Reviewerly/exHarmony huggingface dataset:
POLITENESS STRATEGIES USED BY THE PRESIDENTIAL CANDIDATES OF INDONESIA IN THE 2019 PRESIDENTIAL DEBATES

Presidential debate is one of an interesting examples to be analyzed in terms of politeness strategies. This research analyzed the politeness strategies used by the presidential candidates of Indonesia in a presidential debate program during the election period in 2019. This research employed qualitative descriptive. The data were in the form of spoken utterances which were collected from the debate participants. The sources of the data in this research were the videos and transcripts of the second and the fourth presidential debates taken from youtube and google. The data were analyzed using data condensation, data displayed, drawing and verifying conclusion. The results of the research showed that the presidential candidates used several types of politeness strategies and realizations of politeness. There were four types of politeness strategies found in the debates; they were Bald-On Record, Positive Politeness, Negative Politeness, and Off Record. In realizations of politeness strategies, there were 30 sub-strategies realized in the second debate and 33 sub-strategies realized in the fourth debate. Positive Politeness was found as the dominant strategy since the candidates wanted to omit any gaps, give good sense, choose words selectively and convey their campaign.
Challenges of Training Institutions in Accelerating The Availability of Graphic Design Online Classes for Trainees

The digital era is currently affecting activities in the world of training. This study investigates the challenges training institutions face in accelerating the availability of online classes for today's trainees. This research uses a descriptive qualitative approach. A total of 126 participants came from graphic design training participants. Then, the data collected in this study was by distributing a 4-scale questionnaire, pretest-posttest assessment, and using a Likert Scale observation sheet with five scales (0-4). The results showed that the participants had a reasonable opinion of the availability of online classes at training institutions on the aspects of mastery of the material by the instructor, the accuracy of the presentation, the relevance of the training content, and its suitability for the needs of the training participants. The significance value based on age range, gender, and graduate status is 0.863, 0.094, and 0.107. The results of the pretest-posttest assessment also increased when using online classes. Positive results also impact training participants in understanding graphic design material as a whole with the availability of online courses at training institutions. The presence of this online class has had many positive influences in all aspects but still needs improvement because there are challenges that need to be resolved by the Training Institute. The faster the availability of online classes at the Training Institute, the quicker the fulfillment of training needs.
Digital technology in teaching and learning using DEMATEL

Digital mastering will increase get admission to schooling and knowledge, even as growing mind-sets and skills that set students up for success of their gift and destiny.Social connectivity, pace of communication, multitasking, gaining knowledge of possibilities, automation, data storage, editing, instructors can use extraordinary apps or reliable on line Improve traditional teaching methods for student's Additional engagement resources.Digital school rooms are described by way of the usage of electronic devices or platforms which includes social media, multimedia and cellular telephones to educate college students.Digital equipment and systems are becoming extra imperative to our private and work lives.Digital mastering will increase get admission to schooling and knowledge, even as growing mind-sets and skills that set students up for success of their gift and destiny.Social connectivity, pace of communication, multitasking, gaining knowledge of possibilities, automation, data storage, editing, instructors can use extraordinary apps or reliable on line Improve traditional teaching methods for student's Additional engagement resources.Allowing sufficient time for learning and teaching, enabling learners to develop their knowledge and skills Online digital networks allow educators to share resources and digital tools and services expedite lesson planning.Digital assessment eliminates marking time.DEMATEL (Decision Making Trial and Evaluation Laboratory) They are divided into analysis using the Digital technology in teaching and learning in Course website, Digital camera, Handheld computer, Mobile phone, Internet websites is Evaluation Parameters Digital technology in teaching and learning in the value.Results:Digital technology in teaching and learning.Course website is got the first rank whereas is the Internet websites is having the Lowest rank.
Black box testing on the online quiz application using the Equivalence Partitions method

The Quiz Online application is an android-based application that aims to help elementary school children learn and explore the material taught at school, this application can be used anywhere and anytime, parents and teachers can also monitor children's learning progress. To achieve the objectives of this application, it is necessary to test the online quiz application to ensure that the system functions according to user needs. This test is useful to ensure there are no errors that can later cause losses in the future. Testing on the online quiz application using the Equivalence Partitioning method. The steps taken are to make a test design, test limits, test scenarios and expected results. From the results of tests carried out using the Equivalence Partitioning method in the online quiz application, no errors were found and all system functions were running well.
Benchmarks for Automated Commonsense Reasoning: A Survey

