Reviewerly/exHarmony huggingface dataset:
AN INVESTIGATION OF READING ANXIETY AMONG EFL YOUNG LEARNERS

Reading anxiety is believed as one of factors under affective domain which influences the successfulness of students’ reading comprehension. The aims of this study were to identify the level of anxiety experienced by the EFL young learners while reading in the classroom context and to investigate the symptoms of anxiety suffered by young learners as well as their perception towards reading in English and their current reading ability. The design of this study was quantitative research. The research was carried out in the second-grade students of a junior high school in Makassar with age between 13-14 years old. The sample of this study was 30 students and selected using purposive sampling method. The data were collected using questionnaire named Foreign Language Reading Anxiety Scale (FLRAS) which developed by Saito et al (1999) and consists of 20 items of statement with five-point Likert scale. The data were analyzed with descriptive statistics. The results indicated that the students’ anxiety level was 65.633 which imply that they experienced high level of anxiety although the value is a little bit higher than the limit of moderate level. There are some symptoms of reading anxiety that are commonly recognized by the students. They tend to perceive reading in English as very interesting activity as well as they are satisfied with their ability in reading. The findings of this study might be used as input to teachers and students in an effort to create effective classroom environment.
Efficient robotic path planning algorithm based on artificial potential field

&lt;span lang="EN-US"&gt;Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.&lt;/span&gt;
Enhancing Electronic Voting With A Dual-Blockchain Architecture

Voting is one of the most fundamental aspects of democracy. Over the past few decades, voting methods around the world have expanded from traditional paper ballot systems to electronic voting (e-voting), in which votes are written directly to computer memory. Like any computer system, voting machines are susceptible to technical vulnerabilities that open up opportunities for hackers to tamper with votes, causing the use of electronic voting technology to raise concerns about ballot security. We describe how electronic voting can be supported by blockchain technology to ensure voter secrecy, vote correctness, and equal voting rights. In this paper, we present a system using two separate blockchains, each with separate transactions and consensus algorithms. We describe a prototype implementation that validates our ideas by executing several proof-of-concept simulations of a range of voting scenarios.
Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip

Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. For Arabic Text Compression, a sequential encoding technique was suggested that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques. In this research, a multistage compression process was implemented on Turkish and Arabic languages, by using the new encoding technique, in addition to the 7-Zip application, which has shown a significant file size reduction.
The effect of reframing technique on self-awareness on learning of the 7th grade students

This study is aimed (1) to describe the self-awareness on learning of the 7th grade students, (2) to describe the implementation of technique reframing in affecting the self-awareness on learning of the 7th grade students, and (3) to investigate the effect of reframing technique on self-awarenss on learning of the 7th grade students. Quantitative approach was used in this study by implementing quasi experimental design and using Pretest-Posttest Control Group Design to 24 samples. Data collection techniques applied in this research were self-awareness scale on learning developed by the researcher, observation, and interview. Data analysis technique applied in tis study was independent samples t-test which was conducted through Statistical Product for Service Solustion (SPSS) version 24.0. The study found that (1) the self-awareness on learning of the 7th grade students before the treatment of reframing technique was in low category, while that after the treatment of such technique was in moderate and high categories, (2) the self-awareness on learning of the 7th grade students was increased after the treatment of reframing technique, and (3) reframing technique can increase the self awareness on learning of the 7th grade students.
Improved Fisher MAP Filter for Despeckling of High-Resolution SAR Images Based on Structural Information Detection

Fisher distribution is a popular model for high-resolution (HR) synthetic aperture radar (SAR) images due to its high-peaked and heavy-tailed characteristics as well as its theoretical justification and mathematical tractability. Based on the Fisher modeling of SAR images, the maximum a posteriori (MAP) filter is suggested. In the Fisher model, the parameter of image looks is thought to be fixed to correspond to the formation mechanism of multi-look intensity images, and the other two parameters are accur ately assessed from the SAR image based on second-kind statistics. To improve the Fisher MAP filter especially in the aspect of speckle suppression, the Fisher MAP filter based on recognition of structural information is created using point target detection, the adaptive windowing method, homogeneous region detection, and selection of most homogeneous sub-window. The experiments on despeckling of HR SAR images demonstrate that the improved Fisher MAP filter based on structural information detection can suppress speckle in homogenous and edge regions, and effectively preserve fine details, edges, and point targets.
Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

With the reversible data hiding method based on pixel-value-ordering, data are embedded through the modification of the maximum and minimum values of a block. A significant relationship exists between the embedding performance and the block size. Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data; the smaller the pixel blocks, greater is the embedding capacity. However, it tends to result in the deterioration of the quality of the marked image. Herein, a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method, which realizes the dynamic control of block size by considering the image texture. First, the cover image is divided into non-overlapping 2×2 pixel blocks. Subsequently, according to their complexity, similarity and thresholds, these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4, 4×2, or 4×4 sized blocks. Hence, smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio. Experimental results prove that the proposed method is superior to the other three advanced methods. It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.
More (or Less) Economic Limits of the Blockchain

