Reviewerly/exHarmony huggingface dataset:
A Game-Based Learning for Teaching Arabic Letters to Dyslexic and Deaf Children

In Arab and Muslim countries, the Arabic language is important because it is the Quran’s language. Thus, teaching it to the children is an important issue. However, some of these children might suffer from a learning difficulty or disability such as dyslexia and deaf children. Unfortunately, it has been hardly to find any educational application in Arabic to help them and make their study life easier. Therefore, the aim of this work is to provide a good quality, interesting and encouraging application to teach both the Arabic alphabet and the Arabic sign language for a child who suffers from either a reading difficulty (Dyslexia) or a hearing disability (deaf children). The system teaches the child the Arabic letters systematically in a sequential manner starting for the first letter and ending up with the last one to help the child to learn the letters, step by step and do not move from the first letter to the second one until passing the first letter. It uses an interesting interface and interactive games to provide an encouraging learning environment and achieve a learning and studying life easier for the children with Dyslexia and deaf children.
Improving Interoperability in Multi-domain Enterprise Right Management Applications

In this paper we consider the problem of protecting files, possibly stored using remote storage services, on a device running different and independent third party applications. We present a general architecture that, by exploiting the inherent security of Trusted Execution Environments, and by requiring minimal secure storage onboard the device, is able to provide a general purpose, distributed storage system that allows the cooperation among different applications domains. Our system exposes APIs that can be invoked by other trusted applications, using the standard TEE IPC. Furthermore, we discuss a middleware that allows legacy applications to transparently access secured files.
Tangible VR Multi-user Simulation Methodology for a Balanced Human System Integration

With the purpose of facilitating a balanced Human Systems Integration of complex vision systems for military use and aid to ensure compliance with the expectations of new systems, a methodology for the integration of tangible VR and simulation was established. The methodology incorporates the requirements of stakeholders and users, which are defined through explorative workshops. These are then used to create a highly immersive simulator that takes advantage of Virtual Reality (VR) technology to test the design and performance of new systems at an early design stage. The methodology includes steps for data collection, creation of the virtual environment, construction of a physical mockup and use of tools for measuring different parameters to help in user performance assessment. To test this methodology, a multi-user tangible VR simulator was developed. The simulator allows a three-person crew to communicate and cooperate to carry out their task(s). Assessment of the complete simulation setup and the methodology was done with experienced military crews.
Batch despeckling of SAR images by a convolutional neural network-based method

Synthetic aperture radar (SAR) is a very high and accurate technology for remote sensing. SAR images are very exploited for change detection and monitoring because of their high resolution. Because of the coherent retro diffusion of the backscattered radar waves with the rigorous earth surface, SAR images are characterized by a multiplicative noise called speckle that influences their analysis. For this reason, several methods and algorithms have been introduced for speckle noise reduction from these images. This paper proposes and exploits a convolution neural network (CNN) based technique for SAR image denoising. The performance of the introduced technique is studied firstly by using numerical simulation where we added speckle noise with a different variance; then, a quantitative appraisal is presented by using three metrics: Peak to signal noise ratio (PSNR), Edge preservation index (EPI) and image quality index (Q). Finally, we apply the proposed method on a real SAR image obtained by the Sentinel-l satellite and ERSSAR sensor.
Deep CNN with SE Block for Speaker Recognition

This paper highlights a structure called SECNN. It combines squeeze-and-excitation (SE) components with a simplified residual convolutional neural network (ResNet). This model takes time-frequency spectrogram as input and measures speaker similarity between an utterance embedding and speaker models by cosine similarity. Speaker models are obtained by averaging utterance level features of each enrollment speaker. On the one hand, SECNN can mitigate speaker overfitting in speaker verification by using some techniques such as regularization techniques and SE operation. On the other hand, SECNN is a lightweight model with merely 1.5M parameters. Experimental results indicate that SECNN outperforms other end-to-end models such as Deep Speaker and achieves an equal error rate (EER) of 5.55% in speaker verification and accuracy of 93.92% in speaker identification on Librispeech dataset. It also achieves an EER of 2.58% in speaker verification and accuracy of 95.83% in speaker identification on TIMIT dataset.
Automatic Event Coding Framework for Spanish Political News Articles

