Reviewerly/exHarmony huggingface dataset:
Probabilistic Argumentation: An Approach Based on Conditional Probability –A Preliminary Report–

A basic form of an instantiated argument is as a pair (support, conclusion) standing for a conditional relation ‘if support then conclusion’. When this relation is not fully conclusive, a natural choice is to model the argument strength with the conditional probability of the conclusion given the support. In this paper, using a very simple language with conditionals, we explore a framework for probabilistic logic-based argumentation based on an extensive use of conditional probability, where uncertain and possibly inconsistent domain knowledge about a given scenario is represented as a set of defeasible rules quantified with conditional probabilities. We then discuss corresponding notions of attack and defeat relations between arguments, providing a basis for appropriate acceptability semantics, e.g. based on extensions or on DeLP-style dialogical trees.
Anomaly Detection Using Autoencoder With Feature Vector Frequency Map

Anomaly detection uses various machine learning techniques to identify and classify defective data on the production line. The autoencoder-based anomaly detection method is an unsupervised method that classifies abnormal samples using an autoencoder trained only from normal samples and is useful in environments where it is difficult to obtain abnormal samples. This method uses an abnormal score based on the reconstruction loss function, making it difficult to detect defects, such as stains, having a similar texture to a normal sample. To solve this problem, we propose an anomaly detection method using a vector quantized variational autoencoder and a feature vector frequency map. We use the prototype vector histogram and its frequency for anomaly detection instead of the reconstruction loss function. The prototype vector histogram is obtained from the vector quantized variational autoencoder's codebook in the training stage. The feature vector frequency map of the input image is generated using the prototype vector histogram in the inference stage. We calculated the abnormal score using the generated frequency map and classified the abnormal samples. The experimental results showed that the proposed method has a higher Area Under Receiver Operating Characteristics (AUROC) than the previous method in stain and scratch defects.
Defamation in English and Arabic: A Pragmatic Contrastive Study

Defamation is one of the verbal offences in which the plaintiff is accused of a certain wrongful act by one of the ways of publicity. If that wrongful act is proved, then the accused will be punished by law or lowered by his/her home people. Defamation is surely accusing another person of a wrongful act . Accusing can be done by any spoken or written ways whether truthful or doubtful. The purpose beyond those ways is to make those accusations about the victim true in the people's mind even if they are temporarily made. The aim of this study is to elucidate if there is a similarity or a difference between English and Arabic in terms of defamation. It has been hypothesized that both languages are different from each other in terms of the topic under investigation. This study arrives at: In terms of defamation, English and Arabic are similar to each other in having speech acts, grammatical referencing, conveyed meaning, malicious meaning, and discourse structure and framing with intentionality. English defamation cases include speech acts more than Arabic defamation ones.
Visualization and Manipulation of Air Conditioner Flow via Touch Screen

Though air conditioners (A/Cs) have long been a part of homes and businesses alike, the interfaces for adjusting and managing these devices have not changed significantly over the years. In particular, directional A/Cs such as wall mounted, window, or split units commonly found in smaller homes or Asian countries still predominantly require button-based remotes for adjusting the direction and positioning of air vents and flow direction.In this paper, we present a smartphone controlled interface for both manipulating A/C air flow and visualizing the resulting output as a 3D Augmented Reality (AR) overlay. In contrast with previous work, we generate airflow models based on the A/C's exhaust vents, allowing users to see the effects of interactions on airflow in real time. We also implemented and tested three different methods for manipulation including, swipe, drag and button based manipulations using finger or device gestures. Experiments showed that participants (N=50) were able to control air flow most quickly with the swipe based interface, which outperformed the drag and button median completion times by 26.5% and 36.1%, respectively.
Algorithms Comparison for Non-Requirements Classification using the Semantic Feature of Software Requirement Statements

Noise in a Software Requirements Specification (SRS) is an irrelevant requirements statement or a non-requirements statement. This can be confusing to the reader and can have negative repercussions in later stages of software development. This study proposes a classification model to detect the second type of noise, the non-requirements statement. The classification model that is built is based on the semantic features of the non-requirements statement. This research also compares the five best-supervised machine learning methods to date, which are support vector machine (SVM), naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and Decision Tree. This comparison aimed to determine which method can produce the best non-requirements classification, model. The comparison shows that the best model is produced by the SVM method with an average accuracy of 0.96. The most significant features in this non-requirement classification model are the requirements statement or non-requirements, id statement, normalized mean value, standard deviation value, similarity variant value, standard deviation normalization value, maximum normalized value, similarity variant normalization value, value Bad NN, mean value, number of sentences, bad VB score, and project id.
Investigation of Optimization Algorithms for Neural Network Solutions of Optimal Control Problems with Mixed Constraints

