Reviewerly/exHarmony huggingface dataset:
Questionnaire Test of Safety Psychological Evaluation for Engineering Blaster

Based on the systematic analysis of the evaluation index system of the psychological quality of engineering blaster, a set of safety psychological test questionnaire is developed. The questionnaire meets the design requirements after being corrected by pretest and retest techniques. Through the systematic research of VB computer programming theory and questionnaire testing principle, a computer system of security psychology questionnaire testing is developed. The popularization and application of this system can play an important supporting role in the prevention of engineering blasting accidents.
On Comparative Analysis of Rule-Based Cognitive Architectures

We consider two cognitive architectures which are designed to proceed goal-directed behavior. One of them, Theory of Functional Systems, is developed on the basis of the eponymous biological theory. The other, Non-Axiomatic Reasoning System or NARS, utilizes the formal logic apparatus and weighted probabilistic choice to proceed its inference. The architectures were scrutinized and decomposed and a number of conclusions have been drawn about their advantages and disadvantages regarding the goal-directed behavior processing.
Combined Coverage, Attention and Pointer Networks for Improving Slot Filling in Spoken Language Understanding

Sequence to sequence (Seq2Seq) model together with pointer network (Ptr-Net) has recently show promising results in slot filling task, in the situation where only sentence-level annotations are available, while the model's prediction contains repetition of slot values. In this paper, we add a coverage mechanism to alleviate issues of repeating prediction in slot filling task. We use a coverage vector to record attention history, and then add to the computation of attention, which can force model to consider more about un-predicted slot values. Experiments show that the proposed model significantly improves slot value prediction F1 with 8.5% relative improvement compare to the baseline models on benchmark DSTC2 (Dialog State Tracking Challenge 2) datasets.
Early Detection of Activity in Untrimmed Videos Using Dense Optical Flow and Deep Neural Network

Computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a “seeing” machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities” initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes etc.These lead to the requirement of high power computing devices for recognition, which is considerably costly.In this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively.
Application of Machine Learning In Hematological Diagnosis

Immediate and precise clinical conclusion is fundamental for viable treatment of sicknesses. It utilizes AI calculations and dependent on the consequences of research centre blood tests, we have constructed two models anticipating hematologic sickness. One prescient model uses all accessible boundaries for blood tests and another utilized just a diminished set that is typically estimated in understanding confirmation. The two sorts yield positive results, securing 0.88 and 0.86 judicious data from an overview of five no doubt infections and 0.59 and 0.57 while pondering only the most likely disease. Models it was not altogether extraordinary, showing that the diminished arrangement of boundaries can address the relating “Fingerprints” of the infection. This data upgrades the utilization of the model to be used by broad experts and it shows that the consequences of a blood test contain more data than specialists normally do. A clinical preliminary has shown, the precision of our theoretical models was reliable with hematology treatment specialists. Our examination rushes to show that the learning model is a blood-based judicious model tests alone can be used satisfactorily to expect Hematological sicknesses. This impact can likewise be opened exceptional freedoms for clinical analysis.
Learning Intra-group Cooperation in Multi-agent Systems

Reinforcement learning is one of the algorithms used in multi-agent systems to promote agent cooperation. However, most current multi-agent reinforcement learning algorithms improve the communication capabilities of agents for cooperation, but the overall communication is costly and even harmful due to bandwidth limitations. In addition, de-centralized execution cannot generate joint actions, which is not conducive to cooperation. Therefore, we proposed the Hierarchical Group Cooperation Network (HGCN). Advanced strategy, Group Network (GroNet), learns to group all agents based on their state rather than their location. The Low-level strategy, Group Cooperation Network (GCoNet), is a method of centralized training and centralized execution within a group, which effectively promotes agent collaboration. Finally, we validated our method in various experiments.
Evolution of methods of studying reading as a sociocultural practice

We examine the evolution of reading research methods as a sociocultural practice and show the trajectory of their development from the simplest sociological surveys to complex semio-sociopsychological methods. The main aspects of the study of reading practices related to the change of text carriers are highlighted. A classification of methods of studying reading is given. We substantiate the use of monitoring as a promising method of studying reading for forecasting the development of reading culture and the social situation of reading at various levels: national, regional, local.
Context-Aware Deep Learning Approach for Answering Questions

