Reviewerly/exHarmony huggingface dataset:
Affective Decoding for Empathetic Response Generation

Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) sometimes needs a large amount of data to converge in the training procedure and in some cases, each action of the agent may produce regret. This barrier naturally motivates different data sets or environment owners to cooperate to share their knowledge and train their agents more efficiently. However, it raises privacy concerns if we directly merge the raw data from different owners. To solve this problem, we proposed a new Deep Neural Network (DNN) architecture with both global NN and local NN, and a distributed training framework. We allow the global weights to be updated by all the collaborator agents while the local weights are only updated by the agent they belong to. In this way, we hope the global weighs can share the common knowledge among these collaborators while the local NN can keep the specialized properties and ensure the agent to be compatible with its specific environment. Experiments show that the framework can efficiently help agents in the same or similar environments to collaborate in their training process and gain a higher convergence rate and better performance.
Training for Speech Recognition on Coprocessors.

Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR, like the ones listed above; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges. The paper first describes an ASR model, based on a deep neural network inspired by recent work in this domain, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications

This paper follows up on a recent work of Neu et al. (2021) and presents some new information-theoretic upper bounds for the generalization error of machine learning models, such as neural networks, trained with SGD. We apply these bounds to analyzing the generalization behaviour of linear and two-layer ReLU networks. Experimental study of these bounds provide some insights on the SGD training of neural networks. They also point to a new and simple regularization scheme which we show performs comparably to the current state of the art.
Open science in libraries: re-interpreting the competencies of librarians

The rise and spread of networked communication had a significant impact on scholarly communication as well. Visibility is an essential element of scholarly work. It has also become a key issue in funders’ requirements in the past decade. In most cases, funders require that the research results financed by them should be demonstrated in an open and transparent manner. Such requirements are met usually not only by implementing visibility but also by providing open access to research results. Libraries’ activities in research data management can be conceptualized as falling into four broad categories: publication services, managing open-access repositories, managing data licensing and intellectual property issues, and research metrics. Today’s librarians are required to learn skills and abilities to find, extract, collect, clean, organize, analyze and present data.
Project Manager, Association Read! the leading provider of literature for children and adolescents in Norway

Association Read! was established in 1997 to increase the joy of reading among teenagers. The first initiative came from a corporation of book stores, but many other agents were soon included, that is, associations of book stores, publishers and authors. Association Read! started out as a small-scale project with only one coordinator but now operates with 40 members representing all fields of literature and reading in Norway.
SVA Library Research Guides: Online Resources and Services: Interlibrary Loan

The following guide outlines the collections, services, and support available remotely to students, faculty, and staff.
Peningkatan Kualitas Pembelajaran Daring Guru dengan Produk Multimedia Interaktif di SMA Daarut Tauhiid Boarding School

The education sector in Indonesia the COVID-19 outbreak makes the teaching and learning process online (online).In this situation, teachers are required to be more creative to maintain conducive situations during online learning.It becomes a challenge because situations that are usually more flexible during face-to-face meetings become changed because they no longer meet physically between teachers and students.Interactive multimedia-based learning can be one of the options so that learning can be more interesting in order to make it easier for students to understand learning materials.The purpose of this research is to improve the quality of learning from Daarut Tauhiid Boarding School high school through interactive multimedia products as a learning medium.The data analysis techniques used in this study are camuran research methods or mix methods.Qualitative narrative analysis and quantitative descriptive statistics to be able to process data and conclude research results well.The population of this study is students of class XII IPA and IPS with a sample number of 160 students of class XII men.This research offers solutions in order to add teacher insights to the use of ICT-based Multimedia for learning and further strengthen the ability of teachers to maximize interactive media for those who are accustomed to using it.The result of this study is to analyze and improve the ability of teachers in making learning media in the form of interactive multimedia animation media.It was found that with interactive multimedia high school students Daarut Tauhiid Boarding School as much as 94.5% felt very helped in understanding the material and enthusiastic during online learning.
DAAS: Differentiable Architecture and Augmentation Policy Search

