Reviewerly/exHarmony huggingface dataset:
Arabic Machine Translation: A Survey with Challenges and Future Directions

In recent years, computer language area has witnessed important evolvement with applications in different domains. Machine Translation MT technology, considered as a subfield, has received important development with different approaches and techniques. Although, many MT systems and tools that support Arabic already exist; however, the quality of the translation is moderate and needs some improvement. In addition, the high demand for effective technologies to process and translate information from/to Arabic motivated the researchers in Arabic Machine Translation (AMT) to propose new approaches and solutions following the mainstream method, notably neural machine translation (NMT). In this paper, we provide a broad review and compare different NMT approaches for Arabic-English (and English-Arabic) machine translation research works. The discussed approaches address different linguistic and technical challenges and problems while demonstrating great success compared to traditional methods. The results of this work can serve the researchers and professional to be up-to-date and provide them with the necessary resources for modelling and improving of the AMT. These resources include corpora, toolkits, techniques and new models. The obtained results outline various findings, critics, and open issues in this area.
Investigation of the Quality of Fuzzy IF-THEN Model for a Control System

The main facet of fuzzy logic is relational facet which is associated with IF-THEN rules. They are being increasingly applied to a variety problem as a model of different systems. Up to date research results on investigation of the quality of fuzzy rule-based systems (specificity, transparency, inconsistency) are very scars. In this paper author suggests a new approach to increase quality of IF-THEN rule for a control system.
Approximate Down-Sampling Strategy for Power-Constrained Intelligent Systems

In modern power constrained applications, as with most of those belonging to the Internet-of-Things world, custom hardware supports are ever more commonly adopted to deploy artificial intelligence algorithms. In these operating environments, limiting the power dissipation as much as possible is mandatory, even at the expense of reduced computational accuracy. In this paper we propose a novel prediction method to identify potential predominant features in convolutional layers followed by down-sampling layers, thus reducing the overall number of convolution calculations. This approximation down-sampling strategy has been exploited to design a custom hardware architecture for the inference of Convolutional Neural Network (CNN) models. The proposed approach has been applied to several benchmark CNN models and we achieved an overall energy saving of up to 70&#x0025; with an accuracy loss lower than 3&#x0025;, with respect to baseline designs. Performed experiments demonstrate that, when adopted to infer the Visual Geometry Group-16 (VGG16) network model, the proposed architecture implemented on a Xilinx Z-7045 chip and on the STM 28nm process technology dissipates only 680 and 21.9 mJ/frame, respectively. In both cases, the novel design overcomes several state-of-the-art competitors in terms of energy-accuracy drop product.
Use of Deep Learning Model with Attention Mechanism for Software Fault Prediction

Software defect prediction is a skill in software engineering that can increase program reliability. In the past, most defect prediction studies have been based on size and complexity metrics. In recent years, machine learning based predictive studies have been conducted. To build an accurate prediction model, choosing effective features remains critical. In this paper, we constructed a deep learning model called Defect Prediction via Self-Attention mechanism (DPSAM) to extract semantic features and predict defects automatically. We transferred programs into abstract syntax trees (ASTs) and encoded them into token vectors. With input features, we trained a self-attention mechanism to extract semantic features of programs and predict defects. We evaluated performance on 7 open source projects. In Within-Project Defect Prediction (WPDP), DPSAM achieved 16.8% and 14.4% performance improvement compared to state-of-the-art deep belief network (DBN)-based method and defect prediction via convolutional neural network (DP-CNN)-based method in F1 score, respectively. Besides, in Cross-Project Defect Prediction (CPDP), DPSAM achieve 23% and 60% performance improvement in F1 score compared to DBN-based method and DP-CNN-based method.
Z-Set Based Inference Using ALI-2 Implication for Control System Design