This paper extends the block-chain sustainability framework of Budish (2018) to consider proof of stake (in addition to proof of work) consensus mechanisms and permissioned (where the number of nodes are fixed) networks. It is demonstrated that an economically sustainable network will involve the same cost regardless of whether it is proof of work or proof of stake although in the latter the cost will take the form of illiquid financial resources. In addition, it is shown that regulating the number of nodes (as in a permissioned network) does not lead to additional cost savings that cannot otherwise be achieved via a setting of block rewards in a permissionless (i.e., free entry) network. This suggests that permissioned networks will not be able to economize on costs relative to permissionless networks.
Intuitionistic multi-agent subatomic natural deduction for belief and knowledge

Abstract This paper proposes natural deduction systems for the representation of inferences in which several agents participate in deriving conclusions about what they believe or know, where belief and knowledge are understood in an intuitionistic sense. Multi-agent derivations in these systems may involve relatively complex belief (resp. knowledge) constructions which may include forms of nested, reciprocal, shared, distributed or universal belief/knowledge as well as attitudes de dicto/re/se. The systems consist of two main components: multi-agent belief bases which assign to each agent a subatomic system that represents the agent’s beliefs concerning atomic sentences and a set of multi-agent labelled rules for logically compound formulae. Derivations in these systems normalize. Moreover, normal derivations possess the subexpression property (a refinement of the subformula property) which makes them fully analytic. Relying on the normalization result, a proof-theoretic approach to the semantics of the intensional operators for intuitionistic belief/knowledge is presented which explains their meaning entirely by appeal to the structure of derivations. Importantly, this proof-theoretic semantics is autarkic with respect to its foundations as the systems (unlike, e.g. external/labelled proof systems which internalize possible worlds truth conditions) are not defined on the basis of a possible worlds semantics. Detailed applications to a logical puzzle (McCarthy’s three wise men puzzle) and to a semantical difficulty (Geach’s problem of intentional identity), respectively, illustrate the systems. The paper also provides comparisons with other approaches to intuitionistic belief/knowledge and multi-agent natural deduction.
Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors

The paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form statistical reward functions, referred to as the Expected Rényi information divergence (RID) and the Determinant of the Fisher Information Matrix (FIM), are analysed and discussed in the paper. Being available analytically, the two reward functions are fast to compute and therefore potentially useful for longer horizon sensor trajectory planning. The paper demonstrates, both numerically and from the information geometric viewpoint, that the Determinant of the FIM is a superior reward function. The problem with the Expected RID is that the approximation involved in its derivation significantly reduces the correlation between the target state estimates at two sensors, and consequently results in poorer performance.
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size.
Classification of Professionally Important Abilities and Skills of a Counseling Psychologist

The article presents the results of theoretical and empirical research of professionally important abilities and skills of a counseling psychologist. Based on the analysis of scientific achievements and the results of anonymous questionnaires, the classification of professionally important abilities and skills of a counseling psychologist by certain groups was carried out. Professionally important abilities of a counseling psychologist are presented in four areas - volitional, gnostic, speech and perceptual; and professionally important skills are presented in seven areas - gnostic, communicative, perceptual, didactic, technological, organizational, control and evaluation. It is emphasized that professionally important abilities and skills should be classified separately. Since the abilities are due to the natural inclinations of the individual and mental phenomena, and skills are developed, formed and improved during the life of the person. Abilities and skills are interconnected and interdependent.Classification of professionally important abilities and skills of a counseling psychologist by certain groups provides opportunities: 1) to most systematically present the necessary professionally important characteristics of a counseling psychologist, 2) to review educational training programs for psychologists; 3) select the necessary methods and techniques for the diagnosis and verification of these professionally important characteristics of the candidate for the position of " counseling psychologist". According to the results of a scientific study on the expert assessment of the reference level of development of a counseling psychologist of professionally important abilities and skills, it was found that respondents believe that these abilities and skills should have a very high level of development.
Impact of agricultural land-use change to the surface run-off: a case study of Cereng catchment area, Kulon Progo Regency, Special Region of Yogyakarta

