Reviewerly/exHarmony huggingface dataset:
The Influence of Organizational, Social and Personal Factors on Cybersecurity Awareness and Behavior of Home Computer Users

With the increased use of computers and network systems in a time of digitalization, the digital connectedness frames our daily life at work and at home. To ensure secure systems, all computer users should safely interact with these systems. Prior research indicates insufficient cybersecurity awareness of home computer users who are also difficult to reach as they are not necessarily part of organizational structures. This study therefore investigates organizational, social and personal determinants of an individual’s cybersecurity awareness and its influence on cybersecurity behavior in the home environment, using partial least squares structural equation modeling based on survey data. The results show a low influence of the workplace and weak social influences, while the study confirms a significant effect of personal initiative and a strong effect of information systems knowledge on an individual’s cybersecurity awareness. The results suggest that security strategies aimed at the general public should focus on improving the knowledge and understanding instead of making fear. The study provides valuable insights about cybersecurity awareness and its determinants contributing to the field of research. The findings can be used for reviewing cybersecurity strategies.
Practical quantum private query based on Bell state

Quantum private query (QPQ) is a cryptographic application that protects the privacy of both users and databases while querying the database secretly. In most existing QPQ protocols, the protection of user privacy can only be cheat-sensitive. Cheat-sensitive means that Bob will be found later with a certain probability if he tries to get the address queried by Alice. On the premise of cheat-sensitivity, although Alice can discover Bob’s malicious behavior after a query (transaction), the secret information of Alice was leaked in the completed query, which is likely to be a fatal blow to Alice. Or, to prevent Bob’s malicious behavior, Alice executes one or more additional queries to test Bob’s honesty. However, to bypass Alice’s honesty test, Bob can also provide several honest queries before performing dishonest queries. Therefore, cheat-sensitive should not be the ultimate goal of user privacy protection in QPQ. In this paper, we propose a practical QKD-based QPQ protocol with better user privacy protection than cheat-sensitivity based on order rearrangement of qubits. The proposed QPQ protocol can resist the Trojan horse attack even without wavelength filter and photon number splitter (PNS) equipped with auxiliary monitoring detectors.
Towards Helping Teachers Select Optimal Content for Students

In a personalized learning context, teachers decide which content to assign to students on the basis of data. However, it is not clear that simply providing teachers with data is sufficient to promote good instructional decisions. In this paper, we study data from an online learning platform that gives teachers data on student test performance and then allows them to decide which new skill students should work on. We then apply a knowledge graph algorithm to infer whether the content the teacher assigned the student is a skill that the student is ready to learn (i.e. the skill is within the student's Zone of Proximal Development), whether the student is not yet ready to learn the skill, or whether the student has already learned the skill. In this paper, we study how the teacher's decision of what skills or topics the student should work on correlate to the student's learning outcomes. We study this issue using logistic regression to compare whether students master more skills based on whether they are assigned ready-to-learn skills or unready-to-learn skills according to the knowledge graph. The results demonstrate that in both mathematics and English learning contexts, if the teacher selects skills which the student is assessed by the algorithm to be ready to learn, the student gains more mastery than if he or she is assigned skills he or she is not ready to learn. We conclude by proposing a visualization that more clearly surfaces the knowledge graph predictions to teachers.
WBAT Job Scheduler: A Multi-Objective Approach for Job Scheduling Problem on Cloud Computing

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.
Assembly Instructions with AR: Towards measuring Interactive Assistance Experience in an Industry 4.0 Context

Current developments in digitized industrial manufacturing lead to work processes that are highly complex, frequently change and thus increase workload of staff. In this respect, assistive technologies have the potential to aid workers in better coping with increasing workload and requirements and consequently, maintaining good working experience(s). However, existing research on interactive assistive technologies has hitherto largely neglected work experience as a measurable construct. We addressed this shortcoming by conducting a subjective study that assesses different work experience dimensions in the context of interactive augmented reality assembly instructions, in a realistic industrial setting, with a decent sample size (N=48). In this study, we assessed work experience along the dimensions of perceived utility, hedonic user experience, and satisfaction with the assistive technologies used. On behalf of this approach we were able to detect a significant effect between perceived utility and satisfaction. In addition, we summarize lessons learned from this study as well as resulting implications and discuss open challenges for a research agenda on work experience in industry 4.0 contexts.
Analysis of influencing factors in emergency management based on an integrated methodology

