Reviewerly/exHarmony huggingface dataset:
Evidential Regression by Synthesizing Feature Selection and Parameters Learning

In this paper, we propose an evidential regression (EVREG) that can simultaneously perform feature selection and model parameters learning. To achieve these two functions, an evaluation function for the significance of the features to be selected is proposed, which contains two terms, one for evaluating the impact of the feature to be selected on the prediction accuracy and one for evaluating the redundancy between it and the already selected features, respectively. According to the sequentially forward selection strategy, the features with high degree of significance are selected one by one, while irrelevant or redundant features are eliminated. In contrast to traditional EVREG, instead of performing feature selection as a separate data pre-processing, this method combines it with the parameters learning in model training, thus improving the accuracy of regression prediction. The effectiveness of this method was verified by implementing it on Friedman dataset.
TRICAN: Multi-Modal Hateful Memes Detection with Triplet-Relation Information Cross-Attention Network

Memes are spreading on social networking. Most are created to be humorous, while some become hateful with the combination of images and words, conveying negative information to people. The hateful memes detection poses an interesting multimodal fusion problem, unlike traditional multi-modal tasks, the majority of memos have images and text that are only weakly consistent or even uncorrelated, so various modalities contained in the data play an important role in predicting its results. In this paper, we attempt to work on the Facebook Meme challenge, which solves the binary classification task of predicting a meme's hatefulness or not. We extract triplet-relation information from origin OCR text features, image content features and image caption features and proposed a novel cross-attention network to address this task. TRICAN leverages object detection and image caption models to explore visual modalities to obtain “actual captions” and then combines combine origin OCR text with the multi-modal representation to perform hateful memes detection. These meme-related features are then reconstructed and fused into one feature vector for prediction. We have performed extensively experimental on multi-modal memory datasets. Experimental results demonstrate the effectiveness of TRICAN and the usefulness of triplet-relation information.
A Task-Aware Dual Similarity Network for Fine-Grained Few-Shot Learning

The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a single similarity measure, that is, global or local measure alone. However, for fine-grained images with high intra-class variance and low inter-class variance, exploring global invariant features and discriminative local details is quite essential. In this paper, we propose a Task-aware Dual Similarity Network (TDSNet), which applies global features and local patches to achieve better performance. Specifically, a local feature enhancement module is adopted to activate the features with strong discriminability. Besides, task-aware attention exploits the important patches among the entire task. Finally, both the class prototypes obtained by global features and discriminative local patches are employed for prediction. Extensive experiments on three fine-grained datasets demonstrate that the proposed TDSNet achieves competitive performance by comparing with other state-of-the-art algorithms.
Logical Events Graphs based Emotional Text Generation

We propose a method for generating sentiment text based on event logical inference diagrams, which can be used to uncover the core of an event and the development of the event by creating an event essence diagram and an event logical inference diagram for the event, and by redefining the event so that the model can better fit the target event. The network is end-to-end, with the encoder being a self-attentive mechanism combined with an event-aware enhancement mechanism and a sentiment-enhanced architecture to give sentiment characteristics to the generated text, and the decoder being a GRU network. and enable the generation of text with emotional characteristics for the target event. In addition to this, more attention can be given to the guiding events.
A Novel Tangible Interaction Authoring Tool for Creating Educational Activities: Analysis of Its Acceptance by Educators

The creation of applications based on tangible interaction (TI) applications, particularly on tabletops, is a developing area that requires the collaboration of professionals with expert knowledge in specific domains. Having an authoring tool that facilitates interdisciplinary intervention in the design and implementation of such applications is a current challenge to bring TI to different contexts. This article presents an authoring tool (named EDIT) and analyzes its acceptance by educators for creating educational activities. The novelty of the tool lies in the possibility of creating projects with a schedule of educational activities, sequenced as required for a group of students. In addition, it has specific characteristics for the educational scenario, such as the personalization of feedback and the meta-annotation of projects. Sessions were held with educators ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 38) to analyze variables related to the Technology Acceptance Model (TAM) (such as perceived usefulness and perceived ease of use) when creating TI educational activities on tabletops using the EDIT tool. The sessions were observed and recorded on video, and a Focus Group was held afterward. During the sessions, educators gave a positive assessment in relation to using this type of tool. It was observed that, in general, they find TI valuable mostly for working with children. Finally, the results showed a high acceptance obtained from the TAM and the novel features of EDIT were found to be useful.
DRESS-ML

