Reviewerly/exHarmony huggingface dataset:
Hidden Markov Model Tagger for Applications Based Arabic Text: A Review

The immense increase in the use of the Arabic Language in transmitting information on the internet makes the Arabic Language a focus of researchers and commercial developers. The developing of an efficient Arabic POS tagger is not an easy task due to the complexity of the Language itself and the challenges of tagging disambiguation and unknown words. This paper aims to explore and review the use of Part of speech Tagger for Arabic text based on Hidden Markov Model. Besides, it is discussed and explored the implementation of POS tagger for different languages. This study examined a group of research papers that applied the Part of Speech to Arabic using the Hidden Markov Model. The results have shown that a large number of researchers achieved high accuracy rates in the classification of parts of speech correctly. Handi and Alshamsi achieved a high accuracy rate of 97.6% and 97.4% respectively. Kadim obtained an average accuracy of 75.38% for a Parallel Hidden Markov Model.
Students’ Writing Anxiety and Its Correlation with Their Writing Performance

The objectives of this study were to find out: (1) the dominant aspect in writing anxiety, (2) the correlation between students’ writing anxiety and their writing performance, (3) the differences between male and female students in students’ writing anxiety of the twelfth grade students in Pangkalpinang, Bangka Belitung. The sample of this study was 80 students The instruments used were writing anxiety questionnaire and a writing test. The results showed that, cognitive was the most dominant aspect. There was a fair significant correlation between students’ writing anxiety and writing performance. T-score was -0,430 and T-table was 0,283. The result showed that, there was a negative and strong enough correlation between the students’ writing anxiety and their writing perfomance. English learning anxiety gave positive influences to the students’ writing performance, not necessarily bad result to writing but drove the students to get motivation to be better from others. In addition, the result of writing anxiety showed that the mean score of male students was 100,27 and the females got 102,60. So, there was a significant difference between male and female students. Thus, the teachers are suggested to increase students’ self-confidence so that students’ writing results are maximized.
Research on automatic mosaic ceramic tiling method based on color matching

Mosaic ceramics are widely used in construction-related fields, but with the development of society, architects are no longer satisfied with their utilitarian value and have begun to pay more attention to their artistic value. For example, mosaic ceramics with unique artistic effects are frequently used for paving. This article proposes a comprehensive color difference method based on the probability density of gray values to control the paving system and produce paved mosaic patterns with different artistic effects. Experiments show that our method can easily lay out mosaic ceramic work with a certain aesthetic value based on artistic images. Our method may provide an effective technical reference for the use of mosaic ceramics for entertainment, in the home, and as an aspect of architecture, to enrich people's aesthetic requirements.
Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require significant human involvement, but still suffer from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random field (CRF).A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the effectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two different types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.
Switching median filter for suppressing multi-pixel impulse noise

This paper proposes a new switching median filter for suppressing multi-pixel impulse noise in X-ray images. A multi-pixel impulse is understood as a set of several neighboring pixels, the intensity of each significantly exceeds background intensity. Multi-pixel noise can occur, for example, due to the blooming effect, the reason being the limited value of pixel saturation capacity. This article defines the thresholds for the intensity increment relative to the eight immediate neighbors, above which the current pixel is processed by the median filter. The dependence of these thresholds on the number of pixels in an impulse is presented. The proposed algorithm is based on the median filtering process, which consists of several iterations. In this case, the filter has the smallest possible size, which minimizes image distortion during processing. In particular, to exclude a single-pixel impulse, pixel processing is turned on when intensity surge exceeds 3.5 with the grayscale value ranging from 0 to 1. At the same time, to exclude nine-pixel impulses, three iterations are required with the following thresholds: the first iteration with a threshold 2.0; the second iteration also with a threshold 2.0 and the third iteration with a threshold 3.5. The algorithm proposed was tested on real X-ray images corrupted by multi-pixel impulse noise. The algorithm is not only simple, but also reliable and suitable for real-time implementation and application. The efficiency of the technique is shown in comparison with other known filtering methods with respect to the degree of noise suppression. The main result of the testing is that only the proposed method allows excluding multi-pixel noise. Other advantage of the algorithm is its weak effect on the level of Gaussian noise leading to the absence of image blurring (or preserving image details) during processing.
Future of Learning and Teaching in Higher Education Post-COVID-19

