Reviewerly/exHarmony huggingface dataset:
Development of individual educational path technology of foreign language teaching in non-linguistic universities using the student's self-assessment

The article is devoted to the development of the technology of an individual path of teaching a foreign language, considering the student's self-assessment in a technical university. The theoretical and practical lack of elaboration of this problem both at the psychological and pedagogical levels in the domestic educational system makes our research relevant. Currently, the requirements imposed on a university graduate often do not coincide with the requirements that a future specialist imposes on himself. There is an internal contradiction and conflict, both interpersonal and intrapersonal. To minimize this discomfort, the authors of the article propose to build the educational process according to the requirements of the university and the desires of students, using the technology of individual educational path. The authors consider in detail the concept of «self-assessment» from a psychological point of view, the understanding and awareness of their level of knowledge in a foreign language will help in the development of these skills and abilities. In this paper, students independently determined the level of their skills and abilities to read and write in a foreign language according to the proposed test. Then the level of foreign language proficiency of students was analyzed based on the Common European Framework of Reference for Languages, highlighting the features of self-assessment of foreign language proficiency (reading and writing) of students. The paper provides recommendations on the implementation of the technology of individual learning trajectory in the educational process according to the data obtained on self-assessment and assessment of the level of proficiency in reading and writing based on the Common European Framework of Reference for Languages to ensure the effectiveness of teaching foreign languages to students of technical specialties.
A COMPARISON OF SUPERVISED LEARNING METHODS FOR FORECASTING TIME SERIES IN OUTSIDE PATIENT VISITS

Data is something important because it can be used to help make a decision or policy in an organization.One form of output from the use of data is to produce forecasts in the future.One of the organizations that need forecasting is the hospital.Data that can be used for forecasting is patient visit data.The purpose of this study is to compare several supervised learning methods in the case of forecasting outpatient visit data, by producing a model result from the experimental process of KNN, SVR, Decision Tree, Random Forest and Linear Regression methods.From the Plot Time Series data for outpatient visits, autocorrelation results using the ACF method have a significance level, namely at lag 1 = 0.797, lag 2 = 0.6, and lag 3 = 0.579.So that the formation of the dataset is found that the current data has an influence on the data one month earlier , the previous two months , and the previous three months .The results of the forecasting model carried out resulted that the random forest method had the best model with an evaluation value of the RMSE model of 204.43 and the MAPE value of 12%.Based on the criteria for the MAPE value, the model that has been made has a good category.
DESIGN OF SCHOOL INFORMATION SYSTEMS USING THE TOGAF ADM METHOD AT SMKN 1 KEMLAG

In the process of developing the right information system, of course, there are several aspects that need to be considered. For example, the development of the system has an enterprise architecture basis, which has a pattern in terms of planning, designing, and managing the information system itself. For now, SMK NEGERI 1 KEMLAGI has not integrated information systems in both academic and non-academic fields such as administrative management, library management and school facilities and infrastructure. All activities carried out have been assisted by the existence of IT infrastructure such as computers and networks, but this is not enough to adapt to the dynamic development of application technology. Based on these problems, an architectural design is needed that can be a solution for academic and non-academic information system management. The design uses the TOGAF ADM method, this method is known to be complete and easy to use. In the application of TOGAF ADM, a good understanding of the business processes of the relevant organizations must be mastered. So that it can provide a good guide for creating a blueprint for the required information system components
Data Analytics on the Interagency Task Force Health Protocol Violators: Tracing the Trends and Determining Future Violations using Time Series and ARIMA (1, 0, 1) model

Abstract: The Interagency Task Force (IATF) is the agency that is responsible for ensuring the safety of every individual amidst COVID-19 pandemic. The agency has in placed the minimum health protocols for the community to adhere with as a way of ensuring safety among individual. This study determined the trends of violations committed against the minimum health standards and provides predicted data of its violations. The time series analysis and quantile regression are used for trend analysis and forecasting. The study will provide inputs to the authority as benchmark of their intervention plans in mitigating the violations against health protocol imposed by the national and local government.
A Review of Modern Computer Networks

