Reviewerly/exHarmony huggingface dataset:
Explainable Acceptance in Probabilistic Abstract Argumentation: Complexity and Approximation

Recently there has been an increasing interest in probabilistic abstract argumentation, an extension of Dung's abstract argumentation framework with probability theory. In this setting, we address the problem of computing the probability that a given argument is accepted. This is carried out by introducing the concept of probabilistic explanation for a given (probabilistic) extension. We show that the complexity of the problem is FP^#P-hard and propose polynomial approximation algorithms with bounded additive error for probabilistic argumentation frameworks where odd-length cycles are forbidden. This is quite surprising since, as we show, such kind of approximation algorithm does not exist for the related FP^#P-hard problem of computing the probability of the credulous acceptance of an argument, even for the special class of argumentation frameworks considered in the paper.
Application of Factorial Design for Optimization of PVC-HPMC Polymers in Matrix Film Ibuprofen Patch-Transdermal Drug Delivery System

Ibuprofen is a non-steroidal anti-inflammatory drug (NSAID) class of drugs, and oral use can cause side effects. Transdermal patch dosage forms are useful for minimizing side effects on oral administration. Transdermal patches are formulated using a special membrane that can control drug release in the matrix system. This study was intended to determine the characteristics of the resulting patch, the optimum composition of the formula, and the profile of the release of transdermal ibuprofen release. The film matrix optimization on the ibuprofen patch formulation uses design of experiment (DoE) approach using factorial design 22. The mixture of polyvinyl pyrrolidone (PVC) and hydroxypropyl methylcellulose (HPMC) components gives a pleasant texture, and the release results in vitro show a proper and controlled release of the ibuprofen patch. Based on the research, it can be concluded that the patch has excellent characteristics with a good texture so that the development time is long with the optimum formula of chitosan and HPMC, as well as having a proper and controlled release profile.
Comparison of Image Compressions: Analog Transformations

A comparison between the four most used transforms, the discrete Fourier transform (DFT), discrete cosine transform (DCT), the Walsh–Hadamard transform (WHT) and the Haar-wavelet transform (DWT), for the transmission of analog images, varying their compression and comparing their quality, is presented. Additionally, performance tests are done for different levels of white Gaussian additive noise.
An Enhanced pix2pix Dehazing Network with Guided Filter Layer

In this paper, we propose an enhanced pix2pix dehazing network, which generates clear images without relying on a physical scattering model. This network is a generative adversarial network (GAN) which combines multiple guided filter layers. First, the input of hazy images is smoothed to obtain high-frequency features according to different smoothing kernels of the guided filter layer. Then, these features are embedded in higher dimensions of the network and connected with the output of the generator’s encoder. Finally, Visual Geometry Group (VGG) features are introduced to serve as a loss function to improve the quality of the texture information restoration and generate better hazy-free images. We conduct experiments on NYU-Depth, I-HAZE and O-HAZE datasets. The enhanced pix2pix dehazing network we propose produces increases of 1.22 dB in the Peak Signal-to-Noise Ratio (PSNR) and 0.01 in the Structural Similarity Index Metric (SSIM) compared with a second successful comparison method using the indoor test dataset. Extensive experiments demonstrate that the proposed method has good performance for image dehazing.
Application of Virtual Reality Technologies for Achieving Energy Efficient Manufacturing: Literature Analysis and Findings

Improvement in current manufacturing settings for enabling energy efficiency is a challenge for many manufacturers. Although virtual reality has been so far applied in manufacturing for training, visualization and product development, the use of this technology in manufacturing for increasing energy efficiency has been less addressed. This paper investigates the potential of virtual reality for a better analysis of energy demands in manufacturing. By envisioning and illustrating energy flows and consumption, virtual reality can support energy efficiency. The paper provides a systematic review of the literature. The findings are analysed from the perspective of research gaps in making virtual-based technologies to enable energy-efficient manufacturing. Particularly, the elements and factors (opportunities) and methods that can be transmitted from current research to energy-efficient manufacturing, are identified and discussed.
Virtual Reality as a tool for learning: The past, present and the prospect

&nbsp;The use of computing technologies in human learning is rapidly growing and advancing in various fields of learning and training. Virtual reality (VR) is one of the growing computer techniques used in schools and training institutes to help improve students’ learning experience, create an interactive environment and build students’ confidence while working in a physical environment. The benefits of using virtual reality are yet to be fully explored in all fields of endeavour. Virtual reality has been applied in the field of medicine for rehabilitation of patients and in training of medical students. In addition, it has been used in operations management, manufacturing processes and design as well as in the aviation industry for the dissemination of safety information, and maintenance. VR holds great and promising prospects in education, tourism, entertainment, and architecture. Hence, this paper presents a review of the trends of applications of virtual reality technologies, its potentials and prospects for learning in various fields.
Clustering of District or City in Central Java Based COVID-19 Case Using K-Means Clustering