Training neural networks to read and comprehend natural language documents still poses a great challenge. In the recent past, large scale training and test data sets have been made available for testing machine reading systems based on their ability to answer unseen questions, given the context. In Question-Answering, the model generates answers from the given context for the questions (given as input. Various machine learning methods can be used to build such systems. For understanding natural language, the model should be able to convert the sentences or paragraphs into a representation that is internal to the model (understandable by it), to be able to generate valid answers. Valid answers are the ones which answer the question asked correctly.[1] This work attempts to design a deep learning model to read and comprehend the context provided and provide answers to the posed questions in natural language accurately with very little knowledge of language structure.
Examining Transfer Learning with Neural Network and Bidirectional Neural Network on Thermal Imaging for Deception Recognition

Deception is a common feature in our daily life, which can be recognised by thermal imaging. Previous research has attempted to identify deception with causality features extracted from thermal images using the extended Granger causality (eGC) method. As the eGC transformation is complicated, in this paper we explore whether a transfer learning model trained on the eGC-transformed thermal deception dataset can be applied to the original thermal data to recognise deception. We explore two feature selection methods, namely linear discriminant analysis (LDA) and t-distributed random neighborhood embedding (t-SNE), and three classifiers, including a support vector machine (SVM), a feed forward neural network (NN) and a bidirectional neural network (BDNN). We find that using features selected by LDA, a transfer learning NN is able to recognise deception with an accuracy of 91.7% and an F1 score of 0.92. We believe this study helps foster a deeper understanding of eGC and provides a foundation for building transfer learning models for deception recognition.
Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems. Due to the advancements of medical imaging in healthcare decision making, significant attention has been paid by the computer vision and deep learning (DL) models. At the same time, the detection and classification of colorectal cancer (CC) become essential to reduce the severity of the disease at an earlier stage. The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning (ML) to recognize the existence of diseases. In this aspect, this study focuses on the design of intelligent DL based CC detection and classification (IDL-CCDC) model for bioinformatics applications. The proposed IDL-CCDC technique aims to detect and classify different classes of CC. In addition, the IDL-CCDC technique involves fuzzy filtering technique for noise removal process. Moreover, water wave optimization (WWO) based EfficientNet model is employed for feature extraction process. Furthermore, chaotic glowworm swarm optimization (CGSO) based variational auto encoder (VAE) is applied for the classification of CC into benign or malignant. The design of WWO and CGSO algorithms helps to increase the overall classification accuracy. The performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.
Facial Attributes Translation with Attention Consistency and Self-calibration

The guidance of attention mask in the facial attributes translation tasks is based on the constraints of attribute categories in supervised learning, which are indirect and weak. Hence we propose a generative adversarial network in this paper, which the attention is constrained by a new loss function named attention consistency loss and guided by self-calibration convolution. Stronger constraints on attention improve the quality of facial attributes translation results and self-calibration convolution effectively improves attention coverage. Experiments demonstrate that with attention consistency loss, attention becomes smoother and more focused. In gender swap, hair color change, and age swap, we obtain 70% vote in favor attention consistency loss in the user study. Meanwhile self-calibration convolution improves the mean intersection over union of hair by more than 6%, and improves the mean intersection over union of hair by more than 11% with skip-connection.
COVID-19 and Social Vulnerabilities in Virginia Zip Code Tabulation Areas

