Reviewerly/exHarmony huggingface dataset:
Convolutional Deep Learning Network for Handwritten Arabic Script Recognition

During the last years, deep convolution networks have emerged to become widespread, resulting in substantial gains in various benchmarks. In this paper, Convolutional Deep Belief Networks (CDBN) is applied to learn automatically the finest discriminative features from textual image data consisting of Arabic Handwritten Script. This architecture is able to lay hold of the advantages of Deep Belief Network and Convolutional Neural Network. We subjoin Regularization methods to our CDBN model so that we can address the issue of over-fitting. We evaluated our proposed model on high-level dimension in Arabic textual images. The obtained outcomes from the experiments prove that our model is more effective if compared to the ultra-modern results in handwritten script recognition using IFN/ENIT data sets.
The quality of image encryption techniques by reasoned logic

One form of data is digital images, because of their widespread of frequent exchange over the internet it is necessary to preserve the security and privacy of the images transmitted.There are many image encryption techniques that have different security levels and there are many standards and protocols fortesting the quality of encryption security. The cipher images can be evaluated using various quality measuring criteria, these measures quantify certain features of the image. If there are many methods that can be applied to secure images; the question is what is the most powerful scheme that can be use damong these methods? This research try to answer this question by taking three different encryption methods (rivest cipher 5 (RC5), chaotic and permutation) and measure their quality using the peek signal to noise ratio (PSNR),correlation, entropy, number of pixels changes rate (NPCR) and unified average changing intensity (UACI), the results of these criteria were input to a fuzzy logic system that was used to find the best one among them.
Sign Language Digit Recognition Using Different Convolutional Neural Network Model

An enormous number of world populations in current time are unique in that sense that they have no broad language because of the absence of their hearing capability. The people with hearing impairment have their own language called Sign Language however it is hard for understanding to general individuals [1]. Sign digits are additionally a significant piece of gesture based communication. So a machine interpreter is important to permit them to speak with general individuals. For making their language justifiable to general individual’s computer vision based arrangements are notable these days. In this exploration of work we target to develop a model based on CNN to deal with the recognition of Sign Language digits. A dataset of 10 classes is used to train (70%), validation (20%) and test (10%) of the network. We consider three different models of CNN network to train and test the accuracy of sign digit. Among the three model transfer learning based pre-trained CNN performs better with test accuracy of 92%.
Intelligent Design Based Neural Network Model for Measuring Analysis of the College Teachers’ Teaching Ability

The teaching ability of College Teachers is regarded as one of the core competencies and a critical indicator for measuring comprehensive strength for a college. However, its evaluation process is a highly complex system decision-making, for there are various factors that influence on the assessment of for the College Teachers’ the teaching ability. The traditional methods have drawbacks of strong subjectivity, so they are difficult to correctly evaluate the teaching ability of College Teachers, resulting in decrease of measurement accuracy. Based on the analysis of the relevant factors, this paper presents an intelligent design based neural network model of discrete Hopfield for the measurement and analysis of College Teachers' teaching ability. Firstly, a Hopfield neural network model for the measure analysis of the teaching ability is established, and eleven measure analysis indexes are selected as input information of the Hopfield neural network model. Secondly, the College Teachers' teaching ability grades are chosen as the model output, then the input and output model based on the relationship among the self-learning abilities of neural network is established. Finally, the simulation experiment is obtained by using MATLAB. The simulation results show that the model has the characteristics of high efficiency, objectivity and fairness, which can meet the requirements of the measurement and analysis of College Teachers' teaching ability.
Service-Aware Interactive Presentation of Items for Decision-Making

Current information exploration models present low-level features or technical aspects related to the paradigm used to generate results. While this may increase transparency, it does not help the user form a personal opinion about items because it does not describe the overall experience with them. In order to address this issue, we propose the INTERactivE viSualizaTion model (INTEREST) that supports the exploration and analysis of search results by means of a graphical representation of consumer feedback aimed at making the user aware of the service properties in all the stages of fruition, focusing on the data that is most relevant to her/him. INTEREST is based on the Service Journey Maps for the design and description of user experience with services. We applied it to the home booking domain by developing the Apartment Monitoring application that supports overviewing and analyzing online reviews about rented homes. In a user study, we compared the decision-making support provided by our application with that of a baseline model that enables a temporal filtering of consumer feedback. We found out that Apartment Monitoring outperforms the baseline in user experience, user awareness of item properties, and user control during the interaction with the system. In particular, according to the participants of the study, Apartment Monitoring describes the expectations about the homes and it supports their selection in a more effective way than the baseline. These findings encourage moving from a low-level description of item properties to a service-oriented one in order to improve users’ decision-making capabilities.
Dynamic parallel phase-shifting electronic speckle pattern interferometer

