Reviewerly/exHarmony huggingface dataset:
Students’ Individual Educational Trajectories as a Condition for High-Quality University Education

The article studies the contexts in which the students’ individual educational trajectories are of particular importance. There are introduced and specifi ed the following concepts: individualization in education, individual educational program, educational order, self-competencies. Nowadays, the category of self-competencies is only beginning to be discussed among education researchers. Thus, the authors propose its conceptual structure and defi nitions. Analyzing the special literature and university cases presented within the project «Scientifi c and Methodological Support for the Development of a Quality Management System in Higher Education in the Context of the COVID-19 Coronavirus Infection and after It» (2021), the authors show which universities are making substantive and organizational changes in their educational activities, so that individualization becomes possible. Based on this, the article distinguishes environmental, tutor and mixed types of initiating individualization. When comparing the practices of universities presented within the project above, the authors identifi ed the main stages and features of the HEI’s transition to one or another model of individualization and the formation of students’ individual educational trajectories. The article shows that such trajectories and the principles of individualization are usually introduced due to a number of organizational and managerial conditions related to digitalization, personnel policy, restructuring of educational programs, etc. We conclude that these conditions made it possible to implement the practice of individualization in universities. Based on the analysis carried out, there can already be fi xed certain general results and eff ects that have arisen as a result of implementing such practices.
Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis

In this paper, we propose to build a novel benchmark and neural video coding task named learning based Unmanned Aerial Vehicle (UAV) video coding. We collect the UAV videos with different content variations, including in-door and out-door scenes, object-scale variations and viewpoint distance, different climate condition etc. Then we encode those properly-selected videos using popular end-to-end optimized video codecs and conventional hybrid codecs, to form a comprehensive benchmark for learned drone video compression. We also provide a detailed analysis and envision the challenge of such task for future research. The main contributions of this paper are three folds. First, we construct a comprehensive benchmark for the task of drone video compression which consists of the rate-distortion (R-D) behavior of both hybrid and learned video codecs. To our knowledge, it is the first attempt in end-to-end optimized solution to compress drone videos. Second, we provide the review and analysis of the learned drone video compression schemes and further discuss the challenges of encoding UAV videos. Third, this benchmark and related research is accomplished as a milestone MPAI End-to-end Video (EEV) coding project. The proposed benchmark has constructed a solid baseline for compressing UAV videos and facilitates the future research works for related task.
Symmetric-logarithmic-derivative Fisher information for kinetic uncertainty relations

We investigate a symmetric logarithmic derivative (SLD) Fisher information for kinetic uncertainty relations (KURs) of open quantum systems described by the GKSL quantum master equation with and without the detailed balance condition. In a quantum kinetic uncertainty relation derived by Vu and Saito [Phys. Rev. Lett. 128, 140602 (2022)], the Fisher information of probability of quantum trajectory with a time-rescaling parameter plays an essential role. This Fisher information is upper bounded by the SLD Fisher information. For a finite time and arbitrary initial state, we derive a concise expression of the SLD Fisher information, which is a double time integral and can be calculated by solving coupled first-order differential equations. We also derive a simple lower bound of the Fisher information of quantum trajectory. We point out that the SLD Fisher information also appears in the speed limit based on the Mandelstam-Tamm relation by Hasegawa [Nat. Commun. 14, 2828 (2023)]. When the jump operators connect eigenstates of the system Hamiltonian, we show that the Bures angle in the interaction picture is upper bounded by the square root of the dynamical activity at short times, which contrasts with the classical counterpart.
Beyond Linguistics: Exploring the Cognitive and Motivational Barriers to Essay Writing for Tertiary Students

