЫ ОҚЫТУДА ЖАҢА ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНЫП ОҚУШЫЛАРДЫҢ ФУНКЦИОНАЛДЫҚ САУАТТЫЛЫҒЫН ДАМЫТ

In this article, we will look at ways to improve functional literacy in teaching inorganic chemistry in secondary schools. Students, using their skills and knowledge, relying on comparative data on the composition and properties of the substance, perform tasks using various methods, increase their ability to interact with the external environment in the future, adapt to changing life and make effective decisions. The technologies of teaching chemistry in secondary school and ways of learning through the development of students' abilities and providing feedback are considered.Tasks for functional literacy show that the tasks are based on the curriculum, long-term plan, not only in the form of open answers, but also on the development of reading and mathematical literacy, creative thinking and science literacy. To increase the student's ability to achieve their goals, expand their knowledge and capabilities, to understand and use written texts to participate in public life, to reflect on them and to read. Ability to master and use knowledge by recognizing the natural sciences and asking questions, acquiring new knowledge, understanding natural phenomena and formulating conclusions based on scientific evidence on scientific problems. Helps students to make judgments based on an understanding of the role of mathematical literacy in the world and to make decisions that should be made by constructive, active and reflective citizens, demonstrating access to innovative and effective solutions.
ХИМИЯ ПӘНІН ОҚЫТУДА ПӘН МЕН ТІЛДІ КІРІКТІРЕ ОҚЫТУ ТЕХНОЛОГИЯСЫНЫҢ ТИІМДІЛІГІ

In modern advanced age of science and technology, the effective use of new technology methods in order to deepen students' knowledge, develop scientifically, develop skills to work independently, develop thinking ability, improve speaking skills, search for and use information tools on their own. Integrated learning technology is one of the actual issues. New curricula and new methods of integrated teaching of subjects appeared. And the updating of the educational content requires the use of different types of organization of training, as well as the use of integrated teaching lessons of various subjects. Active use of new CLIL technology in education is very effective. In order to determine the effectiveness of the integrated technology, a pedagogical experiment was conducted, and the subject and language knowledge of the students was determined. That is, the student learns the subject along with the language. Students' knowledge develops well during integrated teaching of general chemistry in English. Students' chemical vocabulary in English will be expanded. In order for the student to easily express his thoughts orally in English in the chemistry class, good results are achieved if audio and video tapes related to the subject are used a lot in English. The importance of teaching using CLIL technology is that it helps students learn English, acquire chemical knowledge, and increase their interest in the lesson.
Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power

In recent years, renewable energy has been paid more attention and utilized more extensively throughout the world [1].
Uncertainty Characterization of Power Grid Net Load of Dirichlet Process Mixture Model Based on Relevant Data

The net load of the power grid is the active power difference between the electricity demand of power users and renewable energy generation.
<i>Paradise Regained</i> and the Prospects of Enlightenment

Milton QuarterlyVolume 56, Issue 3-4 p. 137-152 ORIGINAL ARTICLE Paradise Regained and the Prospects of Enlightenment Sam Hushagen, Corresponding Author Sam Hushagen [email protected] University of Washington Correspondence Email: [email protected]Search for more papers by this author Sam Hushagen, Corresponding Author Sam Hushagen [email protected] University of Washington Correspondence Email: [email protected]Search for more papers by this author First published: 04 May 2023 https://doi.org/10.1111/milt.12428Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume56, Issue3-4October-December 2022Pages 137-152 RelatedInformation
The IMPLEMENTATION OF BLOCKCHAIN AS COVID-19 TEST AND VACCINE CERTIFICATE STORAGE SYSTEM

The existence of the Covid-19 virus, which was first announced at the end of 2019, in a short time was able to cause a pandemic. To overcome this, the government implemented a protocol that integrates Covid-19 test results and vaccination certificates into an application, with the aim that individuals can prove that they are free from Covid-19 infection and can return to normal activities. However, a centralized system is prone to single point of failure and data manipulation from the intervention of certain parties due to a lack of transparency. This paper proposed the use of Ethereum blockchain and smart contracts to solve this problem. By Using blockchain technology and smart contracts, the data management process will be more transparent since every transaction on the blockchain is recorded by each node. Blockchain also prevents a single point of failure because there are more than one data providers. The system that has been developed has fulfilled the security and privacy aspects of patient data by implementing password-based encryption on patient data. However, the system’s response time is strongly influenced by the computational capabilities of the Rinkeby network. On average, the system took 47,9 seconds to register a new certificate.
An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM + Attention and LDA

