Reviewerly/exHarmony huggingface dataset:
The effects of employee commitment and environment uncertainty on product quality: The mediating role of supply chain integration

The main objective of the study was to test the relationship between the uncertainty of supply chain, employee commitment and product quality with the mediating role of supply chain integration. The uniqueness of the study is that it focused on the mediating role of supply chain integration in the automobile sector of United Arab Emirates (UAE). This research is quantitative in nature. For the analysis, structural equation modelling is used. The population of the present research is the employees working in the automobile sector of UAE. A Likert scale questionnaire was used to support this study. The questionnaire was distributed among 481 respondents by using simple random sampling. The usable response rate was 66.66%. The findings of the study confirmed the direct relationship between supply chain integration, product quality, employee commitment and environmental uncertainty. This study fills the gap of limited studies regarding supply chain integration in the UAE automobile sector. The findings of the study are also helpful for policymakers in developing automobile strategies.
Using chatbots in e-retailing – how to mitigate perceived risk and enhance the flow experience

Purpose Chatbots represent an undeniable player between online retailers and customers as they boost operational efficiency and bring cost savings to businesses while offering convenience for customers in terms of timing and immediacy. However, as chatbots represent a new-born online touchpoint in retailing, especially when it comes to online pre-purchase and purchase experience, this study examines whether and how effort expectation, facilitating condition, performance expectancy, social influence, trust, perceived risk and flow affect consumers' intention to use chatbots for online shopping. The purpose of this paper is to address this issue. Design/methodology/approach A total of 226 respondents participated in an online survey. Participants were asked to try a new online service and interact with a chatbot designed using Chatfuel, a platform within the Facebook Messenger setting. Structural equation modelling was used to test the proposed research model regarding the intention to use chatbots. Findings This study discusses the importance of offering useful and trustworthy conversational agents for online shopping and argues and explains the insignificant paths amongst other studied factors and intention to use chatbots concluding with the need to explore more drivers for such contemporary technologies. Moreover, the findings indicate that trust turns out to be an important predictor of behavioural intention towards chatbots, in addition to its role in mitigating perceived risk and enhancing flow experience. Originality/value Given the lack of empirical evidence related to chatbots applied for business purposes, this paper fills a gap in this research field and provides a deeper understanding of what leverages consumers' intention to use chatbots for online shopping.
Optical image encryption based on two-channel detection and deep learning

Aside from its application to the vulnerability analysis of optical cryptosystems, deep learning (DL) technique can also be used as a potential basis for optical cryptography itself. Here, we report a new optical image encryption scheme based on two-channel detection and DL. The encryption process consists of two-channel signal detection within the optical encryption system where three random binary masks are involved, and the training of a neural network to derive an approximate model. The two optically encoded patterns collected with a pair of CCD cameras are recombined into a single speckle image by using two independent sparse matrices. In the training section, the neural network is trained to find the mapping relationship between the speckle images and their corresponding original objects. Besides the model parameters, the decryption requires the use of the matrix keys, which avoids a situation where the DL-based cryptosystem is designed such that security relies entirely on the trained network itself. To the best of our knowledge, this is the first report using a single-wavelength, dual-channel system for diffuse optical encryption. Experimental results demonstrate the feasibility and good performance of the proposed scheme.
G-Value Plateaus: A Challenge for Planning

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.
Special Issue Editorial: Adaptive and Intelligent Gamification Design

This editorial provides an overview of the three accepted papers for the AIS THCI special issue on adaptive and intelligent gamification designs.The first paper examines conversational agents and how one can use gamification to make the design more engaging.The second study focuses on mobile fitness apps and analyzes the role that personality plays in apps and game designs.Finally, the third paper examines gamification in a virtual laboratory environment.Aligned with current work, we present future research directions that involve generative AI, the metaverse, and a shift in gamification research and practice in the future.
VIG: Data scaling for OBDA benchmarks