Knowledge based systems are characterized with bimodal information. There are a lot of works in scientific literature related on fuzzy and probabilistic reasoning. Fuzzy reasoning and probabilistic reasoning have a wide application, in both theoretical and applied works. It is lucid that used implication impacts to result of reasoning. In this work, Z-set based inference using Ali-2 implication for control system design is discussed. Ali 2 logic satisfies the relation μA(x)^(μA(x) → μB(x)) ≤ μB(x) which is essential for progress of fuzzy conditional inference rules. It gives them possibility to utilize it for the creation of developed rules of fuzzy conditional inference and for the modeling of different systems. For clarification of the situation, new approach for approximate reasoning is proposed by author. Proposed approach is based on processing imperfect information.
Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

When implementing intelligent agents for strategy games, we observe that search-based methods struggle with the complexity of such games. To tackle this problem, we propose a new variant of Monte Carlo Tree Search which can incorporate action and game state abstractions. Focusing on the latter, we developed a game state encoding for turn-based strategy games that allows for a flexible abstraction. Using an optimization procedure, we optimize the agent's action and game state abstraction to maximize its performance against a rule-based agent. Furthermore, we compare different combinations of abstractions and their impact on the agent's performance based on the Kill the King game of the Stratega framework. Our results show that action abstractions have improved the performance of our agent considerably. Contrary, game state abstractions have not shown much impact. While these results may be limited to the tested game, they are in line with previous research on abstractions of simple Markov Decision Processes. The higher complexity of strategy games may require more intricate methods, such as hierarchical or time-based abstractions, to further improve the agent's performance.
Loop Verification with Invariants and Contracts

Invariants are the predominant approach to verify the correctness of loops. As an alternative, loop contracts, which make explicit the premise and conclusion of the underlying induction proof, can sometimes capture correctness conditions more naturally. But despite this advantage, the second approach receives little attention overall, and the goal of this paper is to lift it out of its niche. We give the first comprehensive exposition of the theory of loop contracts, including a characterization of its completeness. We show concrete examples on standard algorithms that showcase their relative merits. Moreover, we demonstrate a novel constructive translation between the two approaches, which decouples the chosen specification approach from the verification backend.
Organizational governance in the smart era: the implications of blockchain

Purpose Blockchain is a technical solution integrating multiple technologies, with the potential to overcome the drawbacks of organizational governance. Given the emergence and prevalence of blockchain, its importance for organizational governance has progressively increased. Therefore, this study aims to analyze how blockchain restructure organizational governance. Design/methodology/approach This study presents a structured systematic literature review of blockchain-enabled applications across diverse organizational governance models and several case studies to illustrate them. Based on the above analysis, governance mechanisms, transaction upgrading and challenges are proposed. Findings Based on the literature review and typical applications, the authors summarize the advances in the research on the theoretical and practical applications of blockchain in organizational governance. We also identify the influence mechanisms of organizational governance and investigate transaction upgrading based on blockchain. Finally, the authors discuss three types of challenges (i.e. administrative, technical and environmental) to the relationship between blockchain and organizational governance. Along with the development of blockchain applications, the impact of blockchain on organizational governance has progressed in both theory and practice. Therefore, these findings will have significant implications for both academics and practitioners. Originality/value This paper makes three key theoretical contributions: we review the literature on the impact of blockchain on organizational governance and present typical cases to illustrate it; propose four governance mechanisms for the application of blockchain in organizational governance; and propose an innovating-and-matching-oriented model of transaction upgrading based on blockchain.
An Electromyography Based Shared Control Framework for Intuitive Robotic Telemanipulation

Over the years, robots have seen an increased use in a wide range of applications including exploration, maintenance, and search and rescue operations in remote or hazardous environments. Developing fully autonomous systems for unstructured environments is a challenging task while controlling them from a distance requires highly trained operators. This paper proposes an electromyography (EMG) and fiducial marker-based teleoperation framework that allows for shared task execution between the user and an autonomous operation scheme. The framework offers an intuitive interface that allows the user to take control of a robot arm-hand system and perform tasks that can not be executed autonomously. The EMG subsystem decodes specific gestures performed by the user, triggering a switch between different control states of the robot or setting the desired grasp type for the robot hand. The robot arm is teleoperated through human motion captured by an appropriate vision system that employs fiducial markers. The system performance is validated in a series of five experiments, where the robotic system successfully executes a series of autonomous tasks as well as tasks performed in a shared control manner, in synergy with the user.
Information needs and delivery channels: Experimental evidence from Cambodian smallholders

