Reviewerly/exHarmony huggingface dataset:
Does reusing pre-trained NLP model propagate bugs?

In this digital era, the textual content has become a seemingly ubiquitous part of our life. Natural Language Processing (NLP) empowers machines to comprehend the intricacies of textual data and eases human-computer interaction. Advancement in language modeling, continual learning, availability of a large amount of linguistic data, and large-scale computational power have made it feasible to train models for downstream tasks related to text analysis, including safety-critical ones, e.g., medical, airlines, etc. Compared to other deep learning (DL) models, NLP-based models are widely reused for various tasks. However, the reuse of pre-trained models in a new setting is still a complex task due to the limitations of the training dataset, model structure, specification, usage, etc. With this motivation, we study BERT, a vastly used language model (LM), from the direction of reusing in the code. We mined 80 posts from Stack Overflow related to BERT and found 4 types of bugs observed in clients’ code. Our results show that 13.75% are fairness, 28.75% are parameter, 15% are token, and 16.25% are version-related bugs.
Design of School Information System Using Waterfall Method (Case study: MTsN 8 Bantul)

The development of technology is now getting wider and starting to spread in various fields, one of the areas affected by the development of technology is education. This development has the intention of streamlining existing activities. The design of the school information system at MTsN 8 Bantul is to facilitate activities in data processing at the school. Using the waterfall method is expected to make it easier in this research, as well as telling, analyzing, and classifying problems as a supporting method. The result of this research is a school information system design.
The place of artificial intelligence in the risk management process

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.
PUNet: Temporal Action Proposal Generation With Positive Unlabeled Learning Using Key Frame Annotations

Popular approaches to classifying action segments in long, realistic, untrimmed videos start with high quality action proposals. Current action proposal methods based on deep learning are trained on labeled video segments. Obtaining annotated segments for untrimmed videos is time consuming, expensive and error-prone as annotated temporal action boundaries are imprecise, subjective and inconsistent. By embracing this uncertainty we explore to significantly speed up temporal annotations by using just a single key frame label for each action instance instead of the inherently imprecise start and end frames. To tackle the class imbalance by using only a single frame, we evaluate an extremely simple Positive-Unlabeled algorithm (PU-learning). We demonstrate on THUMOS’14 and ActivityNet that using a single key frame label give good results while being significantly faster to annotate. In addition, we show that our simple method, PUNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , is data-efficient which further reduces the need for expensive annotations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/NoorZia/punet
Models of Factors of the Design Process of Reference and Encyclopedic Book Editions

Reference and encyclopedic book editions are characterized by complex technical and semantic features. Such editions contain articles that answer the predicted questions of potential consumers in a concise form and have semantic and compositional integrity, and text elements are characterized by a clearly defined structure, which is provided by the use of headings, sections, subheadings, etc. It is difficult to imagine the process of cognition of a particular subject area without the use of reference and encyclopedic editions. The challenges facing the humanity require immediate reactions, which is why the lion’s share of reference and encyclopedic editions are now presented in electronic form. This transformation has not solved the design problem, but on the contrary, deepened it, increasing the variability of the necessary parameters selection. Reference and encyclopedic editions have many structural differences from other types of editions (art, popular science, etc.), the identification with which is unacceptable in terms of quality assurance. In view of the above, based on expert decisions, a set of factors of influence on the design process of reference and encyclopedic book editions has been singled out, a semantic network has been constructed and described, priority levels of factors have been established, a model of priority influence of factors on the quality of the analysed technological process has been constructed and optimized. The means of graph and network theory, elements of predicate logic, iterative methods, means of the hierarchical systems theory, methods of multicriteria optimization and pairwise comparisons have been used.
Dealing with Input Mode Confusion During Dual-Language Keyboard Use

Dual-language or multiple-language keyboards are widely used in many parts of the world, where users need to type in more than one scripts. During use of such keyboards, ‘input mode errors’ often occur when the user types in one language mode while the keyboard layout is set to a different one. These errors, along with frequent switching between input modes, are a source of confusion and distraction. In this paper we report on a study of this condition among Greek-speaking dual language keyboard users and describe a tool for automatically detecting this kind of errors and dealing with them in the case of an English-Greek keyboard. We also investigate the effects of this input mode error correction tool on a group of typical users during a study that lasted several days.
REDACT

Refraction networking is a promising censorship circumvention technique in which a participating router along the path to an innocuous destination deflects traffic to a covert site that is otherwise blocked by the censor. However, refraction networking faces major practical challenges due to performance issues and various attacks (e.g., routing-around-the-decoy and fingerprinting). Given that many sites are now hosted in the cloud, data centers offer an advantageous setting to implement refraction networking due to the physical proximity and similarity of hosted sites. We propose REDACT, a novel class of refraction networking solutions where the decoy router is a border router of a multi-tenant data center and the decoy and covert sites are tenants within the same data center. We highlight one specific example REDACT protocol, which leverages TLS session resumption to address the performance and implementation challenges in prior refraction networking protocols. REDACT also offers scope for other designs with different realistic use cases and assumptions.
Development of a Deep Learning Model to Assist with Diagnosis of Hepatocellular Carcinoma

