Reviewerly/exHarmony huggingface dataset:
Niching Multimodal Landscapes Faster Yet Effectively: VMO and HillVallEA Benefit Together

Variable Mesh Optimization with Niching (VMO-N) is a framework for multimodal problems (those with multiple optima at several search subspaces). Its only two instances are restricted though. Being a potent multimodal optimizer, the Hill-Valley Evolutionary Algorithm (HillVallEA) uses large populations that prolong its execution. This study strives to revise VMO-N, to contrast it with related approaches, to instantiate it effectively, to get HillVallEA faster, and to indicate methods (previous or new) for practical use. We hypothesize that extra pre-niching search in HillVallEA may reduce the overall population, and that if such a diminution is substantial, it runs more rapidly but effective. After refining VMO-N, we bring out a new case of it, dubbed Hill-Valley-Clustering-based VMO (HVcMO), which also extends HillVallEA. Results show it as the first competitive variant of VMO-N, also on top of the VMO-based niching strategies. Regarding the number of optima found, HVcMO performs statistically similar to the last HillVallEA version. However, it comes with a pivotal benefit for HillVallEA: a severe reduction of the population, which leads to an estimated drastic speed-up when the volume of the search space is in a certain range.
Semi-inversion of Conditional Constructor Term Rewriting Systems

Inversion is an important and useful program transformation and has been studied in various programming language paradigms. Semi-inversion is more general than just swapping the input and output of a program; instead, parts of the input and output can be freely swapped. In this paper, we present a polyvariant semi-inversion algorithm for conditional constructor term rewriting systems. These systems can model logic and functional languages, which have the advantage that semi-inversion, as well as partial and full inversion, can be studied across different programming paradigms. The semi-inverter makes use of local inversion and a simple but effective heuristic and is proven to be correct. A Prolog implementation is applied to several problems, including inversion of a simple encrypter and of a program inverter for a reversible language.
A Labeled multi-Bernoulli Filter for Multisource DOA Tracking

This paper addresses the problem of multisource direction-of-arrival (DOA) tracking based on the labeled multi-Bernoulli (LMB) filter. In order to avoid information loss, we directly utilize the received signal at an array antenna. The normal superpositional measurement model can be used to describe the relationship between DOAs from multiple sources to the received signal. LMB is not conjugated under the multitarget likelihood function of this distribution, so no analytical solution can be obtained. In order to overcome such difficulty, the likelihood of superpositional measurement model is approximated as a complex Wishart random matrix, and the updated LMB distribution is computed by matching the probability hypothesis density (PHD) of posterior. The auxiliary particle filter is employed to implement the LMB filter, and the performance of proposed algorithm is verified via simulations.
Gravitational Search Based Feature Selection for Enhanced Phishing Websites Detection

Phishing is one of the most prominent online security threat that steals the user credentials through fraudulent websites by masquerading as a legitimate website. Many online users have experienced the monitory losses due to this fraudulent attempt of getting financial credentials of user via illegitimate email. The existing phishing detection strategies mainly consider the web page features and URL characteristics. Though numerous phishing detection strategies are proposed in the literature, very few studies consider the feature selection scheme that eliminates the irrelevant or insignificant features for phishing websites detection problem. In this paper, we explored the significant features for detecting the phishing websites based on their relevance to the detection accuracy. The Gravitational Search Algorithm (GSA) is deployed as the feature selector tool to choose the most significant features from the benchmark phishing datasets. Then, the efficiency of the selected features are evaluated using different classification algorithms. From the experimental results it is stated that the features that are selected using GSA performed better than the other feature subsets for detecting the phishing websites.
Arduino based Virtual Keyboard for Locked-in-Syndrome

Locked in Syndrome, is a disorder where, a victim is familiar with the surroundings but unable to react due to loss of ability to move some voluntary muscles in the body except eye movements and eye blinking. Such persons who are facing this condition can neither be able to speak nor could they communicate and interact with others. These patients would be awake and aware of the surroundings, he could think, but unable to express his thoughts to words. Attimo keyboard is the solution where these persons can able to communicate with others with the help of it. It is a virtual keyboard designed to work on the basis of eye blinks which are served as inputs.
The effect of multiple developers on structural attributes: A Study based on java software