Abstract Agricultural land-use changes can cause a reduction in the green open space area, thereby reducing infiltration and increase surface run-off. One of the phenomena in agricultural land-use changes as indicated by the addition of the percentage of irrigation paddy fields in Cereng Catchment Area (CA) by 2.31%, 2.94%, and 4.39%, respectively, in 1995, 2008, and 2018. Besides, the addition of the settlement area and the plantation area’s reduction were also phenomena of land-use change. The impact of these changes is associated with curve number (CN) and direct run-off. The analysis conducted on the relationship between effective design rainfall and response to the land-use changes to produce direct run-off volume in 2, 5, 10, 20, and 50 years-return period, respectively, in 1995, 2008, and 2018 using the HEC-HMS model. The result showed that have addition to CN that were 81.85, 85.82, and 86.66, respectively, in 1995, 2008, and 2018. This addition caused an increase in the percentage of direct run-off volume from 1995 to 2018; those values were 24.28%, 16.8%, 13.76%, 11.61%, and 9.53%, respectively, at the 2, 5, 10, 20, and 50-years return period. The increase in the value of direct run-off volume causes a high possibility of a flood.
Exploiting Weak Diffusion of Gimli: Improved Distinguishers and Preimage Attacks

The Gimli permutation proposed in CHES 2017 was designed for cross-platform performance. One main strategy to achieve such a goal is to utilize a sparse linear layer (Small-Swap and Big-Swap), which occurs every two rounds. In addition, the round constant addition occurs every four rounds and only one 32-bit word is affected by it. The above two facts have been recently exploited to construct a distinguisher for the full Gimli permutation with time complexity 264. By utilizing a new property of the SP-box, we demonstrate that the time complexity of the full-round distinguisher can be further reduced to 252 while a significant bias still remains. Moreover, for the 18-round Gimli permutation, we could construct a distinguisher even with only 2 queries. Apart from the permutation itself, the weak diffusion can also be utilized to accelerate the preimage attacks on reduced Gimli-Hash and Gimli-XOF-128 with a divide-and-conquer method. As a consequence, the preimage attacks on reduced Gimli-Hash and Gimli-XOF-128 can reach up to 5 rounds and 9 rounds, respectively. Since Gimli is included in the second round candidates in NIST’s Lightweight Cryptography Standardization process, we expect that our analysis can further advance the understanding of Gimli. To the best of our knowledge, the distinguishing attacks and preimage attacks are the best so far.
A Survey of Recent Variants and Applications of Antlion Optimizer

This work proposes a review of a recently developed swarm intelligence-based metaheuristic algorithm called Antlion Optimizer (ALO), its variants, and applications. The suitable blending of a random walk with an adaptive shrinking of hypersphere radius makes this algorithm more effective and impressive over other recent optimization algorithms. This paper elaborates on the recent variants of ALO by reviewing the concerned publications. It also summarized the applications of ALO for solving real-world complex optimization problems of a wide variety of areas. So, this paper comprises of summarized review of various recently published ALO papers. Firstly, the natural phenomena of ALO and the working principle of its various operators are described. Then the recently developed variants of ALO are described in detail depicting in various categories. The real-world applications using ALO and its variants are also described under global optimization, power and system engineering, electronics and communication engineering, machine learning, environmental engineering, and networking.
Video Semantic Analysis

Sparse Representation (SR) and Dictionary Learning (DL) based Classifier have shown promising results in classification tasks, with impressive recognition rate on image data. In Video Semantic Analysis (VSA) however, the local structure of video data contains significant discriminative information required for classification. To the best of our knowledge, this has not been fully explored by recent DL-based approaches. Further, similar coding findings are not being realized from video features with the same video category. Based on the foregoing, a novel learning algorithm, Sparsity based Locality-Sensitive Discriminative Dictionary Learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experimental results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with state-of-the-art approaches. The proposed approach also shows robustness to diverse video environments, proving the universality of the novel approach.
Distributed Virtual Environment Basketball Equipment Embedded Systems’ Research and Development

With the development of social economy and the improvement of material level, people are paying more and more attention to their own health, and they are also constantly improving their fitness awareness. As far as the current sports equipment is concerned, the boring exercise methods can no longer satisfy people’s pursuit of higher quality fitness exercises. The embedding of the distributed virtual environment can incorporate its perceptual, immersive, and interactive characteristics, which increases the realization of multiuser interactive behavior in the field of sports equipment in the virtual scene. Therefore, this paper proposes the research and development of basketball sports equipment embedded system based on distributed virtual environment. First of all, this article adopts the literature method to learn the application of distributed virtual environment and embedded system in depth and, secondly, designs the embedded system architecture of basketball sports equipment suitable for distributed virtual environment and the software and hardware framework of the embedded system. Finally, the performance, delay comparison, system optimization test, and other aspects of the embedded system are analyzed. The average delay of interactive functions of embedded systems based on distributed virtual environment is 1.4 min, which saves 4 min compared with traditional interactive methods. And, the throughput rate of the system has also been greatly improved. The research on the embedded system of sports equipment based on the distributed virtual environment proposed in this paper will be beneficial to the development of society and economy, and it can also help more people to enrich the way of sports and enhance the fun of sports.