In higher education, developing strong writing skills is crucial for students' academic and career success. Unfortunately, this important aspect of education has been somewhat neglected in the context of Maluku province. Therefore, this study, serves the crucial purpose of shedding light on the multifaceted factors that underpin the deficiencies in writing skills. The primary aim of this study is to explore the fundamental factors contributing to the deficiencies in tertiary students' writing skills. To accomplish this, we conducted a quantitative survey, selecting a random sample of 70 Moluccan local students out of 120 who were enrolled in an essay writing class. Results indicate that cognitive factors play the most pivotal role in contributing to deficiencies in writing skills, boasting a remarkably high path coefficient value of 0.846. Linguistic factors and motivational aspects closely follow, with values of 0.556 and 0.528, respectively. The deficiency in writing skills among students can be attributed to a lack of internal motivation for learning, and inadequate opportunities provided by teachers for real-life observation and learning. For effective improvement in writing abilities, students need both an intrinsic motivation to learn and consistent guidance and stimulation from educators. The process of writing not only involves linguistic capability but also a deep cognitive understanding, emphasizing the need for students to process and present their thoughts comprehensively and appealingly. Improving tertiary students' writing skills require a comprehensive approach that addresses cognitive, linguistic, and motivational factors. Implementing these implications can result in more effective teaching methods and improved writing outcomes for students.
The impact of Communicative Language Teaching (CLT) Approach on Students’ Speaking Ability in a Public Indonesian University: Comparison between Introverts and Extrovert Groups

The aim of this research is to explore the effect of implementing the Communicative Language Teaching (CLT) Approach on the speaking ability of students-. The sample size for this study comprises 46 students representing the introvert class and the extrovert class, selected based on the results obtained from the distribution of questionnaires. To conduct the study, an experimental design was employed, which involved gathering quantitative data from the students' responses to the distributed questionnaire, as well as conducting pre-tests and post-tests to assess their speaking abilities. In order to analyze the collected data, the researcher utilized the SPSS program (Statistical Program for Social Science) for precise and efficient processing, generating various necessary outputs to draw meaningful conclusions. The findings of this study strongly indicate a positive impact of implementing the Communicative Language Teaching (CLT) model on the speaking abilities of introverted students. The results not only demonstrate the effectiveness of CLT in the speaking class but also align with previous research in the field. the incorporation of CLT in the speaking class positively impacts the speaking abilities of both introverted and extroverted students. However, a slight distinction can be observed in terms of the extent of improvement between the two groups, with the extrovert class showing a more notable progress. Recognizing and accommodating the diverse learning styles and preferences of students can help create a comprehensive and inclusive learning environment that fosters growth and development for all learners, regardless of their personality traits
High-Performance ECC Scalar Multiplication Architecture Based on Comb Method and Low-Latency Window Recoding Algorithm

Elliptic curve scalar multiplication (ECSM) is the essential operation in elliptic curve cryptography (ECC) for achieving high performance and security. We introduce a novel high-performance ECSM architecture over binary fields to meet the growing demand for performance and security. A low-latency window (LLW) recoding algorithm for hardware implementation is proposed to enhance the resistance toward side-channel attacks (SCAs). Based on the LLW algorithm, we propose an enhanced comb method for ECSM with a unified point addition (PA) and point doubling (PD) pattern. The theoretical analysis demonstrates that the enhanced comb method with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$w=4$</tex-math> </inline-formula> strikes the balance of computation burden for both extreme cases. To achieve short clock cycle latency and high frequency, the data dependency of ECSM is thoroughly analyzed, and we explore a timing schedule with one two-stage pipelined Karatsuba multiplier accumulator (MAC). The datapath of the proposed architecture is well-designed, ensuring that the critical path (CP) only contains minimal logic primitives apart from the MAC. Besides, the ideal placement of pipeline stages for MAC is illustrated. The proposed architecture has been implemented on Xilinx Virtex-7 series field-programmable gate arrays (FPGAs) and performs ECSM in 2.51, 4.93, and 10.85 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$</tex-math> </inline-formula> with 3422, 7983, and 20158 slices over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{GF}(2^{163})$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{GF}(2^{283})$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{GF}(2^{571})$</tex-math> </inline-formula> , respectively. Implementation results reveal that our design shows 53.60%, 39.36%, and 32.64% performance improvement over the existing state-of-the-art works, respectively.
QAOAwith $N\cdot p\geq 200$

