Reviewerly/exHarmony huggingface dataset:
A Sentence-Based Circular Reasoning Model in Multi-Hop Reading Comprehension

Multi-hop Machine Reading Comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantics-related query. The existing researches resort to either document-level or entity-level inferences among relevant information, but such practices may be too coarse or too subtle to result less accurate understanding of the text. To address this issue, this research proposes a Sentence-based Circular Reasoning (SCR) approach, which starts with sentence representation and then unites the query to establish a reasoning path based on a loop inference unit. Further, the model synthesizes the information existing in the reasoning path and receives a probability distribution for selecting the correct answer. In addition, this study proposes a nested mechanism to extend the probability distribution for weighting. And it is proven that this mechanism can assist the model to perform better. Some experiments evaluate SCR on two popular multi-hop MRC benchmark datasets, WikiHop and MedHop, achieving 71.6 and 63.2 in terms of accuracy, respectively, and thus exhibiting competitive results compared with the state-of-the-art model. Additionally, qualitative analyses also demonstrate the validity and interpretability of SCR.
Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model

A frequency response function- (FRF-) based surrogate model for finite element model updating (FEMU) is presented in this paper. Extreme learning machine (ELM) is introduced as the surrogate model of the finite element model (FEM) to construct the relationship between updating parameters and structural responses. To further improve the generalization ability, the input weights and biases of ELM are optimized by Lévy flight trajectory-based whale optimization algorithm (LWOA). Then, LWOA is also applied to obtain the best updating results, where the objective function is defined by the difference between analytical FRF data and experimental data. Finally, a plane truss is used to demonstrate the performance of the proposed method. The results show that, compared with second-order response surface (RS), radial basis function (RBF), traditional ELM, and other optimized ELM, a LWOA-ELM model has higher prediction accuracy. After updating, the FRF data and frequencies have a significant match to the experimental model. The proposed FEMU method is feasible.
Legal Issues of Intellectual Property Rights in Disrupted Technologies Era: Chatbots and Conversational Computing Platforms

This paper represents the complexity of ecosystem of chatbots and related challenges of IPRs. It focuses on the analysis of legal and case law of the European Union, suggest which of the IPR instruments may be used by chatbots developers in order to ensure effective legal protection, rights transfer and etc. The Survey of the Licensing and Terms and Conditions of 20 Conversational Computing Platforms has discovered different legal possibilities to implement IPRs. This paper is organized: 1) introduction and state of art 2) legal analysis; 3) the survey of conversational computing platforms; 4) conclusions and future work.
The economic order quantity taking into account additional information about the known quantile of the cumulative distribution function of the product’s sales volume

In modern logistics and supply chain management, the task of inventory management is paramount.The total costs of the enterprise and consequently, its profit, directly depend on the accuracy of calculating the volumes and terms of orders.In this work, the problem of increasing the accuracy of calculating the economic order quantity for a product was solved by involving additional information about the known quantile of a given level of the distribution function of the volume of product's demand.The quantile information was used to recalculate the annual demand for the product, based on a modified estimator of the sales expectation for the period.The modified estimator is asymptotically unbiased, normal, and more accurate than the traditional sample mean in the sense of mean squared error.New formulas for calculating the economic order quantity and its confidence interval were presented and tested on real data on the monthly sales volumes of goods of a large retail store network over two years.It is shown that the classic way of mean calculation led to an underestimation of the volume of the economic order quantity, which in turn increased the risk of a shortage, and hence a drop in the quality of logistics services.The new calculation method also showed that the period between orders should be one day shorter.The work is practically significant; according to its results, recommendations are given to the enterprise. MODELING OF SOCIAL AND ECONOMIC SYSTEMSKey words: economic order quantity;
A supervised learning approach for detecting erroneous samples in embeddings

Visualizing multidimensional data has been a crucial task in recent years regarding the growing amount of data from various sources. To achieve this, dimensionality reduction algorithms have been used to reduce the number of dimensions for visualization of the data on a screen. However, these algorithms may fail to faithfully represent high dimensional data in lower dimensions and eventually lead to erroneous visualizations. In this work, we propose an error detection algorithm for dimensionality reduction algorithms based on recently developed error prediction algorithms for medical image registration. The proposed algorithm matches the neighborhoods of high and low dimensional data with different similarity measures and predicts the errors using a random forest classifier. The results on three datasets show that the proposed algorithm can successfully detect errors with an accuracy up to 86% and area under the curve score of 0.81.
A 3D Spherical Panorama Modeling Method Based on Double Projective Geometry