Neural architecture search (NAS) has been an active direction of automatic machine learning (Auto-ML), aiming to explore efficient network structures. The searched architecture is evaluated by training on datasets with fixed data augmentation policies. However, recent works on auto-augmentation show that the suited augmentation policies can vary over different structures. Therefore, this work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them. Specifically, 1) for the NAS task, we adopt a single-path based differentiable method with Gumbel-softmax reparameterization strategy due to its memory efficiency; 2) for the auto-augmentation task, we introduce a novel search method based on policy gradient algorithm, which can significantly reduce the computation complexity. Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
Effects of Online Learning and Students Psychomotoric Changes in Writing Short Stories

The purpose of this study was to know the effect of online learning on students' psychomotor changes in writing short stories (Cerpen). The data sources in this study were students of SMPN 4 and SMPN 9 Banda Aceh. The method used is descriptive qualitative and quantitative. Data collection techniques in this study are test, observation, interview and documentation techniques.The data processing technique was carried out using the SPSS program. The results showed that online learning at SMPN 4 Banda Aceh had no significant effect on psychomotor changes in writing short stories. While the results of research at SMPN 9 Banda Aceh showed that online learning had a significant effect on psychomotor changes in students' short stories writing. The results of the interview show that online learning has positive and negative effects depending on the learning pattern.
APPLICATION OF WHATSAPP VOICE NOTE BASED COOPERATIVE LEARNING ON STUDENT COMMUNICATION SKILLS

This research is motivated by the low communication skills of students in Javanese language subjects, especially in class XII students of SMKN 3 Malang. This research was conducted to determine the effect of the Whatsapp Voice Note-based cooperative learning method on online learning on students' communication skills in Javanese. This study uses a pre-experimental method. The subjects consisted of 35 students. Communication skills were measured by written tests (pretest and posttest) and observation sheets. Communication skills tests and observation sheets were prepared and developed based on 21st-century communication indicators. Analysis of written test data (pretest and posttest) in this study were analyzed using statistical tests using the Statistics Package for Social Science (SPSS) software and performed manually using Microsoft Excel 2010 The observation sheet data were analyzed using the percentage calculation results. The data obtained from the study revealed that the application of the Whatsapp Voice Note-based cooperative learning method affected communication skills. This finding might be considered by Javanese language teachers to improve their communication skills.Keywords: Student Communication Skill, Whatsapp Voice Note, Cooperative Learning
Pengaruh Perbandingan Secondary Sheave Spring Standard Dan Racing Terhadap Akselerasi

Currently, automotive products, especially two-wheelers (motorbikes), are equipped with an automatic transmission system. The type of automatic transmission used is CVT (Continously Variable Transmission). Motorbikes with automatic transmission have several advantages, one of which is that they are more practical in use compared to motorbikes with manual transmission. The purpose of this study was to determine the RPM results on the effect of per cvt standard (1000 rpm) and per cvt racing (2000 rpm) on the RPM of a 2015 Honda Beat FI motorcycle. After replacing per cvt, it is expected to improve the performance of the 4 stroke combustion motor. The research method used is an experimental method, namely by starting the engine and holding the throttle at 1600 RPM after stabilizing then the throttle is rotated spontaneously up to 9000 RPM. The results of this study can be concluded that using per cvt2000 rpm only gets good acceleration and RPM at low rotational speeds. Whereas at high rotational speed it is less than optimal, but gets maximum power.
Communicative Reading Comprehension Competency Influences Written Composition Skills Performance of Faculty in English Discipline

James Britton proposed three primary language functions, which researchers tested using faculty members from Tawi-Tawi Regional Agricultural College (TRAC), for communicative reading comprehension—transactional, expressive, and poetic. Tawi-Tawi has many schools with high literacy levels, which contributes to a better society and a more peaceful country. The results revealed that the gender of the respondents had no bearing on their level of communication competency, and no significant differences were found between male and female faculty members. Additionally, there was no significant relationship found between the socio-demographic profiles and teaching performance of the faculty members in the English discipline.
On the Robustness of "Robust reversible data hiding scheme based on two-layer embedding strategy"