Methods for measuring variations in diffuse surfaces using electronic speckle pattern interferometry (ESPI) are widely used and well known. In this research, we present an out-of-plane ESPI system coupled to a Michelson configuration to generate simultaneous parallel interferograms with different phase shifts. The system uses circular polarization states to generate parallel phase shifted interferograms. Due to the polarization states, the fringes do not experience a contrast reduction, thus avoiding measurement errors that affect spatial or temporal phase-shifting in interferometry. The basic operating principle of polarization modulation is described, and results that represent the temporal evolution of an aluminum plate are presented. The generation of two simultaneous patterns allows one to track the dynamic performance of the plate.
Rule-based Interactive Learning Application Model on How to Play Music Instruments

This research aims to develop an interactive application to support people learning to play music instruments of orchestra music. Gamelan, a traditional music orchestra from Java, Indonesia was used as the object study. A model of gamelan orchestra learning application was proposed to support an easy way to learn how to play gamelan music instruments. Rule-based method was used to develop an interactive system to learn gamelan music instru-ments. The system was implemented into an application which supports us-ers in independently learning to play orchestra music instruments, especially in remembering notes sequences and playing the note according to the tem-po. An experiment involving two groups consisted of control and treatment groups were conducted to evaluate the proposed learning model. The results shows that the averages skill value of the members of the treatment group were higher than the members of the control group.
A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process
A Formal Concept Analysis Approach to Cooperative Conversational Recommendation

Group recommender systems A B S T R A C TWe focus on the development of a method to guide the choice of a set of users in an environment where the number of features describing the items is high and user interaction becomes laborious.Using the framework of formal concept analysis, particularly the notion of implication between attributes, we propose a method strongly based on logic which allows to manage the users' preferences by following a conversational paradigm.Concerning complexity, to build the conversation and provide updated information based on the users' previous actions (choices) the method has polynomial delay.
The Symmetries in Film and Television Production Areas Based on Virtual Reality and Internet of Things Technology

To enrich the form of film and television production, improve the level of film and television production, and satisfy the film-watching experiences of audiences, based on Virtual Reality (VR) and the Internet of Things (IoT) technology, with the help of S3 Studio Max and Photoshop software, a VR film-watching system is built, which realizes the interaction with users on different devices through somatosensory interaction sensors. In addition, by utilizing Twirling720, the panoramic sound recording is achieved. Through this system, a smart IoT platform between users, films, and devices is built. Finally, this platform is utilized to produce the film and television work Van Gogh in Dream, which is evaluated and analyzed through questionnaires. The results show that the technology system of this set of film and television production is complete, and the production level of film and television works have been significantly improved. The audience recognition of film and television production based on this technology is 55%, and the impression evaluation is over 56%. However, knowledge acquisition is only 20%, and historical understanding is above 50%. These dimensions show that compared with traditional film production, artificial intelligence films can bring a better experience to audiences, but knowledge acquisition is less. Therefore, professional knowledge will be improved at the later stage. The above results provide a theoretical basis for the application of artificial intelligence technology in film production and production mode.
Pedagogical Resources Indexation Based on Ontology in Intelligent Recommendation System for Contents Production in d-Learning Environment

We witness, today, a strong evolution of learning environments.In parallel, a problem has emerged, consisting in how to capitalize the production of resources when switching from one environment to another.The heterogeneity of the environments, the evolution of the platforms and the will to reuse the educational resources already produced pushed us to design an intelligent system based on cases.In this study, we will focus on the need for resource indexing to facilitate the task of researching and recommending educational resources for authors regardless of the learning environment used.In the literature, this representation can take two forms: Standards or ontologies.The use of standards has partially solved our problem since it is very beneficial for systems that are under construction.On the other hand, it is more interesting to go through the ontologies for systems that are already designed, that we wish to reuse, especially for those that have shown, through the authors, a great satisfaction in the field of knowledge management.Indeed, their use does not require an investment in the environments concerned by the reuse.
Hamiltonian of a flux qubit-LC oscillator circuit in the deep–strong-coupling regime