Long-term software projects employ different software developers who collaborate on shared artifacts. The accumulation of changes pushed by different developers leave traces on the underlying code, that have an effect on its future maintainability, and even reuse. This study focuses on the how the changes by different developers might have an impact on the code: we investigate whether the work of multiple developers, and their experience, have a visible effect on the structural metrics of the underlying code. We consider nine object-oriented (OO) attributes and we measure them in a GitHub sample containing the top 200 ‘forked’ projects. For each of their classes, we evaluated the number of distinct developers contributing to its source code, and their experience in the project. We show that the presence of multiple developers working on the same class has a visible effect on the chosen OO metrics, and often in the opposite direction to what the guidelines for each attribute suggest. We also show how the relative experience of developers in a project plays an important role in the distribution of those metrics, and the future maintenance of the Java classes. Our results show how distributed development has an effect on the structural attributes of a software system and how the experience of developers plays a fundamental role in that effect. We also discover workarounds and best practices in 4 applied case studies.
Using Augmented Reality Technology in Pathfinding

Nowadays advanced technologies with a variety of enormous capabilities and superior quality characteristics enables software product developers to produce the finest developments in software regardless of the underlying challenge. The invention of augmented reality (AR) is often used in almost every field of this era. AR is a brazen phenomenon with smart systems and mobile devices as part of everyday life. In this sense the accessibility navigation intelligence AR system for the University of Petra in Amman-Jordan has been created, allowing students, guests and staff to find a specific position within the campus and to navigate its environment. In addition, the UOP-WF-AR application of Augmented Reality will gather knowledge about campus lecturers and officials and give guidance in the open air area. The UOP-WF-AR application is expected to help and direct students, guests and staff in the university, by providing useful details regarding outdoor mobility positions and by providing information on lecturers and officials who are not in their offices at all times.
On the Hardware Implementation Performance, of Face Recognition Techniques, for Digital Forensics

As face recognition systems constitute a very useful tool in the sector of Digital Forensics, they should provide accurate and fast results. Although several software implementations for face recognition exist, they are unable to achieve high recognition rate in reasonable time, due to the complex and numerous needed calculations. As a result, there is an on-going research in low-cost hardware implementations, especially based on Field Programmable Gate Arrays (FPGAs), in order to reduce the processing time and the false alarm identification rate. In this work, we introduce the fundamental of algorithms, for face recognition, such as Principal Components Analysis (PCA), and its modified methods, as well as the Local Binary Pattern (LBP). These approaches are applied in different architectures, and alternative FPGA implementations are introduced. We also present implementation synthesis results, based on the recognition time and the allocated hardware resources.
An Analytical View on Political Voting System using Blockchain Technology - UAE Case Study

Voting is a pillar of modern society, but the traditional paper voting system is prone to fraud and failure. Votes can sometimes be miscounted or might be lost in transit. Besides, the electronic voting system has been active subject research for decades. However, the old traditional voting system lacks security and privacy features, which has been a challenge for a long time. As a solution, this paper is proposing a prototype blockchain-based electronic voting system for the United Arab Emirates (UAE) political election process. A blockchain system is a distributed decentralized, immutable database; that allows the data to be distributed and shared among all the participants in a network. Also, it has many advantages that can be applied in many use cases, such as electronic voting systems. Implementing the blockchain-based electronic voting system will minimize the costs of running an election while ensuring integrity. It has the potential to limit voter fraud while making the votes traceable and verifiable, preventing unauthorized activity, and fake votes by authenticating each voter in the system. The UAE has a blockchain initiative that seeks to have 50 percent paperless transactions in all its governmental processes by the end of 2021. This paper studied the effectiveness of adopting blockchain in the UAE political election.
Feature Selection for Data Classification based on Binary Brain Storm Optimization

Brain Storm Optimization (BSO) is an effective population-based optimization method inspired by human brainstorming process. This paper proposes a new binary BSO algorithm (BBSO) to develop a new feature selection approach in order to reduce the number of selected features and/or improve the classification accuracy. Specifically, a new update rule mechanism for generating new solutions is proposed, which improves the convergence speed and reduces the possibility of immature convergence in the algorithm. Furthermore, a fuzzy ARTMAP (FAM) neural network, which is an incremental learning model, is utilized as a classification approach to evaluate the effectiveness of the selected feature subsets. The performance of the proposed method is compared with those from the original BSO, particle swarm optimization (PSO) and genetic algorithm (GA) on eight commonly used and well-known benchmark problems. The experimental results indicate the superiority of the BBSO as compared with other state-of-the-art feature selection methods.
Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions

