Reviewerly/exHarmony huggingface dataset:
The Extent of Using E-Learning Among Teachers of Higher Basic Stage in Jordan

This study aimed at exploring the extent of using e-learning among teachers of higher basic stages in one of the southern governorates in Jordan. The researchers used the descriptive approach to collect, analyze the data, and to interpret the results. The study sample consisted of 365 male and female teachers, and it was selected by using the stratified random sampling method. A questionnaire was developed to collect the data, and its validity and reliability were checked. The study results showed that the teachers were using e-learning moderately. In addition, there were no statistically significant differences due to the variables of gender and specialization. However, there were statistically significant differences due to the experience variable. In light of the study results, teachers should use e-learning strategy in their teaching process regardless the subject of the course content. In addition, teachers who have five years and more in teaching experiences should obtain training that focus on the updated use of technology tools.
Anthroposcenic Performance and the Need For ‘Deep Dramaturgy’

In this article I ask: what do we need to relinquish from current ways of doing theatre and what do we need to restore so that performance can play a productive role in responding to the climate emergency? What does it mean to make theatre in the age of the Anthropocene and in the face of potential social collapse or even human extinction? In attempting to think through Anthroposcenic performance or ‘deep dramaturgy’, I will consider a series of performances devised by Colombian-born, Sydney-based artist Maria Fernanda Cardoso, the Cardoso Flea Circus 1994(-2000) and The Art of Seduction (2016/2018).
Guarding Sensitive Sensor Data against Malicious Mobile Applications

With increasing usage of sensor data for medical purposes, the ability to secure sensitive features in mobile sensor data from adversarial applications is a continuous challenge. This paper introduces a random anonymization algorithm, SparCTym, as a method for anonymizing sensitive features in walking accelerometer data while maintaining the utility of the data. SparCTym was implemented in the Android framework of a Nexus S phone and tested with activity recognition applications.
A Reverse Sequence Hash Chain-based Access Control for a Smart Home System

As a smart home system utilizes a machine learning technology, intelligent and user-friendly smart home systems will be trends more and more. However, since a consumer home generally consists of various IoT-devices which are with different capabilities and from different vendors each other, it is difficult to apply centralized high performance security solutions to their smart home systems. A blockchain system, a decentralized authentication solution, is spotlighted as an alternative. However, since a blockchain system needs high resources for its mining process, to apply a blockchain technology to a smart home system, it is needed to improve such a high resource problem. This paper presents a reverse sequence hash chain based authentication scheme for a smart home. Since this authentication system needs small numbers of hash value computation to compute the solution of POW, a user can build a authentication system of his smart home without using a centralized and heavy security system like PKI.
Scalable Measurement-Device-Independent Quantum Key Distribution Networks with Untrusted Relays

We review the recent developments of quantum key distribution networks with untrusted relays based on the concept of Measurement-Device-Independent quantum key distribution (MDI-QKD).
Quantitative Evaluation Model of Network Security Situation Based on D-S Evidence Theory

This paper studies a network security situation assessment model based on D-S evidence theory for the problems that the assessment information source is too single and the accuracy deviation is too large in the situation awareness system. First, the model uses PCA to pre-process the alarm data to shorten the alarm processing time; secondly, it uses the improved DS evidence theory to combine the credibility of multi-source attack data and improve the alarm recognition rate; thirdly, by constructing three kinds of situation assessment indicators of vulnerability, threat and assets, it quantifies the situation component and improves the accuracy of the perception; fourthly, it also uses the weighted average method to synthesize the situation component; finally, the model and algorithm are verified by using network instance data and the results show the correctness of the method.
An Anti-sniffing Protocol for Location-based Services in Wireless Networks

In Wi-Fi networks, a user's location can be determined through the access points (APs) in the network. These kinds of location-based services, LBS, use a calculation of trilateration from the distance away from three routers. This distance is most often determined with received signal strength indication, RSSI. Wi-Fi communications are broadcasted, and RSSI is contained in the header of a packet. As a consequence, simple packet sniffing attacks make it relatively easy for malicious users to track a user's location. In order to increase the security of using LBS, this paper proposes a new algorithm for channel hopping of the routers. In this work, the algorithm is tested for its effectiveness at increasing the difficulty for the malicious user to determine the location of the LBS user. The simulation results support the hypothesis that channel hopping significantly increases the effort needed for the malicious user to track the user, at an acceptable cost of LBS efficiency.
Cost Effective Real Time Vision Interface for Off Line Simulation of Fanuc Robots

