Reviewerly/exHarmony huggingface dataset:
Investigating the pleasure reading habits of EFL learners in lower secondary schools

Reading literacy is significant for learners’ academic success, and high levels of reading literacy can be established through strong engagement in pleasure reading. It seems that the advancement of competing media, such as television and computer games and the fluctuating socio-cultural contexts have made a significant influence on learners’ pleasure reading habits. The purpose of this study was therefore to investigate lower secondary school learners’ pleasure reading habits using descriptive survey design. The data were collected from 377 randomly selected learners in North Wollo Zone, Ethiopia, using a survey questionnaire. Descriptive and inferential (independent samples t-test) statistics were used to analyze the data. The findings of the study indicated that majority of the learners’ habits toward pleasure reading was poor. The findings of the study also showed that the students’ mostly read for academic purposes. It was also found that there was no significant difference (p &gt; .05) between gender and age of learners regarding their pleasure reading habits. Lack of reading materials, low English ability, and lack interest in reading are the major factors that affect learners’ pleasure reading habits. Based on the results, it can be suggested that integrating pleasure reading programme in the curricula, being accessible to a wide range of books (based on the learners’ level and interest) in the school libraries, and establishing school reading clubs in input-poor English as a Foreign Language contexts like Ethiopia is essential to promote learners’ pleasure reading habits.&nbsp
Neuromorphic quantum computing

We propose that neuromorphic computing can perform quantum operations. Spiking neurons in the active or silent states are connected to the two states of Ising spins. A quantum density matrix is constructed from the expectation values and correlations of the Ising spins. As a step towards quantum computation we show for a two qubit system that quantum gates can be learned as a change of parameters for neural network dynamics. Our proposal for probabilistic computing goes beyond Markov chains, which are based on transition probabilities. Constraints on classical probability distributions relate changes made in one part of the system to other parts, similar to entangled quantum systems.
Inferring the fractional nature of Wu Baleanu trajectories

Abstract We infer the parameters of fractional discrete Wu-Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on how clearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimating the fractional exponent and the scaling factor.
Learning Motivation of Students with a Background Behind Broken Home at SMA Negeri 1 Geringging River

This study aims to determine (1) the learning motivation of students is seen from the intrinsic factors with a broken home background (2) The students' learning motivation is seen from the extrinsic factors with a broken home background. This type of research is qualitative research. The data collection method used is interviews. The subjects of this study were JS and DV. while the additional informants are MS, FS, and SD. This data collection technique uses triangulation including method triangulation, inter-researcher triangulation, data source triangulation and theoretical triangulation. Meanwhile, to obtain valid data, this research was conducted by data triangulation. Triangulation is a technique of checking the validity of data by utilizing something outside the data. The results showed that. The results of the study show that: first, students do not get good encouragement from parents, do not provide encouragement and complement the needs of children so that children are enthusiastic and motivated to learn. Second, students have not been able to determine the ideals that exist within themselves. Third, the motivation of students in learning so far, students do not get support and support from parents. Fourth, students do not really care about teaching and learning activities. Fifth, children's learning environment is not conducive, this is because parents are indifferent and never provide facilities to children
Personality Analysis of The Main Characters in Tere Liye's Novel Pulang, Through Literature Psychology Approach

Analysis of the Main Character's Personality in Tere Liye's Pulang Novel Through a Literary Psychology Approach. Maumere: Faculty of Social Sciences and Humanities Education, IKIP Muhammadiyah Maumere, 2021. This thesis discusses the personality psychology of the main character in the novel Pulang Karya Tere Liye through a literary psychology approach. The psychology of the main character's personality in the novel Pulang is interesting to discuss because Bujang's personality tells the story of his journey home through various obstacles, fighting in wars and facing betrayal by his own friends. The purpose of this study is to describe the personality of the main character in the novel Pulang Karya Tere Liye. This type of research is descriptive qualitative, using content with data sources from the novel Pulang by Tere Liye. The analysis technique, with the technique of reading repeatedly, notes the important parts and describes the personality of the main character so that the personality of the main character that will be studied can be known. The results of this study include: first, the results of research on the psychology of the main character's personality in the novel Pulang by Tere Liye examines the personality of the main character Bujang by using personality structures such as Id, Ego and Superego. The two Id structures contained in the personality of the main character Bujang (The Wild Boar) in the Novel Pulang by Tere Liye are impatient, grouchy, while the Ego contained in the personality of the main character Bujang is Bujang's attitude who is a stubborn figure, impatient and selfish, and the Superego contained in the Bujang character is a genius attitude inherited from his parents. From the results of this study, it can be concluded that the psychology of the personality of the main character in the novel Pulang by Tere Liye is contained in the structure of the id, ego and superego
Cross-domain Constrained Network for Zero-shot Object Detection