Background and Methods: The study was performed to investigate the demographic factors impacting COVID-19 cases in Virginia Zip Code Tabulation Areas [VZCTA] in 5 of the Virginia's health planning regions (VHPRs).The data was collected from the Virginia Department of Health (VDH), spanning 5/15/2020-8/6/2020 (3 months) during the state's first COVID-19 peak.Kruskal-Wallis with Bonferroni correction was used to compare the distribution of COVID-19 cases and demographic factors between the VHPRs.Pearson correlation was employed to determine correlations between COVID-19 Cases and demographic factors in VZCTA and VHPRs.Results: Incidence of COVID-19 was the highest in the suburban Northern region and the lowest in the rural-predominant southwestern region, 1017 vs. 420 per 100K population (p <0.05) (Table 1 for details).Overall state-wide and in almost all the VHPRs, the VZCTA with predominantly Hispanics and Blacks ethnicity and high PCR testing rate were strongly associated with COVID-19 incidence in the univariate analyses.Interestingly, the younger age and household crowding (> 1.5 occupants/ room) were also associated with higher COVID-19 cases state-wide and in the Northern VHPR in the univariate analyses.In the multivariate analyses, Hispanic/Black ethnicity was strongly associated with a higher COVID-19 incidence, especially in the Northern region.Considering demographic factors alone, ethnicity, median household income, and household crowding were the most important predictor of the COVID-19 incidence in Virginia ZCTA communities in multivariate analyses with a few important regional differences.The multivariate model's R-value is 0.819 in the Northern region.Conclusions: The study highlights ethnicity as an essential social vulnerability in the contraction of COVID-19, which is also modified by the other factors in a regional manner accounting for the disparity in COVID-19 incidence across VHPRs.The information can guide critical public health decisions, e.g.vaccine distribution or implementation of critical health policies based on the social vulnerability in smaller population units -ZCTAs.As Virginia represents the average U.S. population concerning overall health and COVID-19 cases, the findings are likely to be generalizable to the U.S. population at large.
CSAGAN: Channel and Spatial Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation is to learn a mapping function from one image domain to another with unpaired samples, which is an important task of computer vision. However, current unsupervised image-to-image translation methods only perform well on certain datasets. To handle the limitation, this paper proposes a novel framework termed as CSAGAN which contains a new discriminator structure, a novel attention module, and a new normalized function. The discriminator is an attention-guided feature pyramid discriminator which makes use of low-level and high-level features to determine an image&#x2019;s realness. The new attention module integrating channel attention and spatial attention can guide generators focus on the most discriminative regions of feature maps to generate high-quality translated images. Moreover, our attention module embedded into generators requires less computation compared with other self-attention methods. In addition, the new normalized function helps generators limberly control the variation of shape, color, and texture through learning parameters. Experimental results indicate that our approach performs better than the current state-of-the-art methods.
A trust model for remittance over a blockchain

Remittance is a global service used to transfer money to family and friends. Throughout the world, many companies offer remittance service to families who depend on it for their livelihood. However, the current service of remittance, as provided by remittance companies across the world, assume that the recipient will use the remitted funds for the purpose intended by the sender, such as paying for school fees, medical procedures or groceries. Thus, the current model stops handling the remittance transaction as soon as the beneficiary collects the money. The current service of remittance model does not enforce correct usage of the remittance's funds. This paper proposes a new blockchain and smart contract based remittance trust model that fully considers all the steps, and the stakeholders, necessary to ensure correctness of a remittance transaction: sending the money, acquiring a service, paying a service or refunding the money.
Decision Support for Reading Styles of News Articles

News is an essential resource for understanding politics, economics, local communities, etc. Readers of news articles might browse the text quickly or read thoroughly. In this study, we analyze the contents of news articles and propose a method for encouraging the readers to read the text efficiently. We construct classifiers to infer the reading time required to complete reading the article and localness indicating the region coverage of its content. We utilize the distributed representation of words appearing in news articles, and apply the BERT (Bidirectional Encoder Representations from Transformers) to construct our classifiers. The classification results could be used to support users’ decisions on when and where to read the articles. To learn these classifiers, a dataset is automatically generated from historical access logs of a news site. We carried out experiments to validate the proposed method. The experimental results demonstrates that our classifiers are superior to conventional methods.
A hybrid kernel-based possibilistic fuzzy c-means clustering and cuckoo search algorithm

Possibilistic Fuzzy c-means (PFCM) algorithm is a robustness clustering algorithm that combines two algorithms, Fuzzy c-means (FCM) and Possibilistic c-means (PCM). It addresses the weakness of FCM in handling noise sensitivity and the weakness of PCM within the case of coincidence clusters. However, PFCM works inefficiently when the input data is nonlinear separable. To solve this problem, kernel methods have been introduced into possibilistic fuzzy c-means clustering (KPFCM). KPFCM can address noises or outliers data better than PFCM. But KPFCM suffers from a common drawback of clustering algorithms that may be trapped in local minimum which results in not good results. Recently, Cuckoo search (CS) based clustering has proved to achieve fascinating results. It can achieve the best global solution compared to most other metaheuristics. In this paper, we propose a hybrid method encompassing KPFCM and Cuckoo search algorithm to form the proposed KPFCM-CSA. The experimental results indicate that the proposed method outperformed various well-known recent clustering algorithms in terms of clustering quality.
What Pushes Self-supervised Image Representations Away?