Background: Accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on a pathologist’s experience. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.Methods: We collected a whole-slide image (WSI) of haematoxylin and eosin (H&E)-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noisy-specific deep learning model. The model was trained initially with 137 cases, cropped into multiple-scaled datasets. The patch screening and label smoothing strategies are adopted to handle histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases, with comparable tumor types and differentiations.Findings: Exhaustive experiments demonstrated that our two-step method achieved an 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, our model’s generalization performance was also verified using the Cancer Genome Atlas (TCGA) dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%.Interpretation: The noisy-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion (MVI).Funding: This work was supported by the Natural Science Foundation of Zhejiang Province (LY21H160035), the National Natural Science Foundation of China (81971686).Declaration of Interest: None to declare. Ethical Approval: The study was approved by the Ethics Committee of The First Affiliated Hospital, College of Medicine, Zhejiang University.
Machine Learning As an Advanced Algorithm To Solve Optimization Problems in Physics

It is argued that the standard procedures to solve problems in physics particularly in the field of electrodynamics have in a tacit manner the actions of Machine Learning, such as the criteria of Tom Mitchell, (i) task, (ii) performance, and (iii) experience. In this way, it is presented the case of electric interaction of two charged objects inside a finite cylindric. It is found that Machine Learning concepts is matching well to the requirements to limit the usage of space and energy. Beyond of using such principles as a methodology to solve problems, the concepts of Machine Learning can be projected in the theory of physics to improve and calibrate the mathematical structure of physics equations without touching their fundamental roles.
Enhancing the Anti-Cryptanalysis Ability and Avalanche Effect with Zeckendorf Representation via FPGA Implementation

Recently, many investigations have been conducted on the security of well-established protocols and standards, and it turns out that classical cryptography has seen some plight stemming from statistical cryptanalysis, inadequate avalanche effect, and so forth. To address the aforementioned problems, we resort to Zeckendorf representation whose non-uniqueness feature suggests a novel paradigm for anti-cryptanalysis and avalanche effect enhancement. Specifically, it renders the ciphertext exlusive if we write the plaintext as Zeckendorf representation before encrypting (we call this an "obfuscation" operation). "Obfuscation" squeezes the chance of statistical cryptanalysts: it destroys the basis of frequency analysis by removing repeated segments in substitution ciphers, and disables differential cryptanalysis by running the attackers into difficulties finding the initial plaintext pairs of block ciphers. "Obfuscation" also slightly enhances the avalanche effect by magnifying the change in plaintext. Simulation results on FPGA platform had confirmed our analysis. This paper strives to add a small stone to the wall of security of existing ciphers.
Interest Level Estimation via Multi-Modal Gaussian Process Latent Variable Factorization

This paper presents a method of interest level estimation via multimodal Gaussian process latent variable factorization (mGPLVF). The proposed method estimates user interest levels for contents with high accuracy by using multi-modal features such as contents and users’ behavior. Generally, users’ behavior includes some noise, and it is difficult to prepare a large amount of data. For dealing with the problem, the proposed method newly derives mGPLVF calculating appropriate latent variables that do not overfit a small amount of training data including noise based on a probabilistic generative model. Furthermore, mGPLVF simultaneously performs not only construction of the robust latent space but also estimation of user interest levels via the latent variables based on an idea inspired by a factorization machine. The consistent framework of latent space construction and interest level estimation leads to the improvement of the final estimation accuracy. Experimental results show the effectiveness of the proposed method.
Evaluation on teaching English simple future tense on grade 7 at SMP Tunas Harapan Pasar Kemis

Background: Teaching languages has different ways from teaching another subjects. Purpose: The objective of this research is to know the effectiveness of Active Learning Method in Teaching Simple Future. Design and methods: This research is using an evaluation method. The research was conducted at the seventh grade of SMP Tunas Harapan, Pasar Kemis, Tangerang. The data were collected through observation, teaching learning process, pre-test and post-test, questionnaires. This program contains the planning, how to implement and improve the students’ English Grammar Simple Future through Active Learning Method. The exercise was a mean to evaluate the action conducted in research. Results: The result of this research indicated that: (1) Using Active Learning Method in teaching simple future is one of a good solution to improve the students’ grammar skill, (2) it makes students are more easily in understanding and using simple future tense in daily activities, and (3) students become more active in responding the lessons or questions given by the teacher.
Design and Implementation of Family Doctor System Based on Microservice

research-article Design and Implementation of Family Doctor System Based on Microservice Share on Authors: Qianchao Wang Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Dengtao Liu Beijing University of Technology, China Beijing University of Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 160Pages 1–5https://doi.org/10.1145/3469213.3470365Online:18 August 2021Publication History 0citation26DownloadsMetricsTotal Citations0Total Downloads26Last 12 Months26Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-focus Source Images Reconstruction based on Adaptive Regional Data Hiding

