Reviewerly/exHarmony huggingface dataset:
State Transition in Multi-agent Epistemic Domains Using Answer Set Programming

In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using Answer Set Programming (ASP). The transition function computes the next state upon an occurrence of a single action. Thus it can be used as a module in epistemic planners. Our transition function incorporates ontic, sensing and announcement actions and allows for arbitrary nested belief formulae and general common knowledge. A novel feature of our model is that upon an action occurrence, an observing agent corrects his (possibly wrong) initial beliefs about action precondition and his observability. By examples, we show that this step is necessary for robust state transition. We establish some properties of our state transition function regarding its soundness in updating beliefs of agents consistent with their observability.
A Definition of Sceptical Semantics in the Constellations Approach

We propose a different way to compute sceptical semantics in the constellations approach: we define the grounded, ideal, and eager extension of a Probabilistic Argumentation Framework by merging the subsets with the maximal probability of complete, preferred, semi-stable extensions respectively. Differently from the original work (i.e., [19]), the extension we propose is unique, as the principle of scepticism usually demands. This definition maintains some well-known properties, as set-inclusion among the three semantics. Moreover, we advance a quantitative relaxation of these semantics with the purpose to mitigate scepticism in case the result corresponds to empty-set, which is not very informative.
Enumeration of Minimal Models and MUSes in WASP

Several AI problems can be conveniently modelled in ASP, and many of them require to enumerate solutions characterized by an optimality property that can be expressed in terms of subset-minimality with respect to some objective atoms. In this context, solutions are often either (i) answer sets or (ii) sets of atoms that enforce the absence of answer sets on the ASP program at hand—such sets are referred to as minimal unsatisfiable subsets (MUSes). In both cases, the required enumeration task is currently not supported by state-of-the-art ASP solvers.
Human Eyes Move to the Target Earlier When Performing an Aiming Task with Increasing Difficulties

We examined whether human operators move their eyes earlier to a target before hands when the level of task difficulty increases. We hypothesized that participants would perform less proactive eye movements in the difficult task than in the easy one, as they would need to focus more on their current hand movements. Sixteen university students were recruited to perform the aiming and touching task reciprocally on three paired targets (circles) differing in sizes and distances, while had their eye movements tracked. The movement time, the early eye engagement time (EEET), and the number of eye adjustments were recorded. The EEET was defined as the time that a subject’s eyes fix on a target to the moment that the tool reaches out for it. The movement time increased as the index of difficulty (ID) value increased, echoing with the Fitts’ Law prediction. When aiming to a target with a higher ID, participants’ EEET was longer comparing to when reaching for a target with a lower ID. Participants reduced the movement speed to give themselves a longer time in searching visually for the target information before moving their hands. In contrast to our hypothesis, results suggested a proportional relationship between the task difficulty and the early eye engagement time. Participants also performed an increasing number of eye adjustments over the course of moving from the easy to the hard target. Future research is needed to examine eye hand coordination under the regulation of Fitts’ Law.
Developing a Web-Based Integrated Bayesian Decision Support System for Rangeland Management in a Semi-Arid Watershed

In this research, a Bayesian web-based Decision Support System (DSS) was designed and developed to assist various stakeholders in improving biophysical and socio-economic conditions in a stressed and fragile watershed by systematically presenting the watershed characteristics, introducing feasible solutions as well as choosing the best ones. The need to deliver a flexible DSS has been the motivation to look for a decision toolbox accommodating the concerns and priorities of a wide range of stakeholders as the potential users. This assists the users to make informed decisions based on quantifying outcomes. The DSS main components include a database management system, a model base management system, multi-criteria decision-making models, and a graphical user interface. This prototype DSS provides a brief introduction of a pilot watershed characterized with semi-arid conditions in the Mid-north of Iran and then presents the problem structure using the DPSIR approach. A set of vegetation-based solutions and their spatial distribution considering some scenario rules are provided to the DSS users. The various impacts of the proposed solutions are predicted using several models in the Bayesian network modeling context integrating qualitative and quantitative knowledge and data. The DSS developed in this study facilitates knowledge transfer and involvement of stakeholders including practitioners and local communities in the decision making process. This DSS has been designed into a web-based and open-source framework for easier and inclusive access of various users and is intended to get further applications. The DSS even can be tailored to fit a broader class of environmental problems.
Shearlet-Based Intensity Classification Loop Filter for Video Coding

