Reviewerly/exHarmony huggingface dataset:
Cross-channel Fusion Image Dehazing Network with Feature Attention

In this paper, we propose a cross-channel fusion image dehazing network with feature attention (CFDN), which directly restores the final clear image from the hazy input. The network design is motivated by three strategies, namely cross-channel fusion, feature attention mechanism, and local residual learning. We show that they are effective for image dehazing problem. By separately extracting the features of the RGB three-channel components of the hazy image, the CFDN can effectively learn the different haze information in different color spaces. We develop two key components in our CFDN: internal and external channel feature attention network (Inter-CFAnet and Exter-CFAnet). Inter-CFAnet focuses on assigning more weight to important feature components; while Exter-CFAnet leverages the weighted fusion to adaptively merge cross-channel complementary features effectively. We also embed pixel feature attention network (PFAnet) to solve the problem of uneven haze distribution haze in the image. In addition, local residual learning allows the network to bypass less useful information through multiple skip connections. Experiments demonstrate that the proposed CFDN performs favorably against previous state-of-the-art image dehazing methods both quantitatively and qualitatively.
A New Blockchain Consensus Algorithm for Virtual Power Plants

As one of the new modes of distributed energy aggregation trading and coordination optimization in the energy Internet, Virtual Power Plant (VPP) can effectively improve the power market operation and service capabilities. However, problems such as low efficiency of energy trading, dispatching and opaque transaction information in virtual power plants have become increasingly prominent, which greatly restricts the application and development of virtual power plants. This article first analyzes the feasibility of blockchain technology in virtual power plant energy transactions; then in the designed virtual power plant energy transaction scenario, a homomorphism that takes into account the time overhead of encryption and decryption is adopted. The encrypted data collection scheme realizes the safe transmission of distributed energy production and consumption to the virtual power plant. Finally, a weight-based blockchain consensus optimization algorithm is proposed to realize fast and highly efficient energy trading in the virtual power plant. Experiments have verified the effectiveness of the proposed scheme. Under the premise of ensuring the security of the virtual power plant’s data source, the energy transaction throughput in the virtual power plant is greatly increased and the transaction time overhead is reduced.
Digital Distractions, Note-Taking, and Student Learning

This chapter examines the distractive potential of digital devices and summarizes existing scholarly work in this area. The chapter begins with a background on the overall problem of distractions caused by digital devices and how this problem has changed over time. This is followed by a detailed accounting of the digital distractions research, emphasizing the role of message relevance in this process, as well as discussion of research that has examined the interplay between note-taking and digital distractions. The last major section summarizes scholarly work and additional sources that provide examples of how mobile devices, and technology more broadly, can be used in the classroom to help support student learning.
Sentiment Analysis in Teachers Performance Rating Using Naïve Bayes Algorithm

Sentiment analysis has been extensively researched for the purpose of analyzing qualitative data using a computation technique. However, there are only a few research papers that focus on analyzing sentiments in terms of teachers’ evaluation. Analyzing comments on teacher evaluations can lead to understanding more of what faculty development programs can be provided to improve teachers’ academic performance. Thus, this study presents an opinion-mining system utilizing the Bayesian technique of the Naïve Bayes algorithm. The descriptive research method was used in this study, with a questionnaire serving as the instrument for testing the acceptability of the application. One hundred (100) evaluators were surveyed for the teachers’ evaluations. The application performance attributes are defined using the functionality, usability, reliability, performance and security (FURPS) model. The mean formula was used to analyze the data. The usability and security were evaluated as perfectly acceptable, with a weighted mean of 4.64, and 4.56, respectively. Furthermore, the functionality, reliability, and performance were evaluated at acceptable evaluation ratings with a weighted mean of 4.29, 4.02, and 4.11. The overall quality of the system was given an acceptable rating with a weighted mean of 4.32, indicating that the application provided and managed ratings and comments on individual teachers’ performance.
Community Intelligent Elderly Care Service Platform Based on Internet of Things

With the continuous improvement of living standards and medical technology, the proportion of the elderly population in China is increasing, and the trend of population aging is becoming increasingly obvious. At present, China is in a period of accelerated urbanization development. With the aging of the population, the issue of providing for the aged has become the focus of attention from all walks of life. With the continuous development of information technology, smart old-age care combines traditional old-age care services with modern science and technology to plan all kinds of old-age care services and innovate old-age care modes and provides more extensive, efficient and professional old-age care services for the elderly by means of information technology. The intelligent pension information service platform is also designed to meet the needs of the elderly in daily life entertainment, health care and other multi-level pension services. This project obtains the corresponding information through the sensor technology of Internet of things and builds a huge “community smart pension service platform” for the elderly through the combination of multiple technologies.
Ensemble Feature Selection: Are Stability Metrics a Proxy or a Complement to Predictive Performance?

