Reviewerly/exHarmony huggingface dataset:
A New Method of Latin-To-Balinese Script Transliteration based on Bali Simbar Font

As one of Balinese cultural richness, Balinese script writing is going to extinct because of its decreasing use. This research is one of the ways to preserve Balinese script writing using technological approach. Through collaboration between Software Engineering and Language discipline, this research focused on the development of a new method of Latin-to-Balinese script transliteration that can be used for a web/mobile learning application to give transliteration knowledge as one aspect of Balinese script writing. In this research area, this method utilized Bali Simbar font and was developed based on identified seventeen kinds of special words and three stages of string pattern process. Model-View-Controller (MVC) architectural pattern was used by this method, where each was implemented by using dictionary data structure (as a repository for words belong to seventeen kinds of special words), HTML (as a web/mobile application User Interface), and JavaScript (as controller between Model and View, where transliteration algorithm was written in here). The novelty of this method came from the accommodation of knowledge belong to seventeen kinds of special words through dictionary data structure look-up mechanism. Through the experiment, this method has passed over 98% (148 of 151) testing cases of The Balinese Alphabet writing rules and examples document. This result outperformed the best result of known existing method based on Bali Simbar font, i.e. Transliterasi Aksara Bali, that only has accuracy over 68% (103 of 151) cases of the same testing document.
Arabic Handwritten Character Recognition Using Machine Learning Approaches

Character recognition system of Arabic handwritten is one among of the convincing research and analysis tasks going on in light of the fact that every writer has his own writing style. It is the capacity of the PC to understand and recognize the characters of Arabic handwritten automatically. Due to the advancement within the field of technology and science, there is requirement for character recognition of handwritten script in several real-time applications to reduce the effort of human. Several Algorithms of deep learning along with machine learning are improved which may be utilized for the recognition System of character. This manuscript performs the accuracy analysis and algorithms performance measures of CNN (Convolutional Neural Networks), KNN (K-Nearest Neighbor), and SVM (Support Vector Machine). The authors suggested a new model depend on the methods of CNN, KNN, and SVM together. These various machine learning approaches are integrated to develop the recognition system performance. Experimentation has been performed on AHDB and IFN/ENIT data sets. The outcomes of the new model experimental show an excellent performance with higher accuracy.
Detection and Localization of Copy-Move Tampering Using Features of Locality Preserving Projection

The power of communication corresponding to visual content makes digital images a powerful and effective tool for developing idea and spreading messages. The digital image tampering is done with an intention to mislead the watchers feedback and create the false notion about the original image. One of the commonly used digital image tampering approach is copy-move tampering, in which desired part of image is replicated and undesired part is concealed in the same image. To make the tampering convincing various post-processing operations are performed on copy-move tampered images. In this paper the Locality Preserving Projection (LPP) based copy-move tampering detection scheme is proposed for the detection of copy-move tampering in the presence of such adversely operations. In our approach we are taking advantage of similarity preserving property of LPP. This approach effectively detects and localizes tampering in images with the presence of JPEG compression, additive Gaussian noise, colour reduction and brightness change. We can localize the tampered region even if edges of the pasted region is blur.
Fast division in the residue number system {2 + 1,2 , 2 -1} based on shortcut mixed radix conversion

Unlike the parallelism residue number systems (RNS) provide for addition and multiplication, RNS division is often performed via a straightforward RDF (i.e., reverse conversion, binary division, and forward-conversion) scheme. Given the lack of any faster-than-RDF RNS divider, we designed one for the popular moduli set {2n + 1, 2n,2n − 1}, via mixed radix representation of the operands and two parallel 2n-bit dividers (vs. the 3n-bit counterpart required by the RDF). However, the ensued RNS integer quotient could be off the correct result by ± 1, which is tolerated in many approximate arithmetic processes. Nevertheless, we obtain the accurate quotient via additional hardware (i.e., six parallel n-bit comparators). Our experimental results, for n = 8(16), as compared to RDF, show 33%(36%) less delay, 3%(12%) less energy, and 35%(43%) less energy-delay product in case of the minimal-error approximate divider and 14%(21%) less delay in case of the exact implementation, at the cost of additional area consumption and power dissipation.
Simultaneous Policy and Discrete Communication Learning for Multi-Agent Cooperation