Abstract: A computer network's principal role is to facilitate the transfer of digital information between computers. An interconnected network of computer equipment is all that constitutes a computer network. Distributing tasks among different machines is frequent in a computer network. Most networks confront substantial threats from attacks on their resources. This research focuses on computer networks. It is only through the establishment of a computer network that this is made possible at all. Nodes include things like computers, cell phones, servers, and other networking gear. Computer networks enable the Internet, as well as the shared use of application and storage servers, printers, and fax machines. Through the use of computer networks, many jobs can be completed. Systems can be classified as open or closed. If you want to connect to the network, the open system is ready to go. Closed systems, on the other hand, require further authentication to connect to other networks.
PERENCANAAN STRATEGIS SISTEM INFORMASI UNTUK MENINGKATKAN JUMLAH PENDAFTARAN CALON MAHASISWA (PCM) DAN KEUNGGULAN KOMPETITIF DI STKOM AL MA’SOEM

@font-face{ font-family:"Times New Roman"; } @font-face{ font-family:"??"; } p.MsoNormal{ mso-style-name:Normal; mso-style-parent:""; margin:0pt; margin-bottom:.0001pt; font-family:'Times New Roman'; font-size:12,0000pt; } p.MsoFooter{ mso-style-name:Footer; margin:0pt; margin-bottom:.0001pt; font-family:'Times New Roman'; font-size:12,0000pt; } span.msoIns{ mso-style-type:export-only; mso-style-name:""; text-decoration:underline; text-underline:single; color:blue; } span.msoDel{ mso-style-type:export-only; mso-style-name:""; text-decoration:line-through; color:red; } @page{mso-page-border-surround-header:no; mso-page-border-surround-footer:no;}@page Section0{ } div.Section0{page:Section0;}Al Ma’soem STKOM is one of the Universities under the auspices of the Al Ma’soem Bandung Foundation. STKOM has two study programs, namely Information Systems (S1 &amp; D3) and Accounting Information Systems (D3). With the existence of similar Colleges, it is a challenge for STM Al Ma’soem to increase the number of new Student Candidates (PCMs) each year by increasing the competitive advantage of other Universities. Al Ma'soem's STKOM currently uses information systems in website-based academic and promotional services, but does not yet have an information system strategic plan that can certainly be left behind by other competitors if IS / IT strategic planning is not used in carrying out academic services and activities. PCM. This study aims to analyze and design frameworks of strategic information systems planning to increase the number of PCM and competitive advantage. In this study carried out with the John Ward methodology approach, Pepprad. The methodology used consists of two stages. 1. Stages of input, including: Analysis of the internal business environment, Analysis of the external business environment, internal SI / IT environmental analysis, external IS / IT environment analysis. 2. Output stages, including: Business SI Strategy, IT Strategy and IS / IT Management Strategy. The results of the IS / IT strategic planning are in the form of application portfolios, which are expected to be implemented according to the information system requirements for the next five years. The results of this study are expected to be beneficial for all parties, among others, for the Al Ma'soem STKOM, the PCM Committee, the academic community, namely Managers, Lecturers, Students and Prospective Students. so that increased competitive advantage can be more effective and efficient which can increase the number of new PCMs every year.
Tag-Aware Document Representation for Research Paper Recommendation

Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-of-words techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.
Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface

Strong untestable assumptions are almost universal in causal point estimation. In particular settings, bounds can be derived to narrow the possible range of a causal effect. Symbolic bounds apply to all settings that can be depicted using the same directed acyclic graph (DAG) and for the same effect of interest. Although the core of the methodology for deriving symbolic bounds has been previously developed, the means of implementation and computation have been lacking. Our R-package causaloptim aims to solve this usability problem by implementing the method of Sachs et al. (2022a) and providing the user with a graphical interface through shiny that allows for input in a way that most researchers with an interest in causal inference will be familiar; a DAG (via a point-and-click experience) and specifying a causal effect of interest using familiar counterfactual notation.
FETA: Towards Specializing Foundation Models for Expert Task Applications