In this paper, we consider the problem of selecting the most efficient optimization algorithm for neural network approximation—solving optimal control problems with mixed constraints. The original optimal control problem is reduced to a finite-dimensional optimization problem by applying the necessary optimality conditions, the Lagrange multiplier method and the least squares method. Neural network approximation models are presented for the desired control functions, trajectory and conjugate factors. The selection of the optimal weight coefficients of the neural network approximation was carried out using the gravitational search algorithm and the basic particle swarm algorithm and the genetic algorithm. Computational experiments showed that evolutionary optimization algorithms required the smallest number of iterations for a given accuracy in comparison with the classical gradient optimization method; however, the multi-agent optimization methods were performed later for each operation. As a result, the genetic algorithm showed a faster convergence rate relative to the total execution time.
Social Engineering Based Cyber-Attacks in Kenya

Cybersecurity is a major challenge especially as the world transitions to the fourth industrial revolution. Cybercriminals are always perceived to be using complex sophisticated mechanisms to launch attacks to information systems. It is however worth exploring Social Engineering as one of the arts used to exploit the weakest layer of information security systems, who are the users. In the recent past, the world has witnessed a gradual gain in popularity of Social Engineering attacks propagated through varied forms, including, phishing, vishing and smishing. Hence, this paper presents and demonstrates an analytical approach towards Social Engineering. The study explored the level of understanding of three forms of Social Engineering and the prevalence of Social Engineering attacks with their countermeasures. Qualitative and quantitative data was collected from a random sample through an online survey and face-to-face interviews. Data analysis showed that vishing and smishing are the most commonly used forms of Social Engineering in Kenya with the use of authority featuring as a persuasion strategy used by attackers striving for financial gain. The lack of user education and awareness outstandingly came out as the main reason behind a majority of successful attacks. The study was limited to Kenya as a representative of developing Nations in Africa. The resulting study outcomes could form a foundation for the development of information security policies and awareness programs. This could further translate into National or International Laws on Social Engineering based Cyber-attacks.
Graph-Based Supervoxel Computation from Iterative Spanning Forest

Supervoxel segmentation leads to major improvements in video analysis since it generates simpler but meaningful primitives (i.e., supervoxels). Thanks to the flexibility of the Iterative Spanning Forest (ISF) framework and recent strategies introduced by the Dynamic Iterative Spanning Forest (DISF) for superpixel computation, we propose a new graph-based method for supervoxel generation by using iterative spanning forest framework, so-called ISF2SVX, based on a pipeline composed by four stages: (a) graph creation; (b) seed oversampling; (c) IFT-based superpixel delineation; and (d) seed set reduction. Moreover, experimental results show that ISF2SVX is capable of effectively describing the video's color variation through its supervoxels, while being competitive for the remaining metrics considered.
WaveGuard: Understanding and Mitigating Audio Adversarial Examples

There has been a recent surge in adversarial attacks on deep learning based automatic speech recognition (ASR) systems. These attacks pose new challenges to deep learning security and have raised significant concerns in deploying ASR systems in safety-critical applications. In this work, we introduce WaveGuard: a framework for detecting adversarial inputs that are crafted to attack ASR systems. Our framework incorporates audio transformation functions and analyses the ASR transcriptions of the original and transformed audio to detect adversarial inputs. We demonstrate that our defense framework is able to reliably detect adversarial examples constructed by four recent audio adversarial attacks, with a variety of audio transformation functions. With careful regard for best practices in defense evaluations, we analyze our proposed defense and its strength to withstand adaptive and robust attacks in the audio domain. We empirically demonstrate that audio transformations that recover audio from perceptually informed representations can lead to a strong defense that is robust against an adaptive adversary even in a complete white-box setting. Furthermore, WaveGuard can be used out-of-the box and integrated directly with any ASR model to efficiently detect audio adversarial examples, without the need for model retraining.
Grasping Action Recognition in VR Environment using Object Shape and Position Information