Decentralized multi-agent reinforcement learning has been demonstrated to be an effective solution to large multiagent control problems. However, agents typically can only make decisions based on local information, resulting in suboptimal performance in partially-observable settings. The addition of a communication channel overcomes this limitation by allowing agents to exchange information. Existing approaches, however, have required agent output size to scale exponentially with the number of message bits, and have been slow to converge to satisfactory policies due to the added difficulty of learning message selection. We propose an independent bitwise message policy parameterization that allows agent output size to scale linearly with information content. Additionally, we leverage aspects of the environment structure to derive a novel policy gradient estimator that is both unbiased and has a lower variance message gradient contribution than typical policy gradient estimators. We evaluate the impact of these two contributions on a collaborative multi-agent robot navigation problem, in which information must be exchanged among agents. We find that both significantly improve sample efficiency and result in improved final policies, and demonstrate the applicability of these techniques by deploying the learned policies on physical robots.
Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing

Handwriting was since the start of the history, a higher expression of human skills, and was used for documentation of experiences, and for communication. Existing writing technology require a writing tool, like a pen, and a dedicated writing surface, like paper, or more recently an electronic tablet. These accessories of writing, of writing tool and service, are not available in many daily life situations. Furthermore, the writing accessories, are not natural, in many cases are not ergonomic, and thus can cause fatigue, and in extreme cases contribute to muscular and neurological diseases. In this work, we suggest to step back in history and step forward in technology, and to create, for the first time, an alternate writing solution without any accessories, using one own finger as writing tool, and write on almost any surface. For this, we used directional pressure sensors attached to the fingernail. Changes in the pressure induced on the fingertip in different directions while writing, are projected to the fingernail, and then assessed as a voltage pattern by the sensor. Decoding the pattern, can reveal symbols like letters, punctuations, and writing commands. In this paper, we describe the new pressure sensing modality and tailor processing methods. We tested the new technology on two subjects having different writing patterns while writing alphabet and sentences on different surfaces. We reached letter detection of over 80% while writing on a table, and the word detection rate, was near 70%, after applying the correction algorithm include language priors. The results of this work can revolutionize the way people write and communicate using more convenient, and more approachable, finger-tip writing.
High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem

To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an [Formula: see text]-th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.
On the Security Of A Certificateless Signcryption With Known Session-Specific Temporary Information Security In The Standard Model

Abstract Rastegari et al. recently proposed a certificateless signcryption (CL-SC) scheme. They claimed that their scheme is the first secure CL-SC scheme, which captures the known session-specific temporary information security (KSSTIS), in the standard model. In this paper, we point out that their scheme is insecure, which implies that how to construct a secure CL-SC scheme with KSSTIS in the standard model is still an open problem.
An Improved Artificial Potential Field Method Based on DWA and Path Optimization

The artificial potential field (APF) method is a simple and effective path planning approach. However, there is a fatal problem, which is that the robot can fall into local minima easily before reaching destination. Thus in this paper, we propose an improved APF method based on dynamic window approach (DWA) by evaluating points around robot in local minima with evaluation function, and choose the best point as next path point. Besides, we also propose an optimization algorithm to shorten the path. The main idea is to connect the consecutive points of the planned path while leaving enough secure space between robot and obstacles. The results show that our improved method can solve the problem of local minima and the optimization algorithm can plan a shorter path while consuming little computational time.
Weighted Bayesian Association Rule Mining Algorithm to Construct Bayesian Belief Network

Bayesian network is an appropriate tool to work with the unpredictability and causality which arises in Clinical Domain. A Bayesian network can learn from medical datasets without explicit access to the knowledge of human experts. Thus, to built Bayesian network by learning method, strong rules are needed from datasets. To express statistical dependence relationships, association rules can be considered. This proposal expects to incorporate two techniques to improve the shortcoming of single technique, so this proposal put forward a Weighted Bayesian Association rule Mining Algorithm (WBAR) for the generation of strong Bayesian association rules for the construction of Bayesian network which combines the weighted concept with Association Rule Mining (ARM) to generate Weighted Two-attributes association rules, Weighted Multi-attributes association rules and Weighted Class Association rules. Two interesting dimensions of association rules mining: Weighted Bayes confidence (WBC) and Weighted Bayes lift (WBL) that assess the relationship between different attributes using conditional dependence and independence based on the joint probabilities which are symbolized and then interpreted by the Weighted Bayesian networks using association rules. The proposed algorithm WBAR results the most significant rules according to WBC and WBL.
A Case-Based Reasoning Approach for the Cybersecurity Incident Recording and Resolution