Foundation Models (FMs) have demonstrated unprecedented capabilities including zero-shot learning, high fidelity data synthesis, and out of domain generalization. However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e.g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training. This underlines the necessity to explicitly evaluate and finetune FMs on such expert tasks, arguably ones that appear the most in practical real-world applications. In this paper, we propose a first of its kind FETA benchmark built around the task of teaching FMs to understand technical documentation, via learning to match their graphical illustrations to corresponding language descriptions. Our FETA benchmark focuses on text-to-image and image-to-text retrieval in public car manuals and sales catalogue brochures. FETA is equipped with a procedure for completely automatic annotation extraction (code would be released upon acceptance), allowing easy extension of FETA to more documentation types and application domains in the future. Our automatic annotation leads to an automated performance metric shown to be consistent with metrics computed on human-curated annotations (also released). We provide multiple baselines and analysis of popular FMs on FETA leading to several interesting findings that we believe would be very valuable to the FM community, paving the way towards real-world application of FMs for practical expert tasks currently 'overlooked' by standard benchmarks focusing on common objects.
Multi-signer Strong Designated Multi-verifier Signature Schemes based on Multiple Cryptographic Algorithms

A designated verifier signature scheme allows a signer to generate a signature that only the designated verifier can verify. This paper proposes multi-signer strong designated multi-verifier signature schemes based on multiple cryptographic algorithms and has proven their security in the random oracle model.
Analysis of the shortest vector problems with the quantum annealing to search the excited states

The shortest vector problem (SVP) is one of the lattice problems and is mathematical basis for the lattice-based cryptography, which is expected to be post-quantum cryptography. The SVP can be mapped onto the Ising problem, which in principle can be solved by quantum annealing (QA). However, one issue in solving the SVP using QA is that the solution of the SVP corresponds to the first excited state of the problem Hamiltonian. Therefore, QA, which searches for ground states, cannot provide a solution with high probability. In this paper, we propose to adopt an excited-state search of the QA to solve the shortest vector problem. We numerically show that the excited-state search provides a solution with a higher probability than the ground-state search.
Unsupervised Video Object Segmentation via Prototype Memory Network

Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting features for the most salient common objects within a video sequence. This difficulty can be solved by using motion information such as optical flow, but using only the information between adjacent frames results in poor connectivity between distant frames and poor performance. To solve this problem, we propose a novel prototype memory network architecture. The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps. In addition, the model scores the usefulness of the component prototypes in each frame based on a self-learning algorithm and adaptively stores the most useful prototypes in memory and discards obsolete prototypes. We use the prototypes in the memory bank to predict the next query frames mask, which enhances the association between distant frames to help with accurate mask prediction. Our method is evaluated on three datasets, achieving state-of-the-art performance. We prove the effectiveness of the proposed model with various ablation studies.
Improved Robust Algorithms for Learning with Discriminative Feature Feedback

Discriminative Feature Feedback is a setting proposed by Dastupta et al. (2018), which provides a protocol for interactive learning based on feature explanations that are provided by a human teacher. The features distinguish between the labels of pairs of possibly similar instances. That work has shown that learning in this model can have considerable statistical and computational advantages over learning in standard label-based interactive learning models. In this work, we provide new robust interactive learning algorithms for the Discriminative Feature Feedback model, with mistake bounds that are significantly lower than those of previous robust algorithms for this setting. In the adversarial setting, we reduce the dependence on the number of protocol exceptions from quadratic to linear. In addition, we provide an algorithm for a slightly more restricted model, which obtains an even smaller mistake bound for large models with many exceptions. In the stochastic setting, we provide the first algorithm that converges to the exception rate with a polynomial sample complexity. Our algorithm and analysis for the stochastic setting involve a new construction that we call Feature Influence, which may be of wider applicability.
Topological Quantum Computation on Supersymmetric Spin Chains

Quantum gates built out of braid group elements form the building blocks of topological quantum computation. They have been extensively studied in $SU(2)_k$ quantum group theories, a rich source of examples of non-Abelian anyons such as the Ising ($k=2$), Fibonacci ($k=3$) and Jones-Kauffman ($k=4$) anyons. We show that the fusion spaces of these anyonic systems can be precisely mapped to the product state zero modes of certain Nicolai-like supersymmetric spin chains. As a result, we can realize the braid group on the product state zero modes of these supersymmetric systems. These operators kill all the other states in the Hilbert space, thus preventing the occurrence of errors while processing information, making them suitable for quantum computing.
Pre-Training a Graph Recurrent Network for Language Representation

Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing. Recent work shows that the attention mechanism inside Transformer may not be necessary, both convolutional neural networks and multi-layer perceptron based models have also been investigated as Transformer alternatives. In this paper, we consider a graph recurrent network for language model pre-training, which builds a graph structure for each sequence with local token-level communications, together with a sentence-level representation decoupled from other tokens. The original model performs well in domain-specific text classification under supervised training, however, its potential in learning transfer knowledge by self-supervised way has not been fully exploited. We fill this gap by optimizing the architecture and verifying its effectiveness in more general language understanding tasks, for both English and Chinese languages. As for model efficiency, instead of the quadratic complexity in Transformer-based models, our model has linear complexity and performs more efficiently during inference. Moreover, we find that our model can generate more diverse outputs with less contextualized feature redundancy than existing attention-based models.
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices

Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints simultaneously. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on heterogeneous resource-constrained edge devices. FADE differentially decouples the entire model into small modules to fit into the resource budget of each device, and each device only needs to perform AT on a single module in each communication round. We also propose an auxiliary weight decay to alleviate objective inconsistency and achieve better accuracy-robustness balance in FADE. FADE offers theoretical guarantees for convergence and adversarial robustness, and our experimental results show that FADE can significantly reduce the consumption of memory and computing power while maintaining accuracy and robustness.
Challenges of Proof-of-Useful-Work (PoUW)

Proof-of-Work (PoW) is a popular blockchain consensus algorithm that is used in cryptocurrencies like Bitcoin in which hashing operations are repeated until the resulting hash has certain properties. This approach uses lots of computational power and energy for the sole purpose of securing the blockchain. In order to not waste energy on hashing operations that do not have any other purpose than enabling consensus between nodes and therefore securing the blockchain, Proof-of-Useful-Work (PoUW) is an alternative approach which aims to replace excessive usage of hash functions with tasks that bring additional real-world benefit, e.g. supporting scientific experiments that rely on computationally heavy simulations. This publication consists of two parts: In the first part, important properties of conventional hash-based PoW are described. In the second part, theoretical PoUW concepts such as Coinami, CoinAI and the first successful PoUW cryptocurrency Primecoin are analyzed with respects to how PoW properties can be retained while doing useful work.
On the role of depth predictions for 3D human pose estimation

Following the successful application of deep convolutional neural networks to 2d human pose estimation, the next logical problem to solve is 3d human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2d inputs. With the goal of addressing this depth ambiguity, we build a system that takes 2d joint locations as input along with their estimated depth value and predicts their 3d positions in camera coordinates. Given the inherent noise and inaccuracy from estimating depth maps from monocular images, we perform an extensive statistical analysis showing that given this noise there is still a statistically significant correlation between the predicted depth values and the third coordinate of camera coordinates. We further explain how the state-of-the-art results we achieve on the H3.6M validation set are due to the additional input of depth. Notably, our results are produced on neural network that accepts a low dimensional input and be integrated into a real-time system. Furthermore, our system can be combined with an off-the-shelf 2d pose detector and a depth map predictor to perform 3d pose estimation in the wild.
Prototype Alat Pengusir Hama Burung Pipit Otomatis Berbasis Arduino Menggunakan Sensor PIR (Passive Infra Red)

Pests have long been a problem for farmers in the pre-harvest period. So with that, the author has the idea to make an Arduino-based automatic bird repellent using a PIR sensor. Because according to the author's assumption they only need to put this tool in the field, which will later be connected to an Arduino circuit that uses a PIR sensor as an input intermediary. Later the sensor will send a signal to the servo if the sensor detects an intruder who enters the rice field area. Then the intruder, which is a sparrow, will be surprised or scared because of the movement made by the servo. And the birds will fly away from the rice plants. So the conclusion of all the projects made is to meet the specifications that have been planned by the author in terms of Software and Hardware.