In this paper we describe vig, a data scaler for Ontology-Based Data Access (OBDA) benchmarks. Data scaling is a relatively recent approach, proposed in the database community, that allows for quickly scaling an input data instance to s times its size, while preserving certain application-specific characteristics. The advantages of the scaling approach are that the same generator is general, in the sense that it can be re-used on different database schemas, and that users are not required to manually input the data characteristics. In the vig system, we lift the scaling approach from the pure database level to the OBDA level, where the domain information of ontologies and mappings has to be taken into account as well. vig is efficient and notably each tuple is generated in constant time. To evaluate vig, we have carried out an extensive set of experiments with three datasets (BSBM, DBLP, and NPD), using two OBDA systems (Ontop and D2RQ), backed by two relational database engines (MySQL and PostgreSQL), and compared with real-world data, ad-hoc data generators, and random data generators. The encouraging results show that the data scaling performed by vig is efficient and that the scaled data are suitable for benchmarking OBDA systems.
Iron: managing obligations in higher-order concurrent separation logic

Precise management of resources and the obligations they impose, such as the need to dispose of memory, close locks, and release file handles, is hard---especially in the presence of concurrency, when some resources are shared, and different threads operate on them concurrently. We present Iron, a novel higher-order concurrent separation logic that allows for precise reasoning about resources that are transferable among dynamically allocated threads. In particular, Iron can be used to show the correctness of challenging examples, where the reclamation of memory is delegated to a forked-off thread. We show soundness of Iron by means of a model of Iron, defined on top of the Iris base logic, and we use this model to prove that memory resources are accounted for precisely and not leaked. We have formalized all of the developments in the Coq proof assistant.
Memory-efficient tracking of complex temporal and symbolic dynamics with quantum simulators

Tracking the behaviour of stochastic systems is a crucial task in the statistical sciences. It has recently been shown that quantum models can faithfully simulate such processes whilst retaining less information about the past behaviour of the system than the optimal classical models. We extend these results to general temporal and symbolic dynamics. Our systematic protocol for quantum model construction relies only on an elementary description of the dynamics of the process. This circumvents restrictions on corresponding classical construction protocols, and allows for a broader range of processes to be modelled efficiently. We illustrate our method with an example exhibiting an apparent unbounded memory advantage of the quantum model compared to its optimal classical counterpart.
Statistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfall

Abstract. Statistical hypothesis tests in wavelet analysis are methods that assess the degree to which a wavelet quantity (e.g., power and coherence) exceeds background noise. Commonly, a point-wise approach is adopted in which a wavelet quantity at every point in a wavelet spectrum is individually compared to the critical level of the point-wise test. However, because adjacent wavelet coefficients are correlated and wavelet spectra often contain many wavelet quantities, the point-wise test can produce many false positive results that occur in clusters or patches. To circumvent the point-wise test drawbacks, it is necessary to implement the recently developed area-wise, geometric, cumulative area-wise, and topological significance tests, which are reviewed and developed in this paper. To improve the computational efficiency of the cumulative area-wise test, a simplified version of the testing procedure is created based on the idea that its output is the mean of individual estimates of statistical significance calculated from the geometric test applied at a set of point-wise significance levels. Ideal examples are used to show that the geometric and cumulative area-wise tests are unable to differentiate wavelet spectral features arising from singularity-like structures from those associated with periodicities. A cumulative arc-wise test is therefore developed to strictly test for periodicities by using normalized arclength, which is defined as the number of points composing a cross section of a patch divided by the wavelet scale in question. A previously proposed topological significance test is formalized using persistent homology profiles (PHPs) measuring the number of patches and holes corresponding to the set of all point-wise significance values. Ideal examples show that the PHPs can be used to distinguish time series containing signal components from those that are purely noise. To demonstrate the practical uses of the existing and newly developed statistical methodologies, a first comprehensive wavelet analysis of Indian rainfall is also provided. An R software package has been written by the author to implement the various testing procedures.
Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach

Classical data mining algorithms are considered inadequate to manage the volume, variety, velocity, and veracity aspects of big data. The advent of a number of open-source cluster-computing frameworks has opened new interesting perspectives for handling the volume and velocity features. In this context, thanks to their capability of coping with vague and imprecise information, distributed fuzzy models appear to be particularly suitable for handling the variety and veracity features of big data. Moreover, the interpretability of fuzzy models may assume a particular relevance in the context of big data mining. In this work, we propose a novel approach for generating, out of big data, a set of fuzzy rule–based classifiers characterized by different optimal trade-offs between accuracy and interpretability. We extend a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented under the Apache Spark environment. In particular, we exploit a recently proposed distributed fuzzy decision tree learning approach for generating an initial rule base that serves as input to the evolutionary process. Furthermore, we integrate the evolutionary learning scheme with an ad hoc strategy for the granularity learning of the fuzzy partitions, along with the optimization of both the rule base and the fuzzy set parameters. Experimental investigations show that the proposed approach is able to generate fuzzy rule–based classifiers that are significantly less complex than the ones generated by the original multi-objective evolutionary learning scheme, while keeping the same accuracy levels.
Pivot Machine Translation Using Chinese as Pivot Language

Pivoting through a popular language with more parallel corpora available (e.g. English and Chinese) is a common approach to build machine translation (MT) systems for low-resource languages. For example, to build a Russian-to-Spanish MT system, we could build one system using the Russian–Spanish corpus directly. We could also build two systems, Russian-to-English and English-to-Spanish, as the resources of the two language pairs are much larger than the Russian–Spanish pair, and use them cascadingly to translate texts in Russian into Spanish by pivoting through English. There are, however, some confusing results on the Pivot MT approach in the literature. In this paper, we reviewed the performance of Pivot MT with the United Nations Parallel Corpus v1.0 (UN6Way) using both English and Chinese as pivot languages. We also report our system performance on the CWMT 2018 Pivot MT shared task, where Japanese patent sentences are translated into English using Chinese as the pivot language.
Entanglement distillation by Hong-Ou-Mandel interference with orbital angular momentum states

Entanglement is an invaluable resource to various quantum communication, metrology, and computing processes. In particular, spatial entanglement has become topical, owing to its wider Hilbert space that allows photons to carry more information. However, spatial entanglement is susceptible to decay in the presence of external perturbations such as atmospheric turbulence. Here we show theoretically and experimentally that in a weak turbulence regime, maximally entangled states can be distilled through quantum interference. We generated entangled photons by spontaneous parametric down-conversion, with one photon in the entangled pairs being sent through a turbulent channel. We recombined the paths of the two photons at a beam-splitter in a Hong-Ou-Mandel interference setup and measured in coincidence, using spatial filters, the spatial correlations between photons in the output ports of the beam-splitter. We performed a state tomography and show that, from an ensemble of pure states with very low levels of entanglement, we distil entangled states with fidelities F ≥ 0.90 with respect to the singlet Bell state.
Identifying comparative opinions in Arabic text in social media using machine learning techniques

Social networking sites have become an integral part of everyday life, where people interact, cooperate and quarrel with each other. Social media also encourages them to express their opinions and share their comments about their lives' events or about the product they use. Opinions can be direct without any comparison (I like ABC phone) or they can be comparative (X-phone's camera is better than Y-phone). Comparative opinions are useful in many applications, e.g. marketing intelligence, product benchmarking, and e-commerce. The automatic mining of comparative opinions is an important text mining problem and an area of increasing interest for different languages. This paper focuses on identification of comparative sentence from non-comparative ones in Arabic texts. A corpus was developed consisting of YouTube comments. This paper describes research experiments that aimed to apply data/text mining algorithms, natural language processing and linguistic classification for Arabic comparative text discovery. The results of these experiments along with the analysis are also presented. The results were promising reaching to 91% accuracy for the detection of comparative opinions.
PENINGKATAN KETERAMPILAN BERBAHASA SISWA MELALUI PEMANFAATAN MEDIA TEKA-TEKI SILANG: MENCIPTAKAN KELAS YANG MENYENANGKAN

