Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Classification of Digital Media Traffic

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.
Mixture Culver Filter for Space Object Orbit Determination

Exact sequential state estimation of orbiting objects in space can be found by predicting the state using Fokker Planck Kolmogorov Equation (FPKE) and measurement correction using Bayes’ conditional/posterior Probability Density Function (PDF). Posterior PDF can be expressed as Gaussian density expanded in terms of Hermite polynomials named as Gram Charlier Series (GCS). This research is an extension of an earlier work on second order linearized solution to nonlinear Bayesian filtering using Taylor series and third order GCS expansion of posterior PDF. In this new extension, Bayes’ posterior PDF is approximated by a mixture of GCS functions for which the parameters are propagated using linear propagation theory. The update of weights of different components of GCS mixture model uses the FPKE error as feedback to adapt for the amplitude of different GCS components while solving a quadratic programming problem earlier used for Gaussian Mixture Model (GMM) PDF. Proposed filtering method is applied on tracking of space debris. The simulation results for the filter shows performance is moderately better than single GCS filter, Extended Kalman Filter (EKF) and Gaussian Sum Filter (GSF) under space debris’ highly uncertain initial conditions and sparse measurement availability.
A Deep Learning Method for Ship Detection and Traffic Monitoring in Offshore Wind Farm Waters

Newly build offshore wind farms (OWFs) render a collision risk between ships and installations. The paper proposed a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method is tested in offshore water in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.
DIC system with Euler transformation for structural measurements in wind tunnel

In the wind tunnel testing, structural deformation and motion should be measured using non-contact and precisely calibrated techniques. We designed, developed, and set up a novel DIC method for testing large and lightweight structures in wind tunnel. With the use of a universal mount mechanism and optical advancements for far-field structural evaluations, we increased the functionality of our current in-house stereo DIC system. The pitch and azimuth rotations of cameras are taken into consideration during the calibration of the WT-DIC system utilizing the Euler rotation theorem. Case studies for the developed DIC system include a small MAV flapping wing and a large deployable parachute.
Improved Median Edge Detection (iMED) for Lossless Image Compression

A wide variety of applications are used in lossless image compression models, especially in medical, space, and aerial imaging domains. Predictive coding improves the performance of lossless image compression, which highly relies on entropy error. Lower entropy error results in better image compression. The main focus of this research is to improve the prediction process by minimizing the entropy error. This paper proposes a novel idea for improved Median Edge Detection (iMED) predictor for lossless image compression. MED predictor is improved using k-means clustering and finding the local context of pixels using 20-Dimensional Difference (DDx20) for input images and updates the cluster weights using learning rates (µi) to minimize the prediction errors of pixels. The performance of the proposed predictor is evaluated on the standard grey-scale test images dataset and KODAK image dataset. Results are obtained and compared based on entropy error, bits per pixel (bpp), and computational running time in seconds(s) with the MED, GAP, FLIF, and LBP predictors. The performance of the proposed iMED predictor improves significantly in terms of the entropy error, bpp, and computational running time in seconds(s) after comparison with different state-of-the-art predictors.
The coverage of information science and knowledge organization in the <i>Library of Congress Subject Headings</i>

Purpose This article examines the Library of Congress Subject Headings (LCSH), which is the most used subject heading system in the world and an instance of a controlled vocabulary (CV). Design/methodology/approach The method used to examine the system is based on both authors’ subject knowledge in the field of information science (IS) and the subfield of knowledge organization (KO). Core concepts in this domain were examined (1) by checking if they are present or not in the system; (2) if not, by determining whether LCSH contains alternative terms useful for searching documents about the missing concept, by examining books indexed by the Library of Congress; (3) by identifying the semantic relations between subject headings. Findings The results demonstrate fundamental problems in the logical consistency of the representation of IS and KO in LCSH. Practical implications The implications for CVs in general are discussed. Originality/value No previous study has used our method to examine LCSH’s coverage of IS.
Strengthening civic literacy among students through digital literacy in society 5.0