The new Versatile Video Coding standard (VVC) exhibits a significantly higher coding efficiency in comparison to its predecessor High Efficiency Video Coding (HEVC). Since the finalization of VVC in July 2020 many new in-loop compression tools were suggested, especially with data-driven approaches. The question arises if non-data-driven in-loop filtering approaches can further increase coding efficiency beyond the capability of VVC. In this paper we approach the task of filtering images corrupted by quantization noise from the perspective of applied harmonic analysis, an area of applied mathematics focusing on the efficient representation, analysis and encoding of data. The shearlet-based intensity classification loop filter (SCLF) is a novel non-data-driven approach using an overcomplete and sparsifying transform, the shearlet transform. The basic idea is to achieve a signal-noise separation by applying a shearlet transform. Shearlets can identify important structures of natural images such as edges in the sparse transform domain. Each shearlet transform coefficient is classified into different classes. All coefficients with similar intensities are grouped into the same class, which eventually gives a partition of the set of all shearlet coefficients. This separates important information from noise and ensures an accurate reconstruction of original information by performing an inverse shearlet transform combined with Wiener filtering separately for each class. SCLF effectively removes compression artifacts and therefore restores subjective visual quality. Simulation results show that by adding SCLF to thein-loop stage yet further average bit rate reductions of up to 1.5% are achieved for JVET common test sequences.
Research on the Few-Shot Learning Based on Metrics

Deep learning has been rapidly developed and obtained great achievements with a dataintensive condition. However, sufficient datasets are not always available in practical application. In the absence of data, humans can still perform well in studying and recognizing new items while it becomes a hard task for the computer to learn and generate from a small dataset. Thus, researchers are increasingly interested in few-shot learning. The purpose of few-shot learning is to allow computers to carry out unknown tasks with a few examples. Recently, effective few-shot models have frequently been designed using transfer learning approaches, with the metric method being an important branch in transfer learning. This article reviews the metric methodologies for few-short learning, analyzing the development of the metric based few-shot learning in the following three categories: traditional metric methods, relation network based metric methods and graph based metric methods. Then it compares the effectiveness of those models on a representative dataset and illustrates the feature of each category. Finally, it discusses the potential future research fields.
Research Status of Autonomous Vehicle Path Planning

In recent years, many companies have been developing path planning algorithms and hardware for autonomous vehicles. This paper mainly introduces the latest research and applications of path planning algorithms and related hardware for autonomous vehicles. The introduced algorithms include improvements to Traditional Algorithms, Intelligent Optimization Algorithms, Reinforcement Learning Algorithms and Hybrid Algorithms, such as improving algorithm running speed, path planning ability, and environment learning speed. In terms of hardware research and application, due to the rise of sensor technology and 5G mobile communication technology, this paper mainly introduces the research and application status of multisensor, 5G mobile communication technology and lidar, including improving the measurement accuracy of sensors and the ability of sensors to identify surrounding areas.
An End-to-End Topic-Based Sentiment Analysis Framework from Twitter Using Feature Set Cumulation

Sentiment analysis from Web resources has been a well popular and cultivated area of research in recent years. However, relating a tweet’s sentiment to its particular topic has not been widely explored yet. In this paper, we introduce a framework based on the selection of a new feature set based on information gain and topic extraction from gram selection from the live streamed tweets. We maintain subject topics in tweets from streaming to analyzing them, throughout the entire experimentation pipeline. This helps us to gain insight into the exact sentimental reflection of that particular topic as well as the tweet itself. We build a corpus based on such topic-motivated tweets and subsequently retrieve them to train the system. Followingly, we emphasize selecting distinct features set specifically determined from the nature of the corpus. Here, two feature sets are compared. The first set is bag of words with N-Gram identification. The second set of features is part-of-speech linguistic annotation. We explore comprehensively the choice-dependent decision tree algorithm to train and validate such features. Finally, we develop a Pyplot and Tkinter-based GUI dashboard to provide real-time tweets search and view on any given topic. It introduces two pivotal attributes, a live graph elaborating the popularity and polarity of the search topic with given time.
Digital divide on cybersecurity awareness among the Malaysian higher learning institution students

