Reviewerly/exHarmony huggingface dataset:
Exploring the Opinions of Experts in Conversational Design: A Study on Users’ Mental Models of Voice Assistants

Voice Assistants (VAs) are growing in popularity, but some barriers to these systems' usage and adoption still prevail. Such obstacles may be related to users' mental models, which are unaligned with VAs' actual capabilities. Considering the influence of design aspects on users' perceptions, VA designers have a significant role in designing solutions that improve users' mental models and the quality of interactions. Thus, this study aimed to explore the opinions of professionals experienced in conversational design concerning users' mental models of VAs. Specifically, we aimed to identify the experts' opinions on 1) causes for users' misperceptions of VAs and 2) potential solutions to deal with the issue. To this end, we conducted a three-round questionnaire-based Delphi study with developers and researchers of conversational interfaces. The results showed that design aspects such as VAs' high humanness and the lack of transparency influence users' mental models. Nevertheless, removing VAs' humanness and excessively displaying information about VAs might not be an immediate solution. In turn, designers should assess the context and task domains in which the VA will be used to guide design decisions. Finally, developing teams should have a correct and homogeneous understanding of VAs and possess the necessary knowledge, skills, and tools to employ solutions properly.
Computational Argumentation for Supporting Learning Processes: Applications and Challenges

This book chapter analyzes different applications and challenges of computational argumentation for modeling different aspects of learning processes. Some of the topics included are argument-based recommender systems for educational purposes; argument-based shared knowledge for computer-supported collaborative learning (CSCL) and argument-based opinion mining for eliciting students’ knowledge based on information items corresponding to different topics of study. We also identify and discuss salient challenges associated with argumentation in the current state of the art. The chapter is organized to be self-contained, including an overview of the key elements in computational argumentation. Our contribution is intended to provide a reference point for researchers working on intelligent techniques for educational processes who are interested in incorporating argumentation as a metaphor for modeling intelligent decision making in Intelligent Tutoring Systems (ITS), Computer-Supported Collaborative Learning (CSCL) systems, and other related areas.
Analysis of Smart Grid Automation Technology Based on Data Mining Algorithm

The importance of electric power system makes us have to pay attention to it, because the application of electric power is too extensive. Firstly, this paper expounds the characteristics of smart grid dispatching automation technology, analyzes the existing problems of smart grid dispatching system, and analyzes the key technologies of smart grid automation based on data mining algorithm, in order to provide theoretical reference for relevant practitioners and improve the application of automation technology in smart grid dispatching system, Promote the continuous optimization and development of smart grid dispatching system in China.
OntoRecipe: An Ontology Focussed Semantic Strategy for Recipe Recommendation

In present-day times there is a need for a recipe recommendation system to cater to the demanding culinary needs of several users around the globe. This paper proposes an ontology focused recipe recommendation approach by incorporating semantic models. Its deep learning infused using RNN classification. The dataset uses here is from Food.com Recipes and Interaction which contains 280 k+ recipes and 700 k+ recipe reviews which covers 18 years of user interaction and uploads. The dataset based on it increases a good number of auxiliary knowledge and uses pair of approaches like SemantoSim and Kullback-Leibler divergence under the Shuffled Frog-Leaping algorithm a knowledge-centric recipe is recommended. It shows that the proposed structure performs better than the other baseline approaches in terms of the F-Measure and False Discovery Rate having values 95.03 and 0.06.
Vagueness in Artificial Intelligence: The ‘fuzzy logic’ of AI-related patent claims

Artificial Intelligence is an emerging technology with an average growth rate of 64,70% in patent filings worldwide and 39,71% in Europe between 2015 and 2019, according to Espacenet queries. This trend has raised several concerns regarding the implications for Intellectual Property rights, including disclosure, which is one of the main components justifying the existence of the patent system. Despite its importance, this requirement has been disputed due to certain tendencies that distort the functions that the act of disclosure fulfils, such as promoting innovation, research, or teaching. With Artificial Intelligence-related patents being granted, the disclosure requirement is once again compromised, and perhaps even more due to the particularities that this branch of technology entails. In this sense, one main concern lies in the question of how Artificial Intelligence-related patents should be disclosed, as well as the fulfilment of this requirement. To this aim, two case studies are conducted to show the heterogeneity of compliance with this requirement, focusing on patents filed at the European Patent Office.
Fooling a Face Recognition System with a Marker-Free Label-Consistent Backdoor Attack