The reading index in Indonesia is still relatively low, so many students are victims of fake news that develops on social media. In addition, students have not been able to utilize digital technology as digital literacy to the fullest, so it has implications for the weakness of knowledge about civic literacy. The research design used is a literature review and the type of qualitative research. The research method is literature, while the data collection technique uses document studies and the data analysis used is content analysis. Civic literacy has an important role in facing the era of society 5.0, with the ability of civic literacy, really helps students to participate in realizing the harmony of the life of the nation and state. Society 5.0 has a formidable challenge for higher education if each lecturer does not equip students with civic literacy skills. Strategies to strengthen civic literacy through digital literacy can be carried out effectively, by implementing varied learning methods and using digital-based learning media. Through this, students will be interested in learning, considering that students are a digital native generation who cannot be separated from digital technology. Strengthening civic literacy is very effective by utilizing digital literacy so that every student can adapt to the times, especially society in the 5.0 era.
The use of technology in hybrid learning for student with special needs

This study aims to describe the use of technology in learning for students with special needs during the transition period after the COVID-19 pandemic to the new normal. This study is descriptive research with a survey method. The survey was conducted with 41 special education teachers with variety of special needs students. Data is collected using Google Form questionnaires. Data analysis is carried out by calculating the frequency and is presented as percentages. Participants in this study were chosen randomly for data variation. The presentation of data is carried out qualitatively based on the respondents’ answers to the questions asked. The study participants argued that hybrid learning was feasible, even though it was recognized as encountering obstacles. Based on the results of a survey conducted by special education teachers at special education school (SLB), for hybrid learning, digital platforms are used to support online learning. The platforms used are WhatsApp and video conferencing, such as Google Meet. Integrating technology is considered important for teachers to support the learning process. Conscious and planned efforts and teacher motivation are critical to increasing their experience and skills in utilizing technology in learning.
A Flexible Shared Hardware Accelerator for NIST-Recommended Algorithms CRYSTALS-Kyber and CRYSTALS-Dilithium with SCA Protection

NIST recently decided upon a set of cryptographic algorithms for future standardization. These algorithms are built upon hard mathematical problems which are believed to be difficult for both classical and quantum computers, unlike RSA and ECC which are trivially broken by a quantum computer running Shor’s algorithm. Cryptographic operations are computationally intense, and therefore are often offloaded to dedicated hardware in order to improve performance and reduce energy usage. However, different applications have different needs for performance, energy, and cost. Thus it is beneficial to have a variety of performance options for hardware acceleration. In this work we present a flexible hardware architecture for selected algorithms, Kyber and Dilithium. Our architecture includes separate instances optimized for either Kyber and Dilithium as well as a combined architecture which support both algorithms in one design. Further, the design can be instantiated at three levels of performance: lightweight, mid-range, and high performance. We also present a masked implementation for the Kyber-only implementation which protects against first order differential power analysis attacks and timing attacks. The masked implementation requires $$2.5\!\times $$ more LUTs and $$6.5\!\times $$ more clock cycles for decapsulation.
Deep Ordinal Regression Framework for No-Reference Image Quality Assessment

Due to the rapid development of deep learning techniques, no-reference image quality assessment (NR-IQA) has achieved significant improvement. NR-IQA aims to predict a real-valued variable for image quality, using the image in question as the sole input. Existing deep learning-based NR-IQA models are formulated as a regression problem and trained by minimising the mean squared error. The error measurement does not consider the relative ordering between different ratings on the quality scale, which consequently affects the efficacy of the model. To account for this problem, we reformulate NR-IQA learning as an ordinal regression problem and propose a simple yet effective framework using deep convolutional neural networks (DCNN) and Transformers. NR-IQA learning is achieved by a deep ordinal loss and using a soft ordinal inference to transform the predicted probabilities to a continuous variable for image quality. Experimental results demonstrate the superiority of our proposed NR-IQA model based on deep ordinal regression. In addition, this framework can be easily extended with various DCNN architectures to build advanced IQA models.
Effect of Reliability Variables on Customer Perception Over the Services of Bank