Today, Spanish speaking countries face widespread political crisis. These political conflicts are published in a large volume of Spanish news articles from Spanish agencies. Our goal is to create a fully functioning system that parses realtime Spanish texts and generates scalable event code. Rather than translating Spanish text into English text and using English event coders, we aim to create a tool that uses raw Spanish text and Spanish event coders for better flexibility, coverage, and cost.To accommodate the processing of a large number of Spanish articles, we adapt a distributed framework based on Apache Spark. We highlight how to extend the existing ontology to provide support for the automated coding process for Spanish texts. We also present experimental data to provide insight into the data collection process with filtering unrelated articles, scaling the framework, and gathering basic statistics on the dataset.
A Novel Hand Gesture Recognition Method Based on Illumination Compensation and Grayscale Adjustment

Gesture recognition is a challenging research problem in human–machine systems. Uneven illumination and background noise significantly contribute to this challenge by affecting the accuracy of hand gesture recognition algorithms. To address this challenge, this paper proposes a novel gesture recognition method based on illumination compensation and grayscale adjustment, which can significantly improve gesture recognition in uneven and backlighting conditions. The novelty of the method is in the new illumination compensation algorithm based on luminance adjustment and Gamma correction, which can reduce the luminance value in the overlit image region and enhance the area with low illumination intensity. The grayscale adjustment is used to detect the skin color and hand area accurately. The binary image of the hand gesture is extracted through iterative threshold segmentation, image dilation, and erosion process. Five gesture features including area, roundness, finger peak number, hole number, and average angle are used to recognize the input gesture. The experimental results show that the proposed method can reduce the influence of uneven illumination and effectively recognize the hand gestures. This method can be used in applications involving human–machine interactions conducted in poor lighting conditions.
Improving Effectiveness of Neighborhood-Based Algorithms for Optimization of Costly Pseudo-Boolean Black-Box Functions

Optimization of costly black-box functions is hard. Not only we know next to nothing about their nature, we need to calculate their values in as small number of points as possible. The problem is even more pronounced for pseudo-Boolean black-box functions since it is harder to approximate them. For such functions the local search methods where a neighborhood of a point must be traversed are in a particular disadvantage compared to evolutionary strategies. In the paper we propose two heuristics that make use of the search history to prioritize the more promising points from a neighborhood to be processed first. In the experiments involving minimization of an extremely costly pseudo-Boolean black-box function we show that the proposed heuristics significantly improve the performance of a hill climbing algorithm, making it outperform (1+1)-EA with an additional benefit of being more stable.
Embedded cluster platform for a remote parallel programming lab

Single-board computers have recently grown to offer developers a wide range of options where the common denominators are low power and low cost. In this paper, we present an embedded cluster platform for a remote parallel programming lab to be used in an online course. A remote lab server handles all requests coming from the front-end running on an online learning platform and controls the execution of the parallel programming assignments submitted by students. The embedded cluster where the jobs run is made out of single-board computers connected through a gigabit network among them and to the lab server. In our first working prototype, we have tested six different state-of-the-art single-board computers, evaluating their processing latency, price, and tools compatibility. We found that the Vim3Pro performed best overall, being the fastest in most tests, having a mid-range price, and being only two times slower than a much more expensive high-end Xeon processor when using the same amount of cores.
Dynamic Universal Accumulator with Batch Update over Bilinear Groups

We propose a Dynamic Universal Accumulator in the Accumulator Manager setting for bilinear groups which extends Nguyen’s positive accumulator and Au et al. [4] and Damgård and Triandopoulos non-membership proof mechanism [20]. The new features include support for batch addition and deletion operations as well as a privacy-friendly batch witness update protocol, where the witness update information is the same for all users. Together with a non-interactive zero-knowledge protocol, these make the proposed scheme suitable as an efficient and scalable Anonymous Credential System, accessible even by low-resource users. We show security of the proposed protocol in the Generic Group Model under a (new) generalized version of the t-SDH assumption and we demonstrate its practical relevance by providing and discussing an implementation realized using state-of-the-art libraries.
Power Grid Modeling and Topology Analysis Based on Graph Database Conforming with CIM/E

The power system is becoming larger and larger, and the operation is more frequent, which puts higher requirements on the real-time performance of analysis and calculation. The graph database is a new type of database that originated from the parallel processing of massive data in the Internet. The data model can visually express the topology of the grid and easily realize parallel traversal query. Firstly, the characteristics of graph database are introduced from the aspects of data model and data query, and the potential advantages of applying it to large-scale power system analysis and calculation are analyzed. Secondly, the design method of power system modeling is presented for satisfying the guidelines of integrity, consistency and efficiency conforming with CIM/E standard. Finally, a parallel power network topology analysis algorithm is implemented based on the graph database model for a provincial grid. The calculation results of the actual large-scale provincial power grid show that the proposed method can significantly improve the topology search efficiency.
Advanced Deadline-Sensitive Scheduling Approaches in Cloud Computing

