Reviewerly/exHarmony huggingface dataset:
Penerapan Sistem Informasi Akuntansi Berbasis Aplikasi Microsoft

This study aims to analyze the role of application-based AIS for UD. Meeting Sustenance. This research method is descriptive qualitative. The object of this research is UD. Meet Sustenance which is located in Yosomulyo, Kab. Banyuwangi. The results showed that the existing business processes at UD. Meeting Sustenance in the application application stage through a computer can be described with a Flowchart flow to facilitate identification of business processes. Conclusion, UD. Temu Rezeki has not yet implemented a computer-based accounting information system.&#x0D; Keywords: Accounting Information System (AIS), Sales System, Computer Application, MSME
DESIGN OF A WEB-BASED TOEFL SIMULATION APPLICATION

Currently, technological advancements have touched many aspects of life, one of which is the proliferation of various applications on cell phones, particularly those related to education or commonly referred to as web-based applications. Test Of English Foreign Language (TOEFL) is a measure of a person's ability to speak English. However, taking the TOEFL test is not cheap for one test, so preparation is needed mature to get the score as desired and save the cost of the test because it does not need to repeat. For that, we need a means application that can make it easier for someone in doing exercises or TOEFL Simulations Web-Based, with this application you can measure the ability of somebody in English.
PERANCANGAN SISTEM LATIHAN SOAL DAN UJIAN TOEIC (TEST OF ENGLISH FOR INTERNATIONAL COMMUNICATION) BERBASIS WEB UNIVERSITAS WIDYATAMA

TOEIC which stands for Test Of English for International Communication is one of the well-known English proficiency tests and can be an indicator of participants' ability to use English. In general, this test is divided into two parts, namely listening and reading comprehension tests, where participants fill in their answers on an answer sheet according to the questions they hear in voice and writing. Media The formulation of the problem described in this study is how to build an online and web-based TOEIC exam application so that it can optimize exam and learning activities through TOEIC exercises to be more flexible, effective, and efficient. The focus of the research is how to optimize the evaluation and assessment process. In this case, it makes it easier for performance management (administrators) to make questions, streamline time for correcting answers and examinees know the results quickly, precisely, and accurately. The test results show that the application of practice questions and web-based TOEIC can process data that has been inputted by the administrator and presented to participants in following performance standards in general. The test results can also be used to provide a quick assessment and analyze the results of the scoring system assessment. Suggestions that can be put forward for the development and improvement of this web-based TOEIC test are to think of a better input mechanism to make it easier for users to process data input problems.
TOEIC TEST MANAGEMENT INFORMATION SYSTEM AT WIDYATAMA UNIVERSITY WEB-BASED

The English proficiency of non-English speakers in their daily use in an international work environment is measured by a test, namely the TOEIC (Test of English for International Communication) test. However, this test is still carried out using paper and pencil (paper-based) and still requires people to be supervised during the test. This is still experienced by Widyatama University Bandung students when carrying out the TOEIC test organized by the UPT Language Institute of Widyatama University Bandung. The purpose of this research is to build a website-based TOEIC test management information system. The method used is Waterfall, which includes analysis, system design, program code implementation and system testing. System testing is carried out using the black box method and using a questionnaire to obtain user satisfaction. The SIMTIC admin and user web application has been tested using three different web browsers and can run well, the SIMTIC android application has also been tested using three different android operating systems with the results running well, and testing has been carried out on ten respondents which resulted in a percentage of 83.3%. The application of this information system will facilitate the management of the TOEIC test at the UPT of the Widyatama University Language Institute, Bandung.
Exploring Smart- Quill Technology: Chirography Remembrance in addition to Impression Corroboration

Abstract: There are a variety of pens on the market, but none of them do anything other than write.The extraordinary development of a pen that can do more than just write is the focus of this paper. The Smart Quill is a pen-based computer. Even though this pen takes the place of the keyboard in the workplace, handwritten note-takers will find it extremely useful. The current method of computation has begun to favor small computers. Because of this, companies that make computers now use tools that are similar to computers. The handheld computers can only be reduced to a certain point before they become unusable. Screens require stable indicator control to read text smoothly, and keyboards have become so tiny that you need fingers the size of needles to operate them.
A No-reference Image Quality Assessment Algorithm Based on Human Visual Perception Process Reconstruction