Proper identification of biomarkers, used in the development of drugs, is critical as has been shown with the race to find a vaccine for the Covid19. Gene-expression based marker discovery often entails that feature selection be performed. However, a plethora of feature selection methods exist and they do not result in the selection of the same feature subsets for the same dataset. Often, users are faced with having to select which subset to use. To help in this conundrum, several approaches have been proposed to guide feature subset selection, among which the use of ensemble methods (i.e., combining subsets from multiple methods) has gained attention recently. In an ensemble approach there are two issues that deserve attention: the stability of the feature subsets being combined and the classification performance of the combined feature subsets. Hence the interest in exploring how stability and performance relate, which is the central topic investigated in this paper. First 5/6 different feature selection methods are used to create feature subsets for 3 different transcriptomics datasets. Then, the stability and performance of these feature subsets under a given merging strategy are computed using 5 stability metrics and 3 performance metrics for 3 different classifiers. Our results suggest that performance and stability criteria are complementary and conflicting and that both must be considered to decide on the final selected feature subsets. We use two reference metrics to illustrate such selection.
Medical image processing: an insight to convolutional neural networks

This chapter introduces the convolutional neural networks (CNNs, or ConvNets for short), deep neural networks with special image processing applications. These networks significantly improve the processing of information by providing several new concepts, and are still a great option in many problems involving continuously sampled data in a rectangular grid: audio signals (1D), images (2D), volumetric MRI data (3D), and more. A ConvNet is an old method developed in the 1980s and 1990s. It was relatively long forgotten because it was impractical in real applications of complex images. Since 2012 this method has received considerable attention again and has been used in most computer vision studies. Therefore, it has been evolving rapidly. After introducing the basics of convolutional networks, this chapter analyzes three applications of convolutional neural networks in bioinformatics, namely coronavirus disease (COVID-19) diagnosis, breast cancer predicting, and diabetic retinopathy detection.
Operational Task Decomposition Method based on Extended HTN Planning

As an important premise of operational task planning, the decomposition of operational tasks is of great significance to improve the combat effect and ensure the quality of mission completion. In view of the characteristics of strong randomness and complexity, the characteristics of operational task correlation are analyzed systematically, and the operational task constraint structure is defined, which is composed of task time constraint, logic constraint and function constraint. The measurement method of operational task correlation and task granularity is given. In this paper, domain knowledge is effectively introduced into the process of task decomposition, the method of operational task decomposition based on extended hierarchical task network (HTN) is proposed, and the specific process of task decomposition is given. The method is verified by an example. The results show that the method based on extended HTN is practical and can solve the problem of decomposition optimization of complex combat tasks effectively. The proposed decomposition scheme and improvement suggestions are operable and have certain practical application value.
Language Analysis and the Role of Facebook Hashtags in Pakistan

Over the past few years, online hashtags have gained tremendous fame mainly because of two reasons: first, as digital information trackers and second, as short and spontaneous communication tags. The present study aims to explore language factors in three types of Facebook hashtags, that is, long, short, and multiple mixed. It further investigates each factor’s role in online communication by performing an in-depth examination through activities and close-ended questions. First, it identifies factors through the writing activities where participants wrote different hashtags with their intention behind each hashtag. It follows interpretation activities in which other participants wrote their interpretations about the already written hashtags. Further, a 5-point Likert scale questionnaire was developed to note participants’ opinions about hashtags which were evaluated by the SPSS factor analysis test. Results of the study are divided into three sections and showed that lack of adequate vocabulary and lower casing is an important factor affecting digital communication in Pakistan. It is also suggested that teachers should play their part to improve informal writing and digital literacy in Pakistan.
Neural Network Architecture Based on Wavelet Transform for Electro-mobility Detection

This article discusses the development of a neural network model for long-distance objects and a small-scale of computing. The proposed architecture of wavelet object detection is based on merge other frequency domains of the image into reference. The Experiments is applied for long-distance scenes to provide higher accuracy assume as lower computational complexity. Feasibility of the proposed wavelet neural networks has been evaluated and verified on vehicle detection. The architecture of wavelet object detection promoted low latency and high frame per second (fps) to porting on the NVIDIA JETSON AGX XAVIER evaluation board for artificial intelligence applications.
Accelerating Lifelong Reinforcement Learning via Reshaping Rewards