Abstract Preserving the information of multi-focus source images was neglected in previous image fusion schemes since source image data discarding will happen during the fusion process. Data hiding technology can utilize the redundant bits to embed essential secret data. Inspired from that we proposed a multi-focus image reconstruction algorithm using adaptive regional data hiding. Integrating the Human Visual System (HVS) into the reconstruction module, we further proposed a visual grayscale information entropy operator, which is implemented to segment fused images into texture and flat regions for adaptive data hiding after unfocused region data compression. Our method achieves excellent performances in reconstruction Peak signal-to-noise Ratio (PSNR) above 43dB and maintains the satisfying visual effect of the fused images.
A Comparative Study On Challenges Encountered Among The First And Subsequent Generation Prospective Teachers

The study finds out the Challenges Encountered generally faced by prospective teachers. The need of the study is to investigate the role of the teacher which is not only a transmitter of knowledge, rather he/she has to be the powerful instrument for learning culture and values.  The B.Ed. prospective teachers are the sample group. Among them the investigator selected 1010 students of B.Ed. programme, by applying stratified random sampling technique.  They have lot of challenges or barriers in pursuing their studies in the fast-changing world. The objectives of the study: (A) To find the levels of Challenges Encountered along with its Dimensions among the First and Subsequent Generation Prospective teachers (B) To find whether there is any significant difference in the means scores of “Challenges Encountered” along with its respective dimensions, among the first and subsequent generation prospective teachers with regard to                      (i) All students,  (ii) students from nuclear family and (iii) Students from joint family. Multi-Dimensional Challenges Inventory, validated by R. Doss John (2013) with some modifications had been used to find out Multi-Dimensional Challenges of Prospective Teachers.  The major findings were analysed and discussed in the paper.
Gamification intervention in teaching and learning accounting: ComAcc Card / Nur Syafiqah Hussin, Naqiah Awang and Nurul Afzan Najid

Gamification is expected to make students hooked by the idea of the game and make them engaged in the game's idea and activity, which eventually allows for the possibility of learning process. The features of the game encourage curiosity and exploration to enhance engagement and improve contentcomprehension. Eventually, students will be rewarded with knowledge and skills that the game offered. The game called as ComAcc Card was triggered by the importance of students to be able to understand the components in financial statements. Failure to do so will affect the process of preparation of financial statements which is part of the course requirements. The game is expected to encourage positivestudent attitudes in the learning process and strengthen the students’ ability to classify items according to its components in accounting. Thus, the paper presents a simple method of accounting card game introduced to the non-accounting students, and it disucsses students’ perception towards the use of gamification in teaching and learning process.
Robustness of the Storage in Cloud Data Centers Based on Simple Swarm Optimization Algorithm

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods.
Newton Method for Minimal Learning Machine

Minimal Learning Machine (MLM) is a distance-based supervised machine learning method for classification and regression problems. Its main advances are simple formulation and fast learning. Computing the MLM prediction in regression requires a solution to the optimization problem, which is determined by the input and output distance matrix mappings. In this paper, we propose to use the Newton method for solving this optimization problem in multi-output regression and compare the performance of this algorithm with the most popular Levenberg–Marquardt method. According to our knowledge, MLM has not been previously studied in the context of multi-output regression in the literature. In addition, we propose new initialization methods to speed up the local search of the second-order methods.
Analisis Perbandingan Fingerprint dan Facelock pada Lock Screen Smartphone

Abstrak. Informasi yang bersifat krusial dalam smartphone seperti data pribadi maupun data perusahaan berupa gambar, cetak biru, suara, serta dokumen penting lainnya perlu dilindungi. Satu dari beberapa pengaman dasar pada smartphone adalah pengunci layar (lock screen). Lock screen yang populer digunakan diantaranya fingerprint dan facelock. Serangan sistem pengaman seperti sidik jari palsu (fingerprint-copy attack) dan wajah palsu (face spoof attack) membahayakan informasi yang terdapat pada smartphone. Artikel dibuat dengan tujuan untuk mengetahui lock screen mana yang lebih aman dari sisi kemudahan dalam melakukan serangan oleh pemula, dimana alat dan bahan yang digunakan terbatas yaitu peralatan sehari-hari. Hasil perbandingan menyimpulkan keamanan fingerprint lebih aman dibandingkan facelock dikarenakan fingerprintcopy attack membutuhkan keahlian khusus dalam praktiknya, sehingga serangan ini sulit dilakukan oleh pemula daripada face spoof attack. Kata kunci: Fingerprint, Facelock, Smartphone, Lock Screen Abstract. Crucial information on smartphones such as personal data and company data in the form of images, blueprints, sounds and other important documents need to be protected. One of the most basic safeguards on a smartphone is the lock screen. Popular lock screens those are used include fingerprint and facelock. Security system attacks such as fake fingerprints (fingerprint-copy attacks) and fake faces (face spoof attacks) leave vulnerable on the information contained on smartphones. The article was made with the aim to find out which lock screen is safer in terms of ease in carrying out attacks by beginners, where the tools and materials used are limited to everyday equipment. The comparison results concluded that fingerprint security is safer than facelock because fingerprint-copy attacks require special expertise in practice, so this attack is more difficult for beginners than face spoof attacks. Keywords: Fingerprint, Facelock, Smartphone, Lock Screen
The Influence of Language Transfer on the Grammar Teaching of Second Language Acquisition in the New Media Environment

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.