In the paper "Robust reversible data hiding scheme based on two-layer embedding strategy" published in INS recently, Kumar et al. proposed a robust reversible data hiding (RRDH) scheme based on two-layer embedding. Secret data was embedded into the most significant bit (MSB) planes to increase robustness, and a sorting strategy based on local complexity was adopted to reduce distortion. However, Kumar et al.'s reversible data hiding (RDH) scheme is not as robust against joint photographic experts group (JPEG) compression as stated and can not be called RRDH. This comment first gives a brief description of their RDH scheme, then analyses their scheme's robustness from the perspective of JPEG compression principles. JPEG compression will change pixel values, thereby destroying auxiliary information and pixel value ordering required to extract secret data correctly, making their scheme not robust. Next, the changes in both bit plane and pixel value ordering after JPEG compression are shown and analysed by different robustness-testing experiments. Finally, some suggestions are given to improve the robustness.
Graphics display capabilities in web browsers

The article discusses the issue of displaying graphics in web browsers. A couple of methods related to its display can be distinguished. The methods discussed are: SVG, HTML5 Canvas and the WebGl graphics engine. The research was done using a dedicated web application written in Angular and TypeScript language along with the help of Two.js library for displaying 2D graphics. After analyzing all the results it concluded that, guided by the rendering time of the elements, HTML5 Canvas turned out to be the best method. On the other hand, the best method which achieves the highest number of FPS for animation is WebGl.
A Proposed Blockchain-Based Voting System with User Authentication through Biometrics

Transparency and security in an electoral process are fundamental to the legitimacy of the results and the confidence of voters. Thus, it is necessary to assess opportunities to improve traditional voting systems. Among the main problems is the lack of transparency, due to the impossibility of a voter checking their vote and the lack of access to the source from which the results are obtained. Another problem is mobility, due to the impossibility of performing remote voting, as traditional voting systems continue to require the physical presence of the voter in an electoral zone. Thus, the objective of this work is to propose a voting system that is functional, transparent, safe, and accessible to everyone. Voters can vote through a mobile application with biometric authentication using fingerprint and password access. In our proposal, votes are registered in an Ethereum Blockchain through a Smart Contract, allowing the voter to check their vote. The results are expected to collaborate with the evolution of studies necessary to improve traditional voting systems, especially in fundamental aspects such as security, transparency, and mobility.
Exemplar Normalization for Learning Deep Representation

Normalization techniques are important in different advanced neural networks and different tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network. EN significantly improves flexibility of the recently proposed switchable normalization (SN), which solves a static L2N problem by linearly combining several normalizers in each normalization layer (the combination is the same for all samples). Instead of directly employing a multi-layer perceptron (MLP) to learn data-dependent parameters as conditional batch normalization (cBN) did, the internal architecture of EN is carefully designed to stabilize its optimization, leading to many appealing benefits. (1) EN enables different convolutional layers, image samples, categories, benchmarks, and tasks to use different normalization methods, shedding light on analyzing them in a holistic view. (2) EN is effective for various network architectures and tasks. (3) It could replace any normalization layers in a deep network and still produce stable model training. Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of tasks including image recognition, noisy label learning, and semantic segmentation. For example, by replacing BN in the ordinary ResNet50, improvement produced by EN is 300% more than that of SN on both ImageNet and the noisy WebVision dataset.
The Influence of Vocabulary Mastery Ability to Write Exposition Text of Eighth Grade Students of SMP Negeri 1 Satu Atap Tampahan: The Influence of Vocabulary Mastery Ability to Write Exposition Text of Eighth Grade Students of SMP Negeri 1 Satu Atap Tampahan

