Reviewerly/exHarmony huggingface dataset:
DEPSOSVM: variant of differential evolution based on PSO for image and text data classification

Purpose Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy. Design/methodology/approach A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM. Findings This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well. Originality/value The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.
Minimisation in Logical Form

Recently, two apparently quite different duality-based approaches to automata minimisation have appeared. One is based on ideas that originated from the controllability-observability duality from systems theory, and the other is based on ideas derived from Stone-type dualities specifically linking coalgebras with algebraic structures derived from modal logics. In the present paper, we develop a more abstract view and unify the two approaches. We show that dualities, or more generally dual adjunctions, between categories can be lifted to dual adjunctions between categories of coalgebras and algebras, and from there to automata with initial as well as final states. As in the Stone-duality approach, algebras are essentially logics for reasoning about the automata. By exploiting the ability to pass between these categories, we show that one can minimize the corresponding automata. We give an abstract minimisation algorithm that has several instances, including the celebrated Brzozowski minimisation algorithm. We further develop three examples that have been treated in previous works: deterministic Kripke frames based on a Stone-type duality, weighted automata based on the self-duality of semimodules, and topological automata based on Gelfand duality. As a new example, we develop alternating automata based on the discrete duality between sets and complete atomic Boolean algebras.
Surprises in Knockout Tournaments

SummarySometimes the second-best player in a knockout tournament is more likely to win that to finish second. We characterize this surprising situation, under certain natural conditions.
A New Fast Local Laplacian Completed Local Ternary Count (FLL-CLTC) for Facial Image Classification

Face recognition is one of the most interesting areas of research areas because of its importance in authentication and security. Differentiating between different facial images is not easy because of the similarities in facial features. Human faces can also be covered obscured by eyeglasses, facial expressions and hairstyles can also be changed causing difficulty in finding similar faces. Thus, the need for powerful image features has become a critical issue in the face recognition systems. Many texture features have been used in these systems, including Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC) and Completed Local Ternary Pattern (CLTP). In this paper, a new texture descriptor, namely, Completed Local Ternary Count (CLTC), is proposed by adding a threshold value for the CLBC to overcome its sensitivity to noise drawback. The CLTC is also enhanced by adding the Fast-Local Laplacian filter during the pre-processing stage to increase the discriminative property of the proposed descriptor. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) texture descriptor is evaluated for face recognition task using five different face image datasets. The experimental results of the FLL-CLTC showed that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors in term of recognition accuracy. The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively.
Content-based Image Retrieval Speedup Based on Optimized Combination of Wavelet and Zernike Features Using Particle Swarm Optimization Algorithm

This paper presents a novel method to speedup content-based image retrieval (CBIR) systems. The proposed method can be very useful for retrieving images from a large database. For this task, Zernike and Wavelet features are first extracted from the query image, then an interval of potential matching images is computed from the database images using the extracted feature. Therefore, the query image is compared with images in the interval rather than the whole database which to speedup the retrieval process. Particle swarm optimization is employed to select relevant features among Zernike and Wavelet features, which leads to decrease feature extraction time. Three types of experiments are conducted to evaluate effectiveness the proposed method in terms of database reduction, retrieval accuracy and retrieval time. In the best case, the Corel-1k database is averagely reduced up to 33.98% from its original size, and preserving 71.92% of relevant images. Retrieval accuracy in reduced database is increased by 1% in comparison with retrieving from the original database. Meanwhile, the retrieval time is reduced up to 58.57% in comparison with retrieval time from the original database.
HIDRA: Head Initialization across Dynamic targets for Robust Architectures