One of the central goals of the DARPA Optimization with Noisy Intermediate-Scale Quantum (ONISQ) program is to implement a hybrid quantum/classical optimization algorithm with high <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N\cdot p$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> is the number of qubits and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> is the number of alternating applications of parameterized quantum operators in the protocol. In this note, we demonstrate the execution of the Quantum Approximate Optimization Algorithm (QAOA) applied to the MaxCut problem on non-planar 3-regular graphs with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N\cdot p$</tex> of up to 300 on the Quantinuum 81–1 trapped-ion quantum processor. To the best of our knowledge, this is the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N\cdot p$</tex> demonstrated on hardware to date. Our demonstration highlights the rapid progress of quantum hardware.
DeFi and NFTs Hinder Blockchain Scalability

Many classical blockchains are known to have an embarrassingly low transaction throughput, down to Bitcoin’s notorious seven transactions per second limit. Various proposals and implementations for increasing throughput emerged in the first decade of blockchain research. But how much concurrency is possible? In their early days, blockchains were mostly used for simple transfers from user to user. More recently, however, decentralized finance (DeFi) and NFT marketplaces have completely changed what is happening on blockchains. Both are built using smart contracts and have gained significant popularity. Transactions on DeFi and NFT marketplaces often interact with the same smart contracts. We believe this development has transformed blockchain usage. In our work, we perform a historical analysis of Ethereum’s transaction graph. We study how much interaction between transactions there was historically and how much there is now. We find that the rise of DeFi and NFT marketplaces has led to an increase in “centralization” in the transaction graph. More transactions are now interconnected: currently, there are around 200 transactions per block with 4000 interdependencies between them. We further find that the parallelizability of Ethereum’s current interconnected transaction workload is limited. A speedup exceeding a factor of five is currently unrealistic.
Interaction Techniques to Control Information Clutter in a Pervasive Augmented Reality Scenario

The recent concept of Pervasive Augmented Reality (PAR) predicts the use of Augmented Reality (AR) in every aspect of our daily life to help access, produce and present information and services from various different sources around users. However, as far as the authors know, no study explored how design interaction techniques can be implemented in a complex and overwhelming PAR environment where information clutter is generated from multiple sources with very different political, social and economic interests. The contributions of this paper are 1) an evaluation of a PAR street-like environment with multiple producers of information, 2) a proposition of four different strategies, and their related interaction techniques, to help users tackle information clutter around them and 3) guidelines on expected interactions for managing such flow of information, based on participants’ feedback.
Universal Self-Consistency for Large Language Model Generation

Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs). However, self-consistency relies on the answer extraction process to aggregate multiple solutions, which is not applicable to free-form answers. In this work, we propose Universal Self-Consistency (USC), which leverages LLMs themselves to select the most consistent answer among multiple candidates. We evaluate USC on a variety of benchmarks, including mathematical reasoning, code generation, long-context summarization, and open-ended question answering. On open-ended generation tasks where the original self-consistency method is not applicable, USC effectively utilizes multiple samples and improves the performance. For mathematical reasoning, USC matches the standard self-consistency performance without requiring the answer formats to be similar. Finally, without access to execution results, USC also matches the execution-based voting performance on code generation.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Tanah Berbasis Android

Bawen District is one of the sub-districts in Semarang Regency, Central Java. This region has an area of land used for agriculture around 63.29%. In this area the population still uses soil as a planting medium. Soil is one of the planting media which plays an important role for the survival of plants. With so many types of soil that have different properties and characteristics, the treatment of these soils is also different. So it is necessary to have a soil classification to know how to manage the soil properly. To facilitate the classification of soil types, Deep Learning technology can be utilized with images as input which are then processed using the Convolutional Neural Network (CNN) algorithm. In order to get a model that has a high level of accuracy, an experiment was carried out on several influential parameters and an evaluation of the model was carried out using a confusion matrix. The confusion matrix has several values such as accuracy, precision, recall, and f1-score. Tests have been carried out and the results of this study are models that have a training accuracy of 97% with a loss value of 0.0880 and a testing accuracy of 95% with a loss value of 0.1513.
Immersive capability and spatial presence in virtual reality photo-based tours: implications for distance education