This study aims to determine whether there is a relationship between the mastery of vocabulary and the ability to write exposition text for eighth grade students of SMP N l atap tampahan in  Academic Year 2019/2020. This research is a correlational study using a quantitative approach. The population consists 112 students, and a sample of 24 students. Based on data processing, the average value (m) of vocabulary mastery by eighth grade students of SMP Negeri 1 atap is 78.12 in the good category, while the average value (m) of the ability to write text the exposition is 81.4 in the very good category. Based on the results of the study, it is known that the calculated value is 0.858 while the r table is 0.404 that significance level of 5%. The value of r (count) is greater than r (table) which is 0.858> 0.404. Thus there is a positive and significant relationship between the mastery of vocabulary to write exposition text for eighth grade students of SMP Negeri 1 atap tampahan in academic year 2019/2020 . Then Ha is accepted and Ho is rejected at a significant level of 5%.
Transliteration of Foreign Words in Burmese

This manuscript provides general descriptions on transliteration of foreign words in the Burmese language. Phenomena caused by phonetic and orthographic issues are discussed. Based on this work, we expect to gradually establish prescriptive guidelines to normalize the transliteration on modern words in Burmese.
Evaluation of a conscious control for unmanned autonomous driving vehicles

With the evolution of mobile processor technology and applied features in artificial intelligence, especially in machine learning area allows us to create autonomous driving cars as a reality in our lives. With the acceptance of a car very familiar like a weapon in most of the cultures, autonomous driver engines always seek to protect the owner of the car??s life in case of any accident or immediate decision making progress while driving. This different vendors and different structured AI engines has its own ethic principles and survive thrills so within a manned drive and unmanned autonomous drive car, hybrid environment becomes a drastic problem to evaluate the contradictions and coordination rules. Here a question arises like in case of an accident between two autonomous cars, insurance companies will subject to charge the other side according to which principle? They will subject to same principles and regulations with the manned drive car or we should evaluate a new justice system for AI Engines. If any of driver does not apply the latest updates for his car and AI Engine that severally responsibility to the owner/driver or manufacturer? Also in disaster scenarios or emergency situations we need a centralized management platform to overrule internal AI Engine survive codes and optimization rules that just motives its own vehicle. So we need to identify an interactive communication, listening, ordering and obeying rules that will manipulate the whole traffic. Let??s assume an English autonomous car driving in New York, like the song I am an alien a legal alien I am an English man in New York. Tomorrow will not be easy and technological barriers and engineering problems are just the beginning we need more interaction from philosophy, sociology, anthropology and ethics to create a better organized human, humanoid, android, AI world. In our university we create a multidisciplinary project team to evolve the tomorrow??s world with the tomorrows adults Generation Z and Generation AI. We would like to build cooperation with other universities and institutes for these studies. In my Innovation in technology management class we are creating a framework for autonomous car and social/legal implications.
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

In NLP, a large volume of tasks involve pairwise comparison between two sequences (e.g. sentence similarity and paraphrase identification). Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders. Bi-encoders produce fixed-dimensional sentence representations and are computationally efficient, however, they usually underperform cross-encoders. Cross-encoders can leverage their attention heads to exploit inter-sentence interactions for better performance but they require task fine-tuning and are computationally more expensive. In this paper, we present a completely unsupervised sentence representation model termed as Trans-Encoder that combines the two learning paradigms into an iterative joint framework to simultaneously learn enhanced bi- and cross-encoders. Specifically, on top of a pre-trained Language Model (PLM), we start with converting it to an unsupervised bi-encoder, and then alternate between the bi- and cross-encoder task formulations. In each alternation, one task formulation will produce pseudo-labels which are used as learning signals for the other task formulation. We then propose an extension to conduct such self-distillation approach on multiple PLMs in parallel and use the average of their pseudo-labels for mutual-distillation. Trans-Encoder creates, to the best of our knowledge, the first completely unsupervised cross-encoder and also a state-of-the-art unsupervised bi-encoder for sentence similarity. Both the bi-encoder and cross-encoder formulations of Trans-Encoder outperform recently proposed state-of-the-art unsupervised sentence encoders such as Mirror-BERT and SimCSE by up to 5% on the sentence similarity benchmarks.