In this paper, we propose a method for recognizing the user's grasping action in VR environment. The proposed method utilizes the fact that the positions and shapes of virtual objects are known in VR environment. The position and shape information of the virtual object in addition to the hand posture is used for classification to improve the recognition accuracy. The result of evaluation showed that the recognition rate was improved by adding the position and shape information of the virtual object.
Meta-data Augmentation Based Search Strategy Through Generative Adversarial Network for AutoML Model Selection

Automated machine learning (AutoML) attempts to automatically build appropriate learning model for given dataset. Despite the recent progress of meta-learning to find good instantiations for AutoML framework, it is still difficult and time-consuming to collect sufficient meta-data with high quality. Therefore, we propose a novel method named Meta-data Augmentation based Search Strategy (MDASS) for AutoML model selection, which is mainly composed of Meta-GAN Surrogate model (MetaGAN) and Self-Adaptive Meta-model (SAM). MetaGAN employs Generative Adversarial Network as surrogate model to collect effective meta-data based on the limited meta-data, which can alleviate the dilemma of meta-overfitting in meta-learning. Based on augmented meta-data, SAM self-adaptively builds multi-objective meta-model, which can select the algorithms with proper trade-off between learning performance and computational budget. Furthermore, for new datasets, MDASS combines promising algorithms and hyperparameter optimization to perform automated model selection under time constraint. Finally, the experiments on various classification datasets from OpenML and algorithms from scikit-learn are conducted. The results show that GAN is promising to incorporate with AutoML and MDASS can perform better than the competing approaches with time budget.
A USING EDMODO AS SCAFFOLDING TECHNIQUE IN SMK MASUDIRINI FOR 10th GRADE STUDENTS ON TEACHING WRITING SKILL IN ACADEMIC YEAR 2019/2020

Many studies have been conducted on the application of Edmodo in the classroom. However research on Edmodo as a scaffolding technique in teaching English for a vocational school has been rarely found. This research aims at investigating the implementation of Edmodo as a scaffolding technique in teaching writing in English as a Foreign language. The purpose is to investigate whether there is a significant difference on the writing competencies of the students before and after using Edmodo. The study employed pretest and posttest design. The sample was 39 students of the tenth grade of SMK Masudirini in the academic year of 2019/2020, and all of them were selected as the sample of the study. The data were collected by observing the teachers and students’ activities, interviewing, and using pretest and posttest. The results of the test were analyzed qualitatively and quantitatively. Quantitative data were analyzed using t-test. The result indicated that the students can learn using Edmodo as a scaffolding technique in creating narrative texts. It was found that there was a significant difference on the writing competencies of the students before and after using Edmodo. This implied that Edmodo has been successfully used as scaffolding technique in teaching narrative text in English. It can be concluded that the application of Edmodo is not only effective for teaching writing in English through online learning platform but also promote the 21st century learning in vocational high school in Bali.
RRG-GAN Restoring Network for Simple Lens Imaging System

The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. In this paper, we propose a novel Recursive Residual Groups network under Generative Adversarial Network framework (RRG-GAN) to generate a clear image from the aberrations-degraded blurry image. The RRG-GAN network includes dual attention module, selective kernel network module, and residual resizing module to make it more suitable for the non-uniform deblurring task. To validate the evaluation algorithm, we collect sharp/aberration-degraded datasets by CODE V simulation. To test the practical application performance, we built a display-capture lab setup and reconstruct a manual registering dataset. Relevant experimental comparisons and actual tests verify the effectiveness of our proposed method.
A Survey of Diagnosis of Dental Image Diseases using Soft Computing Techniques

Dental imaging is an area of research where imaging plays important role in disease diagnostic, treatment and analysis. Root Infection is the cause for the Dental cysts and tooth get affected by carious decay. There are mainly three types of Dental cyst i.e. Ameloblastoma, Keratocyst and Dentigerous cyst and treatment depends on the type. Odontogenic, Keratocysts and Ameloblastoma have high reoccurrence rates compared to other types. It is very necessary to differentiate between different forms of cysts to prevent disease progression.
A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned During the Microchip Design

The production of microchips is a complex and thus well documented process. Therefore, available textual data about the production can be overwhelming in terms of quantity. This affects the visibility and retrieval of a certain piece of information when it is most needed. In this paper, we propose a dynamic approach to interlink the information extracted from multisource production-relevant documents through the creation of a knowledge graph. This graph is constructed in order to support searchability and enhance user’s access to large-scale production information. Text mining methods are firstly utilized to extract data from multiple documentation sources. Document relations are then mined and extracted for the composition of the knowledge graph. Graph search functionality is then supported with a recommendation use-case to enhance users’ access to information that is related to the initial documents. The proposed approach is tailored to and tested on microchip design-relevant documents. It enhances the visibility and findability of previous design-failure-cases during the process of a new chip design.
Evaluating the Sensitivity of Face Presentation Attack Detection Techniques to Images of Varying Resolutions

