Reviewerly/exHarmony huggingface dataset:
Where to from here? On the future development of autonomous vehicles from a cognitive systems perspective

Self-driving cars not only solve the problem of navigating safely from location A to location B; they also have to deal with an abundance of (sometimes unpredictable) factors, such as traffic rules, weather conditions, and interactions with humans. Over the last decades, different approaches have been proposed to design intelligent driving systems for self-driving cars that can deal with an uncontrolled environment. Some of them are derived from computationalist paradigms, formulating mathematical models that define the driving agent, while other approaches take inspiration from biological cognition. However, despite the extensive work in the field of self-driving cars, many open questions remain. Here, we discuss the different approaches for implementing driving systems for self-driving cars, as well as the computational paradigms from which they originate. In doing so, we highlight two key messages: First, further progress in the field might depend on adapting new paradigms as opposed to pushing technical innovations in those currently used. Specifically, we discuss how paradigms from cognitive systems research can be a source of inspiration for further development in modelling driving systems, highlighting emergent approaches as a possible starting point. Second, self-driving cars can themselves be considered cognitive systems in a meaningful sense, and are therefore a relevant, yet underutilized resource in the study of cognitive mechanisms. Overall, we argue for a stronger synergy between the fields of cognitive systems and self-driving vehicles.
How <i>sajiao</i> (playing cute) wins forgiveness: The effectiveness of emojis in rebuilding trust through apology

Prior studies have found that emojis can contribute to rebuilding customers’ trust when after-sale staff apologize to them, but studies on the different types of emojis and their different levels of effectiveness in rebuilding trust are still needed. In this paper, we explore the different types and frequencies of emojis and their effectiveness in rebuilding trust based on commercial discourses. Our data are collected from conversations between after-sale staff and customers during Ali Trademanager after-sale service. We find three types of emojis frequently used when after-sale staff apologizing to customers: face-related emojis, gesture-related emojis, and object-related emojis. Moreover, face-related emojis are the most frequently used, as their nature is to express emotion and attitude. The most effective emojis are the whimper emoji and kiss emoji, which are used by after-sale staff for sajiao (literally, ‘playing cute’). On the other hand, we show that the least effective emojis are the sorry emoji and happy emoji, which are deemed insincere because they violate the quality maxim and quantity maxim in the Cooperative Principle. This paper thus contributes to understanding the nature and function of emojis in commercial communication.
Effects of Haptic Feedback on User Perception and Performance in Interactive Projected Augmented Reality

By means of vibrotactile and force feedback, i.e., haptics, users are given the sensation of touching and manipulating virtual objects in interactive Extended Reality (XR) environments. However, research towards the influence of this feedback on the users' perception and performance in interactive XR is currently still scarce. In this work, we present an experimental evaluation of the effects of haptic feedback in interactive immersive applications. By means of a Projected Augmented Reality (PAR) setup, users were asked to interact with a projected environment by completing three different tasks based on finger-tracking and in the presence of visual latency. Evaluations were performed both subjectively (questionnaire) and objectively (i.e. duration and accuracy). We found out that while haptic feedback does not enhance the performance for simple tasks, it substantially improves it for more complex ones. This effect is more evident in presence of network degradation, such as latency. However, the subjective questionnaires showed a general skepticism about the potential of incorporating haptic information into immersive applications. As such, we believe that this paper provides an important contribution toward the understanding and assessment of the influence of haptic technology in interactive immersive systems.
Detection of Synthetic Speech Based on Spectrum Defects

Synthetic spoofing speech has become a threat to online communication and automatic speaker verification (ASV) systems based on deep learning since the synthetic model can produce anyone's voice. The first Audio Deep Synthesis Detection Challenge (ADD 2022) is launched to spur researchers around the world to build innovative new technologies that can further accelerate and foster research on detecting deep synthesis and manipulated speech. This paper presents a spoofing detection system submitted to ADD 2022 Track 3.2 Detection task (FG-D). The system consists of two parts to detect synthetic speech. First, Mel-frequency cepstral coefficients (MFCCs), Linear frequency cepstral coefficients (LFCCs), Delta coefficients, and Delta-Delta coefficients features derived from speech spectrogram are fed into DenseNet for building the DenseNet detection system (DDS). Then Mute segment classifier (MSC), High-frequency classifier (HFC), and Block spectrogram classifier (BSC) algorithms are designed for the defects of the synthetic speech on the spectrogram and the spectrum defect detection system SPECT is formed. The experimental results of the fusion system composed of SPECT and DDS in ADD FG-D demonstrate an EER of 8.5%, and our final submission ranks 6th in the evaluation phase of ADD FG-D.
The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.
PointFace: Point Cloud Encoder-Based Feature Embedding for 3-D Face Recognition