Traditional no-reference image quality assessment algorithms mostly focus on the objective indicators of the image, such as the IE (information entropy) and clarity of the image. These indicators can reflect the quality of the image at the objective level to a certain extent, but it cannot well reflect the human visual evaluation of the image. In fact, it's still a relatively difficult problem to obtain the human visual evaluation of the image through a no-reference image quality assessment algorithm, considering the complexity of the imaging mechanism of the HVS (Human Visual System), for example, the connection mode of photoreceptor cells and ganglion cells which still cannot be well expressed by any specific model, along with the individual differences of human visual perception. This paper starts with the perception process reconstruction of the HVS, uses the similarity between the reconstructed image and the original image to reflect whether the image conforms to the request of human visual perception, carries out parameter optimization and correlation verification. Finally, it is found that the proposed algorithm has better relevance, lightness and universality than the existing algorithms, and can more effectively restore the scoring result of human visual perception of the test image.
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks

In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS. To alleviate this problem, we propose DropMAE, which adaptively performs spatial-attention dropout in the frame reconstruction to facilitate temporal correspondence learning in videos. We show that our DropMAE is a strong and efficient temporal matching learner, which achieves better finetuning results on matching-based tasks than the ImageNetbased MAE with 2X faster pre-training speed. Moreover, we also find that motion diversity in pre-training videos is more important than scene diversity for improving the performance on VOT and VOS. Our pre-trained DropMAE model can be directly loaded in existing ViT-based trackers for fine-tuning without further modifications. Notably, DropMAE sets new state-of-the-art performance on 8 out of 9 highly competitive video tracking and segmentation datasets. Our code and pre-trained models are available at https://github.com/jimmy-dq/DropMAE.git.
Sequence-aware item recommendations for multiply repeated user-item interactions

Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.
Online variable-length source coding for minimum bitrate LQG control

We propose an adaptive coding approach to achieve linear-quadratic-Gaussian (LQG) control with near-minimum bitrate prefix-free feedback. Our approach combines a recent analysis of a quantizer design for minimum rate LQG control with work on universal lossless source coding for sources on countable alphabets. In the aforementioned quantizer design, it was established that the quantizer outputs are an asymptotically stationary, ergodic process. To enable LQG control with provably near-minimum bitrate, the quantizer outputs must be encoded into binary codewords efficiently. This is possible given knowledge of the probability distributions of the quantizer outputs, or of their limiting distribution. Obtaining such knowledge is challenging; the distributions do not readily admit closed form descriptions. This motivates the application of universal source coding. Our main theoretical contribution in this work is a proof that (after an invertible transformation), the quantizer outputs are random variables that fall within an exponential or power-law envelope class (depending on the plant dimension). Using ideas from universal coding on envelope classes, we develop a practical, zero-delay version of these algorithms that operates with fixed precision arithmetic. We evaluate the performance of this algorithm numerically, and demonstrate competitive results with respect to fundamental tradeoffs between bitrate and LQG control performance.
SemiMemes: A Semi-supervised Learning Approach for Multimodal Memes Analysis

The prevalence of memes on social media has created the need to sentiment analyze their underlying meanings for censoring harmful content. Meme censoring systems by machine learning raise the need for a semi-supervised learning solution to take advantage of the large number of unlabeled memes available on the internet and make the annotation process less challenging. Moreover, the approach needs to utilize multimodal data as memes' meanings usually come from both images and texts. This research proposes a multimodal semi-supervised learning approach that outperforms other multimodal semi-supervised learning and supervised learning state-of-the-art models on two datasets, the Multimedia Automatic Misogyny Identification and Hateful Memes dataset. Building on the insights gained from Contrastive Language-Image Pre-training, which is an effective multimodal learning technique, this research introduces SemiMemes, a novel training method that combines auto-encoder and classification task to make use of the resourceful unlabeled data.
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

