Reviewerly/exHarmony huggingface dataset:
Investigation of Cross-Browser Inconsistency Over the Network

The content of the web has increasingly become a focus for academic research, the e-commerce and overall technological aspects. Computer programs are needed in order to conduct any large scale processing of web pages. The online website plays an important role in any business or educational purpose. This field of cross browsing is an important and an emerging branch that deals with finding out about a websites look on different browsers. This paper proposes a solution about how there is a discrepancy in the website when viewed in different browsers few famous ones like Google Chrome, Internet Explorer and Mozilla Firefox etc. It concerns with the front end testing so that the developer is aware of the inconsistency that can trouble the end users.
Research on the Implication of Elderly Education From the Perspective of Active Aging

With economic development and social progress, more and more countries in the world have stepped into an aging society. The concept of active aging has also been recognized by the governments of many countries. China is also facing the problem of population aging. There have been great developments in elderly education in China in the past few decades. However, there is still an imbalance between the society’s increasing demand for elderly education and the supply side of elderly education. The traditional model of elderly education cannot meet the requirements of the development of the new era. It is necessary to learn from the feasible practices of other countries, and combine the possibilities brought by the rapid development of information technology in the new era, to innovate the education for the elderly for the aim of achieving the life-long growth of individual lives, and the realization of sustainable development of society.
Seeing With Sound

Binaural audio is audio with manipulated pitch and volume for each speaker that simulates a 3D audio experience. Many people could benefit from improved spatialized audio in video games, including people with visual impairments and professional video game players. In this paper we explore the research conducted into simulating this effect without needing an expensive multi-microphone setup. We then describe the mathematical principles behind the way we locate audio in 3D space, and explain the computational algorithms needed to create this effect in Unreal Engine 4. We conducted experiments with our algorithm and a control group using Unreal Engine 4’s default audio setup, and the results show that participants using our algorithm were able to locate the audio sources in the virtual environment with greater accuracy than the control group.
PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling

We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in onlin emessaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.
ANALISIS KEAMANAN DATA SELULER TERHADAP SERANGAN SNIFFING MENGGUNAKAN RTL-SDR

Data security on cellular networks is needed to maintain privacy and avoid illegal actions, because the process of communication and sending data remotely using cellular networks, especially GSM and GPRS are still widely used than conventional methods. One of the potential for sniffing data security holes can occur in the air interface, it is signalling system when transmitting data between user devices or Mobile Station (MS) and Base Transceiver Station (BTS). Therefore, to find out the quality of data security on GSM and GPRS cellular networks, a penetration testing method with RTL-SDR device are tested. The results obtained are the cellular network security system used is still secure based on data transmission patterns using timeslot, Temporary Mobile Subscriber Identity (TMSI), and changes in GSM Frame Number on GSM. Then, network security on GPRS is also still safe using logic channels, timeslot, and different security algorithm from GSM.
A Novel Homomorphic Encryption based RSA Algorithm for Machine Learning

In today's internet world the use of Machine Learning as A Service (MLaaS) is increasing day by day especially automated Machine Learning. In which users upload their dataset and auto Machine Learning (ML) will develop models after studying dataset. Due to this, the risk of compromising of privacy of users is also high. So, to secure data on a cloud a new approach is present in this paper. In which, we have been using homomorphic encryption (HE) based Rivest Shamir Adleman (RSA) algorithm to encrypt the data and Azure automated ML is used to develop models. Three different datasets are first encrypted by using the Homomorphic Encryption (HE) based Rivest Shamir Adleman (RSA) algorithm and then uploaded to the Azure automated Machine Learning (ML) platform. The accuracy of machine learning models developed using encrypted data is compared with machine learning models developed using unencrypted datasets.
PELUANG DAN TANTANGAN DALAM PENERAPAN NFC-BASED TRACKING SYSTEM UNTUK GARMENT WAREHOUSING

Garment Industry which has a short product life cycle, fluctuating and unpredictable product demand, and a long production lead time, faces some considerable challenges due to technological innovations in the world. Some technology products such as barcode, RFID, and NFC are generally found in stores only, but not in inventory or production areas that have a lot of repetitions and manual works that makes the data accuracy level is not optimal and well maintained. NFC Technology, which is the development of RFID Technology, is a technology product for identifying goods quickly at close range that can be rewritten according to user preference and can be read with user module. This paper aims to present the possible implementation of NFC Technology in the garment supply chain, especially in warehousing, explore its contributions and analysis its challenges.
WAB Learns: MS WEIRD: Reading Skills

Middle School WAB Extended Independent Reading Days Resources and information about development of reading skills
Domain Adaptation without Model Transferring