Coronavirus Disease 2019 (COVID-19) is a new type of disease that has never been identified in humans. Severe cases of COVID-19 can cause acute respiratory syndrome, kidney failure, and even death. COVID-19 cases have spread all over the world, including in Indonesia. One province with a high number of COVID-19 cases is Central Java Province. Therefore, it is necessary to cluster districts or cities in Central Java based on the COVID-19 case to prevent the spread of COVID-19. Clustering the cases of COVID-19 is done using k-means clustering which is a method of clustering a number of data by means of partitions. The results show that cluster 2 and cluster 3 are areas that the government should pay more attention to because they are areas with a high number of active cases and the high death cases of COVID-19 in Central Java.
Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.
A Uniform Treatment of Aggregates and Constraints in Hybrid ASP

Characterizing hybrid ASP solving in a generic way is difficult since one needs to abstract from specific theories. Inspired by lazy SMT solving, this is usually addressed by treating theory atoms as opaque. Unlike this, we propose a slightly more transparent approach that includes an abstract notion of a term. Rather than imposing a syntax on terms, we keep them abstract by stipulating only some basic properties. With this, we further develop a semantic framework for hybrid ASP solving and provide aggregate functions for theory variables that adhere to different semantic principles, show that they generalize existing aggregate semantics in ASP and how we can rely on off-the-shelf hybrid solvers for implementation.
Yet Meta Learning Can Adapt Fast, it Can Also Break Easily

Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.
Development Navigation, Guidance &amp; Control Program for GPS based Autonomous Ground Vehicle (AGV) using Soft Computing Techniques

In this chapter, an introduction of navigate a vehicle autonomously, the control action of the system must know at least two things, its own current position and direction of movement. Location can be find by either from an outside system source with technology such as the Global Positioning System (GPS), or by calculation of mathematical equation a followed path from a known source point with the use of electronic compasses, inclinometers, and rotational counters. However, under any scheme, the outcome is to somehow generate the positional information of the vehicle so that the vehicle could be guided to follow a path and reach the target. In addition of this, the direction sensing devices are also used for getting the values of current heading of vehicle so that it could be steered to move towards target location. GPS has advantage over other position detection mechanism that it also provides an estimate of vehicle’s current heading based on the previous two positional coordinates. While the current heading provided by GPS is not accurate enough under some conditions, it is envisaged to accommodate GPS as the only sensor for guiding the autonomous vehicle to follow the trajectory in outdoor applications
An open-source end-to-end ASR system for Brazilian Portuguese using DNNs built from newly assembled corpora

In this work, we present a baseline end-to-end&nbsp;system based on deep learning for automatic speech recognition&nbsp;in Brazilian Portuguese. To build such a model, we employ&nbsp;a speech corpus containing 158 hours of annotated speech&nbsp;by assembling four individual datasets, three of them publicly&nbsp;available, and a text corpus containing 10.2 millions of sentences.&nbsp;We train an acoustic model based on the DeepSpeech 2 network,&nbsp;with two convolutional and ﬁve bidirectional recurrent layers. By&nbsp;adding a newly trained 15-gram language model at the character&nbsp;level, we achieve a character error rate of only 10.49% and a&nbsp;word error rate of 25.45%, which are on a par with other works&nbsp;in different languages using a similar amount of training data.
Conceptual model of the concept of digital literacy

Т. А. Бороненко, А
Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system

Abstract Traditional food knowledge (TFK) is an essential aspect of human life. In terms of sociocultural aspects, TFK is necessary to protect ancestral culture. In terms of health, traditional foods contain better and more natural ingredients compared to the ingredients of processed foods. Considering this background, in this study, data acquisition and automatic food recognition were performed for traditional food in Indonesia. The food images were captured in a professional mini studio. The food image data were captured under the same light intensity, camera settings, and shooting distance from the camera. The parameters were precisely measured and configured with a light intensity meter, adjustable lighting, and a laser distance measurement device. The data of 1644 traditional food images were successfully obtained in the data acquisition process. These images corresponded to 34 types of traditional foods, and 30–50 images were obtained for each type of food. The size of the raw food image data was 53 GB. The data were divided into sets for training, testing, and validation. An automatic recognition system was developed to classify the traditional food of Indonesia. Training was performed using several types of convolutional neural network (CNN) models such as Densenet121, Resnet50, InceptionV3, and Nasnetmobile. The evaluation results indicated that when using a high quality dataset, the automatic recognition system could realize satisfactory area under the receiver operating characteristics (AUROC) and high accuracy, precision, and recall values of more than 0.95.
Measles outbreak during the COVID-19 pandemic in Mexico