This article contains a study about the use of crossword media in improving the language skills of elementary school students as an effort to create a fun class. The problem was students’ lack of eagerness to emotionally involve in learning process. This condition caused an impact on students’ unsatisfaying learning outcomes that is 80. To overcome this problem, crossword puzzle media are used in learning by referring to the research models of Kemmis and Taggart. The data were collected by using observation sheets, interview guidelines, questionnaires, tests of student learning outcomes and field observations. At the end of the study, it was found that both the students’ learning outcomes and activities, and the teacher performance in planning and implementing teaching increased from cycle I to cycle II. As the result, the learning exceeded the target of the minimum standard of completeness. Thus, the utilization of crossword puzzles is considered to able to improve students' language skills, as well as create fun classes
Bitcoin and Gold Prices: A Fledging Long-Term Relationship

This study applies threshold regression model in a bivariate framework to explore the nonlinear long-term relationship among Bitcoin and gold prices over the period 2010-2018.Results are threefold: first, we show that gold is a significant predictor of Bitcoin prices.Second, we find evidence of a non-linear relationship between Bitcoin and gold prices characterized rather by a two-regime relationship with a structural break occurring in October 2017.Third, before the break, there is significant, negative but weak causality indicating that Bitcoin is a speculative asset.After the break, the relationship becomes significantly positive revealing diversifier and hedge properties of Bitcoin.
Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Open research data is one of the key areas in the expanding open scholarship movement. Scholarly journals and publishers find themselves at the heart of the shift towards openness, with recent years seeing an increase in the number of scholarly journals with data-sharing policies aiming to increase transparency and reproducibility of research. In this article we present two case studies which examine the experiences that two leading academic publishers, Taylor &amp; Francis and Springer Nature, have had in rolling out data-sharing policies. We illustrate some of the considerations involved in providing consistent policies across journals of many disciplines, reflecting on successes and challenges.
VerifyThis – Verification Competition with a Human Factor

VerifyThis is a series of competitions that aims to evaluate the current state of deductive tools to prove functional correctness of programs. Such proofs typically require human creativity, and hence it is not possible to measure the performance of tools independently of the skills of its user. Similarly, solutions can be judged by humans only. In this paper, we discuss the role of the human in the competition setup and explore possible future changes to the current format. Regarding the impact of VerifyThis on deductive verification research, a survey conducted among the previous participants shows that the event is a key enabler for gaining insight into other approaches, and that it fosters collaboration and exchange.
Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?

Robotization", the integration of robots in human life will change human life drastically.In many situations, such as in the service sector, robots will become an integrative part of our lives.Thus, it is vital to learn from extant research on human-robot interaction (HRI).This article introduces robotic psychology that aims to bridge the gap between humans and robots by providing insights into particularities of HRI.It presents a conceptualization of robotic psychology and provides an overview of research on service-focused human-robot interaction.Theoretical concepts, relevant to understand HRI with are reviewed.Major achievements, shortcomings, and propositions for future research will be discussed.
MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication. With the aim of dealing with this problem, many experts have adopted transfer-learning methods, even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM). The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also, there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets, respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge.
Implementation of Encryption Algorithm and Wireless Image Transmission System on FPGA

In this paper, we proposed to improve bit insertion based on the common chaotic encryption algorithm, which reduces the computational complexity of the chaotic encryption algorithm and is used on low computational systems.We changed the encryption steps from permutation to XOR and XOR to bit insertion, which is our proposed bitwise operation based on encryption.Next, we proposed a fourdimensional chaotic system using the discrete time signal of the chaotic system for the key generator and optimized the floating point operation as well as FPGA resources of the chaotic system.Then, we implemented the algorithm on our Nios II-based wireless image transmission system, where the system's processor clock is 50 MHz.Finally, we performed a security analysis on our encryption algorithm, and the results show that our proposed algorithm consumes less computing resources while maintaining sufficient security.