Various emergencies are happening frequently in recent years, causing great loss of life and property. Therefore, it has become imperative to improve emergency management. Considering that the performance of emergency management could be affected by many different factors, it is hard to improve all the factors under the condition of limited resources. One approach that works well is to optimize the key factors that affect emergency management. To this end, an integrated method based on fuzzy cognitive map (FCM) and Pearson’s product-moment correlation coefficient is presented in this article to determine the critical risk factors in emergency management. First, the causal relationships between the risk factors influencing emergency management are determined according to expert opinions. To verify if the survey results from experts are reliable, an item-average correlation test based on Pearson’s product-moment correlation coefficient is performed. Then, two scenarios are designed and the FCM method is utilized to identifying the critical risk factors of emergency management. Finally, five critical factors are figured out, and the efficiency of emergency management could be enhanced by optimizing these five factors. In addition, a comparative analysis is also conducted to validate the advantages of the proposed method.
The Role of LMX in Explaining Relationships between Organizational Justice and Job Performance

The main aim of this empirical study is to explore the mediation role of the leader-member exchange (LMX) between organizational justice and job performance in several tobacco companies in Pakistan.Two models are represented in this study, the first showing the influence of the dimensions of organizational justice on job performance, and the second the influence of organizational justice on job performance with the mediating role of LMX.The data was collected from 290 employees working within several tobacco companies in Pakistan.The results of the statistical analysis found that the dimensions of organizational justice had both a direct and indirect stronger effect on job performance.It was proved empirically that LMX plays a vital role not only on organizational justice but also on employee performance.This is one of the first studies to empirically examine the mediating role of LMX in the relationship between the dimensions of organizational justice on job performance in tobacco companies in Pakistan.The study highlights the importance of job performance theory when designing strategies to promote performance.
Optimizing Stepwise Animation in Dynamic Set Diagrams

Abstract A set diagram represents the membership relation among data elements. It is often visualized as secondary information on top of primary information, such as the spatial positions of elements on maps and charts. Visualizing the temporal evolution of such set diagrams as well as their primary features is quite important; however, conventional approaches have only focused on the temporal behavior of the primary features and do not provide an effective means to highlight notable transitions within the set relationships. This paper presents an approach for generating a stepwise animation between set diagrams by decomposing the entire transition into atomic changes associated with individual data elements. The key idea behind our approach is to optimize the ordering of the atomic changes such that the synthesized animation minimizes unwanted set occlusions by considering their depth ordering and reduces the gaze shift between two consecutive stepwise changes. Experimental results and a user study demonstrate that the proposed approach effectively facilitates the visual identification of the detailed transitions inherent in dynamic set diagrams.
A Cluster Correlation power analysis against double blinding exponentiation

The exponent and the message blinding countermeasures on modular exponentiation algorithms are effective techniques to defeat simple, collisions, differential and correlation power attacks. However, the high order correlation analyses have shown to be efficient against double blinding exponentiation in theory. But searching for correlation difference of modular multiplication is difficult due to noise in real environments. Moreover, the methods of artificial observation are currently used by setting the threshold in attack process, so the attack effect is heavily dependent on the attacker’s experience. In order to solve these problems, a new cluster correlation power analysis(CPA) is proposed by utilizing correlation characteristics difference between power consumption of modular multiplications to evaluate the effectiveness of power points. We demonstrate how to find the points of interest by using pre-processing and k-means clustering. The utilization of valid information is improved and the noise and artificial participation are reduced. Experiment results show that the proposed cluster CPA can enhances the attack efficiency by comparing with other CPA methods when the exponent was broken.
Kinect Sensor Based Single Person Hand Gesture Recognition for Man–Machine Interaction