Measles is an exanthematous disease caused by a virus of the Morbillivirus genus.On February 23, 2020, the exanthema began in the first confirmed case of measles this year.At the same time, on February 28, 2020, the first case of the new coronavirus disease (COVID-19) was confirmed.Up to June 6, 2020, 176 measles cases have been confirmed: 137 in Mexico City, 37 in the state of Mexico, and two in the state of Campeche.In Mexico City, municipalities with more cases were Gustavo A. Madero, Miguel Hidalgo, and Iztapalapa; in the State of Mexico were Ecatepec, Tlalnepantla, and Netzahualcoyotl; in Campeche, there are only two cases reported.On the other hand, 7,065,133 cases of COVID-19 have been registered worldwide with a global case fatality rate of 5.7%.In Mexico, there have been 113,619 confirmed cases and 13,511 deaths, while in Mexico City, there have been 30,223 cases and 3062 deaths.Iztapalapa and Gustavo A. Madero are the locations with more cases reported.Hence, a campaign of social distancing started as part of the strategies to control the spread of the infection.The potential threat is that as social confinement measures are relaxed and mobility is initiated, both viruses could continue to spread.It is expected that due to the time that has passed since the last reported measles case, control of this disease will be achieved.
Efficient Method Based on Blockchain Ensuring Data Integrity Auditing with Deduplication in Cloud

With the rapid development of cloud storage, more and more cloud clients can store and access their data anytime, from anywhere and using any device.Data deduplication may be considered an excellent choice to ensure data storage efficiency.Although cloud technology offers many advantages for storage service, it also introduces security challenges, especially with regards to data integrity, which is one of the most critical elements in any system.A data owner should thus enable data integrity auditing mechanisms.Much research has recently been undertaken to deal with these issues.In this paper, we propose a novel blockchain-based method, which can preserve cloud data integrity checking with data deduplication.In our method, a mediator performs data deduplication on the client side, which permits a reduction in the amount of outsourced data and a decrease in the computation time and the bandwidth used between the enterprise and the cloud service provider.This method supports private and public auditability.Our method also ensures the confidentiality of a client's data against auditors during the auditing process.
Learning and education in HCI

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Learning Layer-Skippable Inference Network

The process of learning good representations for machine learning tasks can be very computationally expensive. Typically, we facilitate the same backbones learned on the training set to infer the labels of testing data. Interestingly, This learning and inference paradigm, however, is quite different from the typical inference scheme of human biological visual systems. Essentially, neuroscience studies have shown that the right hemisphere of the human brain predominantly makes a fast processing of low-frequency spatial signals, while the left hemisphere more focuses on analyzing high-frequency information in a slower way. And the low-pass analysis helps facilitate the high-pass analysis via a feedback form. Inspired by this biological vision mechanism, this paper explores the possibility of learning a layer-skippable inference network. Specifically, we propose a layer-skippable network that dynamically carries out coarse-tofine object categorization. Such a network has two branches to jointly deal with both coarse and fine-grained classification tasks. The layer-skipping mechanism is proposed to learn a gating network by generating dynamic inference graphs, and reducing the computational cost by detouring the inference path from some layers. This adaptive path inference strategy endows the network with better flexibility and larger capacity and makes the high-performance deep networks with dynamic structures. To efficiently train the gating network, a novel ranking-based loss function is presented. Furthermore, the learned representations are enhanced by the proposed top-down feedback facilitation and feature-wise affine transformation, individually. The former one employs features of a coarse branch to help the finegrained object recognition task, while the latter one encodes the selected path to enhance the final feature representations. Extensive experiments are conducted on several widely used coarse-to-fine object categorization benchmarks, and promising results are achieved by our proposed model. Quite surprisingly, our layer-skipping mechanism improves the network robustness to adversarial attacks. The codes and models are released on https://github.com/avalonstrel/DSN.
Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques

Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
Errors in writing made by Malaysian rural primary school pupils

This study is to identify errors made by rural primary school pupils in writing and to get information about the causes or sources of errors that lead to pupils’ writing problems. The conceptual framework concerns four types of errors committed by rural primary school pupils in writing, namely tense, spelling, and vocabulary. The study is based on Corder’s (1971) Error Analysis (EA) and Richards’ (1974) Causes or Sources of Errors as its theoretical framework. It was conducted at two rural schools with 44 pupils of Primary 5, aged 11, as the research sample. A written task was taken as the study instrument in order to answer two research questions. This study has revealed that tense is the most frequent error committed by the pupils, followed by punctuation, vocabulary, and spelling. Moreover, the pupils’ errors are caused by both interlingual and intralingual transfer. It is concerned with rural primary school settings in Kerian where the majority of the pupils use the Malay language as their medium of instruction. In addition, this study has its implication for English Language Education in Malaysia, in which it affects rural pupils’ performance especially in Primary School Achievement Test also known as Ujian Pencapaian Sekolah Rendah (UPSR) in English Writing paper. Some adjustments in the education system and the total involvement from education departments are meant to better reduce the number of low performers, especially in English writing to enhance the level of English proficiency in rural schools.