Reviewerly/exHarmony huggingface dataset:
An Innovative Technology: Augmented Reality Based Information Systems

In our generation the information systems evolve with new technologies: augmented reality (AR), IoT, artificial intelligence, blockchain etc. Anymore they perform information exchange by sensors. It is estimated that the systems will be in a state of extreme interaction and reach 50 billion devices connected in Internet in 2020. We know that everything around us will be in interaction and they will do everything without any need of human interference. For example, when our dishwasher is full, it will start to wash automatically, or when the run out of the gasoline, our car will drive to the nearest station, or even when a burglar is entered to our house, it will automatically be detected and be announced to the police office. In business life, the processes will be automatical in maximum level and this technology will increase productivity and efficiency. Next to mobile technology, it is thought that these new generation information systems (IS) will take the biggest place in our lives. AR also will be integrated to these systems to augment the information in real world. Humanity will augment its habitat in an innovative way thanks to these AR based IS. This paper surveys the current state-of-the-art AR systems related with aerospace & defense, industry, education, medical and gaming sectors. The connection of AR based IS and innovation is explained with a technological insight. In addition to international use cases HAVELSAN's use cases are also given that are performed from the aspect of applied open innovation strategy. This strategy is addressed specific to the implemented activities of AR based IS.
Hybrid grasshopper optimization algorithm and differential evolution for global optimization

Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper’s behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces.
Towards a Software Sustainability-Quality Model: Insights from a Multi-Case Study

Background. Software sustainability is defined in terms of multiple and interdependent dimensions (economic, social, technical and environmental). Preliminary initiatives have investigated the contribution of certain quality attributes to sustainability dimensions. Problem. Despite these valuable efforts, the characterization of software sustainability is still a key challenge. This entails how sustainability can be embraced in the design of software systems by identifying the relevant software quality attributes (QAs) and their dependencies. Both attributes and dependencies vary heavily with amongst others the type of software system and its operational context. Aim and Method. We followed a multiple case study research method with the main objective of investigating the applicability of the sustainability model in different contexts. We aim also to enrich our model, by means of identifying missing quality attributes or new contributions to the sustainability dimensions. We selected two software projects as cases of our study, where each one was independently conducted in specific situations. Results. The results of the study show that the relevant quality requirements identified in both projects (cases) are covered by most of the QAs related to the social (82%) and technical (83%) dimensions. Moreover, some QAs that were not addressed in the corresponding projects, their relevance like context completeness, and flexibility were acknowledged. These results suggest that the software sustainability model could support the identification of relevant QAs. The case study also contributed to identify QAs that had not been considered in the economic, technical and social dimensions of the sustainability-quality model.
Consistent estimation of rotational optical flow in real environments using a biologically-inspired vision algorithm on embedded hardware

Insects are able to freely navigate through ever-changing environments using predominantly visual inputs, while possessing very minimal processing power compared to humans. Not only are they able to move at high velocities and accelerations, but they are also able to achieve extraordinary levels of obstacle avoidance. We begin to emulate this biological behaviour in a robotic application by first modelling how these visual pathways react to separable degrees of freedom within the motion field, specifically rotation in this case. We have developed upon an existing biologically-inspired algorithm based on the visual pathway of the hoverfly, and statistically compare results to current state-of-the-art algorithms, all the while performing this on computationally-constrained embedded hardware. We have shown that, using a complex, highly-elaborated representation of the hoverfly visual pathway, rotation optical flow estimations can be achieved with a high level of accuracy, at a level of consistency previously unseen in dense-flow algorithms, and they can be achieved at 100 frames per second on an embedded system. This work forms a fundamental basis to understanding one of the two separable components of insect egomotion (rotation and translation), allowing for consistently accurate rotational velocity estimation, providing a building block towards understanding the translational component of insect vision and the application of biologically-inspired egomotion estimation in autonomous vehicles.
The Identity, Fungibility and Anonymity of Money