Abstract Zero-shot object detection (ZSD) aims to recognize and locate unseen objects without training samples. Despite the rapid progress in the ZSD technique, most existing algorithms learn a mapping relationship between visual features and semantic space based on seen classes to apply the unseen classes without considering unseen class possible connections in the inference. Besides, due to the heterogeneous characteristics (category attributes and distribution variations) in different domains, the network’s mapping relationship alone between visual and semantic data will lead to inference bias. To address the above issues, we propose a novel cross-domain constrained network for ZSD, named constraint-ZSD. Specifically, we built a target association strategy to associate seen instances to learn unseen classes, which guarantees that at least one unseen object is associated with a similar seen object during the training period. Then, we model a cross-domain constrained mechanism to set a relational knowledge graph and explicitly constrain the consistency of category correlation under visual and semantic domains. As a result, the detector is guided to learn object-related and domain-independent feature representations. Finally, extensive experiments are presented for two standard benchmarks of ZSD, PASCAL VOC and MSCOCO. Our method outperforms previous state-of-the-art techniques on ZSD and generalized ZSD (GZSD) tasks.
A Novel Approach of Virtual Visualization of Cloth Fitting

The concept of a virtual fitting room in real time has been proposed. The interest in online shopping has grown exponentially. When it comes to buying products like shirts that always require knowledge of how the clothes will fit when worn. This is the main reason why so few clothes are being bought online. As a result, a virtual dressing room that tells consumers how clothes fit personally will be a huge asset to online sellers that can give consumers a wide choice. For online marketers, this would be a great way to grow your market. The proposed system consists of a number of tasks, including locating the user's shirt and specifying the color of the user's shirt. In our proposed system we have used Alpha Channel Masking to mask the user's shirt, and libraries such as Numpy, OpenCV. Finally, we conclude that the concept of virtual fitting rooms that we have implemented helps humans in many ways, such as making their purchases easier, saving them time
A Reversible Digital Image Watermarking Technique for Integrity and Authenticity of MR Images

Digital Watermarking is the art and science of embedding information in existing digital content for Digital Rights Management (DRM) and authentication. Reversible watermarking is a class of (fragile) digital watermarking that not only authenticates multimedia data content, but also helps to maintain perfect integrity of the original multimedia “cover data”. Watermarking of digital images is a well-known technique that is widely used for securing image contents. A successful watermarking method must be accurate, reversible, resilient, and robust against various attacks. The technical revolution associated with the implementation the development of digital technologies in medicine, has led to the emergence and active development of new directions in many areas of medicine. The authenticity and integrity of medical images has to be protected. Hence in this paper a reversible digital image watermarking technique for integrity and authenticity of MR (Magnetic Resonance) images is presented. Reversible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back.
Forensic Data Analysis using the Case Studies on Anti Forensic Devices

Individuals' interactions with machines would change. The technology which started with using buttons have developed to the touchpad screen, which allows people to control devices. merely conversing with them Intelligent house assistants (IHAs), which allow customers to manage their smart devices, read their mail, and occasionally place orders, are becoming increasingly popular. As a result, it’s possible that they'll be discovered at a crime scene shortly for this reason. and are capable of carrying the weight of digital evidence. This research looked into the most famous Alexa Echo and Google Home devices. Documentation was uncovered when this came to forensic research and evidence that made up metadata. Then, by fabricating operations, Changing the device's name, establishing a bogus routine, and so on talent improvement on an individual basis. As a result of the research, cyber defence experts and professors working in this field were given information on the several sorts the digital evidence that can be found in home automation system acts. Anti-forensic was also used to elicit the distinction between actual and false activities.
Classification Facial Skin and Treatment Suggestions for Good Skin Using Deep Learning with Region of Interest (ROI) Patches