Prototype or instance selection techniques is an important field of research in knowledge discovery, data mining, and machine learning. In QSAR, the use of prototype selection techniques in the preprocessing stage of the construction of the QSAR models favors the data set curation, improving the interpretability and accuracy of the models as well as the performance of the algorithms. In this paper, we propose an efficient method for prototype selection to be used in the preprocessing stage of the construction of QSAR classification models. The proposed method is able to generate very high reduction rates in the cardinality of the training set while maintaining or even increasing the accuracy of the classification models. The validation of the method has been carried out by means of the prediction of external molecules, demonstrating that the prediction of new molecules is also maintained or even improved. The method has been tested using 40 benchmark data sets of different sizes and balancing ratios; the results of the tests have demonstrated the wide applicability domain of the proposed method.
Schnorr-Based Implicit Certification: Improving the Security and Efficiency of Vehicular Communications

In the implicit certification model, the process of verifying the validity of the signer's public key is combined with the verification of the signature itself. When compared to traditional, explicit certificates, the main advantage of the implicit approach lies in the shorter public key validation data. This property is particularly important in resource-constrained scenarios where public key validation is performed very often, which is common in vehicular communications (V2X) that employ pseudonym certificates. In this article, we show an alternative Schnorr-based implicit certification procedure that can improve the efficiency of a popular V2X-oriented Vehicular Public Key Infrastructure (VPKI), the Security Credential Management System (SCMS). As an additional contribution, we show that SCMS's underlying certificate provisioning procedure, based on butterfly keys, is vulnerable to existential forgery attacks under certain conditions. We then discuss how this issue can be fixed in an effective and efficient manner.
Enhanced Blowfish Algorithm for Image Encryption Based on Chaotic Map

Image security is essential, topic through the increase of the image usage in most of communications besides assures information security which is hidden in these images such as military and medical images. Blowfish is outstanding symmetric cryptography, which has the benefits of strong ability to resist attackers. Thus it is widely used in data encryption, but it is not suitable for image encryption because of the huge data, higher redundancy and toughly correlation of image pixels. The proposed enhanced method has been employed Blowfish based image encryption technique. Firstly the digital image is decomposed into several key-based blocks randomly to decorrelated the relationship between original and processed image then each block is passed through the Blowfish algorithm. The proposed system is designed to take advantage of the powerful facility, which is supported by a chaotic map resulting in a much-improved security/performance trade-off. As a result, the proposed system offers good performance for image encryption. To ensure improved Blowfish encryption algorithm, the implementation of both techniques has been carried out for experimental purposes which are showed that the original image has a flat histogram after encrypted, a decreasing correlation between adjacent pixels in all colour components and increasing entropy for the cases studied. A comparative study with previous Blowfish algorithm shows the superiority of the modified algorithm.
TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural Networks

Binarization is an attractive strategy for implementing lightweight Deep Convolutional Neural Networks (CNNs). Despite the unquestionable savings offered, memory footprint above all, it may induce an excessive accuracy loss that prevents a widespread use. This work elaborates on this aspect introducing TentacleNet, a new template designed to improve the predictive performance of binarized CNNs via parallelization. Inspired by the ensemble learning theory, it consists of a compact topology that is end-to-end trainable and organized to minimize memory utilization. Experimental results collected over three realistic benchmarks show TentacleNet fills the gap left by classical binary models, ensuring substantial memory savings w.r.t. state-of-the-art binary ensemble methods.
AR Navigation Application based on iOS Platform

In this paper, an Augmented Reality Navigation Application based on iOS platform is designed. This application is composed of five modules: the Location Module, the Digital Map Module, the Approximate Module, the Estimate Module and the AR Module. The Location Module requests current location via GPS in real-time and obtains the details of destination from map server. The Digital Map Module provides a Satellite Imagery View which superimposes navigation routes with marks such as current location, starting and destination. The Approximate module produced an approximation to fix the bias when converting the geographical coordinates between WGS-84 and GCJ-02 system. The Estimate Module produced a 3D position estimation according to a geographical coordinate provided by GPS or map server. This estimation will be used for placing the marks and routes in AR view. The AR Module provides an Augmented Reality scene superimposed destination and the route on a real-world background with a real-world scale.
A Recommendation System for Functional Features to aid Requirements Reuse