Self-supervised models provide on par or superior results to their fully supervised competitors, yet it is unclear what information about images they contain. As a result, a visual probing framework was recently introduced to probe image representations for interesting visual features. While visual probing provides information about semantic knowledge, complexity, and consistency, it does not directly and exhaustively explain which visual features push self-supervised image representations away and which are neutral. In this paper, we fill this gap by proving a method that removes particular visual features from the image and analyzes how such a distortion influences the representation. Our key findings emphasize that discrepancies in features like lines and forms push self-supervised representations away more than brightness, color, shape, and especially texture changes. Our work is complementary to visual probing and provides more direct explanations of the mechanisms behind the contrastive loss.
Brainstorming Multi-Agent Reinforcement Learning for Multi-Vehicles Games

Every year, traffic accidents in the world cause economic losses equivalent to 600 billion dollars. And autonomous driving technology can improve driving safety and traffic efficiency. Therefore, unmanned vehicles are the development direction of future transportation, and decision-making control is an important issue that needs to be faced in the development of unmanned driving technology. The existing reinforcement learning algorithms are mostly limited to the research of single agent. Combining the reality of multi-vehicles driving on the road at the same time, in this work, we propose brainstorming multiagent reinforcement learning (BMARL) to guide the decision-making of multi-vehicles autonomous driving. The basic framework of BMARL is based on the actor-critic network structure. It adopts a centralized training and decentralized execution structure. A Critic network and two Actor networks are trained for each agent. This article uses the intelligent driving simulation environment commonly used in the field of artificial intelligence, and the open source racing simulator (TORCS) simulates the algorithm to verify the effectiveness of the above algorithm in the field of automatic driving decision-making control.
Free-Form Image Inpainting with Separable Gate Encoder-Decoder Network

Image inpainting refers to the process of reconstructing damaged areas of an image. For image inpainting, there are many means to generate not too bad inpainting results today. However, these methods either make the results look unrealistic or have complex structures and a large number of parameters. In order to solve the above problems, this paper designed a simple encoder-decoder network and introduced the region normalization technique. At the same time, a new separable gate convolution is proposed. The simple network architecture and separable gate convolution significantly reduce the number of network parameters. Moreover, the separable gate convolution can learn the mask (represents the missing area) from the feature map and update it automatically. After mask update, weights will be applied to each pixel of the feature map to alleviate the impact of invalid mask information on the completed result and improve the inpainting quality. Our method reduces 0.58M parameters. Moreover, our method improved the PSNR of Celeba and Paris Street View by 0.7–1.4 dB and 0.7–1.0 dB, respectively, in 10% to 60% damage cases. The corresponding SSIM has been increased 1.6 to 2.7 and 0.9 to 2.3%.
Fuzzy Logic Inference System for Quality of Experience Modeling for LTE Video Streaming: Case of Addis Ababa LTE Network

Nowadays, video streaming has become one of the most dominant services due to increasing interest in watching online television programs and video on demand. Providing this service requires a high speed and high capacity network infrastructure. In this work, we propose quality of experience (QoE) model using fuzzy logic inference system for video streaming services. The proposed model is used to measure the user perception from quality of service (QoS) parameters. The model is essential to replace conventional subjective measurement techniques that are costly and inefficient. In addition, the proposed model is helpful for business decision making, network planning, optimization and operational support activities. The result analysis shows that the stall frequency and the start delay play a major impact on user perception by 33 % and 25 %, respectively. Besides, validation of the results shows that the proposed model is accurate, consistent and linear compared to currently existing models.
Detection and Localization of Content deduplication using 64-bit architecture of SHA-256

Management of big data on cloud storage is a big concern now a days. Data is coming on the cloud from various resources like mobile phones, IOT devices, thin clients etc. This overclouded data sometime leads to server overflow. Handling of this exploded data is a great challenge, As according to EMC surves 75% of data on remote servers is duplicate data. Efficient methodology is required for detecting and localizing the duplicate contents for proper utilization of the storage space and to reduce the cost of buying extra storage. Deduplication can be done both at file level as well as block level and different methods are used for eliminating duplicate chunk. In this paper, we proposed a methodology for localization of content deduplication on variety of data such as text files, audio,images and videos. As size of image and video files are very large generation of chunk and assigning index value to the chunks can cause collision so rather than using already existing algorithms,we used 64-bit architecture of SHA-256 which is more scalable than the existing SHA-256.