Reviewerly/exHarmony huggingface dataset:
Light Field Reconstruction in Angular Domain with Multi-Models Fusion Through Representation Alternate Convolution

To alleviate the spatial-angular trade-off in light fields (LFs), LF super-resolution (SR) has been studied. Most of current methods only concern limited relations in LFs, which leads to the insufficient exploitation of the multi-dimensional information. To address this issue, we present a multi-models framework for LFSR in this paper. Models which embody LF from distinct aspects are integrated to constitute the framework. Therefore, the number and the arrangement of these models and the depth of each model determine the frameworkâ€™s performance, we make the comprehensive analysis on these factors to reach the best result. However, models in the framework are isolated to each other as the unique inputs are required. To tackle this issue, the representation alternate convolution (RAC) is introduced. As the fusion is conducted through the RAC, the multi-dimensional information in LFs is fully exploited. Experimental results demonstrate that our method achieves superior performance against state-of-the-arts techniques.
Sort2 Algorithm: A New Sorting Algorithm

In the Computer Field, data arranging is one of the important task. This paper describes about a new data arranging algorithm, “Sort2 Algorithm” which is compared with bubble sort, selection sort, and insertion sort algorithms. Here input is divided into two parts. One part has odd position numbers and other part number has even position numbers. The odd position numbers are arranged by simple comparisons. Then combine even position numbers. Here First-Last logic is applied to get the final sorted list.
Code and Data Synthesis for Genetic Improvement in Emergent Software Systems

Emergent software systems are assembled from a collection of small code blocks, where some of those blocks have alternative implementation variants; they optimise at run-time by learning which compositions of alternative blocks best suit each deployment environment encountered. In this paper we study the automated synthesis of new implementation variants for a running system using genetic improvement (GI) . Typical GI approaches, however, rely on large amounts of data for accurate training and large code bases from which to source genetic material. In emergent systems we have neither asset, with sparsely sampled runtime data and small code volumes in each building block. We therefore examine two approaches to more effective GI under these constraints: the synthesis of data from sparse samples to construct statistically representative larger training corpora; and the synthesis of code to counter the relative lack of genetic material in our starting population members. Our results demonstrate that a mixture of synthesised and existing code is a viable optimisation strategy, and that phases of increased synthesis can make GI more robust to deleterious mutations. On synthesised data, we find that we can produce equivalent optimisation compared to GI methods using larger data sets, and that this optimisation can produce both useful specialists and generalists.
Development of an Electrooculogram (EOG) and Surface Electromyogram (sEMG)-Based Human Computer Interface (HCI) Using a Bone Conduction Headphone Integrated Bio-Signal Acquisition System

Human–computer interface (HCI) methods based on the electrooculogram (EOG) signals generated from eye movement have been continuously studied because they can transmit the commands to a computer or machine without using both arms. However, usability and appearance are the big obstacles to practical applications since conventional EOG-based HCI methods require skin electrodes outside the eye near the lateral and medial canthus. To solve these problems, in this paper, we report development of an HCI method that can simultaneously acquire EOG and surface-electromyogram (sEMG) signals through electrodes integrated into bone conduction headphones and transmit the commands through the horizontal eye movements and various biting movements. The developed system can classify the position of the eyes by dividing the 80-degree range (from −40 degrees to the left to +40 degrees to the right) into 20-degree sections and can also recognize the three biting movements based on the bio-signals obtained from the three electrodes, so a total of 11 commands can be delivered to a computer or machine. The experimental results showed the interface has accuracy of 92.04% and 96.10% for EOG signal-based commands and sEMG signal-based commands, respectively. As for the results of virtual keyboard interface application, the accuracy was 97.19%, the precision was 90.51%, and the typing speed was 5.75–18.97 letters/min. The proposed interface system can be applied to various HCI and HMI fields as well as virtual keyboard applications.
Approximation of optimal control surfaces for 2 × 2 skew-symmetric evolutionary game dynamics

In this paper we study the problem of approximating the general solution to an optimal control problem whose dynamics arise from a 2 × 2 skew-symmetric evolutionary game with arbitrary initial condition.Our approach uses a Fourier approximation method and generalizes prior work in the use of orthogonal function approximation for optimal control.At the same time we cast the fitting problem in the context of a non-standard feedforward neural network and derive the back-propagation operator in this context.An example of the efficacy of this approach is provided and generalizations are discussed.
XRecSys: A framework for path reasoning quality in explainable recommendation