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes the Case-based Cybersecurity Incident Resolution System (CCIRS), a system that implements an approach to integrate case-based reasoning (CBR) techniques and the IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Different types of experimental results so far obtained with the CCIRS show that information security knowledge can be retained with our approach in a reusable memory improving the resolution of new cybersecurity problems.
A Graph-Based Model for Keyword Extraction and Tagging of Research Documents

This paper presents an unsupervised approach for extracting keywords from documents and tagging of research documents with the help of extracted keywords. Keywords are useful for checking the similarity between two documents. This will help us to correctly cluster the related documents with respect to their domains. In this paper, we are describing how research papers are clustered using keywords. A report is generated with respect to the author's contribution in different domains. The system uses Keyword Extraction using Collective Node Weight for extraction of keywords, Node-Edge Rank for ranking Keywords and WordNet Similarity Measurement for tagging of Research papers.
Comparison of CNN Visualization Methods to Aid Model Interpretability for Detecting Alzheimer’s Disease

Advances in medical imaging and convolutional neural networks (CNNs) have made it possible to achieve excellent diagnostic accuracy from CNNs comparable to human raters. However, CNNs are still not implemented in medical trials as they appear as a black box system and their inner workings cannot be properly explained. Therefore, it is essential to assess CNN relevance maps, which highlight regions that primarily contribute to the prediction. This study focuses on the comparison of algorithms for generating heatmaps to visually explain the learned patterns of Alzheimer’s disease (AD) classification. T1-weighted volumetric MRI data were entered into a 3D CNN. Heatmaps were then generated for different visualization methods using the iNNvestigate and keras-vis libraries. The model reached an area under the curve of 0.93 and 0.75 for separating AD dementia patients from controls and patients with amnestic mild cognitive impairment from controls, respectively. Visualizations for the methods deep Taylor decomposition and layer-wise relevance propagation (LRP) showed most reasonable results for individual patients matching expected brain regions. Other methods, such as Grad-CAM and guided backpropagation showed more scattered activations or random areas. For clinically research, deep Taylor decomposition and LRP showed most valuable network activation patterns.
Nonconvex dictionary learning based visual tracking method

Focusing on the heavy decrease of object tracking performance induced by complex circumstances, an object tracking method based on nonconvex discriminative dictionary learning (NDDL) is proposed. Firstly, the object and background samples are acquired according to the temporal and spatial local correlation of objects. Since object and background samples have some common features, an inconsistent constraint is imposed on dictionaries to improve their robustness and discriminability. In what follows, a nonconvex minimax concave plus (MCP) function can be used to penalize sparse encoding matrices to avoid over-punishment via some convex relaxation methods. Based on the sparse representation (SR) theory, a NDDL model can be constructed, which can be tackled by majorization-minimization inexact augmented Lagrange multiplier (MM-IALM) optimization method to achieve better convergence. After obtaining the optimal discriminative dictionary, the reconstruction errors of all candidates are calculated to construct the object observation model. Finally, the object tracking is implemented accurately based on the Bayesian inference framework. Compared to the existing state-of-the-art trackers, simulation results show that the proposed tracker can improve the precision and success rate of the object tracking significantly in complex circumstances.
Real-Time Image Processing Method Using Raspberry Pi for a Car Model

This paper presents the development of a car model that can detect edge, line, and corner of the road image and also the model can detect the red color of a traffic light image. The car model is equipped with a camera which is used for computer vision purpose. The image comes from a camera is read by using Raspberry Pi single-board computer. The algorithms for image processing methods are selected to detect edge, line, corner, and traffic light color of the road model. The algorithms are developed in Simulink diagram block and embedded into Raspberry Pi using Simulink Support Package for Raspberry Pi Hardware. The embedded algorithms for detecting line, edge, corner and red color of traffic light will be tested. The test will be conducted in real-time mode. Based on the test results, the embedded image processing algorithms can successfully detect line, edge, and corner of the road images, and detect the red color of traffic light image.
Taxonomy of Missing Data along with their handling Methods