In this paper, the Foxconn epidemic event in Zhengzhou was taken as an example to analyze the evolution of online public opinion on public health emergencies.In order to improve the performance of online public opinion analysis, based on the life cycle theory and LDA theory, the emotional changes of Internet users in four stages of the Foxconn incident centered on the evolution of inscription were divided.The emotions of netizen speech at different stages are analyzed based on CNN-BiLSTM + Attention model, which uses Convolutional Neural Network (CNN) to extract local features.Bi-directional Long Short-Term Memory (BiLSTM) is used to efficiently extract contextual semantic features and long distance dependencies, and then combined with attention mechanism to add emotional features.Finally, Softmax classifier realizes text emotion prediction.The experimental results show that: compared with TextCNN, BiLSTM, BiLSTM + Attenion, CNN-BiLSTM model, the emotion classification model has better effects in the accuracy rate, accuracy rate, recall rate and F value.By analyzing the emotional distribution and evolution trend of public opinion under "text topic", the paper accurately deconstructs the development characteristics of public opinion in public health emergencies, in order to provide reference for relevant departments to deal with public opinion in public health emergencies.
CandidNet: A Novel Framework for Candid Moments Detection

Candid photography is a relatively recent trend among both professional and amateur photographers. However, capturing decisive candid moments with a natural head pose, body pose, eye gaze, facial expressions, human-object interactions, and background understanding is not a trivial task. It requires the timing and intuition of a professional photographer to capture fleeting moments. We propose a novel and real-time framework for detecting a candid moment in this work. The method includes a two-stream network, namely, Attribute Network and Visual Embedder Network. The former network stream collaboratively learns high-level semantic features from the candid feature pool and the latter network stream focuses on learning visual image features. Lastly, we have extended our solution, by fine-tuning CandidNet, to output candid scores for frames in the range 0 to 4 (0: non-candid; 4: extreme candid). The scoring mechanism allows us to compare images based on their candidness. A detailed ablation study conducted on the proposed framework with various configurations proves the efficacy of the method with a classification accuracy of 92% on CELEBA-HQ [16] and 94% on CANDID-SCORE [13]. With a high processing speed, the proposed solution is suitable for real-time applications like candid moment indication to the user in-camera preview. First-in-the-market, the solutionis being deployed on the Samsung Galaxy S22 flagship phone as part of the Single Take Photo application.
Rain Streak Removal via Spatio-Channel Based Spectral Graph CNN for Image Deraining

Removing rain streaks from the captured single rainy images plays a dominant role in high-level Computer Vision (CV) applications. Since, many existing deraining methods ignores long range contextual information and utilize only local spatial information. To address this issue, a Spatio-channel based Spectral Graph Convolutional Neural Network (SCSGCNet) for image deraining was proposed and two new modules were introduced to extract representations along spatial and channel wise dimensions. Therefore, we integrate deep Convolutional neural network (CNN) with spatial based spectral graph convolutional neural network (SSGCNN) and channel based spectral graph convolutional network (CSGCNN) modules into a single end-to-end network. Therefore, our network was able to model feature representations from local, global spatial patterns and channel correlations. Experimental results on five synthetic and real-world datasets shows that the proposed network achieves state-of-the-art (SOTA) results.
A Novel Scheme for Adversarial Training to Improve the Robustness of DNN Against White Box Attacks

Deep Neural Networks (DNNs) are found to give excellent results in many applications including image classification. DNNs are found to have reduced efficiency in their performance when exposed to adversarial attacks. An adversarial attack is a phenomenon that is used to fool the DNN, by adding imperceptible perturbations to the input. Under white-box attack conditions, when an adversary has complete knowledge of the network and may produce substantial perturbations via repeated iterations, the robustness of current defense methods against these assaults is severely compromised. By observing learned feature space of a DNN it is noted that different class samples are within close proximity due to which by adding imperceptible perturbations, the feature map of input in the learned feature space is being mapped away from its respective class samples. This forces the model to completely change its decision when an unnoticeable perturbation is added to the input. To counter such attacks, this work attempts to force the DNN to learn how to maximize the distance between different class samples.
Traffic Sign Detection and Recognition Using Dense Connections in YOLOv4

A self-driving car is a growing technology in India where detection of traffic sign in an unconstrained environment is a challenging task due to its small size. With the development of deep neural networks, many models for object detection have been developed. In this work, we have used a single-stage detection model YoloV4 with further improvements in detection neck for detection of traffic signs. We have used dense connections in place of normal connections of the model for better feature propagation. This improves the accuracy with less inference time. We have conducted our experiments on bench-marked Chinese traffic sign dataset, Tshigua-Tenscent 100K dataset (TT-100K). We have achieved accuracy of 94.30% with 32 FPS.
Real-Time Violence Detection Using Deep Neural Networks and DTW