This paper presents a review of the field of reinforcement learning (RL), with a focus on providing a comprehensive overview of the key concepts, techniques, and algorithms for beginners. RL has a unique setting, jargon, and mathematics that can be intimidating for those new to the field or artificial intelligence more broadly. While many papers review RL in the context of specific applications, such as games, healthcare, finance, or robotics, these papers can be difficult for beginners to follow due to the inclusion of non-RL-related work and the use of algorithms customized to those specific applications. To address these challenges, this paper provides a clear and concise overview of the fundamental principles of RL and covers the different types of RL algorithms. For each algorithm/method, we outline the main motivation behind its development, its inner workings, and its limitations. The presentation of the paper is aligned with the historical progress of the field, from the early 1980s Q-learning algorithm to the current state-of-the-art algorithms such as TD3, PPO, and offline RL. Overall, this paper aims to serve as a valuable resource for beginners looking to construct a solid understanding of the fundamentals of RL and be aware of the historical progress of the field. It is intended to be a go-to reference for those interested in learning about RL without being distracted by the details of specific applications.
PyQBench: a Python library for benchmarking gate-based quantum computers

We introduce PyQBench, an innovative open-source framework for benchmarking gate-based quantum computers. PyQBench can benchmark NISQ devices by verifying their capability of discriminating between two von Neumann measurements. PyQBench offers a simplified, ready-to-use, command line interface (CLI) for running benchmarks using a predefined parametrized Fourier family of measurements. For more advanced scenarios, PyQBench offers a way of employing user-defined measurements instead of predefined ones.
Understanding Journalists’ Workflows in News Curation

With the increasing dominance of internet as a source of news consumption, there has been a rise in the production and popularity of email newsletters compiled by individual journalists. However, there is little research on the processes of aggregation, and how these differ between expert journalists and trained machines. In this paper, we interviewed journalists who curate newsletters from around the world. Through an in-depth understanding of journalists’ workflows, our findings lay out the role of their prior experience in the value they bring into the curation process, their own use of algorithms in finding stories for their newsletter, and their internalization of their readers’ interests and the context they are curating for. While identifying the role of human expertise, we highlight the importance of hybrid curation and provide design insights on how technology can support the work of these experts.
FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems

Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual information from multiple channels. Specifically for the current task, our approach is to provide two information channels in parallel, Fusing Conversation history and domain knowledge extracted from Candidate provenance (FCC), where candidate responses are curated, as contextual information to improve the performance of multi-turn dialogue response ranking. The proposed approach can be generalized as a module to incorporate miscellaneous contextual features for other context-oriented tasks. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks. Our experimental results show that our framework significantly outperforms the previous state-of-the-art models, improving Recall@1 by 7% and MAP by 4%. Furthermore, we conduct ablation studies to evaluate the contributions of each information channel, and of the framework components, to the overall ranking performance, providing additional insights and directions for further improvements.
Subject-driven Text-to-Image Generation via Apprenticeship Learning

Recent text-to-image generation models like DreamBooth have made remarkable progress in generating highly customized images of a target subject, by fine-tuning an ``expert model'' for a given subject from a few examples. However, this process is expensive, since a new expert model must be learned for each subject. In this paper, we present SuTI, a Subject-driven Text-to-Image generator that replaces subject-specific fine tuning with in-context learning. Given a few demonstrations of a new subject, SuTI can instantly generate novel renditions of the subject in different scenes, without any subject-specific optimization. SuTI is powered by apprenticeship learning, where a single apprentice model is learned from data generated by a massive number of subject-specific expert models. Specifically, we mine millions of image clusters from the Internet, each centered around a specific visual subject. We adopt these clusters to train a massive number of expert models, each specializing in a different subject. The apprentice model SuTI then learns to imitate the behavior of these fine-tuned experts. SuTI can generate high-quality and customized subject-specific images 20x faster than optimization-based SoTA methods. On the challenging DreamBench and DreamBench-v2, our human evaluation shows that SuTI significantly outperforms existing models like InstructPix2Pix, Textual Inversion, Imagic, Prompt2Prompt, Re-Imagen and DreamBooth, especially on the subject and text alignment aspects.
Macroscopic Dynamics of Entangled 3+1-Dimensional Systems: A Novel Investigation Into Why My MacBook Cable Tangles in My Backpack Every Single Day