The world of learning and teaching has changed tremendously as a result of COVID-19 pandemic. The pandemic has forced educational institutions to innovate their ways of teaching, learning processes and learner management and reimagine their approach to education. Learners are facing a similar transition to less familiar online environment and limited in-person interactions in a purpose-built physical environment. As these realities are sinking in, stakeholders are evaluating implications on the future of learning and teaching in higher education post COVID. This chapter adopts a qualitative approach to uncover deeper faculty and learner insights in three key dimensions – digital transformation of education, learner and faculty interactions, and learning and teaching experience. We will examine how disruption can lead to innovation in terms of how and when learners engage with digital content and activities to ensure that the learning outcomes are achieved. This includes looking at different ways of orientating learning to tertiary education experience and a holistic approach to the onboarding of students in the hybrid model of virtual with face-to-face experiences. From the faculty perspective, it involves looking at the kind of support and training required to be fully equipped for the digital world of study. Adopting a community of inquiry framework with essential face-to-face learning implies that strong capabilities will be a major differentiator in an already competitive higher education sector. A key driver of these changes is learner experience and the ability to effectively acquire skills and attributes for the new digital workplace.
Research on Cyberspace Mimic Defense Based on Dynamic Heterogeneous Redundancy Mechanism

With the rapid growth of network technology, the methods and types of cyber-attacks are increasing rapidly.Traditional static passive defense technologies focus on external security and known threats to the target system and cannot resist advanced persistent threats.To solve the situation that cyberspace security is easy to attack and difficult to defend, Chinese experts on cyberspace security proposed an innovative theory called mimic defense, it is an active defense technology that employs "Dynamic, Heterogeneous, Redundant" architecture to defense attacks.This article first briefly describes the classic network defense technology and Moving Target Defense (MTD).Next, it mainly explains in detail the principles of the mimic defense based on the DHR architecture and analyzes the attack surface of DHR architecture.This article also includes applications of mimic defense technology, such as mimic routers, and mimic web defense systems.Finally, it briefly summarizes the existing research on mimic defense, expounds the problems that need to be solved in mimic defense, and looks forward to the future development of mimic defense.
Ethical leadership and employee information security policy (ISP) violation: exploring dual-mediation paths

Purpose A growing number of studies have investigated the effect of ethical leadership on behavioral outcome of employees. However, considering the important role of ethics in IS security, the security literature lacks a theoretical and empirical investigation of the relationship between ethical leadership and employees' security behavior, such as information security policy (ISP) violation. Drawing on social learning and social exchange theories, this paper empirically tests the impact of ethical leadership on employees' ISP violation intention through both information security climate (i.e. from a moral manager's perspective) and affective commitment (i.e. from a moral person's perspective). Design/methodology/approach The research was developed based on social learning theory and social exchange theory. To measure the variables in the model, the authors used and adapted measurement items from previous studies. The authors conducted a scenario-based survey with 339 valid responses to test and validate the research model. Findings Results indicated that information security climate fully mediates the relationship between ethical leadership and ISP violation intention. The authors also found that information security climate enhances the negative effect of affective commitment on ISP violation intention. Originality/value This research contributes to the literature of information security by introducing the role of ethical leadership and integrating two theories into our research model. This study also calls attention to how information security climate and affective commitment mediate the relationship between ethical leadership and employees' ISP violation intention. The theory-driven study provides important pragmatic guidance for enhancing the understanding of the importance of ethical leadership in information systems security research.
Public Wireless Packets Anonymously Hurt You

With growing privacy concerns over the last decade, two of the most notable wireless technologies – i.e., BLE and WiFi – are being more and more investigated in terms of privacy vulnerabilities. In this paper, we explore this problem, prospect the related consequences, and alert the need for privacy-preserving public packets. We identify key flaws in the current design of public packets like beacons and probe requests. We discuss them as the cause of privacy issues that require the community’s attention. We address the flaws in detail and propose solutions that facilitate the devices to protect user privacy. We also give recommendations based on the findings to the standard.
Location Data and Privacy