Cloud computing technology in the field of high-performance distributed computing has become a milestone as it provides shared computing and storage resources as a service upon request of user. Also, with the advent of the computers, scheduling problem got good attention in innumerous fields and arose in industries and technology as well. Service provider’s main requirement is the productive utilization of available resources because it is altogether demanding to supply on-demand resources to the users in a best possible manner for improving performance and faster computation time. In this paper, we present a review to act as an aid to the newcomer researchers to understand various advanced techniques proposed by researchers on deadline-sensitive task scheduling in cloud computing. The schemes reviewed in this paper include grouped task scheduling, deadline-sensitive lease scheduling, scientific workflow scheduling, resource provisioning, pair-based task scheduling, workflow in multi-tenant cloud environment, etc. We also performed a comparative analysis of these approaches and their parameters. Our goal of reviewing the scheduling literature is to scrutinize the interest in different problems among the proposed techniques and algorithms.
Predicting American Sign Language from Hand Gestures Using Image Processing and Deep Learning

The use of computer vision along with machine learning techniques resulted in a breakthrough in medical and social fields. One such intervention is the sign language detection, enabling communication for differently-abled people. Hand gestures are the common method used for this. This paper deals with using image processing techniques like RGB to binary image conversion, skin detection, edge detection to extract the important features of the gesture and provide them as inputs to Convolution Neural Networks (CNN) to improve the accuracy in predicting American Sign Language alphabets (ASL). Using the preprocessed image data set of the hand gesture in our proposed mechanism has predicted the alphabets with higher accuracy up to 99% as compared to the model trained with original data set of images.
On Multi-class Currency Classification Using Convolutional Neural Networks and Cloud Computing Systems for the Blind

Identifying a currencySanjay Kumar,K. K. R. is the easiest of task for a person. But for a blind human, it is not always the same. AlthoughSubramani,Goutham the currencies come in all shapes and sizes, it is always possible for a blind person to get confused with the currency value. WeRishinathh,K. S. analyze the potential usage of cloud computing systems, image processing techniques along with deepIyer,Ganesh Neelakanta learning approaches for identifying the currency based on its denomination which can be accessed by the blind via a mobile app. We examine different approaches including public cloud and private cloud systems for training and deploying the deep learning techniques. We achieve an accuracy of up to 98% in our proposed models. We propose models which can be used under a variety of situations including cost-effective approaches, efficient approaches that suite reduced local storage, etc.
A Novel Study of Different Privacy Frameworks Metrics and Patterns

Learnability has impacted data privacy and security exposing two sides of a coin.Rajendran, Sukumar A breach in security eventually leads to loss of privacy and vice versa. Evolution of technologies has put forth new platforms simplifying data derivation and assimilation providing information on the go. Even though different policies and metrics are in place, the objective varies along with the factors determined by technological advancement.Prabhu, J. This paper describes existing privacy metrics and patterns while providing an overall view of different mathematical framework privacy preserving. Furthermore, maintaining trust and utility becomes a challenge in preserving privacy and security as different techniques and technologies for assimilation of information are readily available without any restraints.
A Novel Evolving Ensemble Approach Based on Bootstrapping and Dynamic Selection of Diverse Learners

In this paper, a novel evolving ensemble approach based on bootstrapping and dynamic selection of diverse learners is presented. This approach combines the use of evolving Fuzzy-rule-based (FRB) classifiers with the dynamic generation of ensembles based on its diversity. In this sense, the method proposed keeps the properties of the evolving classifiers and the advantages of the use of ensembles.
A framework for multimodal sign language recognition under small sample based on key-frame sampling

Sign language recognition is challenging, due to the scarcity of available annotated corpora and the difficulty of large vocabulary. In this paper, we study the task based on a Chinese SL database-DEVISIGN, but it only has a few samples to train the deep network on the scratch. First, we segment the hand to eliminate the disturbance of irrelevant factors. By analyzing the special movement tendency of sign words, we propose two novel Key-frame selection schemes. Since no other datasets can have similar data distribution with our preprocessed data, we invent a novel cross-sampling approach, which successfully prevent the overfitting under small sample. To enhance the diversity of data, we take several samplingbased videos as input, and learn spatiotemporal features based on R(2+1)D-18 layers, which is successful in action recognition tasks. Finally, it is shown that our solution can obtain the state-of-the-art performance.
Resolving Polysemy in Malayalam Verbs Using Context Similarity