Software product lines (SPL) engineering is an efficient means to enhance software quality, support requirement reuse and develop variants of products. Functional and nonfunctional features can be extracted from SRS docs of ancestry built artifacts to aid RR. In this paper we offer a recommendation web tool (prototype) to extract functional features and calculating reusability for amount of data available in the form of SRS of already developed systems. In initial-level, SRS docs are feed into system. System accesses natural language requirements automatically from SRS. Terms extraction is performed which depends on keyword occurrences from several combinations of nouns, verbs, and/or adjectives. Phrases that reflect functional features reside on SRS docs were extracted by using information retrieval (IR). FRs are then stored in knowledgebase automatically. In Secondary-level, requirement analyst inputs summary of prospective system and selects the operation to perform i.e. simple and advance search. System applies POS-tagger technique on software summary for tokenization to search functional features. These tokens are then passed to inference engine to match between knowledgebase to identify which features could be recommended to analyst to aid RR. Matched features with queried features are prioritized using collaborative filtering to assist requirement analyst in making right decision in different software engineering tasks, starting from forming the teams and specifying the requirements to subsequent projects.
A Method for Quantitative Steganalysis Based on Deep Learning

In this paper, a quantitative method of image steganalysis for LSB matching is proposed. It applies Ye network (YeNet) which has the best performance in field of binary steganalysis at present. According to experimental observation that through YeNet the final feature output of stego image changes regularly with the embedding rate, the mapping relationship between embedding rate and feature distribution can be fitted, so as to estimate the embedding rate of a given stego. Compared with previous researches, this method has a better performance and the advantage is more pronounced for low embedding rates.
L0 Regularization based Fine-grained Neural Network Pruning Method

Deep neural networks have made remarkable achievements in many tasks. However, we are not able to deploy such successful but heavy models on mobile devices directly due to the limited power and computing capacity. Thus, an obvious solution to tackle this problem is to compress neural network by pruning useless weights in neural networks. The point is how to remove these redundancies while maintain the performance of neural networks. In this work, we propose a novel neural network pruning method: guiding the weights of a neural network to be sparse by introducing LO regularization during the training stage, which can effectively resist the damage on the performance while pruning as well as dramatically reduce the time overhead of retraining stage. Experiment results on MNIST with LeNet and CIFAR-10 with VGG-16 demonstrate the effectiveness of this method to the classic method.
Combined computational quantum chemistry and classical electrodynamics approach for surface enhanced infrared absorption spectroscopy

Surface enhanced spectroscopy, which enhances the signal intensity of molecules on a surface, facilitates the study of molecular properties, even down to a single-molecule level if a scanning probe is used. To realize the full potential of surface enhanced spectroscopy, a clear theoretical understanding is indispensable. However, quantum chemical calculations for surface enhanced spectroscopy are not simple because of the violation of the widely used dipole approximation. The spatial structure of electric near-field in the close proximity of a surface strongly depends on the geometry of the metal nanostructure as well as on the incident wavelength. Therefore, in principle, a universal model for electric near-field cannot exist. To address this issue, we have developed a generalized light–matter interaction model from first-principles quantum chemical calculations by using the multipolar Hamiltonian, in which the spatial structure of the electric field is fully considered. Here, we incorporate computational electrodynamics for surface enhanced infrared (IR) absorption spectroscopy in the model, where electric near-field around a Ag ellipsoid is obtained and used for IR calculations. Furthermore, we have devised a method to successfully reproduce the peak selectivity observed experimentally.
Blockchain based Competent Consensus Algorithm for Secure Authentication in Vehicular Networks

Vehicular networks are constructed for efficient and secure data flow to make intelligent transportation system. It works by using central storage system which is inefficient as large amount of data is difficult to handle and also becomes the target of malicious activity. The flow of sensitive data on wireless channels for communication of vehicles with other nodes can also be chosen by attackers for attack. These challenges are tackled using blockchain solutions. The blockchain, a farfetched decentralized technology which is undoubtedly going to change overall trends of entire business and professional networking environment in the world to release the burden of centralization on the organizations even on the individuals and several developed countries have started to adopt this trend. This technology would be as dynamic, vibrant, efficient and secure as the category of consensus algorithms is used for the agreement and decision of joining the new block among network nodes. Security and efficiency of vehicular network is maintained through consortium blockchain boasting with Practical Byzantine Fault tolerance (PBFT) algorithm.