The aim of the study is to examine the effect of Reliability variables on customer perception over the services of ICICI Bank Branches in Nagapattinam District.ICICI Bank has delivered its functions through eight branches located in rural and Semi-urban area of Nagapattinam district.Hence the proportionate stratified random sampling was used for selecting the sample respondents from all these eight ICICI bank branches.It has been determined that the optimal sample size for a population of 39,416, a level of confidence at 95%, and a margin of error at 5% would be 380.The result of the study revealed that among the five reliability variables of banking services which used to predict the impact on overall customer perception over the banking services, ATM Facility and Arrangement are creates a significant impact on customer perception regarding banking services.However, the remaining Reliability variables, Appearance of the Bank, Appearance of Bank Physical facilities, Appearance of Waiting area and Security arrangement, were not made any significant impact on overall customer perception.
A Study of Malaria in Telangana

Background: To the best knowledge of the author, there is no available literature on the status of Malaria in Telangana. Hence the preparation of this document.Objectives: To find out the parameters of Malaria in Telangana till as recently as possible.Methods: By studying the documents prepared by the National Centre for Vector Borne Diseases (NCVBD) and doing a web search on Malaria in Telangana.Results: It is seen that the Annual Parasite Incidence (API) of Malaria in Telangana had come down to the very low level of 0.05 in 2018 and that there were only 240 cases of Malaria in Telangana during 2021 as of June 27th of that year.Conclusions: Telangana is very close to achieving near-elimination goals but will have to take active measures to reach it.
KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images

Instance-level graph neural network explainers have proven beneficial for explaining such networks on histopathology images. However, there has been few methods that provide model explanations, which are common patterns among samples within the same class. We envision that graph-based histopathological image analysis can benefit significantly from such explanations. On the other hand, current model-level explainers are based on graph generation methods that are not applicable in this domain because of no corresponding image for their generated graphs in real world. Therefore, such explanations are communicable to the experts. To follow this vision, we developed KS-GNNExplainer, the first instance-level graph neural network explainer that leverages current instance-level approaches in an effective manner to provide more informative and reliable explainable outputs, which are crucial for applied AI in the health domain. Our experiments on various datasets, and based on both quantitative and qualitative measures, demonstrate that the proposed explainer is capable of being a global pattern extractor, which is a fundamental limitation of current instance-level approaches in this domain.
Long-term Forecasting with TiDE: Time-series Dense Encoder

Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model, Time-series Dense Encoder (TiDE), for long-term time-series forecasting that enjoys the simplicity and speed of linear models while also being able to handle covariates and non-linear dependencies. Theoretically, we prove that the simplest linear analogue of our model can achieve near optimal error rate for linear dynamical systems (LDS) under some assumptions. Empirically, we show that our method can match or outperform prior approaches on popular long-term time-series forecasting benchmarks while being 5-10x faster than the best Transformer based model.
University students’ competences in ICT: A view from the education domain

Contemporary university students face the knowledge society, where mastering information and communication technologies (ICT) is an essential requirement to form part of this society. The objective of our study was to validate a basic ICT competences model made up of three ICT competence subsets (technological, pedagogical and ethical) influenced by various personal and contextual factors. For this purpose, a cross-sectional explanatory correlation design was used, with a sample of 646 university students from the University of Valencia (Spain), collecting the information through a questionnaire. A multiple indicators and multiple causes model was used to validate the students’ ICT competences model. The results revealed that ICT competences form a single set made up of three subsets of competences: technological, pedagogical and ethical. An asymmetrical explanatory relation was found between the technological and pedagogical competences on the one hand and between the ethical and pedagogical competences on the other hand. The factors gender, area in which the degree is taught and the frequency of using a computer with the Internet impacted on the three subsets of competences. The model shows the complexity of university students’ ICT competences, with training in ICT competences being an important element to consider. Implications for practice or policy: University leaders have to include the three ICT competence subsets in the curriculum. University teachers must promote the three ICT competence subsets in their classes. Instructional designers and educational technologists should include the three ICT competences subsets in their training plans.
Assessment of Solar Energy Resource for PV Plant Development Using a Low-cost PV Monitoring System

