Reviewerly/exHarmony huggingface dataset:
A Review of Research on the Effect of Digital Learning on Foreign Language Learning Motivation

The influence of digital learning and motivation on foreign language learning is the focus of current research. This paper reviews the impact of digital learning on foreign language learning motivation. It defines digital language learning as language education using a technology-based platform or application. Then, it expounds the background and application of digital language learning, which uses technology to meet the diverse educational needs of society and breaks and surpasses the limitations of traditional teaching. Driven by intelligent devices, digital language learning has become an essential learning tool in the information age. This paper discusses the relationship between motivation and language learning, with emphasis on integrated motivation and instrumental motivation. Explore how digital learning can positively influence foreign language learners' learning motivation through its convenience, flexibility, diverse resources and feedback mechanisms. However, limitations and challenges such as lack of emotional engagement, distraction and reduced interaction in digital language learning are also addressed. Combining emotional factors, strengthening interaction and combining digital teaching methods with traditional teaching methods can effectively enhance the learning motivation of foreign language learners and achieve efficient language learning outcomes.
Internship Management Information System at Batik TV Pekalongan

Batik TV Pekalongan City as a government agency provides opportunities for students and college students to do internships. Batik TV does not yet have an internship information system so that internship management is still manual and not computerized. This makes it less efficient and time-consuming because interns must meet the supervisor every day to report daily activities. The purpose of this research is to create an Information System for Managing Internship Activities at Batik TV Pekalongan to make it easier to manage internship activities. This system uses a prototype development method including listen to customer, build/revise mock up, customer test drives mock up. The result of this research is the establishment of an Internship Management Information System that makes it easier for interns to write daily internship activities, makes it easier for supervisors to check daily activity reports, and makes it easier for admins to manage internship data.
Empowering Children for Social Justice–Design Research in the Context of Children’s Computing Education

In the contemporary digitalized society, it is pivotal to provide children with skills to ideate, design and develop digital technology and to critically reflect on it and its consequences. In the current world full of crises, it is also crucial to make visible for children that digital technology is never value-free and that it can be used for making the world a better place but also for discrimination, marginalization, and oppression. Information Systems (IS) scholars have not addressed these topics with children, while IS as a discipline would provide very suitable expertise for this. We, inspired by frameworks on empowerment and social justice, have invited children to tackle a wicked societal problem of bullying through design of digital technology as part of their computing education, and while doing so, aimed at empowering children to empower those suffering from bullying, i.e., advocating social justice. In this design research study, we analyze and showcase our empowering design process and outcomes. We contribute to IS literature by offering insights on how IS research can contribute to children’s empowerment and social justice oriented computing education. These insights serve also IS researchers working with adults and pursuing social justice and empowerment agendas.
A Tale of Snakes and Horses: Amplifying Correlation Power Analysis on Quadratic Maps

We study the success probabilities of two variants of Correlation Power Analysis (CPA) to retrieve multiple secret bits. The target is a permutation-based symmetric cryptographic construction with a quadratic map as an S-box. More precisely, we focus on the nonlinear mapping X used in the Xoodoo and Keccak-p permutations, which is affine equivalent to the nonlinear mapping of Ascon. We thus consider three-bit and five-bit S-boxes. Our leakage model is the difference in power consumption of register cells before and after one round. It reflects that, &gt;in hardware, the aforesaid cryptographic algorithms are usually implemented by deploying a round-based architecture. The power consumption difference depends on whether the targeted bits in the register flip. In particular, we describe two attacks based on the CPA methodology. First, we start with a standard CPA approach, for which, to the best of our knowledge, we are the first to point out the differences between attacking a three-bit and a five-bit S-box. For CPA, the highest correlation coefficient is the most likely secret hypothesis. We improve on this with our novel combined Correlation Power Analysis (combined CPA), or Snake attack, which uses quadratic map cryptanalysis (e.g., of the function X) to achieve a better attack in terms of the number of traces required and computational complexity. For the Snake attack, sums of absolute or squared values of correlation coefficients are used to determine the most likely guess. As a result, we effectively show that our proposed Snake attack can recover the secret in n22 (or n22+n) intermediate results, compared to 22n for the CPA, where n is the length of the targeted S-Box. We collected power measurements from a hardware setup to demonstrate practical attack success probabilities according to the rank of the correct secret hypothesis both for Xoodoo and Keccak-p. In addition, we explain our success probabilities thanks to the Henery model developed for horse races. In short, after performing 16,896 attacks, the Snake attack or combined CPA on Xoodoo consistently recovers the correct secret bits with each attack using 43,860 traces on average and with only 12 correlations, compared to 61,380 traces for the standard CPA attack with 64 correlations. The Snake attack requires about one-fifth as many correlation values as the standard CPA. For Keccak-p, the difference is more drastic: the Snake attack recovers the secret bits invariably after 771,600 traces with just 20 correlations. In contrast, the standard CPA attack operates on 1,024 correlations (about fifty times more than the Snake attack) and requires 1,223,400 traces.
Student Perceptions of Andalas University Payakumbuh Campus Against the Speed of Access to Academic Information Systems