The reinforcement learning (RL) problem is typically formalized as the Markov Decision Process (MDP), where an agent interacts with the environment to maximize the long-term expected reward. As an important branch of RL, Lifelong RL requires the agent to consecutively solve a series of tasks modeled as MDPs, each of which is drawn from some distribution. A crucial issue in Lifelong RL is how best to utilize the knowledge from the previous tasks for improving the performance in the current task. As a pioneering work in this field, MaxQInit takes the maximum over action-values learned from previous tasks’ environmental rewards as the initial action-value of the current task. In this way, MaxQInit improves the initial performance in the current task and reduces the sample complexity of learning. However, the rewards obtained in the learning process are usually delayed and sparse, dramatically decreasing the learning efficiency. In this paper, we propose a new method, Shaping Rewards for Lifelong RL (SR-LLRL), to speed up the lifelong learning process by shaping timely and informative rewards for each task. Critically, we construct the Lifetime Reward Shaping (LRS) function based on the knowledge of optimal trajectories collected in previous tasks, providing additional reward information for the current task. Compared with MaxQInit, our method exhibits higher learning efficiency and superior performance in Lifelong RL experiments.
OSIP: Tightened Bound Propagation for the Verification of ReLU Neural Networks

Abstraction-based methods for the verification of ReLU-based neural networks suffer from rapid degradation in their effectiveness as the neural network’s depth increases. We propose OSIP, an abstraction method based on symbolic interval propagation in which the choice of the ReLU relaxation at each node is determined via optimisation. We present an implementation of OSIP on top of Venus, a publicly available toolkit for complete verification of neural networks. In the experiments reported, OSIP calculated bounds that were tighter than the state-of-the-art on ReLU networks from the first competition for neural network verification. As a case study we apply the method for the verification of VGG16, a deep, high-dimensional, 300,000 node-strong model used for object classification in autonomous vehicles against local robustness properties. We demonstrate that OSIP could verify the correctness of the model against perturbations that are larger than what can be analysed with the present state-of-the-art.
Parsing Using a Grammar of Word Association Vectors

This paper was first drafted in 2001 as a formalization of the system described in U.S. patent U.S. 7,392,174. It describes a system for implementing a parser based on a kind of cross-product over vectors of contextually similar words. It is being published now in response to nascent interest in vector combination models of syntax and semantics. The method used aggressive substitution of contextually similar words and word groups to enable product vectors to stay in the same space as their operands and make entire sentences comparable syntactically, and potentially semantically. The vectors generated had sufficient representational strength to generate parse trees at least comparable with contemporary symbolic parsers.
Toward Learning Robust Detectors from Imbalanced Datasets Leveraging Weighted Adversarial Training

Machine learning is an attractive technique in the security field to automate anomaly detection and to detect unknown threats. Most of the real-world training samples to learn with neural networks are imbalanced from the viewpoint of their distribution and importance priority on each class. In particular, datasets for security problems are imbalanced in most cases. Learning from an imbalanced dataset may cause the degradation of a classifier's performance, especially in the minority but important classes. We thus propose a new robust learning method for imbalanced datasets using adversarial training. Our proposed method leverages adversarial training to expand classification areas of minority classes. Specifically, we design weighted adversarial training, where the perturbation size of adversarial examples is weighted according to the number of samples in each class. We conducted experiments with real-world datasets, and the results demonstrate that our proposed method increases classification performance in both binary and multiclass classifications. Namely, our proposed method makes classifiers more robust even if the dataset is imbalanced, which is useful for us to apply machine learning to security tasks.
Spatial-Frequency Hevc Multiple Description Video Coding with Adaptive Perceptual Redundancy Allocation

Multiple description coding (MDC) approaches improve the error-resilient performance of video transmission by introducing redundancy. The existing multiple description video coding (MDVC) schemes are rarely designed for particular coding structure of HEVC, and the characteristics of the human visual system (HVS) are seldom considered. In this paper, a spatial-frequency MDVC with adaptive perceptual redundancy allocation framework, named SF-PMDVC, is proposed for HEVC. For descriptions generation, after polyphase down-sampling in spatial domain, a transformation based on integer DCTÂ is expended to adapt to the flexible coding unit partitioning process in HEVC. To further improve the performance of MDVC, an adaptive perceptual redundancy allocation strategy based on visual saliency is proposed, which improves the coding efficiency adapting to the visual perception. Experimental results show that the proposed scheme improves the error resiliency of HEVC by achieving superior objective and subjective reconstructed video quality as compared to the state-of-the-art MDVC methods for HEVC.
An Efficient Watermarking Based Matrix Manipulation and Optimization Based Cryptographic Method for Privacy Preservation in Biomedical Data