In recent years, researchers have been paying increasing attention to the threats brought by deep learning models to data security and privacy, especially in the field of domain adaptation. Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain. However, UDA with representation alignment or self-supervised pseudo-labeling relies on the transferred source models. In many data-critical scenarios, methods based on model transferring may suffer from membership inference attacks and expose private data. In this paper, we aim to overcome a challenging new setting where the source models cannot be transferred to the target domain. We propose Domain Adaptation without Source Model, which refines information from source model. In order to gain more informative results, we further propose Distributionally Adversarial Training (DAT) to align the distribution of source data with that of target data. Experimental results on benchmarks of Digit-Five, Office-Caltech, Office-31, Office-Home, and DomainNet demonstrate the feasibility of our method without model transferring.
Demonstrating robust simulation of driven-dissipative problems on near-term quantum computers

Quantum computers are poised to revolutionize the simulation of quantum-mechanical systems in physics and chemistry. Current quantum computers execute their algorithms imperfectly, due to uncorrected noise, gate errors, and decoherence. This severely limits the size and scope of protocols which can be run on near-term quantum hardware. Much research has been focused on building more robust hardware to address this issue, however the advantages of more robust algorithms remains largely unexplored. Here we show that algorithms for solving the driven-dissipative many-body problem, among the hardest problems in quantum mechanics, are inherently robust against errors. We find it is possible to solve dissipative problems requiring deep circuits on current quantum devices due to the contractive nature of their time evolution maps. We simulate one thousand steps of time evolution for the non-interacting limit of the infinite driven-dissipative Hubbard model, calculate the current through the system and prepare a thermal state of the atomic limit of the Hubbard model. These problems were solved using circuits containing up to two thousand entangling gates on quantum computers made available by IBM, showing no signs of decreasing fidelity at long times. Our results demonstrate that algorithms for simulating dissipative problems are able to far out-perform similarly complex non-dissipative algorithms on noisy hardware. Our two algorithmic primitives are the basic building blocks of many condensed-matter-physics systems, and we anticipate their demonstrated robustness to hold when generalized to solve the full many-body driven-dissipative quantum problem. Building upon the algorithms presented here may prove to be the most promising approach to tackle important, classically intractable problems on quantum computers before error correction is available.
Optical neural networks at Tera-OP/s speeds with soliton crystal microcombs

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS-operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously-enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
SphereFace2: Binary Classification is All You Need for Deep Face Recognition

State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-class classification framework. Despite being popular and effective, these methods still have a few shortcomings that limit empirical performance. In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization. Motivated by these limitations, we propose a novel binary classification training framework, termed SphereFace2. In contrast to existing methods, SphereFace2 circumvents the softmax normalization, as well as the corresponding closed-set assumption. This effectively bridges the gap between training and evaluation, enabling the representations to be improved individually by each binary classification task. Besides designing a specific well-performing loss function, we summarize a few general principles for this "one-vs-all" binary classification framework so that it can outperform current competitive methods. Our experiments on popular benchmarks demonstrate that SphereFace2 can consistently outperform state-of-the-art deep face recognition methods. The code has been made publicly available.
The Necessity of Teaching Pragmatics in Iraqi Foreign Language Classrooms: Focus on Secondary Schools

At present, English plays a key role in the world and it is known as an international language which enables people to connect all around the world and English can be called as a global language because it is helpful in the processes of globalization. As English has played an indispensable role in global communication, it is essential for English language users, both native and non-native, to use clear, comprehensible and educated English that allows smooth communication and avoids misunderstandings in social interactions. Hence, pragmatic competence can facilitate language users to successfully achieve their communicative aims in intercultural communication. Consequently, pragmatic elements have noticeable roles in communication between speakers because such elements can hinder inaccuracies and misunderstandings during communication. This paper discusses pragmatics as a branch of linguistics and its significant role in learning English as a second and a foreign language. In pragmatics, meaning in communication can be classified into two elements; verbal and nonverbal. This classification depends on the contexts, relationship between utterers, and social factors. Teachers should teach pragmatic competence in second language English classes through different activities and tasks.
독자 읽기능력 진단에 기반 한 도서지수 개발 원형연구

This is a pilot study for the establishment of a Korean-Lexile system. The reading camp consisted of 24 elementary school students from grades 1 through 6, with intensive English reading guidance on various books as teaching materials, covering four hours and 12 times per day. A one-to-one diagnosis was conducted for the 24 subjects and the changes in the program’s subjects' pre- and post-reading ability were compared and analyzed. In addition, the trend of Lexile changes was analyzed according to the trend of the reading ability change. The results of the pre-diagnosis ability of the subjects showed that they were generally below benchmarks, and the difference in the ability within the group was large. The results of the post-diagnosis ability of the subjects showed an improvement of reading ability and the Lexile index. The correlation between post-diagnosis reading ability and change in the Lexile index was generally static, but the increase in the number of Lexile per person was not consistent with the increase in reading ability. Based on the results, the study emphasizes the need for follow-up studies and the establishment of a Korean-Lexile system.
Difficulty-Aware Machine Translation Evaluation

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings. In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration. A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely. Experimental results on the WMT19 English-German Metrics shared tasks show that our proposed method outperforms commonly used MT metrics in terms of human correlation. In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them. The source code is freely available at https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation.
교육과정 개정 논의를 위한 제언