The need for information continues to grow rapidly such as the need for an Academic Information System, where Academic Information System is an innovation of technology and information. This happens because of the demands of the times that require information to be transferred quickly. To find out how fast information reaches users who are the main goal, the parameters used are bandwidth and signal strength. The access speed of an information system network is one of the dimensions that is very important for users of the information system. The results of the study state that the Academic Information System of Andalas University access is quite good at normal usage times, but often hangs or errors at peak usage times, both access is done with a computer or laptop or with a smartphone.
Design and Build a Web Service for Financial Technology Dashboard at PT. Mitra Kasih Perkasa with React JS

PT Mitra Kasih Perkasa or MKP is a financial technology or fintech company, as well as a system integrator. MKP processes thousands, even millions, of financial transaction data every day, requiring efficient data management through the use of a dashboard. Information from the dashboard is used to monitor business processes and improve company performance. This research focuses on the development of efficient and effective web services for financial technology dashboards, integrating attractive and user-friendly user interfaces (UI/UX). Additionally, this research will discuss how to access web services using React JS, a popular JavaScript framework for responsive user interface development. The research will also outline the steps in building a financial technology dashboard that can help PT. Mitra Kasih Perkasa efficiently and effectively manage financial information using modern and innovative React JS technology.
Retracted: Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm

[This retracts the article DOI: 10.1155/2021/3927551.].
Retracted: Long Noncoding RNA LINC00473 Ameliorates Depression-Like Behaviors in Female Mice by Acting as a Molecular Sponge to Regulate miR-497-5p/BDNF Axis

[This retracts the article DOI: 10.1155/2022/4244425.].
ElectRA-Decentralized Voting Application

Blockchain technology is one of the most promising modem technologies. The decentralization principle is used to distribute data among the active nodes of the network. There are many different use cases in the contemporary digital era because of the technology’s ability to assure confidentiality, privacy, and immutability. The existing electoral process is centralized and unreliable in operation. Elections must therefore be conducted using secure online methods. The most effective method for guaranteeing election anonymity, security, privacy, and dependability is blockchain technology. The goal of this work is to implement an online voting system based on blockchain technology. It looks for examples of how blockchain technology might be used in the Indian electoral system, how to do it, and what other considerations need to be made. Scalability, application methodology, implementation criteria, blockchain characteristics, and technological components must all be taken into account while managing an online voting application using smart contract writing.
A New Construction Method for Keystream Generators

&lt;p&gt;We introduce a new construction method of diffusion layers for Substitution Permutation Network (SPN) structures along with its security proofs. The new method can be used in block ciphers, stream ciphers, hash functions, and sponge constructions. Moreover, we define a new stream cipher mode of operation through a fixed pseudorandom permutation and provide its security proofs in the indistinguishability model. We refer to a stream cipher as a Small Internal State Stream (SISS) cipher if its internal state size is less than twice its key size. There are not many studies about how to design and analyze SISS ciphers due to the criterion on the internal state sizes, resulting from the classical tradeoff attacks. We utilize our new mode and diffusion layer construction to design an SISS cipher having two versions, which we call DIZY. We further provide security analyses and hardware implementations of DIZY. In terms of area cost, power, and energy consumption, the hardware performance is among the best when compared to some prominent stream ciphers, especially for frame-based encryptions that need frequent initialization. Unlike recent SISS ciphers such as Sprout, Plantlet, LILLE, and Fruit; DIZY does not have a keyed update function, enabling efficient key changing. &lt;/p&gt;
Agglomerative Federated Learning:Empowering Larger Model Training via End-Edge-Cloud Collaboration