Generally, verbs are polysemous in any language as their number is lesser than other categories including nouns. Mostly, the meaning of a verb is decided by the words with which it collocates. The contextual dependency of the verbs reduces the number of verbs in most of the languages or all languages. So by default, verbs become highly polysemous. In textual context or spoken context, the polysemy will not create problems as one can infer the correct meaning of a verb by the context of its occurrence. But in computational context, polysemy will be a problem. As machine does not have the knowledge which human brain has, it must be given knowledge by some means to interpret the meaning of a verb correctly. Polysemy is a problem in the interpretation of Malayalam verbs too. Resolving polysemy in Malayalam verb is needed for any NLP activity in Malayalam including machine translation. In machine translation, ambiguity due to polysemy is a crucial problem. This chapter explores all sorts of ambiguity focusing mainly on ambiguity due to polysemy in verbs. It will also explore resolving polysemy in Malayalam verbs using context similarity.
Contour Classification Method for Industrially Oriented Human-Robot Speech Communication

This paper describes research dealing with application of image recognition as a tool supporting efficient speech communication between humans and collaborative robots in industrial environment. Image-based recognition of objects in robot’s workspace may provide a context for voice commands. In this way the commands can be shorter and more concise. As the robots “understand” abstract technical terms used by human operators, a user-friendly speech communication can be provided. In order to recognize objects properly, classification of their contours must usually take into account the fact that some objects described by one abstract term may differ in dimensions and shapes, whereas some other objects described by different terms may be very similar. Since the object classification rules are usually application-specific, it is impossible to develop general algorithm applicable in all situations. This problem can be solved using Flexible Editable Contour Templates (FECT). However, the crucial factor determining applicability of contour classification method to speech communication is rapidity of the algorithm used for comparison of real contours against the FECTs. Currently, a computationally-expensive algorithm of segment matching is used. In this paper, we propose an alternative method, based on artificial neural networks (ANN).
Combining Parsing Information With Joint Structure for Human Pose Estimation

Recently, with the growing popularity of Convolutional Neural Networks (CNN), major progress has been made in human pose estimation.However, due to the lack of consideration of human body structure, the current methods still suffer from occlusion, complex background, and large pose variations.Human parsing, as a highly relevant task, can provide useful semantic information of body parts for pose estimation.In this paper, we propose a novel convolutional network, which consists of a pose encoder and a parsing encoder in parallel, to combine parsing information with body structure information for effectively assisting human pose estimation in unconstrained environment.The pose encoder can extract discriminative features that contain rich structure information by incorporating a multi-branch parallel module and a hierarchical connection architecture into hourglass network.On the other hand, the parsing encoder can obtain valuable parsing information that serves as extra constraints to help improve the performance of pose estimation.Then, the generated pose features and parsing features will be integrated together for heatmap prediction.In addition, we apply a joint classification loss for preserving the structural consistency between keypoints and body parts.By correctly localizing the joints in their corresponding body parts, the network can better optimize the spatial relationship between different keypoints, thus reducing the errors of estimation.It is worth mentioning that the proposed modules can be introduced into most of the existing methods.The experimental results on the extended PASCAL-Person-Part and LSP datasets demonstrate the effectiveness of our model, and the performance can be greatly improved by utilizing parsing information.
Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

Summary One of the most important challenges of African countries is the effective use of information and communication technologies and its generalization in the different sectors (education, economic, political, and so on). This will have a great impact on different aspects of society and economic activities by making everyday procedures easier and more efficient. In this same context, we are interested in this article by the proposition of a decision support framework based on the use of data mining (DM) techniques. Using DM in e‐government is the process of translating data to appropriate knowledge which can be useful for decision‐making. We propose a framework whose goal is the generation of association rules (ARs) for better decision‐making. This framework includes two approaches: (1) the first approach applies different DM algorithms and (2) the second approach optimizes the first one by considering two different metaheuristics: the Genetic algorithm and the Cuckoo search algorithm. A new relevance measure called “Weighted Dominance” has been considered to evaluate the quality of the generated ARs. Extensive experiments have been conducted using different datasets. The results obtained demonstrated the effectiveness of combining DM and optimization algorithms compared to the first approach. Finally, a case study related to an Algerian mobile phone company has been presented illustrating the use of our framework in the decision‐making process.