A 3D spherical panorama modeling method based on the principle of multi-view geometry is proposed in this study to solve panoramic measurement. First, this method uses homography and essential transformations to obtain parameters. Second, it establishes a double spherical projection geometric relationship to complete relative orientation. Third, the conversion parameters of the relative orientation between this method and close-range photogrammetry are derived and compared. Experimental results show the feasibility of the method proposed in this study.
Design Futures Now: Literacies &amp; Making

This issue of Temes de Disseny addresses a mix of topics concerning how design literacies and design making may be understood as ‘future facing’. By doing so, we convey the ways and means design practice and design education may be positioned and activated to work prospectively and be ahead of current concerns with environmental, social and ethical challenges. Doing so also means tackling tough, complex and often unknown problems and offering potential and imaginary responses. Accordingly, tools and techniques originating in futures studies intertwine with design practices offering exploratory, methodological and anticipatory work on how we might shape our futures through design together. Literacies as design futures making and making futures literacies by design are therefore featured in this special issue of the journal.
A Spatial-temporal Multiplexing Method for Dense 3D Surface Reconstruction of Moving Objects

Three-dimensional reconstruction of dynamic objects is important for robotic applications, for example, the robotic recognition and manipulation. In this paper, we present a novel 3D surface reconstruction method for moving objects. The proposed method combines the spatial-multiplexing and time-multiplexing structured-light techniques that have advantages of less image acquisition time and accurate 3D reconstruction, respectively. A set of spatial-temporal encoded patterns are designed, where a spatial-encoded texture map is embedded into the temporal-encoded three-step phase-shifting fringes. The specifically designed spatial-coded texture assigns high-uniqueness codeword to any window on the image which helps to eliminate the phase ambiguity. In addition, the texture is robust to noise and image blur. Combining this texture with high-frequency phase-shifting fringes, high reconstruction accuracy would be ensured. This method only requires 3 patterns to uniquely encode a surface, which facilitates the fast image acquisition for each reconstruction step. A filtering stereo matching algorithm is proposed for the spatial-temporal multiplexing method to improve the matching reliability. Moreover, the reconstruction precision is further enhanced by a correspondence refinement algorithm. Experiments validate the performance of the proposed method including the high accuracy, the robustness to noise and the ability to reconstruct moving objects.
Shadow Elimination in Soccer Game Video using Background Subtraction and Sobel Operators

Object detection in real time is considered as a challenging problem. However, it is very important in a wide range of applications, especially in field of multimedia. The players and ball are the most important objects in soccer game videos and detecting them is a challenging task because of many difficulties, such as shadow and illumination, ball size, ball occluded by players or merged with lines, and similar appearance of players. To overcome these problems, we present a new system to detect the players and ball in real-time by using background subtraction and Sobel detection. The results were more accurate and approximately two times faster than those using only background subtraction.
Perspectivas sobre la competencia informacional y digital de estudiantes y docentes de Ciencias Sociales antes y durante el confinamiento por la Covid-19

We analyze the perceptions of Social Sciences faculty about their own information and digital competence and that of their students, as well as the changes that have occurred as a consequence of the virtualization of learning caused by the coronavirus pandemic (Covid-19). The methodology used is qualitative, being based on the application of two techniques: discussion groups carried out with experienced faculty from three Spanish universities, and interviews carried out with the same sample after the suspension of face-to-face teaching. The results show that the faculty members participating in this study have a critical vision of the information and digital competence of the student body, indicating that they lack capacity for evaluation, critical use, and communication of information despite their mastery of technological tools and extensive use of mobile devices. In addition, they doubt their own ability to train students in this competence, and attribute the difficulty in achieving and promoting such learning to various factors of the university culture: a lack of coordination among teaching staff, who are sometimes in situations of job insecurity, or the way in which the European Higher Education Area has been applied in universities. Faculty recognize the basic and transversal nature of this competence and advocate its incorporation into the real work methodology of all subjects, not in a segregated way. Finally, the effort made towards the “virtual” development of teaching as a consequence of the Covid-19 pandemic has been a shock for faculty, who declare a positive attitude towards reviewing their own digital-informative practices to adapt their teaching, but believe that this has hardly resulted in any improvement in the information and digital competence of their students so far.
Autonomous Warehouse-Scale Computers