The biomedical processing of images is an important aspect of the modern medicine field and has an immense influence on the modern world. Automatic device assisted systems are immensely useful in order to diagnose biomedical images easily, accurately and effectively. Remote health care systems allow medical professionals and patients to work from different locations. In addition, expert advice on a patient can be received within a prescribed period of time from a specialist in a foreign country or in a remote area. Digital biomedical images must be transmitted over the network in remote healthcare systems. But the delivery of the biomedical goods entails many security challenges. Patient privacy must be protected by ensuring that images are secure from unwanted access. Furthermore, it must be effectively maintained so that nothing will affect the content of biomedical images. In certain instances, data manipulation can yield dramatic effects. A biomedical image safety method was suggested in this work. The suggested method will initially be used to construct a binary pixel encoding matrix and then to adjust matrix with the use of decimation mutation DNA watermarking principle. Afterwards to defend the sub keys couple privacy which was considered over the logical uplift utilization of tent maps and purpose. As acknowledged by chaotic (C-function) development, the security was investigated similar to transmission in addition to uncertainty. Depending on the preliminary circumstances, various numbers of random were generated intended for every map as of chaotic maps. An algorithm of Multi scale grasshopper optimization resource with correlation coefficient fitness function and PSNR was projected for choosing the optimal public key and secret key of system over random numbers. For choosing the validation process of optimization is to formulate novel model more relative stable to the conventional approach. In conclusion, the considered suggested findings were contrasted with current approaches protection that was appear to be successful extremely.
Automatic Information Extraction from Electronic Documents Using Machine Learning

The digital processing of electronic documents is widely exploited across many domains to improve the efficiency of information extraction. However, paper documents are still largely being used in practice. In order to process such documents, a manual procedure is used to inspect them and extract the values of interest. As this task is monotonous and time consuming, it is prone to introduce human errors during the process. In this paper, we present an efficient and robust system that automates the aforementioned task by using a combination of machine learning techniques: optical character recognition, object detection and image processing techniques. This not only speeds up the process but also improves the accuracy of extracted information compared to a manual procedure.
It’s Your Turn! Supporting Social Change Through Networked Learning and Game Playing

Fast Food da Politica (FFDP) is a learning network designed to promote social action in Brazil. In this chapter, we explore the role of key structural elements in FFDP (the tools, tasks and social organization of the network) and highlight their connections to valued learning activity. FFDP cleverly (re)purposes popular (board) games as pedagogical tools, which are then customized for the teaching and learning of a complex topic—the mechanisms and functioning of Brazilian political structures. FFDP has taken their political games to game-playing sessions across the country in varied venues, including schools, government organizations, and to open sessions at market-street events and public protests. FFDP games are shared as open learning resources through blueprints and manuals that explain the many ways a game can be played. These are easily downloadable and shared for reuse through the FFDP website. In this chapter, we draw on the Activity-Centred Analysis and Design (ACAD) Wireframe to explore the alignment of the networks’ elements at the micro and meso levels, focusing on the ways FFDP combines a strategic educational vision deeply grounded on action for social change, with a curriculum that emphasizes gaming elements and promotes the physicality of materials in learning.
A Comparative Study of Punctuation Marks between China and Taiwan ; The process of establishment and Differentiation of Punctuation Marks

This research aimed to compare punctuation marks frequently used in China and Taiwan, exploring the historical establishment process and differences of the current punctuation rules on both sides. In a large sense, current punctuation rules in China are based on “General Rules for Punctuation” (2011). However, in Taiwan, “The Revised Punctuation Handbook” (2008) is currently applied. They are basically rooted in the “New Punctuation” of 1920. Since 1949, they have been slightly different in usage. In particular, as rules have been revised over time, the punctuation marks on both sides showed a plethora of dissimilarities between both sides. Accordingly, this research fully paid attention to the history of Chinese punctuation marks, then briefly compared both current punctuation rules with reference to contemporary use. As a result, a few differences were found: The Chinese rules are more systemic and detailed than those in Taiwan. For instance, Taiwan's rules have no table of contents, definitions and classifications of punctuation marks. In addition, there were no specific usage and examples in Taiwan. It can be assumed that the policy and regulation of both governments have some impact on them. Simply put, Chinese learners and users should get aware of accurate Chinese punctuation marks frequently used on both sides. Furthermore, the higher similarities of the punctuation rules and usage could pave the way for more bilateral culture exchanges and Chinese learners.