Despite numerous policy interventions, poverty still exists. Those most harshly affected are people living in rural areas of low-income countries, regions that are often characterized by information asymmetries leading to market failure. The widespread growth of information and communications technologies (ICTs) in remote areas across the world holds immense potential for lifting the information barriers of the rural poor. However, there is little evidence of the effectiveness of delivery channels, which might be one reason why digital advice differs in its impact. Seeking to ascertain how smallholders can best be served by ICT, the authors investigated information needs and effective ICT delivery channels. Sociodemographic and ICT-related data was collected and a framed field experiment was conducted with smallholders in Cambodia; they were asked to build an object while using various delivery channels for instruction. Employing different regression techniques and matching algorithms, the experiment reveals that multisensory instructions trump all others.
NSTBNet: Toward a nonsubsampled shearlet transform for broad convolutional neural network image denoising

Deep convolutional neural networks (CNNs) have achieved huge success in the image denoising fields. However, there still have three drawbacks to be overcome: firstly, it is a big cost to train a deeper CNN model for better denoising performance, secondly, most models often lack the flexibility to remove spatially variant additive Gaussian noise (AWGN), finally, it is difficult to achieve a good trade-off between denoising performance and details preservation. In this paper, we propose a novel network integrated with nonsubsampled shearlet transform (NSST) and a broad convolutional neural network, namely NSTBNet. The proposed NSTBNet has the following desirable properties: (1) a single model has the ability to deal with different noise levels and spatially variant AWGN; (2) the model combines the two networks make it wider to improve denoising performance without increasing too much computational cost; (3) the NSST and inverse NSST are employed to acquire more texture and to avoid gridding effect. Extensive simulation experiments on AWGN and realistic noise show that NSTBNet has state-of-the-art denoising performance, making it a high application value.
High-Frequency and Low-Latency DSP Architecture for Information Matrix Fusion

The centralized fusion architecture gives optimal global estimate by fusing all the measurements pertaining to a given target. The centralized architectures are computationally huge and requires full data rate requirements. Hence, in practice, decentralized architectures with Information matrix fusion (IMF) is popular to derive an estimate which is equal to optimal global estimate accomplished in centralized architecture. In this paper, a digital signal processing (DSP) architectural minimization technique of pipelining is applied to derive the highspeed IMF. We proposed two different DSP architectures, namely pipelined traditional IMF and pipelined adder-ladder IMF to reduce the critical path, which inturn, increases the architecture's operating frequency. Further, we derived an optimal number of pipeline stages and hardware resources that are required for a generalized <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> sensors case. The proposed pipelined adder-ladder IMF configuration requires a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> + 1 pipeline stage and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> + 2 pipeline stages for an even number of sensors and an odd number of sensors respectively. Besides that, The pipelined traditional IMF requires 2N + 1 stages to optimally pipeline and achieve the same operating frequency as that of pipelined adder-ladder IMF. Furthermore, the proposed pipelined adder-ladder IMF is superior in performance (less hardware and less latency) compared to pipelined traditional IMF. The theoretical analysis is performed with metrics (critical path, number of resources, and maximum achievable frequency) to compare various architectures presented in this research work.
Boost Spectrum Prediction With Temporal-Frequency Fusion Network via Transfer Learning

Modeling and predicting the radio spectrum is vital for spectrum management, such as spectrum sharing and anomaly detection. Nevertheless, the precise spectrum prediction is challenging due to the interference from both intra-spectrum and external factors. To tackle these complex internal and external correlations, we develop a model named TF <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> AN, consisting of three components: 1) a robust signal detection algorithm based on image processing, 2) an attention-based Long Short-term Memory network to capture the temporal-frequency correlations, 3) a generalized fusion module to take the heterogeneous external factors into account. This structure shows prominent effectiveness for spectrum prediction on a single monitoring station with sufficient data. However, when the data derived from a single station is insufficient, the performance of the deep learning model will decline a lot. Considering that more than one monitoring station is deployed in practice, the new challenge becomes how to enhance our model by leveraging the data from multiple stations or frequency bands. Therefore, we further propose T-TF <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> AN, a transfer learning-based framework for data augmentation and knowledge sharing in spectrum prediction. Compared to TF <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> AN, better performance is achieved. Besides, the model interpretability and training efficiency are also discussed with two case studies, respectively.
AutoML Models for Wireless Signals Classification and their effectiveness against Adversarial Attacks