Since from a long time, data mining remains an interested and important domain of research and in this continuation a number of hazards came into the development of this field. One of these hazards is the missing value existed in the datasets. To deal with this a number of methods evolved and deployed. In this paper, we discussed the taxonomy of missing data along with their handling methods. In addition, a sample implementation of the best method in the category is also depicted with the results for the purpose of its explanation.
An Open-Source Platform for Evaluation of Hardware Implementations of Lightweight Authenticated Ciphers

Lightweight implementations of cryptographic algorithms must be evaluated in terms of security, cost, and performance before their deployment in practical applications. The availability of open-source platforms for such evaluation saves researchers' time and increases reproducibility of results. In this work, we improve upon the previous version of the Flexible Opensource workBench fOr Side-channel analysis (FO-BOS) to introduce “FOBOS2,” and utilize it to perform such evaluation tasks for hardware implementations of authenticated ciphers, with special focus on candidates submitted to the NIST Lightweight Cryptography standardization process. We perform power measurements on Artix7 FPGA, and countermeasure evaluation of lightweight hardware implementations of selected NIST Lightweight Cryptography Round-2 candidates and the current NIST standard AES-GCM on the Spartan6 and Artix7 FPGAs. Our results show that Ascon consumes the least power at 50 MHz, and has the lowest change in dynamic power per increase in frequency, while GIFT-COFB consumes the least energy-per-bit. We also show that side-channel countermeasures applied to implementations of Ascon and AES-GCM are effective using leakage detection tests.
Bigraph specification of software architecture and evolution analysis in mobile computing environment

Software system evolution is an active and important research topic in software engineering. In guiding software system evolution, software architecture plays a critical role. In the traditional software architecture, only the link information of components is considered, while the place information of components is usually neglected. However, due to the emerging mobile computing, pervasive computing, and intelligent computing, the place information is as important as the link information in the software architecture. Especially in mobile computing environments, the place changes often lead to changes in software configuration and functionality. In this paper, we study the Bigraph specification of software architecture and use it to describe both link and place information in detail. Based on Bigraph specification, we investigate the structural characteristics in the software architecture, and design checking algorithms for the component's link exceptions and place exceptions. Furthermore, we address the well-evolved software architecture from a new perspective, which includes three basic evolution operation rules and their well-evolved conditions. We discuss the overall software architecture evolution through strong and weak bi-simulation in terms of software functionality. Finally, two case studies about software system in the evolution operation are presented, which illustrate the effectiveness of our approach.
FSRM-STS: Cross-dataset pedestrian retrieval based on a four-stage retrieval model with Selection–Translation–Selection

Pedestrian retrieval is widely used in intelligent video surveillance and is closely related to people’s lives. Although pedestrian retrieval from a single dataset has improved in recent years, obstacles such as a lack of sample data, domain gaps within and between datasets (arising from factors such as variation in lighting conditions, resolution, season and background etc.), reduce the generalizability of existing models. Factors such as these can act as barriers to the practical use of this technology. Cross-dataset learning is a way to obtain high-quality images from source datasets and can assist the learning of target datasets, thus helping to address the above problem. Existing studies of cross-dataset learning directly apply translated images from source datasets to target datasets, and seldom consider systematic strategies for further improving the quality of the translated images. There is therefore room for improvement in cross-dataset learning. This paper proposes a four-stage retrieval model based on Selection–Translation–Selection (FSRM-STS), to help address this problem. In the first stage of the model, images in pedestrian retrieval datasets are semantically segmented to provide information for image-translation. In the second stage, STS is proposed, based on four strategies to obtain high quality translation results from all source datasets and to generate auxiliary datasets. In the third stage, a pedestrian feature extraction model is proposed, based on both the auxiliary and target datasets. This converts each image in target datasets into an n-dimensional vector. In the final stage, the extracted image vectors are used for cross-dataset pedestrian retrieval. As the translation quality is improved, FSRM-STS achieves promising results for the cross-dataset pedestrian retrieval. Experimental results on four benchmark datasets Market-1501, DukeMTMC-reID, CUHK03 and VIPeR show the effectiveness of the proposed model. Finally, the use of parallel computing for accelerating the training speed and for realizing online applications is also discussed. A primary demo based on cloud computing is designed to verify the engineering solution in the future.