Modern face recognition systems are mostly based on deep learning models. These models need a large amount of data and high computational power to be trained. Often, a feature extraction network is pretrained on large datasets, and a classifier is finetuned on a smaller private dataset to recognise the identities from the features. Unfortunately deep learning models are exposed to malicious attacks both during training and inference phases. In backdoor attacks, the dataset used for training is poisoned by the attacker. A network trained with the poisoned dataset performs normally with generic data, but misbehave with some specific trigger data. These attacks are particularly difficult to detect, since the misbehaviour occurs only with the trigger images. In these paper we present a novel marker-free backdoor attack for face recognition systems. We generate a label-consistent poisoned dataset, where the poisoned images matches their labels and are difficult to spot by a quick visual inspection. The poisoned dataset is used to attack an Inception Resnet v1. We show that the network finetuned on the poisoned dataset is successfully fooled, identifying one of the author as a specific target identity.
Obfuscation Techniques for a Secure Endorsement System in Hyperledger Fabric

Blockchain is a distributed ledger that provides a platform for sharing information among the participating nodes. The transactions are transparent and are validated by all the nodes in the network. Though the transparent nature offers advantageous features like provenance and non-repudiation, there are privacy risks that need to be addressed. This paper addresses a privacy problem prevailing in the endorsement system of Hyperledger Fabric, a permissioned blockchain network. The endorsement process reveals the entity authorized to perform endorsement. It is revealed in two ways: one is through the endorser’s signature and the other is through the endorsement policy. Endorsement policy is an expression that enforces, which are the organizations supposed to endorse a transaction. The endorser signature is secured by constructing a linkable ring signature secure in the standard model of security. The second form of leakage is prevented by committing the endorsement policy and providing non-interactive zero-knowledge proofs. This paper formally proves that the privacy-preserving endorsement system achieves anonymity and unlinkability of the endorsers in Hyperledger Fabric.
New Role of Universities: Experiences from Taiwan

In the severe epidemic situation, the role of universities in Taiwan should not only perform traditional education and academic research functions, but also effectively prevent severe and special infectious pneumonia in terms of the university’s social responsibility. Because Taiwan currently does not have a vaccine for COVID-19, the border epidemic prevention strategy is given priority to prevent the virus from entering the community. In terms of colleges and universities, the main strategy is to require everyone wear masks and maintain sufficient social distance to reduce the risk of contracting the virus. At the same time, the real-name system is adopted to improve the accuracy of the epidemic investigation. Accordingly, through cross-domain integration, National Taiwan University not only strengthens NTU COOL's remote teaching services, but also develops an automated temperature-measuring device with a contact tracing system to help the Epidemic Prevention Command Center quickly grasp the chain of infection. However, community infection broke out in May 2021. National Taiwan University immediately adopted to comprehensive remote teaching, and real-name tracing system should also be implemented. Finally, through the sharing of Taiwan’s experience, we hope that Institutions can refer to it and further enhance campus safety and resilience.
Building Together - Towards a Roadmap for African Language Technologies

There has been a rise in Natural Language Processing (NLP) communities across the African continent (Masakhane, AfricaNLP workshops). With this momentum noted, and given the existing power asymmetries that plague the African continent, there is an urgent need to ensure that these technologies move towards shared goals between organisations and stakeholders, to not only improve the representation of African languages in cutting-edge NLP research, but also to ensure that NLP research enables technological advances towards human dignity, well-being and equity for those who speak African languages.This study investigates the motivations, focus and challenges faced various stakeholders who are core of the NLP process. We perform structured stakeholder identification to identify core stakeholders to the NLP process. Interviews with representatives these stakeholders are performed and are collated into relevant themes. Finally, a set of recommendations are proposed for use by policy and AI researchers.
Digitalization in Remote Learning

Actual problems of vocational education development are directly linked with the development of digital learning and implementation of the remote education process. However, consideration of these issues is neglected in the domestic scientific literature. This work is devoted to considering the prospects for the development of distance education. Actualization of which was exacerbated in connection with the self-isolation of participants of the educational process during the past pandemic. The dynamics of distance education development, strengths, and weaknesses of this education are shown, as well as the most significant problems in our country and directions for the development of partnership relations of educational organizations.
Game Level Design in Mobile Gaming Industry: Fuzzy Pythagorean Similarity Approach