Drones are gaining attention due to its possibility to support wide different types of applications. Since they can operate in different environments, it is possible to encounter uncertainties and exceptional situations, not initially predicted, during the use of drone-based applications. In this realm, self-adaptive strategies have been successfully used to guarantee resilience and continuous execution of such applications despite environment changes. Although some modelling approaches emerged to represent drone concepts, they are limited to model only expected flight plans or include few environmental conditions and drone resources, which restrict considerably their use. To mitigate those problems, this work proposes a domain-specific language, called DRESS-ML, which allows modelling exceptional situations and self-adaptive behaviours for drone-based applications. It relies on the Given-When-Then template used in the Behaviour-driven development (BDD) technique and the some of the main Aspect-oriented Programming concepts. We validate the applicability of our language through a proof of concept regarding an example application that uses a drone to monitor a forest to search for fire spots.
Online Multi-Face Tracking With Multi-Modality Cascaded Matching

Tracking multiple faces online in unconstrained videos is a challenging problem as faces may appear drastically different over time and identities can be inferred only based on information available from past frames. Previous tracking methods focus on face information without reference to other modality information such as a person’s overall body appearance, leading to suboptimal performance. In this paper, we propose a new online multi-face tracking method, called online multi-face tracking with multi-modality cascaded matching (OMTMCM), to improve the tracking performance by using both face and body information. The proposed OMTMCM consists of two stages, namely detection alignment and detection association. In the first stage, a detection alignment module is designed to align face detection with body detection from the same person for the subsequent detection association. In the second stage, a cascaded matching module is designed to associate face detections across frames to locate trajectory of each target face by using both face and body information. Specifically, aligned face-body detections in the current frame are matched in a cascade manner with body and face features that are selected from past frames and stored in the designed feature memory. In this way, our method can track multiple faces online with both face and body information while eliminating the possibility of face detection and body detection from the same person being separately assigned with different identities. Experimental results demonstrate our method is on par with or better than other online tracking methods for multi-face tracking.
Improving Labeling Through Social Science Insights: Results and Research Agenda

Frequently, Machine Learning (ML) algorithms are trained on human-labeled data. Although often seen as a “gold standard,” human labeling is all but error free. Decisions in the design of labeling tasks can lead to distortions of the resulting labeled data and impact predictions. Building on insights from survey methodology, a field that studies the impact of instrument design on survey data and estimates, we examine how the structure of a hate speech labeling task affects which labels are assigned. We also examine what effect task ordering has on the perception of hate speech and what role background characteristics of annotators have on classifications provided by annotators. The study demonstrates the importance of applying design thinking at the earliest steps of ML product development. Design principles such as quick prototyping and critically assessing user interfaces are not only important in interaction with end users of an artificial intelligence (AI)-driven products, but are crucial early in development, prior to training AI algorithms.
A Link Prediction Model Based on Improved TuckER Decomposition and Neural Tensor Network

Knowledge graphs usually refer to a knowledge base that stores a large number of triples. It uses triples (head entity, relationship, tail entity) to represent the relationship between entities, which can provide practical and valuable information for disciplinary research. However, they are often incomplete because their construction may be based on only a fraction of all facts, and the implicit relationships among a large number of entities are not sufficiently mined. To solve this problem, we can perform link prediction on the missing parts. Any existing knowledge graph needs to continuously improve the knowledge itself through completion, and even infer new knowledge. This paper will combine the improved Tucker decomposition algorithm and neural tensor network (NTN) to propose a new link prediction algorithm called Improved TuckER-NTN (Im Tu-NTN), which can effectively improve the accuracy of knowledge graph link prediction.
Sentiment Analysis on Food Reviews Using Data Visualization Tools

Scientists are delving deeply into different types of analysis of which sentiment analysis is the prominent one, which in the current times has emerged as a remarkable field of study. Positive, negative, and neutral sentiment analysis are the three types of emotional analysis. Users may share good and negative evaluations, which can be desirable or undesirable depending on the user experience, on the internet, and on e-websites such as Twitter and Facebook. Our research will look at the most popular points of view to determine the scope and kind of sentiment analysis based on Food Reviews. The study's major goal was to discover food-related sentiments among social media users. We want to show how to gather, pre-process, and categorize data from various social media platforms, and how to analyse attitudes based on Food Reviews. We collected Food Review-related data from Amazon Fine Food Reviews, an information-sharing social media network, to investigate public opinions regarding food review emotions. Various tools are employed in our research work such as matplotlib, pandas, word cloud and NLTK package. After conducting the research, it was found that the majority population had positive sentiments related to the food products and in many cases, they revealed negative sentiments as well.
Techniques for Generating Language Learning Resources: A System for Generating Exercises for the Differentiation of Literal and Metaphorical Context