The proliferation of automation has created an increasing demand for robots in every area of the manufacturing industry. FANUC robots are very widely used in automated production lines because of its intelligent functions with flexible open architecture. It allows the users to plugin online additional components such as vision and sensor systems to support the development of robotic systems. FANUC also has an offline simulation tool called Roboguide, which allows the users to simulate a virtual robotic cell in 3D, eliminating the need for expensive physical prototype setup. This tool supports the virtual integration of an integral Robot Vision (iRVision) capabilities. However, this feature is limited to virtual vision or using very expensive FANUC compatible cameras that allow the users to take a real mode image and simulate it in the virtual world. In this paper, we propose an offline plugin for interfacing normal cameras of any type including webcams to the FANUC iRVision system for the virtual simulation of real mode images. It was developed using an Open Computer Vision (OCV) plugin and tested it by physically capturing the real image of the object using a webcam interfaced with the iRVision system and processed it on the virtual robot controller. The test results show that this cost-effective plugin can be used as an OCV in industrial automation and robotic building system without any compromise in terms of performance and quality.
Obstacle Induced Stochastic Tree for Fast Path Planning

Rapidly-Exploring Random Tree (RRT) has been extensively applied to robot navigation field as a fast and effective path planning method. Rapidly-Exploring Random Tree Star (RRT*), one of the extensions of RRT, outperforms other methods because it can approach the optimal path with the number of iterations increases. Nonetheless, as the number of sampling points increases, it takes too long to get an optimal path and the convergence speed slows dramatically for RRT and all its variants, since the range of sampling is the entire configuration space. In this paper, we propose a novel method of robot path planning in the global knowledge scenario: obstacle induced stochastic tree algorithm. This algorithm regards the center point in obstacles as the sampling point, which significantly reduces the sampling range then shorts the time for path planning. Simulation results demonstrate that our algorithm can find a feasible path at a rapid rate. Compared with RRT and its variants algorithms, the algorithm has better performance in both time and path cost.
A Fast Algorithm for Hiding High Utility Sequential Patterns

High utility sequential patterns (HUSPs) are common patterns that can be discovered from the data collected in many domains (e.g. retail, bioinformatics, mobile commerce). To extract these patterns, high utility sequential pattern mining (HUSPM) has been proposed in recent decade. Although the HUSPM algorithms provide us a special perspective to analyze the knowledge behind the collected data, it also arises the risk of the privacy leakage and underlying security issues. This leads to the emergence of high utility sequential pattern hiding (HUSPH) whose purpose is to hide all HUSPs in the sequence database under a specified threshold. Around this topic, many algorithms were proposed. However, the existing algorithms are very time-consuming, which makes them unable to process the real massive data quickly. In this paper, we propose an efficient algorithm named FH-HUSP (fast algorithm for hiding high utility sequential patterns) for HUSPH. Substantial experimental results show that the proposed algorithm can hide all high utility sequential patterns quickly under the specific minimum utility with relatively small modifications.
Ensemble Steganalysis Based on Deep Residual Network

The performance of the steganography detector built on deep learning has been superior to the traditional feature-based methods, and more adaptive methods for steganalysis are beginning to emerge. However a single model may encounter a bottleneck in classification accuracy due to the absent diversity of training data and parameter configuration, it maybe fails to exert a strong fitting performance of the deep learning network. To make full use of the classification performance of the combination of multiple models, we first obtained multiple base learners from different snapshot and different training sets. Then two strategies to combine multiple base learners: one achieves the optimal ensemble effect by majority voting and product combination, another, in view of the insufficient performance of Softmax classifier, propose a scheme of feature extraction based on convolutional neural network. Experiments show that the ensemble scheme proposed can well fuse the output of multiple convolutional neural networks, thus effectively reduce the detection error rate of a single model.
In-Memory Cache and Intra-Node Combiner Approaches for Optimizing Execution Time in High-Performance Computing