In this paper, we study and compare the performance of AutoML models with state-of-the-art models on wireless signal classification and their vulnerability and transferability towards transfer-based white-box and black-box attacks. We designed models of four architectures using AutoML, namely Deep Residual Network (ResNet), Convolutional Long Short-Term Deep Neural Network (CLDNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Using AutoML techniques for model generation helps to reduce time spent on designing, training and tuning hyper-parameters of deep learning models. Using numerical results, we show that AutoML models are a viable and solid candidate approach for the classification of wireless signals. In addition, we show the vulnerability of AutoML models towards adversarial attacks when compared to state-of-the-art models.
A computationally efficient distributed Bayesian filter with random finite set observations

This paper presents a distributed Bayesian filter for tracking a target in the presence of random missed detections and false alarms using a sensor network, in which the Bayesian recursion as well as our proposed multi-sensor posterior fusion is carried out via Gaussian mixture (GM). For better communication and computation efficiencies, only properly selected Gaussian components in the GMs are disseminated and fused between neighbor sensors in which the components are selected following the principle of principal component analysis. Linear/arithmetic average fusion is realized for which thresholds used in GM merging and pruning operations are theoretically derived by the Occam's window method. Furthermore, an improved distributed flooding protocol is devised for GM communication over the network which enables parallelization of internode communication and fusion operations and reduces the node-memory cost. It is demonstrated that under reasonable assumptions, it yields the same result as the original flooding algorithm while having lower node-memory requirements. Our proposed approach is compared with the state-of-the-art approach in both simulation and experiment scenarios for target tracking.
Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation

Abstract This study carries out a systematic intrinsic evaluation of the semantic representations learned by state-of-the-art pre-trained multimodal Transformers. These representations are claimed to be task-agnostic and shown to help on many downstream language-and-vision tasks. However, the extent to which they align with human semantic intuitions remains unclear. We experiment with various models and obtain static word representations from the contextualized ones they learn. We then evaluate them against the semantic judgments provided by human speakers. In line with previous evidence, we observe a generalized advantage of multimodal representations over language- only ones on concrete word pairs, but not on abstract ones. On the one hand, this confirms the effectiveness of these models to align language and vision, which results in better semantic representations for concepts that are grounded in images. On the other hand, models are shown to follow different representation learning patterns, which sheds some light on how and when they perform multimodal integration.
Emotion Recognition from 3D Motion Capture Data using Deep CNNs

Designing computer games requires a player-centered approach. Whilst following guidelines and functional requirement specifications is part of the process, observing and measuring qualities of the players experience is key in providing feedback to game designers. Moreover, it can also be used to create adaptive and personalized experiences for players. With the advancement of affective computing and gaming user interfaces, the opportunity to recognize the player's emotions becomes more feasible and each different modality can offer additional information as affect expression is less defined as compared to action selection. This paper explores the use of 3D skeleton motion data transformed to 2D images that encode pose and movement dynamics to represent annotated emotions. The 2D images are then used to train and test the Inception V3 CNN model on a binary classification emotion recognition between happy and sad emotions. Preliminary results in unseen test data indicate that the above transformation technique can capture emotional information. The paper also discusses future directions that may improve the effectiveness of the proposed method on a wider scale.
Object Tracking Method Based on Kalman Filter and Camshift Algorithm for UAV Applications

Since the object tracking system for unmanned aerial vehicles (UAVs) and evaluating its effectiveness are a demanding task, we have focused our work on this topic. In this paper, we presented a method for tracking an object in image sequences. Our proposed method is based on the Kalman filter and Camshift algorithm. The Kalman filter is used to predict the location of an object, while the selected target is tracked by the Camshift algorithm. The results of our experiments demonstrated that the proposed method can effectively track an object in various videos.