Reviewerly/exHarmony huggingface dataset:
Evolutionary dataset optimisation: learning algorithm quality through evolution

Abstract In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the ‘best performing’. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric. These datasets can be studied so as to learn what attributes lead to a particular progression of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a case study in clustering is presented. This case study demonstrates the performance and nuances of the method which we call Evolutionary Dataset Optimisation. In this study, a number of known properties about preferable datasets for the clustering algorithms known as k -means and DBSCAN are realised in the generated datasets.
IMPACT OF KNOWLEDGE ORIENTED LEADERSHIP ON KNOWLEDGE MANAGEMENT PROCESSES IN THE MIDDLE EASTERN AUDIT AND CONSULTING COMPANIES

Purpose – this article aims to evaluate the influence of knowledge oriented leadership on knowledge management processes and the influence of those processes on the performance of the Middle Eastern audit and consulting companies. Research methodology – analysis of scientific literature, structural equation modelling and expert evaluation (structured questionnaire) were used in order to create hypotheses and research model, to collect data from the audit and consulting companies’ experts and to test research model and related hypotheses. Findings – the results of this research supported eight out of ten suggested hypotheses. Empirical evidence shows that leadership has a positive impact on knowledge acquisition, storage, and sharing processes in the Middle Eastern audit and consulting companies and knowledge management processes (creation, acquisition, sharing, storage and application) have positive influence on organizational performance. Research limitations – conducting the research in the Middle Eastern audit and consulting companies limits the possibility of generalizing the results to other types of businesses sectors as well as other geographical areas. Practical implications – conducted research results have practical value for audit and consulting companies as this study analyses the concept of knowledge oriented leadership and its impact on knowledge management processes and organizational performance. Originality/Value – this research investigated one of rare studies in the Middle East business sector, where the audit and consulting companies’ performance affected by the knowledge management processes was assessed considering knowledge oriented leadership as an influential factor that affects the knowledge management processes implementation.
Blockchain-based Object Name Service with Tokenized Authority

Today, the Internet of Things (IoT) technology is applied everywhere, providing tremendous amounts of IoT service. The GS1, a non-profit international standards organization, has established standards for IoT technology. Especially, the GS1 standardized an Object Name Service (ONS) leveraging DNS's distributed and federated infrastructure, enables users to manage and discover IoT services such as the retail, food, healthcare, smart city, and so on. However, the ONS is vulnerable to the data tampering, privilege escalation, and service fault caused by DNS attacks including the man in the middle, cache poisoning, replay, hijacking, and denial of service attacks. Nowadays, IoT services are used in security-sensitive areas, such as finance and healthcare. Therefore, the security of ONS should be strengthened before causing severe problems such as data breach, economic loss, and social loss. In this paper, we propose a blockchain-based ONS with a tokenized authority called the BlockONS. The BlockONS provides strength in the data tampering and privilege escalation allowing a fault tolerance. The BlockONS consists of a content off-chain scaling, a data tampering validation method, a fault-tolerance method, and a Blockchain Token-Based Access Control (BTBAC) method. We designed the BlockONS into two parts: A BlockONS Node part manages the validation method and BTBAC model. A BlockONS Agent part manages the off-chain scaling and fault tolerance. Finally, we implemented the BlockONS leveraging a Hyperledger Sawtooth blockchain. We proved the proposed validation method, fault tolerance method, and BTBAC method through use cases and security analyses on attack situations. We deployed the BlockONS in the Daejeon city and evaluated the performance to show the feasibility of the BlockONS.
Guidelines to Evaluate the Usability and User Experience of Learning Support Platforms: A Systematic Review

Nowadays, the usability and the user experience, are important aspects for the success of any software product. In the educational domain, Learning Support Platforms are not the exception, and these quality attributes become essential to guarantee an appropriate teaching process. For this reason, in this study, we present a systematic literature review whose purpose was to identify the characteristics that these types of systems must meet to obtain usable products. In the same way, the features that contribute to the assurance of a satisfying user experience have been cataloged. The purpose of this study is to establish the basis towards the elaboration of a future framework to quantify the level of usability and user experience of learning support platforms. The systematic search retrieved a total of 105 studies, from which 23 were selected as relevant. A set of 15 sub-attributes with their corresponding guidelines were identified to serve as a guide to design graphical user interfaces in a correct way in this type of software systems.
How Do Image Quality Metrics Perform on Contrast Enhanced Images?