There is increasing evidence that recommendations accompanied by explanations positively impact on businesses in terms of trust, guidance, and persuasion. This advance has been made possible by traditional models representing user–product interactions augmented with external knowledge modeled as knowledge graphs. However, these models produce textual explanations on top of reasoning paths extracted from the knowledge graph without considering relevant properties of the path entities. In this paper, we present XRecSys, a Python framework for the optimization of the reasoning path selection process according to properties deemed relevant by users (e.g., time relevance of the linking interaction or popularity of the entity linked to the explanation). Our framework leads to a higher reasoning path quality in terms of the considered properties and, consequently, textual explanations more relevant for the users.
Evaluating the use of Google Maps as navigation application by identifying hazards and assessing risks using HIRA matrix

Abstract Google Maps as a navigation application provides route information with the shortest route or fastest travel time, but this technology has a weakness as it does not consider the suitability of road condition with the type of vehicles, therefore the choice of the routes does not accommodate traffic safety aspects. This research aims to evaluate the use of Google Maps as a navigation application by identifying hazards and assessing risks. The method used is based on an analysis using the HIRA (Hazard Identification and Risk Assessment) matrix to determine the level of risk thus the step of mitigation can be executed. The data source obtained was from the results of questionnaires distributed to 1,114 respondents. As the results of analysis, it was found that 11 hazards from the usage of Google Maps came from the driver’s aspect, Google Maps feature’s aspect, and road conditions aspect. Evaluation of risks came from the three sources of hazard classified into 2 risks in the intolerable risk category and 9 risks in the tolerable risk category. After the mitigation efforts were arranged to control the risk, all risks are expected to meet the acceptable risk level. To anticipate the road traffic accident due to the usage of Google Maps, the recommendation for this navigation application is to add more features to warn the users about the road characteristics in the form of voice notification or pop up if not disturb the driver while driving and providing a specific route for truck/heavy vehicle as now the Google Maps is only available for light vehicle.
“I definitely felt like I was there”: enacting empathy and negotiating a virtual reality Indian Residential School

We examine how 20 university students interacted with a virtual Residential School that was designed in partnership with a group of Indigenous Survivors of Canada’s Residential Schools system. We begin by offering a brief history of Canada’s Residential Schools system as well as the origins and ambitions of the Embodying Empathy project. We also reflect on the idea of empathy as it has been taken up both in the design and testing phases of the project and on how student participants enacted their empathy. We demonstrate how participants worked through their immersion in the virtual Residential School, selecting empathy frames that allowed them to grapple with its content and connect with the Survivor narratives presented therein.
CONTAIN4n6: a systematic evaluation of container artifacts

Abstract A container provides an environment where applications are packaged and run with the supporting libraries and dependencies. Due to scalability and efficient software deployment, the popularity of container technology has increased and its services are also available on cloud platforms. The container environment is prone to a variety of threats and vulnerabilities that lead to security breaches and attacks. Investigation is required to analyze the attack and the digital forensics processes have also been implemented in the container environment. In this paper, we present a systematic evaluation of container artifacts. An interface named CONTAIN4n6 is developed to collect data from container environment that extracts the data using introspection libraries, container file systems, and is also capable to trace the system call of running container. The functionality of system calls traces is implemented in an open source containerization software, i.e, Moby project. Container’s artifacts are associated with environmental information, log files, directories, link files, repositories, etc. Data collected from multiple sources are stored in a database and created a hash values to maintain the integrity of collected data. A case study of privilege escalation attacks has been demonstrated which is used to validate the data collection tool, called, CONTAIN4n6. Research challenges associated with security and forensic investigations on containerized applications are also presented.
Reform mode of sports training and competition organization based on data mining

Summary China has continuously strengthened its efforts in reforming the sports training and competition organization mode, issued a series of measures and made some innovations, but the current optimized policies and measures still have certain limitations. Therefore, this article first uses data mining technology to model students' sports training and competition mode, considering multi event competition, the scoring of the project itself, the uncertainty of the number of students in sports training and other factors. The results show that the average prediction accuracy of the time series data mining method applied in this article is 89.82%, which is 19.71% and 44.78% higher than that of ANN and traditional clustering methods, respectively. The average error rate of ARMA time series algorithm data mining is reduced by 21.95% and 18.89% compared with AR and Ma time series algorithm. In addition, compared with the convergence time of multidimensional sports data mining realized by AR algorithm and Ma algorithm, ARMA time series algorithm is shortened by 77.13% and 70.02%, respectively.
PARENTAL ROLES IN THE VOCABULARY DEVELOPMENT OF CHILDREN AGED 5-6 YEARS DURING THE PANDEMIC

