Reviewerly/exHarmony huggingface dataset:
Heuristic Evaluation of eGLU-Box: A Semi-automatic Usability Evaluation Tool for Public Administrations

This paper illustrates the heuristic evaluation of a web-based tool for usability testing for Public Administrations called eGLU-box. eGLU-box is an online platform aiming at supporting practitioners in the process of designing usability tests, analyzing data, and helping step-by-step participants to complete assessment tasks. Web users of Public Administrations can report their perceived quality of experience by completing a library of questionnaires shown to them by eGLU-box at the end of the test. This work is part of a multi-step user experience (UX) evaluation methodology to assess the platform. The UX evaluation methodology of eGLU-box uses standard and bio-behavioural evaluation methods. This work shows the results of the heuristic evaluation of eGLU-box involving five human factors experts and 20 practitioners working in Italian Public Administrations. Findings show that most of the problems are rated as minor problems and related to Nielsen’s heuristic, “visibility of the system.” Only 9% of problems are rated as major problems. These major problems are related to the “problematic match between system and the real world” heuristic. Evaluators provided indications for improvements that will be applied for the next version of the platform.
Blockchain Technologies in E-commerce: Social Shopping and Loyalty Program Applications

With the rapid advancement of cryptography and distributed computing systems, blockchain technologies are highly anticipated to transform many industries with better transparency, high security, and low transaction costs. However, the scalability and performance of blockchains are limiting their utility and suitability in online services, especially e-commerce. This paper provides a survey of blockchain technologies to highlight their benefits and challenges in online shopping. We, therefore, propose two blockchain-based e-commerce applications with detailed design guidelines: social shopping and loyalty program. The study contributes to the cumulative theoretical development of social computing and blockchains. It also provides a number of implications for academic bodies, platform operators, and developers of blockchain technologies.
TeleSight

In this paper, we propose "TeleSight", proof-of-concept prototype enabling asymmetric collaboration in VR between HMD user and Non-HMD users. TeleSight provides two interaction layers for designing asymmetric interaction, a physical layer for direct tangible interaction with players in VR space and visual layer that visually expresses VR space. Each layer is constructed by an avatar robot that imitates the motion of the HMD user, and a projection system. Non-HMD users can understand what happens in the virtual world by each interaction layers. Also, Non-HMD users can interact tangibly with the players in VR space throughout avatar robot. TeleSight provides that cooperative co-located gameplay between HMD user and Non-HMD users through experience scenarios using two layers.
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization

Smooth finite-sum optimization has been widely studied in both convex and nonconvex settings. However, existing lower bounds for finite-sum optimization are mostly limited to the setting where each component function is (strongly) convex, while the lower bounds for nonconvex finite-sum optimization remain largely unsolved. In this paper, we study the lower bounds for smooth nonconvex finite-sum optimization, where the objective function is the average of $n$ nonconvex component functions. We prove tight lower bounds for the complexity of finding $\epsilon$-suboptimal point and $\epsilon$-approximate stationary point in different settings, for a wide regime of the smallest eigenvalue of the Hessian of the objective function (or each component function). Given our lower bounds, we can show that existing algorithms including KatyushaX (Allen-Zhu, 2018), Natasha (Allen-Zhu, 2017), RapGrad (Lan and Yang, 2018) and StagewiseKatyusha (Chen and Yang, 2018) have achieved optimal Incremental First-order Oracle (IFO) complexity (i.e., number of IFO calls) up to logarithm factors for nonconvex finite-sum optimization. We also point out potential ways to further improve these complexity results, in terms of making stronger assumptions or by a different convergence analysis.
An Improved Ensemble Learning for Imbalanced Data Classification

In the field of data mining, imbalanced data is widespread, and ensemble learning algorithms are a classifier that is more effective in classifying imbalanced data. However, the ensemble learning algorithm itself is not optimized for imbalanced data. Therefore, an imbalanced data processing method based on data level of ensemble learning SE-gcForest is proposed. This method introduces the data level processing idea after the Multi-grained Scanning window process of the gcForest algorithm. The SMOTE algorithm and the EasyEnsemble idea deal with imbalanced data. Experiments show that this method is more effective when the data imbalanced ratio is lower and higher.
A Holy Quran Reader/Reciter Identification System Using Support Vector Machine