Skin is the outermost layer of the human body. It serves as a protective layer for the internal organs of the human body. We should keep that healthy by knowing the respective skin type of ourselves rather than using different skin type products for skin without knowing our skin type can lead to damage to skin cells and maybe be responsible for epidermal diseases. There are four different skin types: Normal skin, Oily skin, dry skin, combination skin. We have taken two skin types: Normal skin, Oily skin of faces. The main objective of this paper is to classify facial skin as normal skin or oily skin. After classification, this paper suggests treatment for respective skin. For this task we used deep learning and open CV. In order to enhance performance, we exploit knowledge related to the human face structure. We train our model by employing automatically created facial regions of interest (ROI) to this end. By jointly learning the network parameters and optimized network output combination weights, each facial region appropriately contributes to the final classification result
A Study on Various Techniques on Data Partitioning and Sampling in Big Data Analysis

Recognition of hand gestures has great importance for Human-computer interaction (HCI). The human hand is very small with complex junctions compared with the entire human body, hence recognizing the human hand is not an easy task. By using the hand gesture recognition the hand point/coordinates of hands can be detected using which we can make many impossible happens. Our work indicates one such finding, that is, VIRTUAL PAINTER. In our project, the main objective is to display the words on the monitor screen which we write on air in front of the webcam. This is done by recognizing the human hand through the normal webcam of a computer and the hand points are detected using the MediaPipe python library. Using the detected hand points the count of opened fingers is stored. When the index and middle fingers are open it means it is in Selection mode and when only the index finger is open it is in drawing mode. In Selection mode, the colors using which we want to draw can be selected from the list of colors that are made displayed on the screen. In drawing mode, the content that is written in front of the camera on air is drawn on the monitor screen. The application of this implementation can be used in many places where immediate implementation or explanation is needed.
An Optimized Approach for Privacy Preserving of Big Data using GDTM and Random Number Generators with GNN

In this paper we propose a Privacy preserving mechanism of big data using GDTM along with Random Number generators. Given the rapid explosion of data being used across Enterprises, Individuals and Sensors, billions of data is being streamed and exchanged across the network. There is a high possibility of sensitive data being exchanged and stored, it's important to preserve sensitive data of Individuals and Enterprise data.Most of the current techniques of privacy preserving in particular in the areas of data perturbation has been done on Static data. Given the dynamic nature of the applications and the huge data that is being generated it's important to evaluate the privacy preserving on big data without losing the accuracy.Our research contribution is on Privacy preserving of big data using Geometric data transformation, random number generator and GNN techniques [5].We would like to extend our research further on improving the accuracy of big data.
A Bibliometric Analysis on the Presence of a Quantitative Measure of the Development Hindering Behavior of Individuals and Institutions – The ‘Bad Behavior Index’

Objective. Political instability, corruption, exclusive institutions et al. are some of the hypotheses backed by literature as to why some nations are more developed than others. One hypothesis which has not been intensively studied is the culpability of individual and institutional behavior and its impact on development. To examine the validity of such a hypothesis, a composite index which quantifies such development hindering behavior must be developed. The prelude to developing this index is to investigate whether such a quantitative measure exists to begin with. To achieve this objective, a bibliometric analysis of Scopus and Web of Science databases will be conducted. Method. A bibliometric and content analysis of Scopus and Web of science databases using Excel, VOSviewer, and R software. Results. The findings of the bibliometric analysis indicate the absence of such measure particularly within the scope of &amp;lsquo;bad behavior&amp;rsquo;, &amp;lsquo;unethical behavior&amp;rsquo;, and &amp;lsquo;development&amp;rsquo;. Conclusions. The study findings provide the greenlight to proceed with the construction of the &amp;lsquo;Bad Behavior Index&amp;rsquo;. Contribution. The practical contribution of this study is that it provides researchers with an improved methodology on how to conduct a bibliometric analysis to identify the absence of knowledge and provide a justification for the creation of such knowledge by integrating and analyzing two journal databases instead of one, using three mediums: Excel, VOSviewer, and R software.
Research on Innovation of College Classroom Instructional Strategy from the Perspective of Deep Learning