The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model. Recent works show that there exist weight initializations from which optimization procedures can find the task-specific parameters faster than from uniformly random initializations and that such a weight initialization can be learned by optimizing a specific model architecture across similar tasks via MAML (Model-Agnostic Meta-Learning). Current methods are limited to populations of classification tasks that share the same number of classes due to the static model architectures used during meta-learning. In this paper, we present HIDRA, a meta-learning approach that enables training and evaluating across tasks with any number of target variables. We show that Model-Agnostic Meta-Learning trains a distribution for all the neurons in the output layer and a specific weight initialization for the ones in the hidden layers. HIDRA explores this by learning one master neuron, which is used to initialize any number of output neurons for a new task. Extensive experiments on the Miniimagenet and Omniglot data sets demonstrate that HIDRA improves over standard approaches while generalizing to tasks with any number of target variables. Moreover, our approach is shown to robustify low-capacity models in learning across complex tasks with a high number of classes for which regular MAML fails to learn any feasible initialization.
A Cloud Gaming Framework for Dynamic Graphical Rendering Towards Achieving Distributed Game Engines

Cloud gaming in recent years has gained growing success in delivering games-as-a-service by leveraging cloud resources. Existing cloud gaming frameworks deploy the entire game engine within Virtual Machines (VMs) due to the tight-coupling of game engine subsystems (graphics, physics, AI). The effectiveness of such an approach is heavily dependant on the cloud VM providing consistently high levels of performance, availability, and reliability. However this assumption is difficult to guarantee due to QoS degradation within, and outside of, the cloud - from system failure, network connectivity, to consumer datacaps - all of which may result in game service outage. We present a cloud gaming framework that creates a distributed game engine via loose-coupling the graphical renderer from the game engine, allowing for its execution across cloud VMs and client devices dynamically. Our framework allows games to operate during performance degradation and cloud service failure, enabling game developers to exploit heterogeneous graphical APIs unrestricted from Operating System and hardware constraints. Our initial experiments show that our framework improves game frame rates by up to 33% via frame interlacing between cloud and client systems.
Bounded-Leakage Differential Privacy

We introduce and study a relaxation of differential privacy [Dwork et al., 2006] that accounts for mechanisms that leak some additional, bounded information about the database. We apply this notion to reason about two distinct settings where the notion of differential privacy is of limited use. First, we consider cases, such as in the 2020 US Census [Abowd, 2018], in which some information about the database is released exactly or with small noise. Second, we consider the accumulation of privacy harms for an individual across studies that may not even include the data of this individual. The tools that we develop for bounded-leakage differential privacy allow us reason about privacy loss in these settings, and to show that individuals preserve some meaningful protections.
Phonological Words in the Syntax and in the Lexicon: A Study of Russian Prepositions

Phonological words play a crucial role in phonology, but where exactly they are produced in syntax is not clear. I propose a theory whereby the syntax issues phonological word diacritics to the complex constituents it creates. Additionally, certain morphemes can be specified in the lexicon as possessing these diacritics. The phonology then interprets the diacritics—sometimes it ignores them, and other times it makes phonological words to satisfy language-specific prosodic requirements. The resulting theory is demonstrated on the complex patterning of prepositions in Russian. The class of prepositions in Russian has certain syntactic traits in common, but there are many patterns where prepositions diverge according to their phonological word status. There are correlations between morphosyntactic structure and phonological word status: morphologically complex prepositions are always words. On the other hand, the presence of a morphological root, phonological size, and stress do not align with word status. The large range of phonological and morphosyntactic patterns involving prepositions in Russian demonstrates the need for an explicit and rich theory of word formation at the phonology-syntax interface.
Constructing Geospatial Concept Graphs from Tagged Images for Geo-Aware Fine-Grained Image Recognition

While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species of birds need to be distinguished. Especially for recognizing geospatial concepts, which are observed only at specific places, geographical locations of the images can improve the recognition accuracy. However, such geo-aware fine-grained image recognition requires prior information about the visual and geospatial features of each concept or the training data composed of high-quality images for each concept associated with correct geographical locations. By using a large number of images photographed in various places and described with textual tags which can be collected from image sharing services such as Flickr, this paper proposes a method for constructing a geospatial concept graph which contains the necessary prior information for realizing the geo-aware fine-grained image recognition, such as a set of visually recognizable fine-grained geospatial concepts, their visual and geospatial features, and the coarse-grained representative visual concepts whose visual features can be transferred to several fine-grained geospatial concepts. Leveraging the information from the images captured by many people can automatically extract diverse types of geospatial concepts with proper features for realizing efficient and effective geo-aware fine-grained image recognition.
General Legal Limits of the Application of the Lethal Autonomous Weapons Systems within the Purview of International Humanitarian Law