Purpose The purpose of this study is to describe an educational virtual reality (VR) photo-based tour used in an online course and investigate the influence of immersive capability on the dimensions of spatial presence and their relationship with learning-related variables. Design/methodology/approach The study employs a descriptive and an experimental methodological approach. The research objectives were achieved using a two-group (n1 = 29 and n2 = 30) experiment, employing descriptive statistics, t -test and correlation analysis. Findings The t -test revealed that the immersive capability had a significant effect on the sense of physical space (SP) , Engagement (EN) and negative effects (NE) dimensions. Correlations between the dimensions of spatial presence were found to confirm reports from the literature. Furthermore, some of the dimensions were found to be correlated with motivational and learning variables. Research limitations/implications The study reported the results of a one-off experiment among 59 participants. While the results were promising, a longitudinal qualitative study could confirm the results in an actual distance learning context. Practical implications The study confirmed that adding VR photo-based tours as learning activities may enhance the learning experience of distance learners. Social implications The study shared a case of a learning activity that can be employed for flexible education. Virtual tours can support the need for context-based learning that the geographical or political constraints may limit. Originality/value While the paper confirms previous reports on the benefits of using VR photo-based tours as learning activities, this paper has empirically shown the relationship between the dimensions of spatial presence and immersive capability in this specialized context.
Generate Adversarial Attack on Graph Neural Network using K-Means Clustering and Class Activation Mapping

Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing complex structured data, including social networks, biological networks, and recommendation sys-tems. However, their susceptibility to adversarial attacks poses a significant challenge, especially in critical tasks such as node classification and link prediction. Adversarial attacks on GNNs can introduce harmful input graphs, leading to biased model predictions and compromising the integrity of the network. We propose a novel adversarial attack method that leverages the combination of K-Means clustering and Class Activation Mapping (CAM) to conduct subtle yet effective attacks against GNNs. The clustering algorithm identifies critical nodes within the graph, whose perturbations are likely to have a substantial impact on model performance. Additionally, CAM highlights regions of the graph that significantly influence GNN predictions, enabling more targeted and efficient attacks. We assess the efficacy of state-of-the-art GNN defenses against our proposed attack, underscoring the pressing need for robust defense mechanisms. Our study focuses on countering attacks on GNN networks by utilizing K-Means clustering and CAM to enhance the effectiveness and efficiency of the adversarial strat-egy. Through our observations, we emphasize the necessity for stronger security measures to safeguard GNN-based applications, particularly in sensitive environments.Furthermore, our research highlights the importance of developing robust GNNs that can withstand adversarial attacks, ensuring the reliability and trustworthiness of these models in critical applications. Strengthening the robustness of GNNs against adversarial manipulation is crucial for maintaining the se-curity and integrity of systems that heavily rely on these advanced analytical tools. Our findings underscore the ongoing efforts required to fortify GNN-based applications, urging the research community and practitioners to collaborate in developing and implementing more robust security measures for these powerful neural network models.
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noising Defense Network (ETNDNet), which treats occlusion as a noise disturbance and solves occluded person re-ID from the perspective of adversarial defense. In the proposed ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature map to create an adversarial representation with incomplete information, enabling adversarial learning of identity loss to protect the re-ID system from the disturbance of missing information. Secondly, we introduce random transformations to simulate the position misalignment caused by occlusion, training the extractor and classifier adversarially to learn robust representations immune to misaligned information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians, and employ adversarial gaming in the re-ID system to enhance its resistance to occlusion noise. Without bells and whistles, ETNDNet has three key highlights: (i) it does not require any external modules with parameters, (ii) it effectively handles various issues caused by occlusion from obstacles and non-target pedestrians, and (iii) it designs the first GAN-based adversarial defense paradigm for occluded person re-ID. Extensive experiments on six public datasets fully demonstrate the effectiveness, superiority, and practicality of the proposed ETNDNet. The code will be released at https://github.com/nengdong96/ETNDNet.