The fungibility of money is a characteristic which contributes to the quality of money. Fungibleness is itself related to the technical ability to associate a unit of currency with its past instances of exchange. This history is analogous to the identity of money. The identity of an individual unit of exchange is increasingly important as cash becomes less common, and banks require more information about the provenance of money. Private currencies, including Bitcoin and Libra, are themselves subject to tracking. The prior financial – and potentially political – activities of a user determine the fungibility of the currency they hold. Different money technologies provide varied levels of privacy, while cryptocurrencies offer users the potential to choose the level of information they share.
Preference enhanced hybrid expertise retrieval system in community question answering services

Here, we propose a preference enhanced hybrid expertise retrieval (PEHER) system in community question answering services. PEHER consists of three segments, namely, preferability estimator, authority estimator, and expertise estimator. The preferability estimator utilizes the textual information to determine both intra-profile and inter-profile preferences of answerers for each term. The intra-profile preferences consider the preference of a term using the answering history of a given answerer. The inter-profile preferences incorporate the preferences of all answerers for a term. These preferences are then used to determine the preferability of each answerer for each of the archived questions. The authority estimator considers the textual familiarity between each archived question and the profile of each answerer as the weight of the associated link in the network. The expertise estimator is composed of three blocks, namely, question similarity finder, proficiency estimator, and expert list generator. The question similarity finder finds the similarities between the new question and each of the archived questions. The proficiency estimator uses the said similarities of the archived questions along with their preferabilities to decide the proficiencies of answerers for the new question. Finally, the expert list generator considers the authorities and proficiencies to generate a list of experts for a given question. We compare PEHER with twenty existing methods on four real-world datasets using five performance measures. We find that PEHER outperforms the comparing algorithms in 92.00% (368 out of 400) cases.
Enhancing Hospitality Sentiment Reviews Analysis Performance using SVM N-Grams Method

Sentiment analysis or opinion mining is an analysis conducted to derive meaningful information or sentiments contained in an opinion. The use of sentiment analysis has spread in various fields, also exists in the tourism sector. Many tourists are actively reading and writing reviews on travel websites or travel platforms. Whereas in the review information contained useful information for the company or hotel manager, considering that the hospitality industry is very competitive. This analysis produces knowledge about sentiment from the review text data using approaches of n-grams to increase the level of accuracy according to the literature proven. This research uses SVM as a review classification method with positive and negative sentiment. The results of this research indicate an average level of accuracy of 94% which is greater than the level of accuracy in previous research using the same data. In addition, this research shows that the use of SVM as a classification model produces a higher level of accuracy than the Recursive Neural Tensor Network (RNTN).
Could SOAR save skills-short SOCs?

Over the past year or so it's becoming clear that, when it comes to a cyber breach, it's no longer a matter of if it will happen, but when. With threats coming in thick and fast, organisations, and the cyber security professionals in their employ, are increasingly struggling to resist each and every threat; and they know it. A 2018 CyberEdge survey revealed that 77% of respondent organisations were compromised during the 12 months ahead of the study. 1 With a skills shortage and working with tight budgets, security operations centres (SOCs) are struggling with limited resources. The problems faced by SOCs need a solution, and embedded security orchestration, automation and response (SOAR) promises to be just that. A SOAR approach automates a lot of the mundane tasks usually undertaken by SOCs. By reducing the day-to-day workload through improved intelligence and reporting, streamlined workflows and playbooks for automated response actions, SOAR can enable cyber security professionals to put their talents and knowledge to better use, explains Ross Brewer of LogRhythm.
A Study on Convolution Neural Network for Breast Cancer Detection

Mammography is a widely used imaging technology for the detection and diagnosis of breast cancer. A computer-aided automatic classifier with the help of machine learning can improve the diagnosis system in terms of accuracy and time consumption. These types of system can automatically distinguish a benign and malignant pattern in a mammogram. Deep learning algorithms have gained a lot of popularity in recent years. Convolution Neural Network has become a preferred choice for images analysis including a mammogram. In this paper, we review various deep learning concepts applied to breast mammogram analysis and summarizes contributions to this field. We present a summary of the recent developments and a discussion about the best practices done using CNN in mammogram analysis and improvements that can be done in future research.
Further Optimizations of CSIDH: A Systematic Approach to Efficient Strategies, Permutations, and Bound Vectors