Hadoop is a leading framework for processing and analyzing big data. MapReduce is a programming model for processing data in parallel and distributed computing environment. One of the issues in HDFS is the number of disk I/O operations involved in fetching input data blocks to memory from disk for every task. Since data-intensive tasks expect predominant file access operations, In-Memory Cache approach is proposed in this paper so as to reduce the number of disk I/O operations which in turn results in reduced execution time and increased throughput. The intermediate data from mapper having a large number of duplicate <key, value> pairs are to be delivered across the network to the reducers. This may cause increase in network traffic and requires high bandwidth. Therefore, Intra-Node Combiner approach is proposed in this paper which aggregates the duplicate <key, value> pairs present in the intermediate data in every node before transferring to the reducers. This may result in reduced network traffic and bandwidth. Experimental analysis on the proposed approaches has been carried out on Click count and Sessionization applications of a Web server log file with varied size. The experimental results reveal that the average execution time is decreased by 23% in respect of In-Memory Cache approach.
A Competitive Analysis on Digital Image Tamper Detection and Its Secure Recovery Techniques Using Watermarking

Digital images play a vital role in human life. Hence, its protection from unauthorized access is a serious matter of concern. Even if the contents are modified then its detection and recovery must be defined. Nowadays, a number of methods are proposed to protect digital images based on digital watermarking. But all are not with similar capability in terms of security, authenticity, recovery. This paper represents the basics of digital watermarking techniques along with their competency and weakness for the detection of tampered images and their recovery process. A series of watermarking techniques with simulated results show their working efficiency with quantitative result analysis.
DCIGAN: A Distributed Class-Incremental Learning Method Based on Generative Adversarial Networks

Class incremental learning has received wide attention due to its better adaptability to the changing characteristics of online learning. However, data cannot be shared between organizations in the data isolated islands scenario. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIGAN. It uses GAN generators to store the information of past data and continuously update GAN parameters with new data. In particular, we proposed CIGAN to ensure that the distribution of generated pseudo data is as close as possible to the real data on a single node, which guarantees the accuracy of class-incremental learning. Furthermore, we propose GF, a generators fusion method to integrate local generators of multi-nodes into a new global generator. To evaluate the performance of DCIGAN, we conduct experiments on six datasets under various parameter settings on both two and multi nodes distributed scenarios. Extensive experiments confirm that DCIGAN outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.
“Interdisciplinarity” and “Synergy” in the Œuvre of Judit Bar-Ilan

Abstract Both “interdisciplinarity” and “synergy” are desirable features from a policy perspective: can surplus be found in the interactions among (disciplinary) bodies of knowledge? We have recently developed measures for “interdisciplinarity” and distinguished these measurements from those of “synergy.” In this study, we analyze three review papers by Judit Bar-Ilan (Scientometrics 50(1):7–32, 2001, Ann Rev Inf Sci Technol (ARIST) 38:231–288, 2004, J Informetr 2(1):1–52, 2008a) in terms of whether they rank high on interdisciplinarity and synergy values among the 130 papers of her œuvre . Review papers can be expected to fulfill a synergetic and perhaps also interdisciplinary function in scientific literature more than research articles, since the literature is considered from a broader perspective. Both the interdisciplinarity and synergy indicators point to Bar-Ilan (2004). The three reviews have high synergy scores. Whereas Bar-Ilan (2008a) contributed to the redefinition and shaping of the discipline of “information science,” Bar-Ilan (2004) added the broader perspective of the theoretical and practical relevance of the discipline. Bar-Ilan (2001) reviews various methods for data collection at the Internet. An article of Bar-Ilan and Peritz (2002) in Library Trends scores also high on synergy.
A Snapshot of Bystander Attitudes about Mobile Live-Streaming Video in Public Settings

With the advent of mobile apps such as Periscope, Facebook Live, and now TikTok, live-streaming video has become a commonplace form of social computing. It has not been clear, however, to what extent the current ubiquity of smartphones is impacting this technology’s acceptance in everyday social situations, and how mobile contexts or affordances will affect and be affected by shifts in social norms and policy debates regarding privacy, surveillance, and intellectual property. This ethnographic-style research provides a snapshot of attitudes about the technology among a sample of US participants in two public contexts, both held outdoors in August 2016: A sports tailgating event and a meeting event. Interviews with n = 20 bystanders revealed that many are not fully aware of when their image or speech is being live-streamed in a casual context, and some want stronger notifications of and ability to consent to such broadcasting. We offer design recommendations to help bridge this socio-technical gap.
Distributed Adaptive Learning of Graph Processes via In-Network Subspace Projections

In this paper, we introduce a novel adaptive method for distributed recovery of graph processes, which are observed over a dynamic set of vertices. The proposed algorithm hinges on proximal gradient optimization techniques, while leveraging in-network projections as a mechanism to enforce graph bandwidth constraints in a cooperative and distributed fashion, and thresholding operators to identify anomalous sparse components hidden in the signals. The theoretical analysis illustrates the mean-square stability of the proposed adaptive method. Finally, numerical tests on synthetic and real data assess the performance of the proposed distributed strategy for adaptive learning of graph processes.
A Sub-Quadratic Algorithm for the Longest Common Increasing Subsequence Problem.