This research describes how parental roles in developing a vocabulary for children aged 5-6 years during the pandemic at Aisyiyah Kindergarten 14 Ampang Padang. The type of this research is descriptive quantitative, and the sampling technique is total sampling. Analysis techniques are descriptive analysis data. The results showed that parental roles in vocabulary development are good, so parents play a role in 1. Children have already understood using cleanliness, washing, and cleaning vocabulary to get used to a clean lifestyle. 2. Children have already understood using comfortable, safe and peaceful vocabulary to create a comfortable environment. 3. As role models, children already understand using vocabulary to set examples and give instructions. 4. Accompanying children while learning, children have already understood using accompanied and guiding vocabulary. 5. Playing together, children have already understood using the vocabulary of collaboration, appreciation, and respect.
Towards causal physical error discovery in video analytics systems

Video analytics systems based on deep learning models are often opaque and brittle and require explanation systems to help users debug. Current model explanation system are very good at giving literal explanations of behavior in terms of pixel contributions but cannot integrate information about the physical or systems processes that might influence a prediction. This paper introduces the idea that a simple form of causal reasoning, called a regression discontinuity design, can be used to associate changes in multiple key performance indicators to physical real world phenomena to give users a more actionable set of video analytics explanations. We overview the system architecture and describe a vision of the impact that such a system might have.
Flexible and scalable annotation tool to develop scene understanding datasets

Recent progress in data-driven vision and language-based tasks demands developing training datasets enriched with multiple modalities representing human intelligence. The link between text and image data is one of the crucial modalities for developing AI models. The development process of such datasets in the video domain requires much effort from researchers and annotators (experts and non-experts). Researchers re-design annotation tools to extract knowledge from annotators to answer new research questions. The whole process repeats for each new question which is time-consuming. However, since the last decade, there has been little change in how the researchers and annotators interact with the annotation process. We revisit the annotation workflow and propose a concept of an adaptable and scalable annotation tool. The concept emphasizes its users' interactivity to make annotation process design seamless and efficient. Researchers can conveniently add newer modalities to or augment the extant datasets using the tool. The annotators can efficiently link free-form text to image objects. For conducting human-subject experiments on any scale, the tool supports the data collection for attaining group ground truth. We have conducted a case study using a prototype tool between two groups with the participation of 74 non-expert people. We find that the interactive linking of free-form text to image objects feels intuitive and evokes a thought process resulting in a high-quality annotation. The new design shows ≈ 35% improvement in the data annotation quality. On UX evaluation, we receive above-average positive feedback from 25 people regarding convenience, UI assistance, usability, and satisfaction.
A robust edge-guided and color-guided network for single image dehazing

Most current dehazing methods tend to cause color distortion and blurred edge details. In this work, we design a robust edge-guided and color-guided dehazing network (ECGDN) to mitigate these issues. ECGDN consists of a cross-aggregation dual U-Net (CADU), a color feature extractor (CFE), a prior attention module(PAM), and a prior fusion group (PFG). Specifically, the CADU can take advantage of the complementarity between edge and texture features in a cross-aggregation manner. The CFE focuses on Hue- and Saturation-channel features to solve color distortion under the constraint of color correction loss. Finally, the PAM and the PFG can handle different priors(i.e., edge, texture, color) with attention mechanism and fusion strategy, respectively, to guide the feature learning procedures. To demonstrate the effectiveness of the proposed method, we perform extensive experiments on synthetic and real-world hazy images. The experimental results show that our method outperforms existing state-of-the-art methods in terms of accuracy and robustness.
Progressive Face Super-Resolution Reconstruction Network Based on Relational Modeling

Aiming at the imprecise details of the reconstructed face image caused by the large scale and ignoring the relationship modeling between different pixels in the upsampling process of most existing face super-resolution reconstruction algorithm models, a new progressive face super-resolution reconstruction network based on relationship modeling is proposed. The network mainly includes a detail information generation module based on progressive upsampling and a detail information enhancement module based on relational modeling. The step-by-step upsampling detail information generation module realizes the step-by-step generation of the face image detail information through the step-by-step upsampling operation. The detail information enhancement module based on relational modeling which adopts a linear and nonlinear relational modeling method optimizes the channel-level and spatial feature-level modeling of the detail information of the face image, and combines with the progressive upsampling detail information to achieve accurate reconstruction. Finally, through the experimental verification, the effectiveness of the algorithm proposed in this paper is proved.
Learning Smooth Motion Planning for Intelligent Aerial Transportation Vehicles by Stable Auxiliary Gradient

