Reviewerly/exHarmony huggingface dataset:
Evaluation of Usability Dimensions of Smartphone Applications

This study analyses different techniques used for evaluation of various usability dimensions of software applications (apps) being used on the smartphones.The scope of this study is to evaluate various aspects of the usability techniques employed in the domain of smartphone apps.Usability assessment methodologies are evaluated for different types of applications running on different operating systems like Android, Blackberry and iOS, etc. Usability evaluation techniques and methodologies with respect to usability heuristics like field experiments, laboratory experiments models and usability standards are discussed in detail.The issues for evaluation of usability of smartphone apps are identified by considering limitations and areas of improvement outlined in the contemporary literature.A conceptual framework for usability evaluation of smartphone apps is also designed which would be validated through experimentation in the thesis work.This study is particularly useful to comprehend usability issues and their likely remedies to produce high quality smartphone apps.
Generalization of the experience of using research on psychology of behavior for designing UX design software products

The article deals with the analysis of the influence of the psychology of human behaviour on user experience. The evolution of the conceptual apparatus in the field of designing the user interface, taking into account user experience. Based on recent research and publications, psychological concepts that are fundamental in designing software products based on user experience are summarized. The process of the cognitive load formation and its influence on user experience is described. Also, the article discusses the topic of the effect of cognitive load on user experience, and provides examples of how the research results of behavioural psychology improve the practice of developing user experience. The field of UX (user experience) is being studied in an area where design and experimental research are in conflict. Designing UX has gone a long way from commercial websites, where convenience and efficiency have almost never been taken into account for optimized interfaces designed to interact with users. Previously, the goal was to have as much content as possible on the interface, now the field of user experience has been extensively explored and optimized to offer the user the right content, functionality at the right time, and it also takes into account psychological and emotional needs of users. In the article psychological concepts which are fundamental to the design of UX, the formation of cognitive load and the effects on user experience are discussed. Since cognitive load plays an important role in the development of UX models, it is important to understand and take into account the laws that form it. In order to design an effective interface, one must study the psychology of a user who uses psychological concepts and theories. The analysis of user interaction with technology is aimed at developing and testing theories that explain or predict human behaviour. Based on the existing sources, the article describes how user experience is formed, as well as investigates the theory of levels of the emotion system.
Automatic Identification Methods on a Corpus of Twenty Five Fine-Grained Arabic Dialects

This research deals with Arabic dialect identification, a challenging issue related to Arabic NLP. Indeed, the increasing use of Arabic dialects in a written form especially in social media generates new needs in the area of Arabic dialect processing. For discriminating between dialects in a multi-dialect context, we use different approaches based on machine learning techniques. To this end, we explored several methods. We used a classification method based on symmetric Kullback-Leibler, and we experimented classical classification methods such as Naive Bayes Classifiers and more sophisticated methods like Word2Vec and Long Short-Term Memory neural network. We tested our approaches on a large database of 25 Arabic dialects in addition to MSA.
Comparison of feature point detectors for multimodal image registration in plant phenotyping

With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in “difficult” image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.
Mining the Boardgamegeek

Abstract Phil Woodward and Sam Woodward use some wisdom of the crowd to discover meeple001 gold on a popular online database of board game reviews
PrivStream: Enabling Privacy-Preserving Inferences on IoT Data Stream at the Edge

Differential privacy (DP) has been recognized as a useful technique to achieve the trade-off between privacy guarantee and data utility. However, applying DP on the continuous data stream to protect sensitive inferred information faces two significant challenges. Firstly, directly adding noise into the continuous data stream may significantly reduce the data utility. Secondly, it is difficult to effectively and efficiently sample the data stream since the distribution of the data stream is usually unknown in advance. In this paper, we propose PrivStream, a privacy-preserving IoT data stream analytical framework based on edge computing, to address the two challenges. Specifically, PrivStream is split into two parts distributed at the IoT device and edge server, respectively. The first part, deployed at the device side, adaptively samples the data stream, leverages a tailored autoencoder to conduct data minimization and obfuscates the learnt features by injecting Laplace noise. The second part, deployed at the edge server side, reconstructs the perturbed features into obfuscated data stream for useful inferences without privacy disclosure. Moreover, we present the implementation details of PrivStream on a practical IoT system and theoretically analyze that Privstream can provide provable privacy guarantee. The experimental results based on realistic IoT data stream demonstrate that Privstream can preserve the data utility of useful inferences and mitigate the sensitive inferences simultaneously with efficient system overheads.
Mobile robot wall-following control using a behavior-based fuzzy controller in unknown environments