Holy Quran Reader Identification is the process of identifying the reader or reciter of the Holy Quran based on several features in the corresponding acoustic wave.In this research, we build our own corpus, which contains 15 known readers of the Holy Quran.The Mel-Frequency Cepstrum Coefficients (MFCC) are used for the extraction of these features from the input acoustic signal.These MFCCs are the reader's features matrix, which is used for recognition via Support Vector Machine (SVM) and Artificial Neural Networks (ANN).According to our experimental results, the Holy Quran Reader Identification System identifies the reader with 96.59% accuracy when using SVM, in contrast to accuracy of 86.1% when using ANN.
Multiscale connected chain topological modelling for microcalcification classification

Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47±1.30%, 90.0±0.00%, 82.5±2.63%, 76.75±0.66% for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.
An Efficient Motion Planning Algorithm for UAVs in Obstacle-cluttered Environment

A novel algorithm is presented in this paper for motion planning of an unmanned aerial vehicle (UAV) from a start position to a goal position in a two-dimensional environment cluttered with stationary obstacles. The algorithm, termed `GSE', leverages a generalized shape expansion (GSE)-based sampling strategy, the main contribution of the paper, to explore the workspace efficiently. Once the shortest path is found from start position to goal position, a locally optimal trajectory is obtained within the homotopy class using sequential convex programming. Numerical simulations on the performance of the GSE algorithm and comparison of the same with that of some existing well-established algorithms are performed. The computational efficiency of the GSE algorithm is found to be significantly higher than that of the algorithms in comparison, while the trajectory costs obtained by the GSE algorithm is found to be marginally better in comparison with others.
Exploiting Semi-Supervised Training Through a Dropout Regularization in End-to-End Speech Recognition

In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework. The first step in our approach involves training a seed model on the limited amount of labelled data. Additional unlabelled speech data is employed through a data selection mechanism to obtain the best hypothesized output, further used to retrain the seed model. However, uncertainties of the model may not be well captured with a single hypothesis. As opposed to this technique, we apply a dropout mechanism to capture the uncertainty by obtaining multiple hypothesized text transcripts of an speech recording. We assume that the diversity of automatically generated transcripts for an utterance will implicitly increase the reliability of the model. Finally, the data selection process is also applied on these hypothesized transcripts to reduce the uncertainty. Experiments on freely available TEDLIUM corpus and proprietary Adobe's internal dataset show that the proposed approach significantly reduces ASR errors, compared to the baseline model.
From a User Model for Query Sessions to Session Rank Biased Precision (sRBP)

To satisfy their information needs, users usually carry out searches on retrieval systems by continuously trading off between the examination of search results retrieved by under-specified queries and the refinement of these queries through reformulation. In Information Retrieval (IR), a series of query reformulations is known as a query-session. Research in IR evaluation has traditionally been focused on the development of measures for the ad hoc task, for which a retrieval system aims to retrieve the best documents for a single query. Thus, most IR evaluation measures, with a few exceptions, are not suitable to evaluate retrieval scenarios that call for multiple refinements over a query-session. In this paper, by formally modeling a user's expected behaviour over query-sessions, we derive a session-based evaluation measure, which results in a generalization of the evaluation measure Rank Biased Precision (RBP). We demonstrate the quality of this new session-based evaluation measure, named Session RBP (sRBP), by evaluating its user model against the observed user behaviour over the query-sessions of the 2014 TREC Session track.
On the Hardness and Inapproximability of Recognizing Wheeler Graphs