The advancement of cameras and sensors has made a skyline for man–machine interaction (MMI). This paper aims to present an improved man–machine interaction by single person hand gesture recognition using Microsoft’s Kinect sensor. Using the skeletal images obtained from the sensor, six features are extracted from 20 body joints’ coordinates in 3D space. Artificial neural network with backpropagation learning algorithm is employed for classification. The proposed work outperforms the state-of-the-art algorithms and produces a recognition rate of 97.8%.
Underwater image enhancement and restoration based on local fusion

Underwater imaging and image processing play important roles in oceanic scientific research. However, because the light is absorbed and scattered, the obtained underwater images are seriously degraded. Color distortion, low contrast, and detail (edge information) loss are the major problems of underwater images. We propose a method to solve these problems. First, a local adaptive proportion fusion algorithm is proposed to produce a color-balanced image, which is the first input image. Second, an edge-enhanced image is produced as the second input image. Third, a proportion fusion image is produced as the third input image. Finally, the image formation model-based local triple fusion method is used to merge these three input images and get the final result. Experimental results show that the proposed method could effectively balance color distortion and enhance edges of the degraded images. Subjective and objective evaluations show that our method is superior to many state-of-the-art methods.
Rewritability in Monadic Disjunctive Datalog, MMSNP, and Expressive Description Logics

We study rewritability of monadic disjunctive Datalog programs, (thecomplements of) MMSNP sentences, and ontology-mediated queries (OMQs) based onexpressive description logics of the ALC family and on conjunctive queries. Weshow that rewritability into FO and into monadic Datalog (MDLog) are decidable,and that rewritability into Datalog is decidable when the original querysatisfies a certain condition related to equality. We establish2NExpTime-completeness for all studied problems except rewritability into MDLogfor which there remains a gap between 2NExpTime and 3ExpTime. We also analyzethe shape of rewritings, which in the MMSNP case correspond to obstructions,and give a new construction of canonical Datalog programs that is moreelementary than existing ones and also applies to formulas with free variables.
Reconstructing Trees from Traces

We study the problem of learning a node-labeled tree given independent traces from an appropriately defined deletion channel. This problem, tree trace reconstruction, generalizes string trace reconstruction, which corresponds to the tree being a path. For many classes of trees, including complete trees and spiders, we provide algorithms that reconstruct the labels using only a polynomial number of traces. This exhibits a stark contrast to known results on string trace reconstruction, which require exponentially many traces, and where a central open problem is to determine whether a polynomial number of traces suffice. Our techniques combine novel combinatorial and complex analytic methods.
Debugging Quadrocopter Trajectories in Mixed Reality

Debugging and monitoring robotic applications is a very intricate and error-prone task. To this end, we propose a mixed-reality approach to facilitate this process along a concrete scenario. We connected the Microsoft HoloLens smart glass to the Robot Operating System (ROS), which is used to control robots, and visualize arbitrary flight data of a quadrocopter. Hereby, we display holograms correctly in the real world based on a conversion of the internal tracking coordinates into coordinates provided by a motion capturing system. Moreover, we describe the synchronization process of the internal tracking with the motion capturing. Altogether, the combination of the HoloLens and the external tracking system shows promising preliminary results. Moreover, our approach can be extended to directly manipulate source code through its mixed-reality visualization and offers new interaction methods to debug and develop robotic applications.
An Opinion Mining for Indian Premier League Using Machine Learning Techniques

Social media has dramatically changed the way people express their opinion, appraisals or feelings towards entities or brand. Among many social media sites one of the free social service websites i.e. twitter, that permits users to publish their everyday life related events. As we know that twitter blog posts are being originated continually and Twitter having character short or limit to the Twitter posts (tweets) and also extremely compatible origins of continuous flow data for finding or detect opinion mining. Blog posts will reveal general or people emotions once taken in collection as an example throughout events like IPL 2016. Here our work presents, and provide the effectiveness of a machine learning model as positive or negative sentiment on tweets. The data collection of tweets and processing them by filtration best of the authorized IPL hastags (#IPL 2016 and #IPL 9)which can be done through using of Twitter's API(Application Programming Interface) service. We analyze the performance of the `Random Forest' against existing supervised machine learning algorithms with respect to its accuracy, specificity, sensitivity etc. here explanation of our paper is to performed opinion mining for the event like Indian Premier League 2016 effectively.
The Argument Against Quantum Computers