Today, Cybersecurity is one of the most critical challenges for anyone who is connected to the Cyberspace. However, there are limited visibility and public awareness towards this issue. This paper review the fundamentals of the Cybersecurity awareness among the Higher Learning Institution students in Malaysia. The aim is in line with the Malaysia’s Cyber Defense initiatives on the National Cybersecurity policy. This study aims to bridge the gap of the levels of Cybersecurity awareness on Cybersecurity threats among the students in the Malaysian Higher learning Institutions. Awareness in this project conceived of as comprising knowledge, behavior, and attitudes, and its relationship among these elements that influences towards Cybersecurity awareness. Since there has not been any standardized Cybersecurity policy in Higher Learning Institutions, there is a need for this research to do a critical analysis of factors affecting students’ Cybersecurity awareness in Higher Learning Institutions. The findings of this study will be helpful for the students to improve their livelihood and people around them, higher learning institutions, academicians, future employers and indirectly will help the Cybersecurity Malaysia (CSM) department. In line with the Cybersecurity Malaysia, the study contributes to a betterment of society in Malaysia.
Empathy: How Can Technology Help Foster Its Increase Rather Than Decline in the 21st Century?

Empathy (a caring concern for the thoughts and feelings of others) has been a focus of studies at the national and international level since the late 1980s when Japan delayed introduction of microcomputers into elementary schools in part due to concerns that technology would turn young children into “non-thinking machines.” (Children & Computers in Schools, 1996). In the early 1990s, three years of study by the USA authors of the current chapter, in conjunction with colleagues in Japan and Mexico, found no tangible evidence of those specific concerns at the early primary school level, but did uncover evidence of a construct they labeled Computer Seclusion emerging at the middle school level (Computers in the Schools, 1996). Since those early studies, many other trends based on the original IEA items customized by Japan have emerged. Among these are the extensive gender gap regarding empathy that is now known to exist for young learners from the first grade through completion of secondary education. Another is that empathy is positively related to many other characteristics, such as self-concept, study habits, and creative tendencies. In this chapter findings from empathy data gathered from 5000 middle school students in 2009, compared with previous studies using the same item set and more recent findings in the literature, will be used to present implications for best practices regarding the current strong interest in empathy as a contributor to twenty-First century skills that appear to be declining among today’s youth in our society. Conjectured reasons for these declines are also included in the chapter, as well as coaching historical findings in the context of the newly-emerging importance of social emotional intelligence.
An Artificial Intelligence Approach to the Prediction of Global Solar Irradiation in India

Due to a high demand in solar power generation, the predictions and integration of solar energy sources have become an important area of research work. Previous researches have shown that the artificial intelligent-based method for approximation of global solar radiation provides better accuracy and efficiency. The objective of the present work is to find the global solar radiation using multilayer feedforward neural network (MLFF) for the year 2016 pertaining three cities Kolkata, Roorkee and Chennai based on five years (2011–2015) hourly radiation database collected from Photovoltaic Geographical Information System (PVGIS). Out of 1, 31,325 collected data, 70% were used for training, 15% were used for validation and the rest 15% were used for testing. This neural network model is developed by taking into consideration different parameters like month, day, time, latitude, longitude, elevation, slope, azimuth as inputs and global irradiance on the inclined plane as output. The obtained results indicate that the proposed technique predicts global solar irradiation with a high accuracy (98.74%) which proves the superiority of the applied method over the conventional one.
Feature Selection Based on Regularization of Sparsity Based Regression Models by Hesitant Fuzzy Correlation

In this chapter, inspired by the preceding works that show that the use of fuzzy sets and their extensions are useful in machine learning applications, we discuss the introduction of a regularization term to the sparsity based regression models. In particular, we describe how the Ridge, LASSO, and Elastic Net regression methods are revised to handle feature selection. This revision includes the addition of a Hesitant Fuzzy Correlation Matrix (HFCM) to the objective function of each of these models so that the result has a minimum redundancy in the selected features.
Tracking Untrained Objects Based On Optical Flow Approach