In recent years several compressed indexes based on variants of the Burrows-Wheeler transformation have been introduced. Some of these are used to index structures far more complex than a single string, as was originally done with the FM-index [Ferragina and Manzini, J. ACM 2005]. As such, there has been an increasing effort to better understand under which conditions such an indexing scheme is possible. This has led to the introduction of Wheeler graphs [Gagie et al., Theor. Comput. Sci., 2017]. Gagie et al. showed that de Bruijn graphs, generalized compressed suffix arrays, and several other BWT related structures can be represented as Wheeler graphs, and that Wheeler graphs can be indexed in a way which is space efficient. Hence, being able to recognize whether a given graph is a Wheeler graph, or being able to approximate a given graph by a Wheeler graph, could have numerous applications in indexing. Here we resolve the open question of whether there exists an efficient algorithm for recognizing if a given graph is a Wheeler graph. We present: - The problem of recognizing whether a given graph G=(V,E) is a Wheeler graph is NP-complete for any edge label alphabet of size sigma >= 2, even when G is a DAG. This holds even on a restricted, subset of graphs called d-NFA's for d >= 5. This is in contrast to recent results demonstrating the problem can be solved in polynomial time for d-NFA's where d <= 2. We also show the recognition problem can be solved in linear time for sigma =1; - There exists an 2^{e log sigma + O(n + e)} time exact algorithm where n = |V| and e = |E|. This algorithm relies on graph isomorphism being computable in strictly sub-exponential time; - We define an optimization variant of the problem called Wheeler Graph Violation, abbreviated WGV, where the aim is to remove the minimum number of edges in order to obtain a Wheeler graph. We show WGV is APX-hard, even when G is a DAG, implying there exists a constant C >= 1 for which there is no C-approximation algorithm (unless P = NP). Also, conditioned on the Unique Games Conjecture, for all C >= 1, it is NP-hard to find a C-approximation; - We define the Wheeler Subgraph problem, abbreviated WS, where the aim is to find the largest subgraph which is a Wheeler Graph (the dual of the WGV). In contrast to WGV, we prove that the WS problem is in APX for sigma=O(1); The above findings suggest that most problems under this theme are computationally difficult. However, we identify a class of graphs for which the recognition problem is polynomial time solvable, raising the open question of which parameters determine this problem's difficulty.
Energy-Efficient Task Scheduling for Heterogeneous Cloud Computing Systems

Virtual machine migration and consolidation technologies can reduce the number of active physical machines (PMs) in the cloud platform, which can effectively reduce the power consumption of cloud platform. However, migration and consolidation of virtual machines (VMs) requires a suitable time to perform. When VMs' migration and consolidation are not performed, how to minimize power consumption of cloud platform through task scheduling is still one major challenge. Existing task scheduling methods mainly aimed at load balancing or minimizing task execution time, without considering the power consumption optimization of heterogeneous server cluster during task execution. To solve the high power consumption problem, this paper proposes a power-aware task scheduling (PATS) method for heterogeneous cloud platform. First, we construct a task scheduling model, which can aware the PM workload in real time according to the VM state, and predict the busy and idle power consumption of the PM, so as to obtain the power consumption of the cloud platform. This model also formulates the task scheduling optimization problem, which tries to minimize the power consumption of heterogeneous cloud platform. Second, we analyze the impact of PM type and task execution time on power consumption of cloud platform, and then propose a task scheduling algorithm to solve the above optimization problem. Finally, the algorithm is evaluated against other existing task scheduling algorithms under a CloudSim framework. Results demonstrate that PATS consumes 23.9-6.6% less total power consumption in comparison to the state-of-the-art.
The E-commerce Law of the People's Republic of China: E-commerce platform operators liability for third-party patent infringement

The E-Commerce Law of the People's Republic of China(E-commerce Law) was enacted on August 31, 2018. One of the hottest topics is e-commerce platform operators (EPOs) liability for third-party patent infringement. The EPOs, which provide platform services rather than participating in trade directly, must try to maintain the balance between patentees and social interests. Due to the particularities of the patent infringement review, it is difficult for the EPOs to fulfil the task. Thus, the EPOs liability for copyright and trademark infringement is likely unworkable in the patent area. A China practice review will be helpful in analysing the problem; the guiding case No. 83 is an especially meaningful reference. The E-Commerce Law is principled and advisory - it is impossible to distinguish the limits of EPOs liability for third-party patent infringement by differentiating the patent from other intellectual property rights. After reviewing the regulations on the general and intellectual property duty of care of EPOs, this article argues that the E-Commerce Law still has some specific problems. Consequently, to implement the E-Commerce Law, judicial interpretation and detailed rules for implementation should be formulated as soon as possible, and the potential solutions are described in the conclusion.
Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory

Kernel ridge regression (KRR) is a powerful method for nonparametric regression. The time and space complexity of computing the KRR estimate directly are O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) and O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), respectively, which are prohibitive for large-scale data sets, where n is the number of data. In this article, we propose a novel random sketch technique based on the circulant matrix that achieves savings in storage space and accelerates the solution of the KRR approximation. The circulant matrix has the following advantages: It can save time complexity by using the fast Fourier transform (FFT) to compute the product of matrix and vector, its space complexity is linear, and the circulant matrix, whose entries in the first column are independent of each other and obey the Gaussian distribution, is almost as effective as the i.i.d. Gaussian random matrix for approximating KRR. Combining the characteristics of the circulant matrix and our careful design, theoretical analysis and experimental results demonstrate that our proposed sketch method, making the estimate kernel methods scalable and practical for large-scale data problems, outperforms the state-of-the-art KRR estimates in time complexity while retaining similar accuracies. Meanwhile, our sketch method provides the theoretical bound that keeps the optimal convergence rate for approximating KRR.
Engaging Students in Covariational Reasoning within an Augmented Reality Environment

This chapter considers the application of augmented reality (AR) technology for developing covariational reasoning and aims also to show general potentialities of AR technology and to give some hint of the theoretical principles behind them. Specifically, the chapter discusses the first design-cycle from a design-based research study that aims at both the development of an AR toolkit within iterative research cycles, and at its scientific investigation. Our main claim is that engaging students in coordinating continuous real phenomenon (e.g., a moving object along inclined plane) with its mathematical representations (e.g., plotting points of graph, ordered pairs in a table of values) through visual-kinesthetic activities may help the students to experience the multiple levels of sophistication and develop the multiple meanings of covariational reasoning. Insights from the first design cycle are presented and ideas for future design-cycles are also discussed.
A model for evaluating the vital factors affecting cloud computing adoption

Purpose Nowadays, the rapid growth of information technology strategies such as cloud computing is very noticeable in organizations. The advantages of the cloud environment are unavoidable because of an increase in innovation, flexibility and economy. Therefore, the critical topic is considering the factors affecting the adoption of cloud computing. This study aims to understand the factors of the adoption of cloud computing and its benefit in companies. Design/methodology/approach A research framework with four hypotheses has been developed based on the results of previous studies. Structural equation modeling has been used for data analysis. Findings The proposed model is verified by the results. In addition, the results have shown that cloud computing adoption is affected by four variables as follows, including human factor (with sub-indicator personal innovativeness and knowledge), organizational factor (with sub-indicator size, adequacy of resources and top management support), technical factor (with sub-indicator compatibility and security) and environmental factor (with sub-indicator regulatory environment, competitive pressure and trading partner). Research limitations/implications There are crucial implications in the findings: they have an essential contribution to the research community, administrators and Information and Communications Technology providers with respect to framing improved tactics for the adoption of cloud computing. The proposed model can enhance the perception of service providers about why some services sectors accept cloud computing amenities, whereas apparently the same ones having the same market situations do not. In addition, the above providers should enhance their interaction with the services sectors contributed to the cloud computing experience to make a well-organized setting for the adoption of cloud computing, and eliminate any ambiguity about this sort of technology. Moreover, the sample has been limited to Iran respondents. Practical implications The research studies about the usage of cloud computing have shown its effects on organizations today. Also, the different impacts of cloud computing on other contexts and organizations are in the center of attention. By carefully considering and managing cloud computing adoption logics, organizations could get significant advantages. Originality/value Cloud computing’s technical and operational issues have been central in most of the previous studies. Some surveys have referred to the adoption of cloud computing by the organizations in terms of human characteristics or the contextual factor. Therefore, there should be a model and outline to assess the effect of aforesaid factors on cloud computing adoption.
Multi-prover interactive proofs: how to remove intractability assumptions

research-article Share on Multi-prover interactive proofs: how to remove intractability assumptions Authors: Michael Ben-Or Hebrew University Hebrew UniversityView Profile , Shafi Goldwasser MIT MITView Profile , Joe Kilian MIT MITView Profile , Avi Wigderson Hebrew University Hebrew UniversityView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 373–410https://doi.org/10.1145/3335741.3335758Published:04 October 2019Publication History 3citation107DownloadsMetricsTotal Citations3Total Downloads107Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access