We give a computational complexity argument against the feasibility of quantum computers. We identify a very low complexity class of probability distributions described by noisy intermediate-scale quantum computers, and explain why it will allow neither good-quality quantum error-correction nor a demonstration of "quantum supremacy." Some general principles governing the behavior of noisy quantum systems are derived. Our work supports the "physical Church thesis" studied by Pitowsky (lyuun Jerus Philos Q 39:81–99, 1990) and follows his vision of using abstract ideas about computation to study the performance of actual physical computers.
Task Resource Usage Analysis and Failure Prediction in Cloud

To improve the reliability of the cloud computing system it is important to understand the failure characteristics and to predict failures earlier to avoid it. A statistical analysis of workload data on the cloud provides insights into failure characteristics, which can be used as a cue to improve the reliability of the system. This manuscript discusses a statistical analysis of resource usage data of tasks on the large Google cluster dataset, further failure prediction algorithms are developed to predict the failure. Based on the study, it is observed that there is variation in the resource usage pattern, duration of execution and amount of resource used by a failed task as compared to that of a finished task. Different resampling techniques along with XGboost classifier is used to predict the failure of a task in the highly imbalanced dataset and it is observed that Synthetic minority oversampling along with XGboost predicted the task status with precision of 92% and recall of 94.8%.
Fast and In Sync: Periodic Swarm Patterns for Quadrotors

This paper aims to design quadrotor swarm performances, where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using a few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.
Model-Free Reinforcement Learning Algorithms: A Survey

This paper aims to provide a comprehensive survey of the reinforcement learning algorithms given in the literature. Especially model-free reinforcement learning algorithms are given in details and the differences of these algorithms are handled. Finally, some open problems in reinforcement learning are presented for future researches.
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? To approach this question, deep multi-task learning is extended in this paper to the setting where there is no obvious overlap between task architectures. The idea is that any set of (architecture,task) pairs can be decomposed into a set of potentially related subproblems, whose sharing is optimized by an efficient stochastic algorithm. The approach is first validated in a classic synthetic multi-task learning benchmark, and then applied to sharing across disparate architectures for vision, NLP, and genomics tasks. It discovers regularities across these domains, encodes them into sharable modules, and combines these modules systematically to improve performance in the individual tasks. The results confirm that sharing learned functionality across diverse domains and architectures is indeed beneficial, thus establishing a key ingredient for general problem solving in the future.
The Notorious Difficulty of Comparing Human and Machine Perception

With the rise of machines to human-level performance in complex tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These works have the potential to deepen our understanding of the inner mechanisms of human perception and to improve machine learning. Drawing robust conclusions from comparison studies, however, turns out to be difficult. Here, we highlight common shortcomings that can easily lead to fragile conclusions. First, if a model does achieve high performance on a task similar to humans, its decision-making process is not necessarily human-like. Moreover, further analyses can reveal differences. Second, the performance of neural networks is sensitive to training procedures and architectural details. Thus, generalizing conclusions from specific architectures is difficult. Finally, when comparing humans and machines, equivalent experimental settings are crucial in order to identify innate differences. Addressing these shortcomings alters or refines the conclusions of studies. We show that, despite their ability to solve closed-contour tasks, our neural networks use different decision-making strategies than humans. We further show that there is no fundamental difference between same-different and spatial tasks for common feed-forward neural networks and finally, that neural networks do experience a recognition gap on minimal recognizable images. All in all, care has to be taken to not impose our human systematic bias when comparing human and machine perception.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs

Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set---the set of plausible transition probabilities---which is usually constructed as a multi-dimensional confidence region. Existing methods construct ambiguity sets as confidence regions using concentration inequalities which leads to overly conservative solutions. This paper proposes a new paradigm that can achieve better solutions with the same robustness guarantees without using confidence regions as ambiguity sets. To incorporate prior knowledge, our algorithms optimize the size and position of ambiguity sets using Bayesian inference. Our theoretical analysis shows the safety of the proposed method, and the empirical results demonstrate its practical promise.