Automatic generation systems for language learning, as well as computer-assisted language learning (CALL) systems have evolved according to the demands of teachers and students. For those systems, it is important to review Natural Language Processing (NLP) techniques aimed at that task, considering other disciplines such as Computational Sciences, Computational Linguistics and Creativity for teaching and learning other languages. This work is twofold. First, it presents an effort to review the main characteristics, methods and techniques used for its implementation, relevance and profitability of the systems developed in recent years; considering the importance to develop the abilities to recognize literal use of language as well as its non literal use, particularly metaphorical expressions in the natural process of learning a new language. For the second part, it presents a system that, based on the Trofi dataset (Gao G. et al. 2018), is able to generate different exercises to strengthen the students’ abilities to read and recognize the use of some verbs in literal and non-literal contexts.
Toward Methods To Develop Experience Measurements For Mixed Reality

In recent years, Mixed Reality (MR) has become more popular with more affordable devices and the introduction of new applications. These applications and devices create MR experiences which can be completely different from the experience in the real world. Different approaches have been developed to measure those expe-riences. Besides behaviour observations or incorporating devices to measure physiological signals, using questionnaires is the most popular practice. However, the usage of questionnaires which were developed not based on any rational theory or well-defined model can lead to potential issues. In this paper, we present methods to design measurement models which can be used to develop instru-ments to measure experiences in MR. In addition, an illustration of developing a model for the sense of presence in MR is introduced.
Quantum Radiating Arrays and Their Directivity: A Dynamical Open System Approach

We investigate the directive properties of radiating quantum antennas comprised of coupled two-level (qubit) systems interacting with bosonic modes and excited by classical fields. A directivity formula inspired by classical antenna theory is proposed. The density operator of a coupled two-level quantum dot (qubit) array, excited by classical external signals with variable amplitude and phase, is evolved in time using the Gorini–Kossakowski–Sudarshan–Lindblad master equation. We illustrate the method in a numerical example where it is shown that manipulating the phase excitations of individual quantum dots may significantly enhance the directive radiation properties of the quantum dot antenna system.
Labeling of Cultural Heritage Collections on the Intersection of Visual Analytics and Digital Humanities

Engaging in interdisciplinary projects on the intersection between visualization and humanities research can be a challenging endeavor. Challenges can be finding valuable outcomes for both domains, or how to apply state-of-the-art visual analytics methods like supervised machine learning algorithms. We discuss these challenges when working with cultural heritage data. Further, there is a gap in applying these methods to intangible heritage. To give a reflection on some interdisciplinary projects, we present three case studies focusing on the labeling of cultural heritage collections, the problems and challenges with the data, the participatory design process, and takeaways for the visualization scholars from these collaborations.
Human Detection and Localization in Indoor Disaster Environments Using UAVs

This paper presents human detection and localization module for GPS-denied indoor environments in disaster scenario. We evaluate the performance of three human detection algorithms: Histogram of Oriented Gradients, YOLO v3, and Mask RCNN using occluded human dataset. We compare them on performance metrics such as runtime, precision, and recall. YOLO v3 has the best trade-off between accuracy and computational runtime. This model is used to implement a human detection and localization module using ROS. The module was tested in simulated Gazebo environments that depict indoor disaster scenarios. The localization errors of detected humans show the this approach is suitable for localizing humans which can help in better planning of rescue operations.
Blockchain-Based Confidential Payment System with Controllable Regulation

Blockchain-based payment systems (e.g., Bitcoin) have been wildly adopted for many scenarios since the transaction details are publicly accessible. Blockchain-based anonymous payment systems (e.g., Monero and Zerocash) have also been proposed to protect on-chain privacy, such as the sender’s or receiver’s balance, and the transaction amount. However, overly privacy preserving systems are sometimes abused for malicious behavior in lieu of suitable regulation. Thus, balancing between requirements for regulation and privacy has become an important issue for such systems. This paper proposes a blockchain-based confidential payment system with controllable regulation. To protect user privacy and provide controllable regulations, we realized the proposed system using threshold homomorphic encryption to encrypt user transaction values and balance. The encryption was performed with thresholded regulators’ keys and hence limits regulator abilities to decrypt a transaction. In addition, we can update the user’s balance using the homomorphic property, without decrypting the transaction value or user’s balance, preserving on-chain privacy, while satisfying all security requirements. A prototype implementation is provided for performance analysis.
Group Activity Representation Learning with Self-supervised Predictive Coding