With the increased coverage of CCTV cameras across major metropolitan cities, there has been an increased need to continuously monitor these video feeds for them to be effective, this incurs human labor which is prone to boredom and flaws. We propose a system that can augment the capabilities of surveillance systems by automatically tagging sequences of violence in real-time. Instead of relying on a bigger network like CNN-LSTM, our system uses DTW to measure the similarity between two sequences of body joint angles over time and uses this feature as an input to different classifiers, thus reducing the need for a big video dataset to perform action recognition. For person detection on the individual frames we employ YoloV3 and then DeepSort is used to track different individuals over time then use this as an input to OpenPose to obtain the key points, the obtained key points are used to calculate different joint angles. The joint angles can be seen as temporal signals which can be compared with different joint angle signals over time and find the similarity measure and use this as a feature to classifiers like SVM, KNN, and Random forests. The results are at par with the ones that use CNN-based classifiers trained on larger data.
A Compact-Structured Convolutional Neural Network for Single Image Denoising and Super-Resolution

In this work, a simple and compact-structured convolutional neural network (CNN) entitled as Denoising and Super-Resolution (DnSR) network for single image denoising and super resolution (SISR) is presented. Using sparsity property (a smaller number of large intensity values and a larger number of small intensity values), the training of DnSR is made to converge early. We obtain the sparsity by learning residuals of image pixels instead of learning raw image pixels particularly for SR. We use a smaller receptive field of the image patch for faster training. We use gradient clipping to train DnSR net with an extremely large learning rate to overcome gradient inconsistency during training. An end-to-end mapping is done by learning the complex mapping between low-resolution (LR) image patches and the corresponding residual of high resolution (∆HR) image patches using DnSR net. The proposed DnSR net uses a smaller number of parameters (approximately 3 times lesser) than the existing state-of-the-art (SOTA) SRCNN method. The proposed DnSR provides superior reconstruction accuracy with less artifacts when compared to other SOTA methods. The performance of the DnSR is assessed qualitatively and quantitatively using different image quality metrics.
Single Image Dehazing Using Multipath Networks Based on Chain of U-Nets

Single-image haze removal is a necessary pre-processing step for several computer vision applications and may be a challenging task. The images captured outdoors are seriously degraded in color and contrast due to extreme weather conditions like fog, haze, snow and rain. This poor visual quality can inhibit the performance, which is intended to operate on clear conditions like object detection. In recent years, single-image dehazing, which recovers clean images from haze-affected images, has gained popularity. This paper proposes a unique and efficient multipath supported U-Net called Ladder-Net to directly restore a hazy free image from a hazy image. Utilizing only a single set of encoder and decoder branches within the U-Net structure, the proposed Ladder-Net has numerous encoder and decoder branches and dense skip connections between each pair of adjoining encoder and decoder branches at every level. The encoder and decoder are designed to acquire the contextual information in the input images and to evaluate the role of each input to the resulting hazy-free images, respectively, without relying on the atmospheric scattering model. The proposed model is trained and tested using RESIDE benchmark dataset. Experiments conducted on hazy images and a synthetic dataset obtained naturally show that the proposed method proves effective in achieving superior performance over previous methods. The experimental results convey that the proposed architecture achieves exceptional performance over the prevailing methods concerning the qualitative and the quantitative performance, PSNR and SSIM.
Improvising the CNN Feature Maps Through Integration of Channel Attention for Handwritten Text Recognition

Convolutional Neural Network (CNN) based encoder and Recurrent Neural Network (RNN) based decoder architectures are widely used in the design of Handwritten Text Recognition (HTR) systems. Effective encoder representation plays a vital role in improving the performance of HTR systems. Squeeze and Excitation Networks, used in the context of image classification, object detection and scene classification, capture global inter-channel dependencies. ECA-Net learns channel attention via local Cross Channel Interaction (CCI). The current work proposes an encoder-decoder architecture for HTR which combines the benefits of local and global cross-channel attention for effective encoder representation. Experimental results on the IAM dataset show that there is an 8.98%, 3.24% reduction in Character Error Rate (CER) and an 8.98%, 3.45% reduction in Word Error Rate (WER) when the proposed module is applied to the state-of-the-art HTR Flor model and Puigcerver model respectively. The proposed work also presents a detailed error analysis at the character level on the IAM dataset.