A key axiom of equilibrium statistical physics is that all microstates are equally probably in a thermally isolated system. Coupled with the laws of Newtonian mechanics, quantum mechanics, chemistry, and thermal physics, one can build from this axiom both complex and satisfactory models for macroscopic phenomena. Here, we apply the precepts of statistical physics to a problem that has puzzled scientists and engineers since its discovery in the 1980's: The Entangled Laptop Cable Problem. Using a stochastic 2-dimensional simulation to approximate projections of the 3+1-dimensional system, we shall see that the overwhelmingly most probable state for a laptop cable is a severely tangled one.
Hidden Layer Interaction: A Co-Creative Design Fiction for Generative Models

This paper presents a speculation on a fictive co-creation scenario that extends classical interaction patterns with generative models. While existing interfaces are restricted to the input and output layers, we suggest hidden layer interaction to extend the horizonal relation at play when co-creating with a generative model's design space. We speculate on applying feature visualization to manipulate neurons corresponding to features ranging from edges over textures to objects. By integrating visual representations of a neural network's hidden layers into co-creation, we aim to provide humans with a new means of interaction, contributing to a phenomenological account of the model's inner workings during generation.
The 3D-DCT transform: didactic experiment and possible useful tool

This article is about an image transform called 3D-DCT, or three-dimensional discrete cosine transform. This is an extension of the well-known 1D and 2D-DCT, which is extensively used, mostly in multimedia coding. A modification of 1D-DCT (MDCT) is used in audio coding, whereas 2D-DCT is the key concept for JPEG standard (still image coding) and also for very popular video coding standards: MPEG-1 and 2, H.261 & H.263. Other, more modern methods like MPEG-4/H.264 and the newest MPEG-H/H.265 use a modified version of 2D-DCT. Nevertheless, 3D-DCT is less known. In this work, a simple implementation for 3D-DCT is constructed and some applications are explored. This is designed as a material for class work but, as it will be shown in this paper, some practical applications can be interesting. 3D-DCT can be applied for creating a video coder much simpler than those following MPEG-x/H.26x and with only slightly less performance. 3D-DCT has also been tested for 3D medical imaging (CT studies) coding, yielding very interesting results.
Universal properties of truth

We investigate abstract model theoretic properties which holds for models in which a truth or satisfaction predicate for a sublanguage of the signature is definable. We analyse in which cases those properties in fact ensure the definability of the respective truth predicate. In some cases, we formulate different axiomatic theories which are indispensable for such properties to hold and we analyse the mutual definability relations between those theories.
Scalable Attestation of Virtualized Execution Environments in Hybrid- and Multi-Cloud

Existing attestation mechanisms lack scalability and support for heterogeneous virtual execution environments (VEEs), such as virtual machines and containers executed inside or outside hardware isolation on different vendors' hardware in clouds managed by various organizations. To overcome these limitations, hardware vendors and cloud providers implement proprietary mechanisms (Intel DCAP, Amazon NitroTPM, Google Titan) to support their offerings. However, due to their plurality, the attestation becomes cumbersome because it increases maintenance and integration costs and reduces portability required in hybrid- and multi-cloud deployments. We introduce WAWEL, a framework that enables scalable attestation of heterogeneous VEEs. WAWEL can be plugged into existing hardware-specific attestation mechanisms, offering a unified interface. WAWEL supports the widely adopted trusted platform module (TPM) attestation standard. We implemented a prototype and integrated it with three different VEEs. It supports runtime integrity attestation with Linux integrity measurement architecture (IMA) and legacy applications requiring zero-code changes. The evaluation demonstrated that the WAWEL prototype achieves very good performance and scalability despite the indirections between the VEE and hardware root of trust.