Location data consist of information regarding the geographical position (coordinates) of a device or tool collected through different methods. New generation devices collect a large amount of personal data, including location data. Such data could be revealing about the habits of individuals. The article addresses the issue of location data usage in different contexts and for different purposes from a privacy perspective. Lastly, the article focuses on the theme of the use of location data in the context of contact tracing during the pandemic.
Strong Equivalence of TAG and CCG

Tree-adjoining grammar (TAG) and combinatory categorial grammar (CCG) are two well-established mildly context-sensitive grammar formalisms that are known to have the same expressive power on strings (i.e., generate the same class of string languages). It is demonstrated that their expressive power on trees also essentially coincides. In fact, CCG without lexicon entries for the empty string and only first-order rules of degree at most 2 are sufficient for its full expressive power.
Focus on Interaction: A Novel Dynamic Graph Model for Joint Multiple Intent Detection and Slot Filling

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. Since the two tasks are closely related, the joint models for the two tasks always outperform the pipeline models in SLU. However, most joint models directly incorporate multiple intent information for each token, which introduces intent noise into the sentence semantics, causing a decrease in the performance of the joint model. In this paper, we propose a Dynamic Graph Model (DGM) for joint multiple intent detection and slot filling, in which we adopt a sentence-level intent-slot interactive graph to model the correlation between the intents and slot. Besides, we design a novel method of constructing the graph, which can dynamically update the interactive graph and further alleviate the error propagation. Experimental results on several multi-intent and single-intent datasets show that our model not only achieves the state-of-the-art (SOTA) performance but also boosts the speed by three to six times over the SOTA model.
Optimization of Music Teaching in Colleges and Universities Based on Multimedia Technology

Multimedia technology refers to the computer as the core, integrated text, graphics, images, sound, animation, video and other multimedia information, and make the information to establish a logical connection, in order to express richer and more complex ideas or methods, each of these sensory teaching information in a short time to the students, so that students a hitherto unknown deep feelings, the use of multimedia courseware is an ideal modern teaching means. In music appreciation, contemporary college students will enjoy psychological pleasure and enjoy aesthetic enjoyment through their emotional experience of music and art works, so that they can have an eternal understanding of life and future. The computer multimedia technology is very important to assist the optimization of music teaching in Colleges and universities. The paper presents optimization of music teaching in Colleges and Universities Based on Multimedia Technology.
Maximizing Legibility in Stochastic Environments

Making an agent’s intentions clear from its observed behavior is crucial for seamless human-agent interaction and for increased transparency and trust in AI systems. Existing methods that address this challenge and maximize legibility of behaviors are limited to deterministic domains. We develop a technique for maximizing legibility in stochastic environments and illustrate that using legibility as an objective improves interpretability of agent behavior in several scenarios. We provide initial empirical evidence that human subjects can better interpret legible behavior.
Individual Academic Productivity vs New Managerialism in Academic Research

The paper considers individual academic productivity and the new managerialism in academic research as a set of social relations, common and opposite interests of a scholar and a university. The balance of interests is a necessary condition for regulating the contradictions among participants in public relations, including academic research. Reforming higher education results in new managerialism spreading wider and in scientific results paid attention to. The increasing accountability with a lack of mutual trust and information asymmetry creates the illusion of an imbalance of academic researchers’ interests in favor of management. The power potential of the new managerialism can become an instrument of both pressure and encouragement of individual academic productivity. As is shown in our review, this productivity, mainly published papers, is influenced by the principles of its assessment. The evolution of approaches to the academic results promotion provides a large variety of criteria for the selection of indicators to assess scientific activity. The game theory allows to reduce this variety to one common ground, where winning is considered to be the basis of relationships in academic research. As a result, there is a matrix model of four strategies – the extreme forms of scholar-and-management relationship manifestation within the system of academic research. Only one of these strategies means a balance of interests and long-term cooperation, the other three imply the contradiction of individual academic productivity vs the new managerialism and are short-term. The use of winning in a game as a basis and criterion of assessment for the individual academic productivity normalization contributes to opportunistic behavior neutralization. The author makes the conclusion that the type of strategy affects the combination of simple and qualitative indicators and professional expertise when assessing scientific results. It is reasonable to choose the indicators of assessment according to the most balanced strategy of regulating the contradictions among participants in academic research.
Lightweight Connectivity In Graph Convolutional Networks For Skeleton-Based Recognition

Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, namely graphs. Their success is highly dependent on how the topology of input graphs is defined and most of the existing GCN architectures rely on predefined or handcrafted graph structures. In this paper, we introduce a novel method that learns the topology (or connectivity) of input graphs as a part of GCN design. The main contribution of our method resides in building an orthogonal connectivity basis that optimally aggregates nodes, through their neighborhood, prior to achieve convolution. Our method also considers a stochasticity criterion which acts as a regularizer that makes the learned basis and the underlying GCNs lightweight while still being highly effective. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs w.r.t. the related work.
An Exploration of Freehand Crossing Selection in Head-Mounted Augmented Reality

Crossing, or goal crossing, has proven useful in various selection scenarios, including pen, mouse, touch, and virtual reality (VR). However, crossing has not been exploited for freehand selection using augmented reality head-mounted displays (AR HMDs). Using the HoloLens, we explore freehand crossing for selection and compare it to the state-of-the-art “gaze and commit” (head gaze) method. We report on three studies investigating freehand crossing in multiple use cases. The first study shows that crossing outperforms head gaze in selection scenarios of varying target arrangements. The second explores crossing, head gaze, and hand pointing in radial menu and dynamic interface scenarios. The third explores crossing as a function carrier for a variety of basic interaction functions in a drawing application. This work builds on existing knowledge on the goal-crossing paradigm by demonstrating its potential as a useful interaction method in 3D AR HMD interfaces.
Research on Key Technologies of Intelligent Recommendation Based Online Education Platform in Big Data Environment

The information education mode supported by emerging technologies such as big data technology, cloud computing, communication and the Internet of Things is called intelligent education. The purpose of promoting intelligent education is to make use of developed countries and emerging technological means to create intelligent, effective and accurate education methods and adopt correct talent training methods based on the results of big data calculation, thus laying a good foundation for the cultivation of high quality technology and technical talents. Intelligent education platform is a new way of education communication which is constantly improved and developed along with the Internet and education digitization and information. On the one hand, it brings students great convenience, but also provides a new way of learning; On the other hand, it also proposes a solution to the phenomenon of "information overload" caused by the rapid increment of learning resources. Secondly, students who have no basic knowledge of courses will have more choices in choosing courses and learning paths.
An Investigation of Bug Algorithms for Mobile Robot Navigation and Obstacle Avoidance in Two-Dimensional Unknown Static Environments

A significant problem in mobile robot navigation is obstacle avoidance in an unknown environment. Though there are efficient global path planning algorithms for known environments, they are not feasible in layouts where the robot does not have a comprehensive knowledge about the obstacle locations. Bug Algorithms are navigation and obstacle avoidance algorithms that can be implemented in a 2D environment with previously unknown static obstacles. The Bug family of algorithms includes some of the most fundamental navigation and obstacle avoidance algorithms which are still used at a higher level in any mobile robot navigation problem. In this paper, three algorithms belonging to the Bug algorithm family have been implemented with Python3 software and the results have been compared against each other with cost of the path and the computational time as criteria. Finally, the probability density functions were plotted and the relative performances of three Bug algorithms which are Bug0, Bug1 and Bug2, were compared using simulations with varying parameters and layouts.
Analysis of a hybrid quantum network for classification tasks

In the era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here, we report about an analysis, where a hybrid quantum-classical network is applied to classify non-trivial datasets (finance and MNIST data). In comparison to a pure classical network, we find an advantage when looking at several performance measures. As in classical machine learning, problems around overfitting the dataset arise. Therefore, we explore different possibilities to regularise the network.
Towards three-parameter group encryption scheme for MST3 cryptosystem improvement

The article proposes a general approach of the encryption schemes implementation based on the group of the automorphism. The three-parameter group is used with logarithmic signatures outside the center of the group. This time we have applied for function field of Hermite. The phased de-encapsulation of the key from the encrypted text is an advantage of this implementation. The attack complexity and the message size depends on the strength of the group.