Abstract We derive the Hamiltonian of a superconducting circuit that comprises a single-Josephson-junction flux qubit inductively coupled to an LC oscillator, and we compare the derived circuit Hamiltonian with the quantum Rabi Hamiltonian, which describes a two-level system coupled to a harmonic oscillator. We show that there is a simple, intuitive correspondence between the circuit Hamiltonian and the quantum Rabi Hamiltonian. While there is an overall shift of the entire spectrum, the energy level structure of the circuit Hamiltonian up to the seventh excited states can still be fitted well by the quantum Rabi Hamiltonian even in the case where the coupling strength is larger than the frequencies of the qubit and the oscillator, i.e., when the qubit-oscillator circuit is in the deep–strong-coupling regime. We also show that although the circuit Hamiltonian can be transformed via a unitary transformation to a Hamiltonian containing a capacitive coupling term, the resulting circuit Hamiltonian cannot be approximated by the variant of the quantum Rabi Hamiltonian that is obtained using an analogous procedure for mapping the circuit variables onto Pauli and harmonic oscillator operators, even for relatively weak coupling. This difference between the flux and charge gauges follows from the properties of the qubit Hamiltonian eigenstates.
MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.
A Scheduler for Serverless Framework base on Kubernetes

This paper mainly studies the optimization of pod scheduling in the large-scale concurrent scenario of Serverless framework base on Kubernetes platform. In Serverless cloud computing paradigm, rapid deployment and running of pods is the most critical step to improve resource efficiency. However, the deployment and operation of pods depend on images, and Kubernetes default scheduler currently base on pod-by-pod mode for scheduling, it cannot fully meet the needs of Serverless in resource scheduling. In view of this problem, we propose an algorithm using the same pod simultaneous scheduling to further optimize the efficiency of pod scheduling in Serverless cloud paradigm. Through preliminary verification, we can greatly reduce the delay of pod start-up while ensuring the balance of node resources.
The Ethical Perspective of Millennial Accountants in Responding to Opportunities and Challenges of Blockchain 4.0

Research aims : This study aims to provide a perspective on the professional ethics of a millennial accountant in applying blockchain technology to improve his professionalism, and discusses the issue of blockchain ethical challenges for an accountant's profession Design/Methodology/Approach : A research approach is a qualitative approach with interviews with the millennial accountants, namely millennial corporate accountants, millennial research accountants, and millennial internal auditors. Research findings : The study results indicated that a millennial accountant in the 4.0 revolution era is required to adapt to technology. The blockchain technology extends the ethical approach to utilitarianism to a broader social level with decentralization in serving stakeholders’ information needs. Blockchain changes the task of millennial accountants who previously produced information into information evaluators. Millennial accountants must change their mindset to absorb the essence of the accountant's code of ethics and harmonize ethical values. It is the basis for acting ethically to avoid the moral dilemma of technological adaptation. Theoretical contribution/Originality : The contribution of this research for accountants is to build a moral-ethical perspective in adopting blockchain technology that prevents potential impacts on business and society and to describe the challenges that the accounting profession will face in adopting blockchain in the industrial revolution 4.0. Practitioner/Policy implication : The implications of this study illustrate that the blockchain can change perspectives in real-time accounting to reduce practitioners' mistakes from the accounting profession by considering the ethical and moral aspects of a millennial accountant. Research limitation/Implication : The limitation of this study is the first; it has not yet deepened the three challenges faced by millennial accountants in the revolutionary 4.0 era. Secondly, millennial educator accountants have not yet to be involved as interviewees.
Smallest Explanations and Diagnoses of Rejection in Abstract Argumentation