In the last decades, emerging techniques for face Presentation Attack Detection (PAD) have reported a remarkable performance to detect attack presentations whose attack type and capture conditions are known a priori. However, the generalisation capability of PAD approaches shows a considerable deterioration to detect unknown attacks. In order to tackle those generalisation issues, several PAD techniques have focused on the detection of homogeneous features from known attacks to detect unknown Presentation Attack Instruments without taking into account how some intrinsic image properties such as the image resolution or biometric quality could impact their detection performance. In this work, we carry out a thorough analysis of the sensitivity of several texture descriptors which shows how the use of images with varying resolutions for training leads to a high decrease on the attack detection performance.
Techniques to Improve B2B Data Governance Using FAIR Principles

Sharing data along the economic supply/demand chain represents a catalyst to improve the performance of a digitized business sector. In this context, designing automatic mechanisms for structured data exchange, that should also ensure the proper development of B2B processes in a regulated environment, becomes a necessity. Even though the data format used for sharing can be modeled using the open methodology, we propose the use of FAIR principles to additionally offer business entities a way to define commonly agreed upon supply, access and ownership procedures. As an approach to manage the FAIR modelled metadata, we propose a series of methodologies to follow. They were integrated in a data marketplace platform, which we developed to ensure they are properly applied. For its design, we modelled a decentralized architecture based on our own blockchain mechanisms. In our proposal, each business entity can host and structure its metadata in catalog, dataset and distribution assets. In order to offer businesses full control over the data supplied through our system, we designed and implemented a sharing mechanism based on access policies defined by the business entity directly in our data marketplace platform. In the proposed approach, metadata-based assets sharing can be done between two or multiple businesses, which will be able to manually access the data in the management interface and programmatically through an authorized data point. Business specific transactions proposed to modify the semantic model are validated using our own blockchain based technologies. As a result, security and integrity of the FAIR data in the collaboration process is ensured. From an architectural point of view, the lack of a central authority to manage the vehiculated data ensures businesses have full control of the terms and conditions under which their data is used.
The Value of Bitcoin in the Year 2141 (and Beyond!)

The emergence of bitcoin poses an important question for monetary theorists: can bitcoin compete with or even replace existing fiat monies? To answer this question, one must be able to determine what gives intrinsically useless monies their value, what determines the coexistence of alternative monies, and under what conditions economic agents would prefer to hold one money relative to another. We attempt to answer these questions in light of the emergence of bitcoin. In particular, we outline a theoretical model in which an intrinsically useless money is essential.
WITHDRAWN: Corrigendum to “Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application” [Swarm and Evolutionary Computation 63 (2021) 1–40/100868]

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( http://www.elsevier.com/locate/withdrawalpolicy ). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.
Modulated Variable-Rate Deep Video Compression

Rate adaption is one of the decisive factors for the applications of video compression. However, previous deep video compression methods are usually optimized for a single fixed rate-distortion (R-D) tradeoff. While they can achieve multiple bitrates by training multiple independent models, the realized bitrates are limited to several discrete points on the R-D curve and the storage cost increases proportionally to the number of models. In this paper, we propose a variable-rate scheme for deep video compression, which can achieve continuously variable rate by a single model, i.e., it can reach any point on the R-D curve. In our scheme, two deep auto-encoders are used to compress the residual and the motion vector field respectively, which directly generate the final bitstream. The basic rate adaptation can be achieved by using the R-D tradeoff parameter to deeply modulate all the internal feature maps of the auto-encoders. However, other modules in our scheme, notably motion estimation and motion compensation, also affect the final bitrate indirectly. We further use the R-D tradeoff parameter to modulate them via a conditional map, which effectively improves the compression efficiency. We use a multi-rate-distortion loss function together with a step-by-step training strategy to optimize the entire scheme. Our experiments show that the proposed scheme achieves continuously variable rate by a single model with almost the same compression efficiency as multiple fixed-rate models. The additional parameters and computation of our model are negligible when compared with a single fixed-rate model.