This paper addresses a behavior-based fuzzy controller (BFC) for mobile robot wall-following control.The wall-following task is usually used to explore an unknown environment.The proposed BFC consists of three sub-fuzzy controllers, including Straight-based Fuzzy Controller (SFC),Left-based Fuzzy Controller (LFC), and Right-based Fuzzy Controller (RFC).The proposed wall-following controller has three characteristics: the mobile robot keeps a distance from the wall,the mobile robot has a high moving velocity, and the mobile robot has a good robustness ability of disturbance.The proposed BFC will be used to control the real mobile robot.The Pioneer 3-DX mobile robot has sonar sensors in front and sides, and it is used in this study.The inputs of BFC are sonar sensors data and the outputs of BFC are roboti¦s left/right wheel speed.Experimental results show that the proposed BFC successfully performs the mobile robot wall-following taskin a real unknown environment.
Rule-based Emotion AI in Arabic Customer Review

The e-commerce emotion analysis is notable and the most pivotal advance since it catches the customer emotion in a product, and emotions with respect to product to decide if the customer attitude is negative, positive, or neutral.Posting on the customer's reviews have turned into an undeniably famous path for individuals to share with different customers their emotion and feelings toward a product.This review has a significant impact on sales in the future.The proposed system utilizes mixed word from an adjective (adj) and adverb to improve the emotion analysis process utilized a rule-based emotion analysis.The system extracts an Arabic customer review and computes the frequency of each word.At that point, it computes the emotion and score of each customer review.The system likewise computes the emotion and score of straightforward Arabic sentence.
Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

Sugar cane disease is a major factor in reducing sugar cane yields. The low intensity of experts to go into the field to check the condition of sugar cane causes the handling of sugarcane disease tends to be slow. This problem can be solved by instilling expert intelligence on sugar cane into an expert system. In this study the method of classification of sugar cane disease was proposed using Extreme Learning Machine (ELM). However, ELM alone is not enough to classify multilabel and multiclass disease case data in this study. Therefore, it is proposed to optimize the weight of hidden neurons in ELM using Particle Swarm Optimization (PSO). The experimental results show that the classification using ELM alone can reach an accuracy rate of 71%. After the weight of hidden neurons from ELM was optimized, the accuracy rate became 79.92% or an increase of 8.92%.
Outsourcing Hierarchical Threshold Secret Sharing Scheme Based on Reputation

Secret sharing is a basic tool in modern communication, which protects privacy and provides information security. Among the secret sharing schemes, fairness is a vital and desirable property. To achieve fairness, the existing secret sharing schemes either require a trusted third party or the execution of a multiround protocol, which are impractical. Moreover, the classic scheme requires expensive computing in the secret verification phase. In this work, we provide an outsourcing hierarchical threshold secret sharing (HTSS) protocol based on reputation. In the scheme, participants from different levels can fairly reconstruct the secret, and the protocol only needs to run for one round. A cloud service provider (CSP) uses powerful computing resources to help participants complete homomorphic encryption and complex verification operations, and the CSP cannot be aware of any valuable information. The participants can obtain the secret with a small number of operations. To avoid collusion, we suppose that participants have their own reputation value, and they are punished or rewarded according to their behavior. The reputation value of a participant who deviates from the protocol will decrease; therefore, the participant will choose a cooperative strategy to obtain better payoffs. Lastly, our scheme is proved to be secure, and experiments indicate that our scheme is feasible and efficient.
Multi-owner Secure Encrypted Search Using Searching Adversarial Networks

Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $$\mathcal {O}(\log N)$$ . In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.
Speaker-adapted neural-network-based fusion for multimodal reference resolution

Humans use a variety of approaches to reference objects in the external world, including verbal descriptions, hand and head gestures, eye gaze or any combination of them.The amount of useful information from each modality, however, may vary depending on the specific person and on several other factors.For this reason, it is important to learn the correct combination of inputs for inferring the best-fitting reference.In this paper, we investigate speaker-dependent and independent fusion strategies in a multimodal reference resolution task.We show that without any change in the modality models, only through an optimized fusion technique, it is possible to reduce the error rate of the system on a reference resolution task by more than 50%.
Optical-Flow Based Nonlinear Weighted Prediction for SDR and Backward Compatible HDR Video Coding

Tone Mapping Operators (TMO) designed for videos can be classified into two categories. In a first approach, TMOs are temporal filtered to reduce temporal artifacts and provide a Standard Dynamic Range (SDR) content with improved temporal consistency. This however does not improve the SDR coding Rate Distortion (RD) performances. A second approach is to design the TMO with the goal of optimizing the SDR coding rate-distortion performances. This second category of methods may lead to SDR videos altering the artistic intent compared with the produced HDR content. In this paper, we combine the benefits of the two approaches by introducing new Weighted Prediction (WP) methods inside the HEVC SDR codec. As a first step, we demonstrate the interest of the WP methods compared to TMO optimized for RD performances. Then we present the newly introduced WP algorithm and WP modes. The WP algorithm consists in performing a global motion compensation between frames using an optical flow, and the new modes are based on non linear functions in contrast with the literature using only linear functions. The contribution of each novelty is studied independently and in a second time they are all put in competition to maximize the RD performances. Tests were made for HDR backward compatible compression but also for SDR compression only. In both cases, the proposed WP methods improve the RD performances while maintaining the SDR temporal coherency.
Data Virtualization for Decision Making in Big Data

Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization.Data analytics emphasizes on algorithms to control the relationship between data offering insights.The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data.Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis.Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on.Data needs to be integrated from disparate sources in order to derive valuable insights.Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses.Recently Data virtualization has been used to speed up the data integration process.Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store.In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Model digitalnog nastavnog okruženja za strani jezik struke

The aim of this paper is to present a digital environment teaching model, based on critically observed problems in teaching the foreign language for specific purposes and the analysis of the educational potentials of digital technologies, in the context of the current strategies and legal solutions in the field of education in Serbia, which would advance and improve the teaching of the foreign language for specific purposes at the faculties of social sciences and humanities in Serbia. The subject of this research has been examined from both theoretical-conceptual and evaluation aspects. The theoretical and conceptual aspect is based on the scientific, theoretical and methodological connotation of teaching the foreign language for specific purposes and educational technology, as factors which are relevant to university teaching. The evaluation aspect refers to a critical analysis of digital technologies which can be efficiently and effectively used in creating a model for teaching a foreign language for specific purposes. The theoretical starting points of SAMR and TPACK models as well as the possibilities and challenges of using digital technology in teaching of the foreign language for specific purposes are considered. The conducted analysis shows that teachers have the opportunity to apply smart devices, Web applications and tools for preparing, implementing and evaluating teaching, but that they are not prepared to engage in the planning and construction of a digital teaching environment, since they have not been trained for this type of teaching during initial education.
Fuzzy Pruning for Compression of Convolutional Neural Networks

Pruning can be used in Convolutional Neural Networks(CNNs) to reduce the consumption of computations. In the iteration of pruning, a fixed ratio of filters or weights are pruned after evaluating their importance according to a certain criterion, which has the risk of leading to mis-pruning when the distribution of the importance is imbalanced. We propose a strategy to assist pruning for CNNs at filter level. Firstly, in order to evaluate importance and non-importance about filters in the network, we design two fuzzy membership functions respectively, then we evaluate all filters by their membership function value, finally, α-cut set is employed to determine the ones to be pruned . Experimental results demonstrate the effectiveness of our strategy, since it can obtain more compression than the popular pruning algorithms at similar accuracy level.
Deep Multi-label Classification in Affine Subspaces

Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet provides more expressiveness than multi-class classification. However, to train MLCs, most methods have resorted to similar objective functions as with traditional multi-class classification settings. We show in this work that such approaches are not optimal and instead propose a novel deep MLC classification method in affine subspace. At its core, the method attempts to pull features of class-labels towards different affine subspaces while maximizing the distance between them. We evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi-label frameworks. This method can be seen as a plug-in replacement loss function and is trainable in an end-to-end fashion.
Lens-based Focus+Context Visualization Techniques for Interactive Exploration of Web-based Reachability Maps

Reachability maps are powerful means to help making location-based decisions, such as choosing convenient sites for subsidiaries or planning vacation trips.Existing visualization approaches, however, introduce drawbacks concerning an effective acquisition of relevant information and an efficient data processing.In this paper, we introduce the first approach known so far to apply focus+context techniques to web-based reachability maps.We therefore propose a real-time enabled combination of an isochrone-based and a network-based representation of travel times obtained from multi-modal routing analysis using interactive lenses.We furthermore present a GPUaccelerated image-based method to compute isochrones based on a travel time-attributed network on client-side and thus achieve reduced data transmission efforts while yielding isochrones of higher precision, compared to generalized geometry-based approaches.
Optimizing Sensor Combinations and Processing Parameters in Dynamic Sensor Networks

The algorithms used to provide robust Position, Navigation and Timing (PNT) information for autonomous navigation purposes normally rely on accurate, precise, reliable and continuous information, captured with different sensors mounted on the vehicles. In addition, the availability of these sensors and the growth and development of the wireless communication systems enable the distribution of the information between both dynamic and fixed agents of the scene (Collaborative/Cooperative Positioning, CP). In collaborative scenarios, the characteristics of the used sensors (precision, geometry, limitations, etc.) together with the heterogeneous environments in which the vehicles navigate reveal that a single fixed sensor configuration might not be always optimum. This paper discusses the results from an in-house developed simulation tool that enables and assists the optimum selection of sensors and processing parameters for collaborative navigation in dynamic sensor networks by means of Monte Carlo techniques. Given that the sensor characteristics and the chosen processing parameters in the simulation are often associated with the sensor costs, the reader will learn from the outcome of the study the best performing sensor combinations that drive the cost of the sensor combination down, but still achieve the desired performance.