The mobile game industry has reached an enormous volume due to the increasing use of mobile phones and this volume is increasing day by day. Depending on this situation, many game developer companies release new games and generate income from the advertisements shown in these games. Users need to play the game longer to increase revenue in this context. Game companies also attach great importance to the design of various data analyses and in-game sections to increase users’ time consumed in the game. But finding the optimum sequence of in-game sections is quite a challenging process. Because this optimum sequence may vary from user to user, analyses made in this direction also show that user-based dynamization may be more effective than static in-game section designs since users have different characteristics. In this study, Using the user-based in-game activities of a crossword puzzle game of a mobile game company, the fuzzy similarity of the users most similar to themselves was calculated. Although many methods are suggested for the similarity calculation in the literature, since the similarities of the users have fuzzy characteristics, this similarity calculation was made with the Pythagorean Fuzzy Membership Degree method on the Pythagorean Fuzzy Set. In this way, it is aimed to increase the time spent in the game by enabling users to progress through the pattern in which the users most similar to them completed the game.
Cycle Consistency and Fine-Grained Image to Image Translation in Augmentation: An Overview

Generative adversarial networks (GANs) have gained considerable momentum in I2I translation. In recent years, I2I has attracted a growing amount of interest and achieved significant advancements due to its broad range of applications in various computer vision and image processing challenges, including image synthesis, segmentation, style transfer, restoration, and posture estimation. This article presents an overview of the I2I works created during the last several years. We review the major approaches to the current I2I results, outline the most significant advances the community achieves, and discuss the critical challenges such as Mode Imploding, Insufficient Indicators, Absence of Diversification, Non-Convergence and Hyperparameter Volatility, and Unbalanced Delinquent. Furthermore, we provide an overview of possible future research topics in the GANs I2I translation, including image enhancement, video production, facial animation synthesizing, and 3D face modeling.
Various Algorithms Used for Image Compression

In real world situations, working with images have been playing significant role in recent technologies. In image analysis, compression of images is a process to reduce the size of data in the image to represent the information required, known as data compression. The type of compress applied to the digital images is known as image compression in order to reduce the size thus reduced storage and easy transmission of image information. It plays an important role in various tasks related to images like transferring of data and storing data. Various compression techniques are available today for image compression like Embedded zero trees of wavelet transforms, wavelet difference reduction, set partitioning in hierarchical trees, spatial orientation tree wavelet etc. These algorithms of image compression applied to the images give better and efficient results. The image compression algorithms are applied to both types of compression lossy as well as lossless compression.
A Study on Transfer Functions of Binary Particle Swarm Optimization for Energy-Efficient VM Placement

Cloud computing has emerged as the backbone of the IT industry for infrastructural support. In cloud computing, resources are virtualized in form of Virtual Machines which eventually is mapped to physical infrastructure. Energy-efficient virtual machine placement is an important problem in cloud computing and has attracted the attention of researchers in recent. As virtual machine placement is an NP-hard problem, meta-heuristics have often been applied vastly for this. In one of our earlier works, Modified Binary Particle Swarm Optimization algorithm has been applied for the VM placement. It was observed therein that the transfer function, which plays an important role to obtain an optima, does not completely avoid the problem of local optima. Therefore, in this work, we have studied the behavior of eight different transfer functions towards this property. For this, energy-efficient VM placement problem is modelled as multi-objective optimization problem and Binary Particle Swarm Optimization is applied. The study is done by simulation and their statistical analysis.
Personalized Fashion Recommendation With Discrete Content-Based Tensor Factorization

Fashion outfit recommendation has attracted lots of attention recently. The problem becomes even more interesting and challenging when considering users’ personalized fashion preferences. Although existing works have successfully improved the recommendation accuracy, the efficiency issue of computation and storage is still under-investigated and often ignored. In this paper, we propose a discrete content-based tensor factorization model that maps items and user to binary codes for efficient fashion recommendation. We introduce a probabilistic perspective for learning to hash, where the binary codes are sampled from a set of underlying Bernoulli variables. To demonstrate the effectiveness of our model, we collect a large-scale outfit dataset together with user label information from a fashion-focused social website. Extensive experiments on our dataset show that the proposed model outperforms other state-of-the-art methods.
A Joint Model for Hierarchical Nested Information Extraction