&lt;p&gt;Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale by the weakest end devices. To address this issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedAgg recursively organizes computing nodes among all tiers based on Bridge Sample Based Online Distillation Protocol (BSBODP), which enables every pair of parent-child computing nodes to mutually transfer and distill knowledge extracted from generated bridge samples. This design enhances the performance by exploiting the potential of larger models, with privacy constraints of FL and flexibility requirements of EECC both satisfied. Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53% accuracy gains and remarkable improvements in convergence rate. Our code is available at https://github.com/wuzhiyuan2000/FedAgg.&lt;/p&gt;
Sistem Informasi Reservasi Kamar Hotel Snukel Menggunakan Metode Analisis System Usability Scale

Web-Based Snukel Hotel Reservation Information System, is an information system that focuses on reservation data entered by the receptionist relating to reservations in the hotel's business transactions. In the previous system, data was entered manually using MS.Excel. The risk that is owned is management that is less effective and efficient. The purpose of building this system is to help manage reservation data to be more effective and efficient, make it easier to enter reservation data and help manage data. So that transactions are recorded and stored properly. The system design is made using the system development life cycle (SDLC) approach with the Waterfall design method, the stages include system requirements analysis, system design, system implementation, system testing and system maintenance. Hotel guest reservation system, using PHP programming language MySql. The results of this study are systems that have been tested with a black box, the results of the black box test state that the system is running well and as expected. System analysis uses the SUS method so that it gets a score or accuracy of 77.33 which means that the system is feasible and well received by the user.
Multi-task Image Restoration Guided By Robust DINO Features

Multi-task image restoration has gained significant interest due to its inherent versatility and efficiency compared to its single-task counterpart. Despite its potential, performance degradation is observed with an increase in the number of tasks, primarily attributed to the distinct nature of each restoration task. Addressing this challenge, we introduce \mbox{\textbf{DINO-IR}}, a novel multi-task image restoration approach leveraging robust features extracted from DINOv2. Our empirical analysis shows that while shallow features of DINOv2 capture rich low-level image characteristics, the deep features ensure a robust semantic representation insensitive to degradations while preserving high-frequency contour details. Building on these features, we devise specialized components, including multi-layer semantic fusion module, DINO-Restore adaption and fusion module, and DINO perception contrastive loss, to integrate DINOv2 features into the restoration paradigm. Equipped with the aforementioned components, our DINO-IR performs favorably against existing multi-task image restoration approaches in various tasks by a large margin, indicating the superiority and necessity of reinforcing the robust features for multi-task image restoration.
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology

Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informative data such as additional immunohistochemical (IHC) stains and spatial transcriptomics. We present TriDeNT, a novel self-supervised method for utilising privileged data that is not available during inference to improve performance. We demonstrate the efficacy of this method for a range of different paired data including immunohistochemistry, spatial transcriptomics and expert nuclei annotations. In all settings, TriDeNT outperforms other state-of-the-art methods in downstream tasks, with observed improvements of up to 101%. Furthermore, we provide qualitative and quantitative measurements of the features learned by these models and how they differ from baselines. TriDeNT offers a novel method to distil knowledge from scarce or costly data during training, to create significantly better models for routine inputs.
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation

Transferability of adversarial examples on image classification has been systematically explored, which generates adversarial examples in black-box mode. However, the transferability of adversarial examples on semantic segmentation has been largely overlooked. In this paper, we propose an effective two-stage adversarial attack strategy to improve the transferability of adversarial examples on semantic segmentation, dubbed TranSegPGD. Specifically, at the first stage, every pixel in an input image is divided into different branches based on its adversarial property. Different branches are assigned different weights for optimization to improve the adversarial performance of all pixels.We assign high weights to the loss of the hard-to-attack pixels to misclassify all pixels. At the second stage, the pixels are divided into different branches based on their transferable property which is dependent on Kullback-Leibler divergence. Different branches are assigned different weights for optimization to improve the transferability of the adversarial examples. We assign high weights to the loss of the high-transferability pixels to improve the transferability of adversarial examples. Extensive experiments with various segmentation models are conducted on PASCAL VOC 2012 and Cityscapes datasets to demonstrate the effectiveness of the proposed method. The proposed adversarial attack method can achieve state-of-the-art performance.
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability.
STEREOFOG -- Computational DeFogging via Image-to-Image Translation on a real-world Dataset

Image-to-Image translation (I2I) is a subtype of Machine Learning (ML) that has tremendous potential in applications where two domains of images and the need for translation between the two exist, such as the removal of fog. For example, this could be useful for autonomous vehicles, which currently struggle with adverse weather conditions like fog. However, datasets for I2I tasks are not abundant and typically hard to acquire. Here, we introduce STEREOFOG, a dataset comprised of $10,067$ paired fogged and clear images, captured using a custom-built device, with the purpose of exploring I2I's potential in this domain. It is the only real-world dataset of this kind to the best of our knowledge. Furthermore, we apply and optimize the pix2pix I2I ML framework to this dataset. With the final model achieving an average Complex Wavelet-Structural Similarity (CW-SSIM) score of $0.76$, we prove the technique's suitability for the problem.
New Evaluation Metrics Capture Quality Degradation due to LLM Watermarking

With the increasing use of large-language models (LLMs) like ChatGPT, watermarking has emerged as a promising approach for tracing machine-generated content. However, research on LLM watermarking often relies on simple perplexity or diversity-based measures to assess the quality of watermarked text, which can mask important limitations in watermarking. Here we introduce two new easy-to-use methods for evaluating watermarking algorithms for LLMs: 1) evaluation by LLM-judger with specific guidelines; and 2) binary classification on text embeddings to distinguish between watermarked and unwatermarked text. We apply these methods to characterize the effectiveness of current watermarking techniques. Our experiments, conducted across various datasets, reveal that current watermarking methods are detectable by even simple classifiers, challenging the notion of watermarking subtlety. We also found, through the LLM judger, that watermarking impacts text quality, especially in degrading the coherence and depth of the response. Our findings underscore the trade-off between watermark robustness and text quality and highlight the importance of having more informative metrics to assess watermarking quality.
Adaptive spectral proper orthogonal decomposition of tonal flows

An adaptive algorithm for spectral proper orthogonal decomposition (SPOD) of mixed broadband-tonal turbulent flows is developed. Sharp peak resolution at tonal frequencies is achieved by locally minimizing bias. Smooth spectrum estimates of broadband regions are achieved by locally reducing variance. The method utilizes multitaper estimation with sine tapers. An iterative criterion based on modal convergence is introduced to enable the SPOD to adapt to spectral features. For tonal flows, the adaptivity is controlled by a single user input; for broadband flows, a constant number of sine tapers is recommended without adaptivity. The discrete version of Parseval's theorem for SPOD is stated. Proper normalization of the tapers ensures that Parseval's theorem is satisfied in expectation. Drastic savings in computational complexity and memory usage are facilitated by two aspects: (i) sine tapers, which permit post hoc windowing of a single Fourier transform; and (ii) time-domain lossless compression using a QR or eigenvalue decomposition. Sine-taper SPOD is demonstrated on time-resolved particle image velocimetry (TR-PIV) data from an open cavity flow and high-fidelity large-eddy simulation (LES) data from a round jet, with and without adaptivity. For the tonal cavity flow, the adaptive algorithm outperforms Slepian-based multitaper SPOD in terms of variance reduction, local bias, mode convergence, and memory usage. For both the tonal cavity and the broadband jet flows, results comparable to or better than those from standard SPOD based on Welch's overlapped segment averaging are obtained with up to 75% fewer snapshots. Based on these examples, best practice is established.