Deciding acceptance of arguments is a central problem in the realm of abstract argumentation. Beyond mere acceptance status, when an argument is rejected it would be informative to analyze reasons for the rejection. Recently, two complementary notions---explanations and diagnoses---were proposed for capturing underlying reasons for rejection in terms of (small) subsets of arguments or attacks. We provide tight complexity results for deciding and computing argument-based explanations and diagnoses. Computationally, we identify that smallest explanations and diagnoses for argumentation frameworks can be computed as so-called smallest unsatisfiable subsets (SMUSes) and smallest correction sets of propositional formulas. Empirically, we show that SMUS extractors and maximum satisfiability solvers (computing smallest correction sets) offer effective ways of computing smallest explanations and diagnoses.
An Elitist Non-Dominated Multi-Objective Genetic Algorithm Based Temperature Aware Circuit Synthesis

IntroductionP ARAMETERS such as area, power, and delay of highly complex logic circuits used in the field of digital systems can be appropriately optimized by utilizing AND-XOR based circuits rather than AND-OR based Boolean function [1]- [2].Besides, XOR-based circuits are well suited for testability [3]-[4] and easily mapped into the Field Programmable Gate Arrays (FPGAs).Minimization of AND-XOR nodes (area count) is possible by sharing the sub-functions/ product terms within the multi-output functions.With the reduction of node count, the signal transitions among the sub-function get reduced.'Espresso, ' the two-level AND-OR minimizer was developed to eliminate redundant literals from canonical form using unite function decomposition [5].Low power approaches are established by searching a suitable input variable polarity for maximum sharing of internal nodes to reduce the switching activity of a Boolean function [6]-[8].There are several sub-classes of AND-XOR circuit synthesis which are of interest.The most general 2-level AND-XOR form is EXOR Sum-Of-Product (ESOP).Due to its non-canonical nature, ESOPs are very hard for optimization.It is observed that any Boolean function can be represented in modulo-2 AND-XOR based algebraic expressions.These expressions are elaborated with the help of Davio functions [9], and are termed as Reed-Muller (RM) expansions.A Boolean function represented in RM form is unique and canonical in nature, consumes less area, reduces power dissipation, and is in readily testable form [4]-[6], [8].Depending on application-specific advantages, RM circuits are represented in the Positive Polarity Reed-Muller (PPRM) expansion, Fixed Polarity Reed-Muller (FPRM) expansion and Mixed Polarity Reed-Muller (MPRM) expansion.A thorough search on literature review has shown that optimization of MPRM is superior over the FPRM expansions on circuit performance regarding the area, switching activity (dynamic power) and/or delay [6]-[8], [10]- [11].A fast minimization algorithm (FMA) using the binary differential evolution (BDE) method to minimize the FPRM product term is proposed in [12].A comparative study of proposed FMA with the genetic algorithm (GA) and simulated annealing genetic algorithm (SAGA) is also reported.An incompletely specified FPRM (ISFPRM) acquisition algorithm is proposed by He et al. in [13].The authors proposed a chromosome conversion technique to convert zero polarity ISFPRM to the FPRM for power reduction.A hybrid simulated annealing (SA) and discrete particle swarm optimization (DPSO) based area optimization approach is proposed in [14].Authors in [8], considered the NSGA-II algorithm to find an optimal polarity for power and area optimization of MPRM network.Authors in [8] proposed a chromosome encoding method based on ternary input polarity and binary don't care allocation.By exploiting the don't care condition, authors in [15] proposed a delay optimization approach for MPRM based logic.In [15], the authors minimized the weighted
Deepfake Detection using Capsule Networks and Long Short-Term Memory Networks

With the recent advancements of technology, and in particular with graphics processing and artificial intelligence algorithms, fake media generation has become easier.Using deep learning techniques like Deepfakes and FaceSwap, anyone can generate fake videos by manipulating the face/voice of target subjects in videos.These AI synthesized videos are a big threat to the authenticity and trustworthiness of online information and can be used for malicious purposes.Detecting face tampering in videos is of utmost importance.We propose a spatio-temporal hybrid model of Capsule Networks integrated with Long Short-Term Memory (LSTM) networks.This model exploits the inconsistencies in videos to distinguish real and fake videos.We use three different frame selection techniques and show that frame selection has a significant impact on the performance of models.The combined Capsule and LSTM network have comparable performance to state-of-the-art models and about 1/5 th the number of parameters, resulting in reduced computational cost.