CSIDH is a recent post-quantum key establishment protocol based on constructing isogenies between supersingular elliptic curves. Several recent works give constant-time implementations of CSIDH along with some optimizations of the ideal class group action evaluation algorithm, including the SIMBA technique of Meyer et al. and the "two-point method" of Onuki et al. A recent work of Cervantes-Vázquez et al. details a number of improvements to the works of Meyer et al. and Onuki et al. Several of these optimizations—in particular, the choice of ordering of the primes, the choice of SIMBA partition and strategies, and the choice of bound vector which defines the secret keyspace—have been made in an ad hoc fashion, and so while they yield performance improvements it has not been clear whether these choices could be improved upon, or how to do so. In this work we present a framework for improving these optimizations using (respectively) linear programming, dynamic programming, and convex programming techniques. Our framework is applicable to any CSIDH security level, to all currently-proposed paradigms for computing the class group action, and to any choice of model for the underlying curves. Using our framework we find improved parameter sets for the two major methods of computing the group action: in the case of the implementation of Meyer et al. we obtain a 13.04% speedup without applying the further optimizations proposed by Cervantes-Vázquez et al., while for that of Cervantes-Vázquez et al. under the two-point method we obtain a speedup of 5.23%, giving the fastest constant-time implementation of CSIDH to date.
A Communication Aid System for Deaf and Mute using Vibrotactile and Visual Feedback

Assistive technology is quite necessary to establish a two-way communication among deaf, mute and normal people where none of them needs to acquire knowledge of sign languages. In this paper, we propose a novel communication aid system that assists the deaf and mute to communicate independently with no use of sign language. Usually, deaf and mute, and normal people communicate with each other using visual references and simple sentences when none of them knows the sign languages. These small talks are mainly based on some contexts or keywords which can be delineated visually. Again these keywords can be classified based on syllables for the purpose of vibrotactile outputs so that the speech is easily comprehended. Therefore our proposed system is emphasized on both visual and vibrotactile feedback while communicating. An Android app has been developed which is focused on multimodal approach can be able to convert speech to visual contexts and vibrations, and similarly, the contexts and vibrations can be converted to speech. To validate the program, the system was experimented by six deaf and mute participants, two normal persons, and two examiners. Finally, the study of our experimentation reflects the effectiveness and usability of our system.
A revisit to solar radiation estimations using sunshine duration: analysis of impact of these estimations on energy yield of a PV generating system

(2019). A revisit to solar radiation estimations using sunshine duration: analysis of impact of these estimations on energy yield of a PV generating system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. Ahead of Print.
An effective cybernated word embedding system for analysis and language identification in code-mixed social media text

The language used by the users in social media nowadays is Code-mixed text, i.e., mixing of two or more languages. This paper describes the application of the code mixed index in Indian social media texts and comparing the complexity to identify language at word level using Bi-directional Long Shor t Term Memory model. Social media platforms are now widely used by people to express their opinion and interest. The major contribution of the work is to propose a technique for identifying the language of Hindi-English code-mixed data used in three social media platforms namely, Facebook, Twitter, and WhatsApp. We recommend a deep learning framework based on cBoW and Skip gram model that predicts the origin of the word from language perspective in the sequence based on the specific words that have come before it in the sequence. The context capture module of the system gives better accuracy for word embedding model as compared to character embedding.
Eye Controlled Wheelchair Using Transfer Learning

The freedom of mobility affects an individual's sense of prominence and confidence. Because of diseases injuring the nervous system like Amyotrophic Lateral Sclerosis (ALS) and Parkinson disease, people lose their ability to move outmoded wheelchairs. This paper presents a novel technique to control a wheelchair by using eye movements. Eye controlled chair comprised of an electric wheelchair, a webcam in front of the user's eye capturing eyeball movements with a low-cost Raspberry pi system, serially communicating with Arduino microcontroller to drive wheelchair in the desired direction. The transfer learning approach was adopted instead of traditional image processing techniques, making wheelchair more reliable and accurate. Keras deep learning pre-trained VGG-16 model led us to achieve excellent performance, with very little training dataset. Unlike conventional wheelchairs, presented methodology makes this wheelchair equally suitable for people wearing eye glasses.
A Formal Methods Approach to Security Requirements Specification and Verification