Deep Reinforcement Learning (DRL) has been widely attempted for solving real-time intelligent aerial transportation vehicle motion planning tasks recently. When interacting with environment, DRL-driven aerial vehicles inevitably switch the steering actions in high frequency during both exploration and execution phase, resulting in the well known flight trajectory oscillation issue, which makes flight dynamics unstable, and even endangers flight safety in serious cases. Unfortunately, there is hardly any literature about achieving flight trajectory smoothness in DRL-based motion planning. In view of this, we originally formalize the practical flight trajectory smoothen problem as a three-level Nested pArameterized Smooth Trajectory Optimization (NASTO) form. On this basis, a novel Stable Auxiliary Gradient (SAG) algorithm is proposed, which significantly smoothens the DRL-generated flight motions by constructing two independent optimization aspects: the major gradient, and the stable auxiliary gradient. Experimental result reveals that the proposed SAG algorithm outperforms baseline DRL-based intelligent aerial transportation vehicle motion planning algorithms in terms of both learning efficiency and flight motion smoothness.
Rethinking Bipartite Graph Matching in Realtime Multi-object Tracking

Data association is a crucial part for tracking-by-detection framework. Although many works about constructing the matching cost between trajectories and detections have been proposed in the community, few researchers pay attention to how to improve the efficiency of bipartite graph matching in realtime multi-object tracking. In this paper, we start with the optimal solution of integer linear programming, explore the best application of bipartite graph matching in tracking task and evaluate the rationality of cost matrix simultaneously. Frist, we analyze the defects of bipartite graph matching process in some multi-object tracking methods, and establish a criteria of similarity measure between trajectories and detections. Then we design two weight matrices for multi-object tracking by applying our criteria. Besides, a novel tracking process is proposed to handle visual-information-free scenario. Our method improves the accuracy of the graph-matching-based approach at very fast running speed (3000+ FPS). Comprehensive experiments performed on MOT benchmarks demonstrate that the proposed approach achieves the state-of-the-art performance in methods without visual information. Moreover, the efficient matching process can also be assembled on approaches with appearance information to replace cascade matching.
A Study of Mongolian Emotion Classification Incorporating Emojis

A Mongolian emotion classification algorithm incorporating emojis is proposed to address the problems of small Mongolian emotion classification corpus, poor classification results, and underutilization of emoji emotion features. Firstly, we extract Mongolian text data from the corpus, vectorize it using FastText algorithm and further learn the Mongolian text features. Secondly, emojis data are extracted from the corpus, vectorized and trained in GRU network to fully learn the emotion features of emoji. Then the attention mechanism is used to adjust the attention dynamics of text and emoji features in the model. Finally, the sentiment features of text and emoji are classified with softmax layer for sentiment classification. The experimental results show that the Mongolian sentiment classification algorithm with fused emojis outperforms FastText, Word2vec&#x005F;BiLSTM and Glove &#x005F; BiLSTM sentiment classification algorithms in terms of precision, recall, F1 value and accuracy. The results show the effectiveness of the proposed method and provide a reference for Mongolian sentiment analysis and opinion prediction.
Label-Aware Recurrent Reading for Multi-Label Classification

Multi-label classification (MLC) is an essential branch of natural language processing where a given instance may be associated with multiple labels. Recently, neural network approaches invested considerable dependency between labels and the instance, achieving state-of-the-art performance. However, the existing methods ignore the hidden correlations between each document&#x0027;s semantic information and labels. In this paper, inspired by the cognitive process of human reading, we propose a Label-Aware Recurrent Reading (LARD) network based on neuroscience. LARD modeled the MLC problem as a decision-making process of recurrent reading and constructs label-aware document representation according to the top-down mechanism of neuroscience. The model outputs the prediction of all labels after each reading, and in the process of recurrent reading, the prediction accuracy is improved. Besides, the attention mechanism is applied to make the weight of words dynamically adjust according to the topdown classification prediction information, taking into account the different contributions of words to labels. Experiments show that our model has better performance than the existing models.