During the long-term power construction process, the power dispatching department has saved many notification texts related to adjustment of grid operation mode. There is an urgent need to study named entity recognition techniques to automatically recognize the power equipment and operation mode, in order to support automatic verification of grid operation mode. By analyzing the characteristics of notification texts, a classification method of hierarchical nested named entities is proposed for the first time in power domain. The entities are divided into two layers with nested relationships, and the corpus of grid operation mode is constructed. We further propose a joint model based on character-word feature fusion and attention mechanism. The model is based on the parameter sharing approach for joint recognition of hierarchical nested entities in the corpus and further introduces an attention mechanism to optimize the feature interaction between hierarchical nested entities. In addition, we splice embeddings of characters and words as feature input to obtain richer semantic features. Experimental results show that our model achieves state-of-the-art results. Eventually, the recognition results can be stored as a standardized verification information chain, providing effective data support for automatic verification of the grid operation mode and ensuring safe and stable operation of the grid.
Design and Implementation of Platform Protocol and Client of Hakka Residential System Based on Artificial Intelligence

Nowadays, with the rapid development of science and technology and the wide popularization and application of Internet and computer technology, many emerging technologies have emerged, among which AIT (Artificial Intelligence Technology) is one. With the increasing demand for high-quality life, a variety of smart home systems and platforms emerge in endlessly. In this paper, in order to explore the effect of the application of AIT in the design and implementation of Hakka residential system platform protocol and client, we select two companies specializing in Hakka residential system as the experimental research object. The results of the experimental research show that the application of AIT in the design and implementation of Hakka residential system platform protocol and client H company, which has developed AIT, has higher design efficiency and client utilization rate than G company due to traditional methods. The highest design efficiency and utilization rate of H company are 98.71% and 92.35% respectively, while the highest design efficiency and client utilization rate of G company are 84.39% and 72.36% respectively.
Explainable Natural Language Inference via Identifying Important Rationales

Natural language inference (NLI) is an important task in the field of natural language processing (NLP), which requires certain common sense and logical reasoning abilities. Existing pretrained models applied to NLI have difficulty achieving good performance, and inference models based on deep learning also have the problem of poor interpretability. In this article, we propose an explainable NLI model by identifying important rationales, which can be obtained by the specially designed rationale extractor and selector. The pretrained models can be fine-tuned on the rationale-enhanced dataset. In addition, the extracted rationales can be used to generate natural language explanation (NLE) with a higher quality. Not only can NLE serve as an explanation for the prediction results, but it can also be combined with the original dataset to improve the performance of pretrained models. Experimental results on the Stanford natural language inference corpus (SNLI) show that the proposed model could obtain state-of-the-art results of 94.19% and 94.08%, respectively, on the SNLI development set, as well as on the test set and obtain good performance when transferred to the out-of-domain dataset (multiNLI). Furthermore, the proposed model has a certain improvement in computational efficiency due to the focus on important rationales.
Analysis of fMRI Images in a Mobile Application with the Implementation of Artificial Neural Networks

The mobile application market is growing dynamically, and the computing power of smartphones is also growing. The main goal of this chapter is to present the possibilities of using mobile devices to analyze fMRI images with the use of neural networks.
Component-Wise Scrutiny of Existing Rule-Based Punjabi Grammar System and Implication for Accuracy Determination

The use of Computational Linguistics (CL) to process Natural Languages (NL) is an important domain of Natural Language Processing (NLP). Fewer grammar checkers are available implicitly for Indian literary languages despite listed twenty two languages as per eighth schedule of Indian Constitution. Traditionally, “linguistic units"—token of a sentence in literary context are grouped together according to a set of predefined rules which can be stated as “Grammar” and hence directs research to a Grammar checker which performs the task of detecting and correcting grammatical errors in the text. This paper categorically explores existing Rule-based Punjabi Grammar System by providing a framework for quantitatively measuring the effect of each component and thus overall implication of the grammar checker using precision and recall as parameters for accuracy criteria and digs out Morphological Analyzer and POS Tagger as the faulty components generating false-alarms and errors to the tune of 58.13% and 26.74%, respectively. Based on these detections, further research can be carried out for developing a model to overcome these ambiguities using Machine Learning techniques.