This article focuses on the problem of regulation of the application of the autonomous weapons systems from the perspective of the norms and principles of international humanitarian law. The article discusses the question of what restrictions are imposed on the application of such weapons in the international humanitarian law. The article presents a number of principles that must be met by both the weapons and their method of their application: distinction between civilians and combatants, military necessity, proportionality, prohibition on causing unnecessary suffering, and humanity.&#x0D; &#x0D; The author concludes that from the perspective of the principles of the international humanitarian law, it is doubtful if autonomous systems would be able to comply with these principles. Weapons that hit targets without human intervention have been applied for a long time, but they have never had the independence that they have now. The issue of compliance of autonomous weapons systems with the international humanitarian law can be considered if sufficient experience of application of such weapons in real conditions is accumulated. &#x0D; &#x0D; This study demonstrates that it is impossible to say that autonomous weapons systems do not comply with the principles of humanitarian law in general. The paper provides policy recommendations and assessments for each of the principles under consideration. The author also concludes that it would be necessary not to prohibit autonomous weapons, because they do not comply with the principles of international humanitarian law, but to develop rules for their application and for human participation in their functioning. A significant challenge to the development of such rules is the opacity of these autonomous weapons systems, if we look at them as at the complex intelligent computer systems.
Classification and Analysis of Security Techniques for the User Terminal Area in the Internet Banking Service

As the Internet banking service has become popular, many users have been able to exchange goods online. Even though this offers advantage, there are cases of incidents in the Internet banking service arising from security threats. In order to counteract this problem, various security techniques have been applied over the whole area of the Internet banking service. In this paper, we analyze the results of security techniques applied in the user terminal area. In detail, we classify the existing security technologies into network domain, financial institution domain, and user terminal domain, to ensure the security of the Internet banking service. This paper focuses on security techniques in the user terminal domain. Specifically, we classify the security technologies in the user terminal domain into secure keyboard program, PKI applications, E2E encryption, antihacking program, personal firewall, removable media security, and antireverse engineering technique and describe detailed and key techniques of each security technology. For easy understanding and security analysis of security technologies, we describe each security technology in detail and explain each technology by way of example. We consider that this paper will provide criteria for evaluating the security of Internet banking services.
Performance Management System of Jordanian Public Sector Organizations: Greater Amman Municipality’s (GAM) Experience

The Performance Management System (PMS) is one of the most critical systems within the context of public organizations. Without proper implementation of the PMS, these organizations will encounter challenges to deliver their services. Ideally, the PMS framework might cover several fields, including strategic planning and goals setting, strategies and plans, performance appraisal, the reward and punishment system, and performance information. Due to limitations in the existing literature about the PMS in the developing countries, this paper aims to reflect on the implementation of the PMS in the Greater Amman Municipality (GAM) in Jordan because it is one of the biggest employers of the Jordanian public sector in the Middle East region, which is an integral part of the developing contexts. To this end, a descriptive analysis of the secondary data has been conducted, including the related literature, published documents, and archival data. According to Otley’s (1999) framework and the analysis of the experiences of the PMS of public entities in the developing world, the GAM system has failed to implement the performance indicators formulation. Also, its indicators are excessively reliant on archival measures. The GAM system is missing out on two major processes, which are appraising performance and the reward and punishment system. The findings revealed that the GAM system has failed to consider the results of the community satisfaction survey as a valuable source of performance for the performance inputs and planning process. Accordingly, a comprehensive framework of PMS has been synthesized and introduced in this paper.
Genetic Optimization in Hybrid Level Sentiment Analysis for Opinion Classification