As Computer Vision is being used in various fields these days, the fields that require object tracking increase, and the performance starts to improve accordingly. In object tracking, State of the Art models are methodologies that obtain high accuracy by learning the objects to be tracked, whereas they have low accuracy for unlearned objects. To compensate for this, some models provide an environment for transfer learning on the object to be tracked, but there is rarely a large amount of preprocessed data that can be trained. In such a problem situation, it is possible to track an object universally and accurately using Lukas Kanade optical flow and additionally derive center coordinates.
A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

In recent years, a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.Scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years, mostly with a focus on things (a well-defined objects that has shape, orientations and size) with a less focus on stuff classes (amorphous regions that are unclear and lack a shape, size or other characteristics Stuff region describes many aspects of scene, like type, situation, environment of scene etc. and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time, accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes, overlapping objects, fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes Dataset.Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+ with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.
Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

This paper proposes an algorithm for scheduling Virtual Machines (VM) with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource utilization for VM deployment in cloud data centers is modeled by a combination of "Virtual Machine Scheduling using Bayes Theorem" algorithm (VMSBT) and Virtual Machine Migration (VMMIG) algorithm.It is shown that the overall data center's consumption of energy is minimized with a combination of VMSBT algorithm and Virtual Machine Migration (VMMIG) algorithm.Virtual machine migration between the active physical servers in the data center is carried out at periodical intervals as and when a physical server is identified to be under-utilized.In VM scheduling, the optimal data centers are clustered using Bayes Theorem and VMs are scheduled to appropriate data center using the selection policy that identifies the cluster with lesser energy consumption.Clustering using Bayes rule minimizes the number of server choices for the selection policy.Application of Bayes theorem in clustering has enabled the proposed VMSBT algorithm to schedule the virtual machines on to the physical server with minimal execution time.The proposed algorithm is compared with other energy aware VM allocations algorithms viz."Ant-Colony" optimization-based (ACO) allocation scheme and "min-min" scheduling algorithm.The experimental simulation results prove that the proposed combination of 'VMSBT' and 'VMMIG' algorithm outperforms other two strategies and is highly effective in scheduling VMs with reduced energy consumption by utilizing the existing resources productively and by minimizing the number of active servers at any given point of time.
Visualization of the Social Atmosphere Using Comments on News Sites

When an emergency such as an infectious disease or natural disaster occurs, a negative atmosphere will usually spread throughout society—increasing people’s dissatisfaction and anxiety. Because of this, it is rather difficult to thoroughly investigate the actual situation. However, people can post sentimental comments on news sites, allowing for their attitudes either for or against the topics to be better observed. This study extracts the positive, negative, and neutral comments by using sentiment analysis. Then, the social atmosphere is visualized by calculating the approval rating of the comments. This methodology is demonstrated in articles regarding COVID-19. The large volume of comments about two topics, Go To campaigns and PCR tests, were analyzed by using ML-Ask to classify the comments into three categories: negative, positive, and neutral. The results indicate that the social atmosphere about the Go To campaigns tended to be negative.
Semi-Supervised Text Detection With Accurate Pseudo-Labels

Recent scene text detection methods have made great progress. However, existing methods rely heavily on extensive labeled data, which is very time-consuming and expensive. In this letter, we propose a novel semi-supervised text detection method to alleviate the dependence of text detectors on labeled data by generating accurate pseudo-labels and performing effective data augmentations. Specifically, a dual-threshold pseudo-label generation algorithm is designed to divide the prediction results into background regions, text regions, and uncertain regions. The definition of uncertain regions obviously improves the accuracy of pseudo-labels. To obtain accurate pseudo-labels for text at various scales, we first design a scale-aware loss function to adaptively adjust the loss weight of different scale texts. Then, a multi-scale feature extraction module is proposed to extract multi-scale text features and adaptively weight these features according to the scale of the text. Moreover, effective data augmentations are explored to use unlabeled data to improve the robustness of the model to various texts. Experiments show that our method achieves state-of-the-art performance on several datasets(e.g., outperforms existing methods by 2.0&#x0025; on TD500).