The Longest Common Increasing Subsequence problem (LCIS) is a natural variant of the celebrated Longest Common Subsequence (LCS) problem. For LCIS, as well as for LCS, there is an $O(n^2)$-time algorithm and a SETH-based conditional lower bound of $O(n^{2-\varepsilon})$. For LCS, there is also the Masek-Paterson $O(n^2 / \log{n})$-time algorithm, which does not seem to adapt to LCIS in any obvious way. Hence, a natural question arises: does any (slightly) sub-quadratic algorithm exist for the Longest Common Increasing Subsequence problem? We answer this question positively, presenting a $O(n^2 / \log^a{n})$-time algorithm for $a = \frac{1}{6}-o(1)$. The algorithm is not based on memorizing small chunks of data (often used for logarithmic speedups, including the Four Russians Trick in LCS), but rather utilizes a new technique, bounding the number of significant symbol matches between the two sequences.
A comparitive study of Image Filters and Machine Learning for use in Machined Part Recognition

The use of filters for image processing has long existed and is well established within industrial practice and in academia. The wide-spread adoption of machine learning into industrial spaces, however, has presented opportunities for the use of machine learning and artificial intelligence applications to be further developed and researched in relation to image processing. This paper aims to identify three of the main types of filters used in the machine learning process and to test their ability for target recognition of a basic/standard industry part.
What Phenomenal Contrast for Bodily Ownership?

Abstract In a 1962 article, ‘On Sensations of Position’, G. E. M. Anscombe claimed that we do not feel our legs crossed; we simply know that they are that way. What about the sense of bodily ownership? Do we directly know that this body is our own, or do we know it because we feel this body that way? One may claim, for instance, that we are we aware that this is our own body thanks to our bodily experiences that ascribe the property of myness to the body that they represent. Here I approach this issue from the perspective of the debate on the admissible content of perception, appealing to the method of phenomenal contrast. After rejecting the myness hypothesis, I criticize alternative accounts of the contrast in somatosensory, cognitive, and agentive terms. I conclude that the phenomenology of ownership consists in the affective awareness of the unique significance of the body for survival.
Efficient learning-based blur removal method based on sparse optimization for image restoration

In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications.
Backflash Light as a Security Vulnerability in Quantum Key Distribution Systems

Based on the fundamental rules of quantum mechanics, two communicating parties can generate and share a secret random key that can be used to encrypt and decrypt messages sent over an insecure channel. This process is known as quantum key distribution (QKD). Contrary to classical encryption schemes, the security of a QKD system does not depend on the computational complexity of specific mathematical problems. However, QKD systems can be subject to different kinds of attacks, exploiting engineering, and technical imperfections of the components forming the systems. Here, we review the security vulnerabilities of QKD. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks. We equally highlight the method for quantifying backflash emission and the different ways to mitigate this effect.
Markerless Human-Manipulator Interface with Vibration Feedback Using Multi-sensors

This paper proposes a novel markerless human-manipulator interface, which tracks the posture of the operator's hand for robot teleoperation and provides vibration feedback in the course of operation. In this system, five Leap Motions (LMs) and a wireless wearable GEAK Watch were integrated to track the postures of the human hands so that the workspace was enlarged, and the data was more reliable by fusing measured data from multiple sensors. However, due to the native noises and tracking errors of the sensors, the measurement errors were found increasing over time, which reduces the accuracy of the system. To solve this problem, a hybrid filter algorithm was integrated for the posture estimation of human hands in this paper. Kalman Filter (KF) was used to estimate the position, and the particle filter (PF) was used to estimate the orientation. To improve the fault tolerance of the system, the GEAK Watch was also used to provide vibration feedback when the robot was about to collide with obstacle or the operator's hand moved outside the workspace. Finally, a series of experiments were conducted with this human-manipulator interface and the results indicate that the system works effectively and exactly in the process of robot teleoperation.