More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed and many aspects of common sense remain untested. Consequently, we do not currently have any reliable way of measuring to what extent existing AI systems have achieved these abilities. This paper surveys the development and uses of AI commonsense benchmarks. We discuss the nature of common sense; the role of common sense in AI; the goals served by constructing commonsense benchmarks; and desirable features of commonsense benchmarks. We analyze the common flaws in benchmarks, and we argue that it is worthwhile to invest the work needed ensure that benchmark examples are consistently high quality. We survey the various methods of constructing commonsense benchmarks. We enumerate 139 commonsense benchmarks that have been developed: 102 text-based, 18 image-based, 12 video based, and 7 simulated physical environments. We discuss the gaps in the existing benchmarks and aspects of commonsense reasoning that are not addressed in any existing benchmark. We conclude with a number of recommendations for future development of commonsense AI benchmarks.
Service quality model analysis on the acceptance of information system users’ behavior

Website technology have created both opportunities and challenges for higher education. Information systems as online learning medium need to pay attention to access, quality and user needs in order to improve the quality of e-learning services. The research objective is to determine user acceptance of the system. The service quality method as identification in solving problems. The research focus on the analysis of five dimensions namely measurable, reliability, responsiveness, assurance and empathy. The research was conducted at the research college at Muria Kudus University. The results state that the assessment model of a system on the website can be completed properly. The level of effectiveness is carried out with respondents as users through the distribution of questionnaires. The results of the analysis with a statistical correlation performance test of 0.985 were declared accepted with a validity level of 97% indicating that the success of the system implemented was from the acceptance side. The higher the empathy with service quality and performance expectations, the greater the student's intention to receive online education services. This research is a reference for developing information systems on e-learning.
Efficient hardware implementation for lightweight Loong algorithm using FPGA

Recently low-resource devices such as radio frequency identification (RFID), internet of things (IoT), and wireless sensor networks (WSN) using lightweight cryptography (LWC) to protect devices. Created or design low-resource devices with a lightweight cryptographic technique should take into account important factors such as the battery life and the amount of data to be processed. This paper provides a new hardware designed for Loong lightweight cryptographic algorithm that takes into account the previously described constraints. The new hardware architecture for Loong algorithm with resource sharing to reduce system designed. The proposed approach is implemented using ISE Xilinx V14.7 using Virtex 4 field programmable gate array (FPGA) platform. The synthesis analysis for ISE showed the throughput of 851.264 Mbps with efficiency of 2.282 Mbps/slice, and a power consumption of 0.193 Watt. The implementation designed show the all-algorithms size consists of 373 slices, and the maximum possible operating frequency is 212.816 MHz. To the best of our knowledge, this is the first time that Loong algorithm has been implemented on FPGA using very high-speed integrated circuit hardware description language (VHDL).
A survey: medical health record data security based on interplanetary file system and blockchain technologies

The adoption of modern health records is growing more mature, yet security issues always accompany it. Interplanetary file system (IPFS) and blockchain are developing technologies with decentralization, distributed fault tolerance, and trustworthiness. Using IPFS and blockchain technology to tackle medical health record data security issues is a very promising trend, and it is presently being utilized to secure medical health record data security. This article first explains the idea of IPFS and highlights the classification of existing IPFS and blockchain techniques before briefly discussing distributed ledger to tackle the existing medical health record data security challenges and faults. Finally, to preserve medical health records, a new medical health record storage architectural model based on IPFS and blockchain technologies is presented.
NOVA-a virtual nursing assistant

The majority of people are medically unqualified to research or comprehend the severity of their ailments or symptoms. Natural language processing plays a critical role in healthcare in this area. These chatbots collect patient health data and, based on that data, provide more relevant information to patients about their physical ailments, as well as advise next steps. Artificial intelligence (AI)-powered healthcare chatbots are useful in the medical industry for supporting patients and directing them to the most appropriate resources. Chatbots are more useful for online searches that users or patients conduct when they are searching for answers to their health-related questions. With this application, a user can make health requests via text message and might also get relevant health suggestions/recommendations through it. This Chatbot is developed to be both educational and conversational. Chatbot delivers medical information, such as symptoms and remedies for diseases. Patients’ personal and medical information is stored in a database for further study, and patients receive real-time advice from experts. AI-powered apps in healthcare have experienced a significant increase in recent days. As a result, office wait times are reduced, saving money and energy. Patients may be learning medical information and assisting at their own pace and location.
Joint Multisided Exposure Fairness for Recommendation

Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric -- that incorporates existing user browsing models that have previously been developed for information retrieval -- to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
Social Practices: a Complete Formalization

Multi-agent models are a suitable starting point to model complex social interactions. However, as the complexity of the systems increase, we argue that novel modeling approaches are needed that can deal with inter-dependencies at different levels of society, where many heterogeneous parties (software agents, robots, humans) are interacting and reacting to each other. In this paper, we present a formalization of a social framework for agents based on the concept of Social Practices as high level specifications of normal (expected) behavior in a given social context. We argue that social practices facilitate the practical reasoning of agents in standard social interactions. Thus they can support deliberations for complex situations just like conventions and norms. However, they also come with a social context that gives handles for social planning and deliberation in top of the normal functional deliberation. The main goal of this paper is to give a formalization of social practices that can be used as a basis for implementations and defining precise structures within which social learning can take place.
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known Local Linear Explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on image four datasets as benchmark where we show REVEL's descriptive and analytical power.
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation

Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on the privacy-invasive collection of users' explicit and implicit feedback to build a central recommender model. One-shot federated learning has recently emerged as a method to mitigate the privacy problem while addressing the traditional communication bottleneck of federated learning. In this paper, we present the first unsupervised one-shot federated CF implementation, named FedSPLIT, based on NMF joint factorization. In our solution, the clients first apply local CF in-parallel to build distinct client-specific recommenders. Then, the privacy-preserving local item patterns and biases from each client are shared with the processor to perform joint factorization in order to extract the global item patterns. Extracted patterns are then aggregated to each client to build the local models via knowledge distillation. In our experiments, we demonstrate the feasibility of our approach with standard recommendation datasets. FedSPLIT can obtain similar results than the state of the art (and even outperform it in certain situations) with a substantial decrease in the number of communications.
MEGALITHIC STONE HERITAGE TRAIL MAPPING FOR RURAL TOURISM USING GEOGRAPHIC INFORMATION SYSTEM (GIS) IN TAMBUNAN, SABAH

Heritage trail is an important part of rural tourism especially for community development, community participation, for discovering community heritage, and for involvement by community in developing trails to promote the history, natural and the cultural heritage of the area. One of the cultural heritages that are increasingly extinct is the megalithic stoneritual which was a daily practice in the lives of the people in the past. Megalithic stone can be found scattered in rural area of Sabah such as Penampang, Keningau, Kota Belud, Tuaran, Papar, Tenom and Tambunan. The megalithic stone or Menhir was usually used for worshipping the ancestral spirit. The megalithic stone is unique as it has many shapes such as flat, horn, triangle and round. However, this was not practice anymore and the stone is disappearing or broken one by one. Since the numbers of megalithic stones in Sabah are many, it is not feasible for all location of the stones to be gazetted for the preservation of the stones. Therefore, heritage trails is the best solutions that can be used to protect the stones and at the same time get the local community involved. However, in order to make it as one of the rural tourism in the area, several criteria need to be met to attract tourists to participate the heritage trails. These criteria include; accessiability to the megalithic site, near to accommodation, closer to other side attraction, transportation availability andsupport services around the area. Thus, GIS mapping is needed to assists this as it can show the suitability of the heritage trails location by identifying whether all the criteria can be found near the area. The purpose of this study is to show the effectiveness of GIS in analyse the suitability of the heritage trail route and its location for the tourists. In this case,the area of Tobilung village in Tambunan was used as a study area in this paper. Having known all the location for each criteria in the study area using google map, the proximity analysis was then conducted to identify the suitability of the heritage trail location. Based on the result, the heritage trail that features the megalithic stone in Tobilung, Tambunan issuitable for rural tourism as it has most of the criteria required for a tourist especially in terms of accommodation, transportation, places of attraction and services such as food and beverages, accessibility and utilities. This paper presents the significant role that GIS can play in rural tourism development in Tobilung, Tambunan and subsequently enhance the local economy of the village.
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized

Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.
MAD for Robust Reinforcement Learning in Machine Translation

We introduce a new distributed policy gradient algorithm and show that it outperforms existing reward-aware training procedures such as REINFORCE, minimum risk training (MRT) and proximal policy optimization (PPO) in terms of training stability and generalization performance when optimizing machine translation models. Our algorithm, which we call MAD (on account of using the mean absolute deviation in the importance weighting calculation), has distributed data generators sampling multiple candidates per source sentence on worker nodes, while a central learner updates the policy. MAD depends crucially on two variance reduction strategies: (1) a conditional reward normalization method that ensures each source sentence has both positive and negative reward translation examples and (2) a new robust importance weighting scheme that acts as a conditional entropy regularizer. Experiments on a variety of translation tasks show that policies learned using the MAD algorithm perform very well when using both greedy decoding and beam search, and that the learned policies are sensitive to the specific reward used during training.
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization

Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology gap between tabular data and text make it difficult for PLMs to yield faithful text. Low-resource generation likewise faces unique challenges in this domain. Inspired by how humans descript tabular data with prior knowledge, we suggest a new framework: PromptMize, which targets table-to-text generation under few-shot settings. The design of our framework consists of two aspects: a prompt planner and a knowledge adapter. The prompt planner aims to generate a prompt signal that provides instance guidance for PLMs to bridge the topology gap between tabular data and text. Moreover, the knowledge adapter memorizes domain-specific knowledge from the unlabelled corpus to supply essential information during generation. Extensive experiments and analyses are investigated on three open domain few-shot NLG datasets: human, song, and book. Compared with previous state-of-the-art approaches, our model achieves remarkable performance in generating quality as judged by human and automatic evaluations.