Modern Warehouse-Scale Computers (WSCs), composed of many generations of servers and a myriad of domain specific accelerators, are becoming increasingly heterogeneous. Meanwhile, WSC workloads are also becoming incredibly diverse with different communication patterns, latency requirements, and service level objectives (SLOs). Insufficient understanding of the interactions between workload characteristics and the underlying machine architecture leads to resource over-provisioning, thereby significantly impacting the utilization of WSCs. We present Autonomous Warehouse-Scale Computers, a new WSC design that leverages machine learning techniques and automation to improve job scheduling, resource management, and hardware-software co-optimization to address the increasing heterogeneity in WSC hardware and workloads. Our new design introduces two new layers in the WSC stack, namely: (a) a Software-Defined Server (SDS) Abstraction Layer which redefines the hardware-software boundary and provides greater control of the hardware to higher layers of the software stack through stable abstractions; and (b) a WSC Efficiency Layer which regularly monitors the resource usage of workloads on different hardware types, autonomously quantifies the performance sensitivity of workloads to key system configurations, and continuously improves scheduling decisions and hardware resource QoS policies to maximize cluster level performance. Our new WSC design has been successfully deployed across all WSCs at Google for several years now. The new WSC design improves throughput of workloads (by 7-10%, on average), increases utilization of hardware resources (up to 2x), and reduces performance variance for critical workloads (up to 25%).
Centerline Tracking of the Single Coronary Artery from X-ray Angiograms

Accurate determination of physiology and morphology of individual coronary artery is a prerequisite for its quantification as well as for planning procedures such as angioplasty. Therefore, in this paper, we propose a semi-automatic method for centerline extraction and tracking of the single coronary artery. Our approach is based on ridge edge detection and thresholding, after which the centerline of a vessel tree is extracted using skeletonization. After that, we manually select two points: at the start and the end of the single coronary artery. Finding the shortest path between selected points is used for single coronary centerline extraction. Finally, the template matching of selected points allows centerline tracking through the whole sequence. Obtained results show successful centerline extraction and tracking with satisfactory accuracy, precision, sensitivity and F1-score, of 81%, 79%, 81% and 0.76, respectively.
A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms

As data centers have become increasingly important in recent years their operational management must attain higher efficiency and reliability. Moreover, the power consumption of a data center is extremely large, and it is anticipated that it will continue to increase, so energy saving has become an urgent issue concerning data centers. In the meantime, the environment of the server rooms in data centers has become complicated owing to the introduction of virtualization technology, the installation of high-heat density information and communication technology (ICT) equipment and racks, and the diversification of cooling methods. It is very difficult to manage a server room in the case of such a complicated environment. When energy-saving measures are implemented in a server room with such a complicated environment, it is important to evaluate “temperature risks” in advance and calculate the energy-saving effect after the measures are taken. Under those circumstances, in this study, two prediction models are proposed: a model that predicts the rack intake temperature (so that the temperature risk can be evaluated in support of energy-saving measures implemented in the server room) and a model that evaluates the energy-saving effect (in relation to a baseline). Specifically, the models were constructed by using machine learning. The first constructed model evaluates the temperature risk in a verification room in advance, and it was confirmed that the model can evaluate the risk beforehand with high accuracy. The second constructed model—“baseline model” hereafter—supports energy-saving measures, and it was confirmed that the model can calculate the baseline (energy consumption) with high accuracy as well. Moreover, the effect of proposal process of energy-saving measures in the verification room was verified by using the two proposed models. In particular, the effectiveness of the model for evaluating temperature risk in advance and that of a technology for visualizing the energy-saving effect were confirmed.
Cornea Radius Calibration for Remote 3D Gaze Tracking Systems