Contrary to the fact that East Asia has won the empathy as a new method of history research in the history academia, there are still negative viewpoints on the formation of in history education. Thus, in the condition of facing the revision of curriculum in 2022, and the complete application of high school credit system in 2025, this study considered the effectiveness of as an elective subject, and also suggested the desirable look of .The meanings of are as follows. First, it is meaningful because it aims for the common peace and prosperity, which has been even presented by Korea that was a victim of colonization in East Asia. Second, it is meaningful as the measures for uniformly solving the problems with the education of national history and world history in Korean history education. Third, it is meaningful as the first textbook developed before academic researches, contrary to the textbooks following the academic researches and general development process of curriculum and textbook. Considering the reality of East Asia as the ‘necessity to form the East Asian community’ based on such meanings, the has sufficient meanings to exist as an elective subject in the necessity of education for understanding East Asia, and also cultivating the talent to support the relations of East Asia.However, based on such meanings and necessities, in order to realize it as the academia aimed to implement, and also to become a significant choice of subject to students, it should become the regional history thoroughly composed of subjects focusing on modern & contemporary history including Southeast Asia. However, the current performance might not be good enough to fully realize the including Southeast Asia. Thus, if it is started in the method of connecting Southeast Asia focusing on consultative East Asia, the contents of textbook could be secured.
Sentiment analysis using fuzzy naïve bayes classifier on covid-19

Fuzzy Naive Bayes Classifier method has been widely applied for classification. The Fuzzy Naive Bayes method which consists of a combination of two methods including fuzzy logic and Naive Bayes is used to create a new system that is expected to be better. This research aim to find out the society's sentiments about COVID-19 in Indonesia and the use of the results of the Fuzzy Naive Bayes Classifier. The data of this research is obtained by scraping on Twitter in the period from January 1, 2020 to April 30, 2020. The classification method used in this research is the Fuzzy Naive Bayes Classifier method by applying the fuzzy membership function. In this research, sentiment analysis uses input data whose source is taken from tweets and the output data consists of sentiment data which is classified into three classes, namely positive class, negative class, and neutral class. In the distribution of training and testing data of 70%: 30%, the accuracy of the classification model using the confusion matrix is 83.1% based on 1199 tweet data consisting of 360 testing data and 839 training data. Also the presentation of each sentiment class was obtained which was dominated by positive sentiments, namely the positive class by 36.7%, the negative class by 35.0%, and the neutral class by 28.3%. Based on the results of the presentation, it can be concluded that there are still many people who have positive opinions or give positive responses to the presence of COVID-19 in Indonesia.
Empowering Student’s Activity with ICT: Case of Weekly Discussion in University of Darussalam

In the Covid-19 era, almost everything was done online. Likewise student discussion activities. At Darussalam University, all students are required to take part in weekly discussions. That is supervised by semester 7 with supervision from the presenters lecturer. This field research collected data on students' weekly discussions, as well as techniques in improving the quality of their literacy. This study resulted in the finding that discussion management can be carried out using an online system. The platform used is Google. Especially drives and email. Google Drive is used as a material bank and discussion reference along with the schedule of the presenters. Google Forms is used for monitoring absent activities and documentation.
How to Evaluate Your Dialogue Models: A Review of Approaches

Evaluating the quality of a dialogue system is an understudied problem. The recent evolution of evaluation method motivated this survey, in which an explicit and comprehensive analysis of the existing methods is sought. We are first to divide the evaluation methods into three classes, i.e., automatic evaluation, human-involved evaluation and user simulator based evaluation. Then, each class is covered with main features and the related evaluation metrics. The existence of benchmarks, suitable for the evaluation of dialogue techniques are also discussed in detail. Finally, some open issues are pointed out to bring the evaluation method into a new frontier.
Sentiment Analysis of Twitter Data Using Hybrid Classification Methods and Comparative Analysis

Recently, there are emergence and advent of data Inter-personal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g. tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociology, and etc., which are resulted from offering immense opportunities for studying and analyzing human views and sentiment. The objective of sentiment analysis is to classify a speaker's or a writer’s attitude towards various events or topics and arranging data into positive, negative or neutral categories. Sentiment analysis means determining the views of a user from the textual content regarding that topic i.e. how one feels about it. It might be used to classify the text content. Various researchers have used a widespread sort of methods to teach the classifiers for the Twitter dataset with various results. The research uses a hybrid method of using Swarm Intelligence optimization (PSO) algorithms with classifiers.The Support Vector Machine (SVM), k-nearest neighbours algorithm (KNN), k-nearest neighbor (KNN), hybrid classification method, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and planned optimised feature sets model is offered to progression the tweet features and to recognise the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analysing millions of variety conversations with human echelon accurateness. The projected optimised feature sets model Sentiment Analysis exercises the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. Sentiment Analysis with planned optimised feature sets achieves 82 percent accuracy, compared to SVM's 78.6% and ACO 75%. Furthermore, we measured only tweets in English that were acknowledged by the Twitter streaming API while evaluating sentiments of tweets.