In this work, we propose the use of a low-cost PV monitoring system for providing accurate and comprehensive data required in the development of efficient and reliable solar PV plants. The system is developed based on an open-source Arduino platform with the capability to monitor solar irradiance, and electric outputs and temperature of multiple solar panels, which should enable the accurate assessment of solar energy resource, as well as electrical energy produced by PV plant under real operating conditions. To demonstrate its applicability, the system has been installed at the campus of Politeknik Negeri Pontianak in Pontianak City, and the data collected by the system is used to assess solar energy resource at the location. Data collected by the system from August 2020 to September 2021 shows that the values of solar irradiation are in the range of 2.9–4.1 kWh/m2 per day, and electric energy produced by commercial solar panels are in the range of 0.30–0.46 kWh/m2 per day, correspond to the monthly averaged efficiencies of 8–13%. The values of solar irradiation are 15–40 % lower than those obtained from the space-averaged satellite data which are in the range of 4.1–5.8 kWh/m2 per day. This shows the significant effects of local conditions, and confirms the advantage of assessment using the low-cost monitoring system that directly accounts for the effects of local conditions without the need of simulation using complex model and sophisticated software that required in the assessment using satellite-derived data.
SoK: The MITRE ATT&amp;CK Framework in Research and Practice

The MITRE ATT&amp;CK framework, a comprehensive knowledge base of adversary tactics and techniques, has been widely adopted by the cybersecurity industry as well as by academic researchers. Its broad range of industry applications include threat intelligence, threat detection, and incident response, some of which go beyond what it was originally designed for. Despite its popularity, there is a lack of a systematic review of the applications and the research on ATT&amp;CK. This systematization of work aims to fill this gap. To this end, it introduces the first taxonomic systematization of the research literature on ATT&amp;CK, studies its degree of usefulness in different applications, and identifies important gaps and discrepancies in the literature to identify key directions for future work. The results of this work provide valuable insights for academics and practitioners alike, highlighting the need for more research on the practical implementation and evaluation of ATT&amp;CK.
Self-collaboration Code Generation via ChatGPT

Although Large Language Models (LLMs) have demonstrated remarkable code-generation ability, they still struggle with complex tasks. In real-world software development, humans usually tackle complex tasks through collaborative teamwork, a strategy that significantly controls development complexity and enhances software quality. Inspired by this, we present a self-collaboration framework for code generation employing LLMs, exemplified by ChatGPT. Specifically, through role instructions, 1) Multiple LLMs act as distinct ``experts'', each responsible for a specific subtask within a complex task; 2) Specify the way to collaborate and interact, so that different roles form a virtual team to facilitate each other's work, ultimately the virtual team addresses code generation tasks collaboratively without the need for human intervention. To effectively organize and manage this virtual team, we incorporate software-development methodology into the framework. Thus, we assemble an elementary team consisting of three ChatGPT roles (i.e., analyst, coder, and tester) responsible for software development's analysis, coding, and testing stages. We conduct comprehensive experiments on various code-generation benchmarks. Experimental results indicate that self-collaboration code generation relatively improves 29.9%-47.1% Pass@1 compared to direct code generation, achieving state-of-the-art performance and even surpassing GPT-4. Moreover, we showcase that self-collaboration could potentially enable LLMs to efficiently handle complex real-world tasks that are not readily solved by direct code generation, as evidenced in case study.