Cornea radius estimation is a key technique for 3D gaze estimation in the single-camera 3D gaze tracking system. Traditional methods with one-camera-one-light-source systems or one-camera-two-light-source systems cannot achieve 3D gaze estimation. The 3D line-of-sight can be estimated only when the cornea radius is pre-calibrated by the user. A cornea radius calibration method based on the iris radius is proposed in this paper for 3D gaze estimation in remote one-camera-two-light-source systems. We first calibrate the iris radius based on the binocular strategy, estimate the spatial iris center using the calibrated iris radius, and then calibrate the cornea radius by a set of non-linear equations under the constraint of equivalent distances from the cornea center to the iris edge points. The calibrated cornea radius is verified by binocular optimization constraints. Simulations and physical experiments validate the effectiveness of the proposed method. The iris-based cornea radius calibration approach is novel; it can be used to obtain the cornea radius and 3D gaze using remote one-camera-one-light-source or one-camera-multi-light-source systems.
Introducing Nap-Break to Increase Work Performance A Case of Business Public Schools in Pakistan

This study aimed to examine the implementation nap break in organizations and its impact on employees’ productivity currently serving in public sector universities. Having reviewed the literature, the factors including employee health, employee happiness, employee productivity, and employee motivation, were found to be affecting positively with the introduction of nap break resulting in better performance of employees. Using quantitative research method, the data was gathered from the employees currently serving in four Public Sector universities operational in Pakistan. The Universities were selected using a convenient sampling technique. Data from the employees were gathered to examine if the explained aspects have any influence on their performance. After it, the gathered data was analyzed through Statistical Package for the Social Sciences by employing both the descriptive and inferential analysis, using regression test. The findings revealed that each of the influencing factors found while the reviewing literature was found having a positive impact on the employees’ performance. The study recommends the management of Public Sector universities in Pakistan to pay attention to employees’ nap break to increase their performance.
A survey on application of machine learning to manage the virtual machines in cloud computing

Abstract Virtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for detecting the PM overloading, the selection of VMs from over-utilized PM and VM placement as the main activities. This article aims to identify and classify research done in the area of scheduling and placement of VMs using the ML with resource utilization history. Energy efficiency, VM migration counts and Service quality were the key performance parameters that were used to assess the performance of the cloud datacenter.
Analyzing the Interplay Between Regulatory Compliance and Cybersecurity

Today, regulatory compliance is a critical component of any cyber-security program. However, although compliance is often the driver for developing or improving cyber-security, it may be incomplete as a cyber-security measure itself. The result is that even a compliant organization may have gaps in its security posture. Through an in-depth literature review, this paper investigates the complexity surrounding compliance and the factors that have an impact on the interplay between compliance and cyber-security.
Dual-Energy CBCT Pre-Spectral-Decomposition Filtering with Wavelet Shrinkage Networks

Convolutional Neural Networks (CNNs) are reshaping signal processing and computer vision by providing data-driven solutions for inverse problems such as noise reduction. However, their relationship with established signal processing methods is sometimes unclear and its development not fully exploiting the existing knowledge. In this paper, rather than improving existing CNNs with wavelet transformations as explored earlier, we improve the wavelet shrinkage approach to noise-reduction with a data-driven solution. The resulting CNN has clear encoding, decoding and processing paths. As application, we perform noise reduction in Dual-Energy Cone- Beam CT. The obtained results were compared to a UNet-like architecture, which reveal better noise-free images without aliasing artifacts. This indicates that that our architecture is able to preserve well the information contained in the images because the architecture exploits explicitly the underlying signal representation.
The impact of green productivity strategy on environmental sustainability through measurement of the management support: A field study in industry sector in Jordan

This study intends to distinguish the green productivity concepts, its strategy and the roadmap of its implementation throughout the adaption of green products, green production, and green innovation process dimensions. Further, it studies the impact of green productivity on environmental sustainability by identifying the mediating role of the management support to these green practices in the Jordanian factories. Methodology population of this study en-tails industry mining firms listed in the Amman Stock Exchange (ASE). A purposive sample was adapted in this study and consisted of specialized employees in terms of operating factories in general and in green production specifically. 100 questionnaires were precisely collected and analyzed via Smart Partial Least Square (PLS) statistics in order to analysis the mediation role. The study results point out a statistically significant impact of the independent variables, namely, the green productivity on environmental sustainability as a dependent variable in the existence of the mediating variable. Further, the results demonstrate the partial mediating effect of the dependent variable. However, there is no mediating effect of the independent to the mediator variable. Accordingly, the study recommends companies to concentrate on the roles played by the top management in the efforts towards green productivity adoption and strategic implementation in the Jordanian environment. The implementation of green productivity is considered at the early stages in Jordan. Thus, the value of the study lies in the search for issues that are eco-friendly practices.