The specification and the verification of security requirements is one of the major computer-based systems challenges. Security requirements need to be precisely specified before a tool can manipulate them, and though several approaches to security requirements specification have been proposed, they do not provide the scalability and flexibility required in practice. We take this problem towards an integrated approach for security requirement specification and treatment during the software architecture design time. The general idea of the approach is to: (1) specify security requirements as properties of a modeled system in a technology-independent specification language; (2) implement the developed model in a suitable language with tool support for requirement satisfaction through model verification; and (3) suggest a set of security policies to constrain the operation of the system and to guarantee the security properties. In the scope of this paper, we use first-order logic as a formalism that is abstract and technology-independent and Alloy as a tooled language used in modeling and software development. To validate our work, we explore a set of representative security properties from categories based on CIA classification in the context of secure component-based software architecture development.
Real-time Detection of Vehicle and Traffic Light for Intelligent and Connected Vehicles Based on YOLOv3 Network

Real-time detection of vehicle and traffic light is essential for intelligent and connected vehicles especially in urban environment. In this paper, a new vehicle and traffic light dataset is established and a real-time detection model of vehicle and traffic light based on You Look Only Once (YOLO) network is presented. A joint training method for target classification and detection is proposed by YOLOv3, aiming to balance the detection accuracy and speed. The YOLOv3 network has lower requirements on hardware devices than other target detection algorithms like Faster R-CNN. Through the experimental analysis of the measured images in urban environment, it is shown that the designed model can not only satisfy the real-time requirements, but also improve the accuracy of the detection of vehicles and traffic lights.
Analytical Formulation for the Quantum Radar Scattering of the Rectangular Plate

There has been much recent interest in quantum information for applications to remote sensing such as in quantum radar. In order to analyze and solve the prediction of scattering problems better, an analytical expression has been proposed for the quantum cross section (QRCS) of a rectangular target with single photon pulse, and it can predict the QRCS in both monostatic and bistatic scenes for different pitch and azimuthal angles. In addition, the comparison of our numerical simulation results with the analytical solutions reveals that they agree with one another extremely well. Finally, we also used the predicted data to illustrate the cause of the sidelobe enhancement effect between monostatic QRCS and corresponding the classic radar cross section (CRCS).
A Survey on: Facial Emotion Recognition Invariant to Pose, Illumination and Age

Facial Emotion Recognition (FER) gained considerable interest among scientists working in areas like medical diagnosis, forensics and communication technology. Numerous studies have already been conducted on facial emotion recognition (FER) but none seems to deal with pose, illumination and age invariance simultaneously. This paper reviews several challenges for pose, illumination and age invariance for FER. Here we also provide a survey on deep learning methodologies used in FER. It is noted that deep learning algorithms provide greater accuracy than all other algorithms previously used.
Design of a Trustless Smart City system: The #SmartME experiment

A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered to cooperate for the greater good of the respective communities, and one of the most powerful ways to attain that is by actually decentralizing any process that can escape single-authority control and the grip of centrally-managed bureaucracies, whilst at the same time not relying on any single party trusting any other. In line with this key insight, in this paper we have designed and implemented a decentralized, trustless DLT-based system for (environmental) sensing, data acquisition, storage, and consumption, in the context of a real-world Smart City deployment, #SmartME, and with the participation of independent, institutional, stakeholders. This Open Data system has been devised to be user-friendly, with an end-user wizard for trustless (i.e., independent) data audit. Indeed, the data collection layer does not trust any IoT node by default, but requires nodes to authenticate themselves when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party. In this work we extend our preliminary proof-of-concept design, and explore the expected outcomes of scaling the system to comprise an even greater number of active institutions participating in the validation process, by carrying out simulation experiments. Results from these experiments help us in validating our approach and assessing its scalability.
Comparison Of Grapheme-to-Phoneme Conversions For Spoken Document Retrieval

The article contains analysis of spoken document retrieval techniques which apply word similarity based on phonemic transcriptions building or approximate string matching. Results are obtained on the collection of spoken documents with speech on Russian language. Grapheme-to-phoneme conversion methods based on a hidden Markov model and 1,2-order finite Markov chain is discussed on the article.