This paper aims to learn the group activity representation in an unsupervised fashion without manual annotated activity labels. To achieve this, we exploit self-supervised learning based on group predictions and propose a Transformer-based Predictive Coding approach (TransPC), which mines meaningful spatio-temporal features of group activities mere-ly with data itself. Firstly, in TransPC, a Spatial Graph Transformer Encoder (SGT-Encoder) is designed to capture diverse spatial states lied in individual actions and group interactions. Then, a Temporal Causal Transformer Decoder (TCT-Decoder) is used to anticipate future group states with attending to the observed state dynamics. Furthermore, due to the complex group states, we both consider the distinguishability and consistency of predicted states and introduce a jointly learning mechanism to optimize the models, enabling TransPC to learn better group activity representation. Finally, extensive experiments are carried out to evaluate the learnt representation on downstream tasks on Volleyball and Collective Activity datasets, which demonstrate the state-of-the-art performance over existing self-supervised learning approaches with fewer training labels.
Enabling Human Interaction in Virtual Reality: An Explorative Overview of Opportunities and Limitations of Current VR Technology

Virtual reality (VR) enables users to experience immersive virtual environments and has become an important tool in different domains such as industry, healthcare, professional services, or education. In many of the VR use cases, humans do not only need to interact with the virtual surroundings (e.g., machines), but also with other humans (e.g., business meetings or creativity workshops). However, human (to human) interaction in VR leads to technological challenges. Hence, researchers and practitioners need to analyze which VR technology actually supports features for human interaction that fit their use cases. A current overview in that regard, however, is missing. Therefore, based upon a market analysis, this paper provides a summary of current VR hardware, software applications and frameworks. We compare the general VR hardware ecosystems, sensory capabilities, technical specifications, available software applications that focus on human interaction, and development tools. The results show that, while many technologies provide specific features, only few solutions allow for the full range of human interaction in VR.
Analysis of Teacher Difficulties in Teaching Comparative Writing Using E-Learning

The Covid-19 pandemic has affected all aspects of life, including education. For this reason, the improvement of information technology is alternatives to help the learning process in the education sector. One such information technology is learning through the internet or can be called e-learning. With this e-learning, it is hoped that the difficulties experienced by students can be overcome during the pandemic. These difficulties include: difficulty understanding lessons, difficulty accessing the internet, concentrating difficulty, etc. On the other hand, the application of e-learning in the learning process is also an obstacle for teachers. Related to this problem, the researcher found a similar problem in a vocational school in Sidoarjo, East Java. Based on interviews conducted with one of the English teachers at the school, the obstacles experienced were: difficulty accessing the internet, and there is no internet quotas support from the school or the government that makes this online school happened.
A Quantitative Field Study of a Persuasive Security Technology in the Wild

Persuasive techniques and persuasive technologies have been suggested as a means to improve user cybersecurity behaviour, but there have been few quantitative studies in this area. In this paper, we present a large scale evaluation of persuasive messages designed to encourage University staff to complete security training. Persuasive messages were based on Cialdini’s principles of persuasion, randomly assigned, and transmitted by email. The training was real, and the messages sent constituted the real campaign to motivate users during the study period. We observed statistically significant variations, but with mild effect sizes, in participant responses to the persuasive messages. ‘Unity’ persuasive messages that had increased emphasis on the collaborative role of individual users as part of an organisation-wide team effort towards cybersecurity were more effective compared to ‘Authority’ messages that had increased emphasis on a mandatory obligation of users imposed by a hierarchical authority. Participant and organisational factors also appear to impact upon participant responses. The study suggests that the use of messages emphasising different principles of persuasion may have different levels of effectiveness in encouraging users to take particular security actions. In particular, it suggests that the use of social capital, in the form of increased emphasis of ‘unity’, may be more effective than increased emphasis of ‘authority’. These findings motivate further studies of how the use of Social capital may be beneficial for encouraging individuals to adopt similar positive security behaviours.