Now-a-days the major task in Natural Language Processing (NLP) is Sentiment Analysis (SA).Identifying the polarity of the textual data has been a complex problem which is applied in the vast range of areas from product feedback analysis to user statement understanding.Yet many approaches and models were developed for this problem, classification problem still remained.To overcome this problem and to train the machine to perfection, we introduced a feature optimization algorithm with CNN (Convolutional Neural Network) as a classification algorithm.The main aim of our project is to attain more accuracy than the proposed capsule-based model.Our experimented results showed huge outcomes for this model.
Analisis Debit Banjir Rancangan Sub DAS Air Bengkulu Menggunakan Analisis Frekuensi dan Metode Distribusi

Abstract&#x0D; The Design discharge is the important data needed for design of the water infrastructure. The purpose of this study is to estimate the design discharge with a variety of times using a frequency analysis in the Air Bengkulu Sub-watershed. Rain fall data were obtained from the BMKG of Bengkulu province, its starting from 2009 to 2018 years. The design flood estimation method used a statistical method. It is using the distribution method. The initial stage of the research is to process rain data and then conduct frequency analysis. The mathematical equation for frequency analysis includes statistical equations to determine the average value, standard deviation, coefficient of variation, skewness coefficient and kurtosis coefficient. The method used in determining the type of distribution for frequency analysis in this study uses the Gumbel Type I distribution method, Pearson Log Type III, Normal, and Normal Log. Distribution fit test was performed with chi square test and Kalmogorof Smirnov test. The results of estimated design discharges for the 2 years return period, 5 years return period, 10 years return period, 25 years return period, 50 years return period and 100 years returns period in the Air Bengkulu down stream Sub-watershed are respectively 125.16 m3 / sec (2 years return period), 145.24 m3 / sec (5 years return period), 157.59 m3 / sec (10 years return period), 172.43 m3 / sec (25 years return period), 183.03 m3 / sec (50 years return period) and 193.34 m3 / sec (100 years return period). &#x0D; &#x0D; Keywords: watershed, Air Bengkulu, Design Discharge, Frequency Analysis, Distribution Analysis
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents. This task is particularly relevant for supporting forensic investigation as documents may contain criminal evidence. As an alternative to the laborious and time-consuming manual process, several researchers have been investigating ways to perform automatic digital reconstruction. A central problem in automatic reconstruction of shredded documents is the pairwise compatibility evaluation of the shreds, notably for binary text documents. In this context, deep learning has enabled great progress for accurate reconstructions in the domain of mechanically-shredded documents. A sensitive issue, however, is that current deep model solutions require an inference whenever a pair of shreds has to be evaluated. This work proposes a scalable deep learning approach for measuring pairwise compatibility in which the number of inferences scales linearly (rather than quadratically) with the number of shreds. Instead of predicting compatibility directly, deep models are leveraged to asymmetrically project the raw shred content onto a common metric space in which distance is proportional to the compatibility. Experimental results show that our method has accuracy comparable to the state-of-the-art with a speed-up of about 22 times for a test instance with 505 shreds (20 mixed shredded-pages from different documents).
Rewriting the Description Logic ALCHIQ to Disjunctive Existential Rules

Especially in data-intensive settings, a promising reasoning approach for description logics (DLs) is to rewrite DL theories into sets of rules. Although many such approaches have been considered in the literature, there are still various relevant DLs for which no small rewriting (of polynomial size) is known. We therefore develop small rewritings for the DL \ALCHIQ -- featuring disjunction, number restrictions, and inverse roles -- to disjunctive Datalog. By admitting existential quantifiers in rule heads, we can improve this result to yield only rules of bounded size, a property that is common to all rewritings that were implemented in practice so far.
RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task

Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Experiments on the Spider dataset show that our approach is more effective compared to the previous works at predicting the nested SQL queries. In addition, we achieve an overall accuracy that is comparable with state-of-the-art approaches.