For many years different objective Image Quality Metrics (IQMs) have been introduced with the goal of modeling the perceptual quality scores given by observers. While these IQMs perform well on evaluating the quality of distorted images, little attention has been paid on how they perform when applied on enhanced images. Therefore, we focus on the performance of IQMs on contrast enhanced images. We have collected a dataset of images and applied four contrast enhancement techniques on them. A subjective test is then performed on the images in our dataset. Finally, we measured the objective quality of images using different IQMs. The results showed the IQMs are not well correlated with the perceived quality of images when contrast has been enhanced.
Genetic Fuzzy Systems for Decentralized, Multi-UAV Cargo Handling

Genetic-fuzzy control methodology is utilized to enable an intelligent and adaptive multi-UAV system to transport a load attached with cables to a given target location. The UAVs (considered as simplified quadcopters) are controlled in a decentralized fashion, reducing communication complexity, and increasing the adaptability and success rate of the overall system. A set of Fuzzy Inference Systems (FIS) tuned via a Genetic Algorithm determines the behavior of each quadcopter based on its current state, the state of the load, and its relation to a set of its neighbors. The combination of all the drones’ behaviors results in group intelligence and the completion of the assigned task. The controllers are created and tuned using a cascading Genetic Fuzzy Tree structure to minimize computational cost. Results show the collective of drones transporting the load progressively closer to a target location over 100 generations, while avoiding collisions with one another and the payload. The proposed architecture shows the potential for intelligent controllers enabling adaptable and collaborative systems.
Personalized recommendation via network-based inference with time

Wide attention has been given to the bipartite network-based recommendation method because of its higher accuracy, better diversity and lower computational cost than the global rank method and the standard collaborative filtering method. However, prior studies on bipartite network treated equally the ratings obtained at different time windows, which clear mismatches with the practical situation because the items collected recently should maintain more significance than those collected long ago. Besides, time impact has not yet been systematically studied as an essential context with the consideration of user preference drift in bipartite network. This paper proposes a personalized recommendation method named network-based inference with time (NBIt). We process time information firstly by mapping the ratings in short-time windows to long-time windows. Then, a suitable time attenuation function is selected to ensure a real reflection of user preference. And then, we set the initial resource and attractive power of network. Finally, the recommendation process is elaborated. To avoid the risk of optimized bias and over-fitting, we employ the triple division technique to optimize the long time window parameter and the attraction power parameter. Experimental results from two benchmark datasets of different scales show that the proposed NBIt algorithm surpasses the other five representative and advanced bipartite network recommendation methods in both accuracy and personalization. Furthermore, the proposed NBIt method can be used as a framework in which other network-based recommendation algorithms along with their variants are run with accuracy and diversity likely to be improved.
Point Siamese Network for Person Tracking Using 3D Point Clouds

Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.
Deep learning based QoE evaluation for internet video

The issue of quality of experience (QoE) evaluation in Internet video is challenging for mainly two facts. One is the sophisticated interactions between features from different entities: user, system and context. The other is the heterogeneity of feature type, i.e., sequential and non-sequential characteristics of features. To address above challenges, we propose a hybrid network model that integrates a deep neural network (DNN) and an improved recurrent neural network (RNN) for representation learning of view-level QoE evaluation. Non-sequential side information and time difference of sequential features are incorporated to different layers of RNN. Attention mechanism is applied for further improvement of RNN. Based on the output of attention network, we propose a graph-based ranking algorithm to conduct user behavior analyzing, which is useful to online decision of service providers. We conduct experiments on a real-world dataset from a large-scale VoD services provider. The results demonstrate that our method is more effective over the state-of-the-art methods for QoE evaluation.
C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time - rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p <; 10^-5.
Introduction to Wearable Computers

This chapter introduces wearableWearability computingComputing, beginning with fundamentalFundamental conceptsConcepts and definitions about wearable technologiesTechnology. Then, it provides a historic view of the fieldField, describing how wearable technologiesTechnology evolved since 1970 throughout the past decades. To illustrateIllustrates several different examplesExample of wearable devicesDevices, a varietyVariety of formForm factorsFactors are presentedIllustrates, ranging from wristWrist-wornWrist-worn wearables and head-mountedHead-mounted displaysDisplay to smartSmart garmentsGarment. This chapter also describes wearable sensorsSensor used to collect inward dataData from usersUser, such as physiologicalPhysiological signalsSignal, as well as environmentalEnvironmental clues from the users’User surroundingsSurroundings, such as temperatureTemperature, lightLight levelsLevel, and humidityHumidity. The main featuresFeatures of wearable technologiesTechnology are characterized, concerning dataData collectionCollection, processing, and deliveryDelivery of informationInformation, servicesServices, and resources for endEnds usersUser. The lastLast part of the chapter concludes with an extensive list of examplesExample of applicationApplication domainsDomain in which wearables have been successfully employed thus far. Specifically, the examples describe wearableWearability applicationsApplication implemented to support humanHuman activitiesActivity in education, health careHealth care, industryIndustry, fitnessFitness and sportsSport, assistiveAssistive technologiesTechnology, musicMusic, leisureLeisure, artsArts and entertainmentEntertainment, safetySafety criticalCritical systemsSystem and the militaryMilitary, and userUser interactionInteraction.
Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval

Retrieving relevant information from Twitter is always a challenging task given its vocabulary mismatch, sheer volume and noise. Representing the content of text tweets is a critical part of any microblog retrieval model. For this reason, deep neural networks can be used for learning good representations of text data and then conduct to a better matching. In this paper, we are interested in improving both representation and retrieval effectiveness in microblogs. For that, a Hybrid-Deep Neural-Network-based text representation model is proposed to extract effective features’ representations for clustering oriented microblog retrieval. HDNN combines recurrent neural network and feedforward neural network architectures. Specifically, using a bi-directional LSTM, we first generate a deep contextualized word representation which incorporates character n-grams form FasText. However, these contextual embedded existing in a high-dimensional space are not all important. Some of them are redundant, correlated and sometimes noisy making the learning models over-fitting, complex and less interpretable. To deal with these problems, we proposed a Hybrid-Regularized-Autoencoder-based method which combines autoencoder with Elastic Net regularization for an effective unsupervised feature selection and extraction. Our experimental results show that the performance of clustering and especially information retrieval in microblogs depend heavily on features’ representation.
LACS: A High-Computational-Efficiency Accelerator for CNNs

Convolutional neural networks (CNNs) have become continually deeper. With the increasing depth of CNNs, the invalid calculations caused by padding-zero operations, filling-zero operations and stride length (stride length 1) represent an increasing proportion of all calculations. To adapt to different CNNs and to eliminate the influences of padding-zero operations, filling-zero operations and stride length on the computational efficiency of the accelerator, we draw upon the computation pattern of CPUs to design an efficient and versatile CNN accelerator, LACS (Loading-Addressing-Computing-Storing). We reduce the amount of data movements between registers and the on-chip buffer from O(k × k) to O(k) by a bypass buffer mechanism. Finally, we deploy LACS on a field-programmable gate array (FPGA) chip and analyze the factors that affect the computational efficiency of LACS. We also run popular CNNs on LACS. The results show that LACS achieves an extremely high computational efficiency, 98.51% when executing AlexNet and 99.66% when executing VGG-16, significantly exceeding state-of-the-art accelerators.
Review and Open issues of Cryptographic Algorithms in Cyber Security

Security has been a primary focus in all the areas of communications and also has become a big problem for all kind of companies dealing with heterogeneous data. To overcome the security issues, we have cybersecurity specialists and experts around the world but the finding and hiring these people are difficult due to expenses accordance with projects. The researchers have focused on some of the issues of cryptographic algorithms to review it and provide solutions in terms of research and development. Furthermore, we discuss cryptographic algorithms which may help to reduce security issues around the world, but some issues are rigorous, so will discuss here and review it according to standard encryption and decryption methods in cryptography. Some of the encryption and decryption algorithms consider and explain some excellent examples for the researchers to find it for a better comparison in the field of cybersecurity. Our effort may help researchers to enhance security capabilities in terms of cryptographic algorithms in general. We provide a summary of different algorithms in this paper and elaborate open issues together with discussions that may help us to reduce problems of security issues.
Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

To solve problem of the reliability and consistency of silver-zinc batteries after being sorted into groups, a proposed classification strategy of zinc-silver battery based on least squares support vector machine with PSO (PSO-LSSVM) was proposed in this paper.Sample data was extracted from the charging curve of silver-zinc batteries to pre-sort training samples using FCM clustering.The least squares support vector machine model parameters were optimized and improved using particle swarm optimization algorithm.The method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, and enhances model training speed and accuracy.In the end, experimental data was used for battery classification model training and testing.Test results show that the battery pack obtained by the group strategy has good dynamic consistency, the rate of capacity decay is significantly reduced.The rate of capacity decay is no more than 10% after 30 cycles of life test.The silver-zinc battery group classification strategy proposed to this paper improves the consistency and reliability of the battery and the life of battery packs.
A Hybrid Cultural Harmony Search Algorithm for Constrained Optimization Problem of Diesel Blending

This paper studies the constrained optimization problem for nonlinear diesel blending. A new hybrid algorithm called cultural harmony search algorithm is presented to solve the proposed optimization problem, which uses cultural knowledge in the belief space of the cultural algorithm to guide the evolving and searching process of the harmony search algorithm. Then, an improved harmony improvisation in the population space of cultural algorithm is developed for new harmony generation to enrich the population diversity. Moreover, in order to accelerate convergence, the domain of decision variables is scaled down by a simplex method at the beginning of the algorithm, and a simplex improved cultural harmony search algorithm is provided. Finally, benchmark functions and the results of application in nonlinear diesel blending of a real-world refinery show the feasibility and effectiveness of the proposed algorithms. The contrasted experiments show that our proposed hybrid algorithm is better than other hybrid algorithms, especially in diesel blending optimization problem.