Deep learning is an understanding and inquiry learning approach based on the development of higher-order thinking, which is of great importance to improve the quality of classroom teaching in colleges. This paper explains the connotation of deep learning for college students, and analyzes the shortage of current classroom teaching in Chinese colleges from the perspective of deep learning. It also puts forward the targeted measures as follows: establishing the teaching goal of promoting the development of students’ higher-order thinking; refining the structured teaching content focusing on knowledge connection; innovating classroom teaching methods to promote students’ cooperative inquiry; and adopting the multiple evaluation pointing to the development of students’ ability.
Different approaches to modeling response styles in Divide-by-Total IRT models (Part II): Applications and novel extensions

Many approaches in the Item Response Theory (IRT) literature have incorporated response styles to control for potential biases. However, the specific assumptions about response styles are often not made explicit. Having integrated different IRT modeling variants into a superordinate framework, we highlighted assumptions and restrictions of the models (Henninger &amp;amp; Meiser, 2019). In this article, we show that based on the superordinate framework, we can estimate the different models as multidimensional extensions of the Nominal Response Models in standard software environments. Furthermore, we illustrate the differences in estimated parameters, restrictions, and model fit of the IRT variants in a German Big Five standardization sample and show that psychometric models can be used to debias trait estimates. Based on this analysis, we suggest two novel modeling extensions that combine fixed and estimated scoring weights for response style dimensions, or explain discrimination parameters through item attributes. In summary, we highlight possibilities to estimate, apply, and extend psychometric modeling approaches for response styles in order to test hypotheses on response styles through model comparisons.
Perceptions of Electrical Engineering Department Students

The development of technology plays an important role in everyday life, especially in the field of education. Currently developing online learning. Currently, innovation is needed in learning English using the Zoom application. Qualitative descriptive research method.The subjects of this study were 36 students of the Department of Electrical Engineering, State Islamic University of Sultan Syarif Kasim Riau (UIN Suska Riau) who took part in online English learning using the Zoom application. Data was collected through the distribution of online questionnaires using Google Forms. The results of the questionnaire were analyzed using an interactive qualitative model with three stages, namely data reduction, data presentation, and conclusion drawing and verification. The results showed that the use of the Zoom application improved student subject mastery, memory, relevance, benefits, new experiences, efficiency, understanding, monitoring the learning process, and motivation to learn English. Based on the perception of students who choose to agree with an average percentage of above 50%, it is clear that the Zoom application can function as a means to support learning.
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving

Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g. as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agents. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on vision-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art, by 17%.
Palindromic Polynomials over Finite Fields

For any finite field $\mathbb{F}$ and any positive integer $n$ we count the number of monic polynomials of degree $n$ over $\mathbb{F}$ with nonzero constant coefficient and a self-reciprocal factor of any specified degree. An application is given for systems of linear equations over $\mathbb{F}$ of index $2$.
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy

More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings. However, deepening the backbone of neural networks will inevitably and significantly increase computation and parameter size. To mitigate these problems, we propose a simple-yet-effective Recurrent Attention Strategy (RAS), which implicitly increases the depth of neural networks with lightweight attention modules by local parameter sharing. The extensive experiments on three widely-used benchmark datasets demonstrate that RAS can improve the performance of neural networks at a slight addition of parameter size and computation, performing favorably against other existing well-known attention modules.
PatchRot: A Self-Supervised Technique for Training Vision Transformers

Vision transformers require a huge amount of labeled data to outperform convolutional neural networks. However, labeling a huge dataset is a very expensive process. Self-supervised learning techniques alleviate this problem by learning features similar to supervised learning in an unsupervised way. In this paper, we propose a self-supervised technique PatchRot that is crafted for vision transformers. PatchRot rotates images and image patches and trains the network to predict the rotation angles. The network learns to extract both global and local features from an image. Our extensive experiments on different datasets showcase PatchRot training learns rich features which outperform supervised learning and compared baseline.