The accuracy of 2D face recognition (FR) has progressed significantly due to the availability of large-scale training data. However, the research of deep learning based 3D FR is still in the early stage. Most of available 3D FR generate 2D maps from 3D data and apply existing 2D CNNs to the generated 2D maps for feature extraction. We propose in this paper a light-weight framework, named PointFace, to directly process point set data for 3D FR. In this framework, two weight-shared encoders are designed to directly extract features from a pair of 3D faces and the distances between embeddings of the same person and different person are minimized and maximized, respectively. The framework also use a feature similarity loss to guide the encoders to obtain discriminative face representations. A pair selection strategy is proposed to generate positive and negative face pairs to further improve the FR performance. Extensive experiments on Lock3DFace and Bosphorus show that the proposed PointFace outperforms state-of-the-art 2D CNN based FR methods.
Securing online accounts and assets: An examination of personal investments and protection motivation

This paper examines how personal investment influence the use of multi-factor authentication (MFA) for securing online accounts. We draw from the Protection Motivation Theory (PMT) that accounts for intrapersonal factors that increase one’s understanding of their threat and coping mechanisms and, subsequently, their motivation to protect such investments. We integrate size of investment – the amount of time, effort, and other personal resources that an individual has put into their online account. Based on PMT’s framework, we develop a research model that examines the influence of investment size as an information source that initiates and builds threat and coping appraisals. The model is tested with a survey of 263 responses, and the result shows that investment size influences threat and coping appraisals, which in turn increases MFA protection motivation and use. These results highlight the importance of eliciting individuals’ personal investments in order to improve their protective security behaviors.
Emojis to conversion on social media

Abstract Emojis have become popular in online marketing communications. Marketers use emojis to humanize brand voice and elicit an emotional response from target audiences. However, little is known about how emojis are perceived and what kind of reactions they evoke. Therefore, this study aims to verify whether exposure to emojis leads to an increased intention to purchase and whether the use of emojis impacts campaign effectiveness in a real‐life environment. In a theory build‐up process, we draw upon the Dual Coding Theory and emotional contagion concept and develop seven hypotheses. We performed two data collection studies to test the hypothesized relations. The first study is based on a questionnaire ( N = 318), while the second is based on experimental design in a real‐life business environment. Surprisingly, we found that using emojis had a negative effect on purchase intention, while the effect was positive when mediated by positive affect. Emojis increased the effectiveness of marketing campaigns for hedonic products and strongly impacted the return on advertising spent. The findings of this study have both theoretical and practical implications in the observed domains, particularly about the type of products emojis are most effective in promoting, gender differences, and real‐life consumer behaviour. This is the first study to use an experimental design in a real‐life scenario to capture the role of emojis on campaign effectiveness and decipher the differences between genders and their perceptions of emojis. Theoretical and practical implications together with future prospects are discussed.
Using digital media through sequential worksheets: an astronomy activity

Learning from digital media is one of the notable learning environments used outside of school. The effect of using digital technology in education is well known today, as where such tools are used appropriately in lessons, they can make an effective contribution to the teaching and learning of abstract science subjects such as astronomy. The purpose of this study was to design an astronomy activity for elementary level students. We worked with 30 preservice primary school teachers (PPSTs). The activity was based on the fifth-grade elementary level topic of “Space systems: Stars and the Solar system.” The materials used were digital media tools and worksheets prepared in accordance with curriculum standards, and were applied sequentially. At the end of the activity, a feedback paper was applied to enable the PPSTs to identify their thoughts about the activity. The PPSTs noted that learning open digital media tools for the purpose of astronomy education was seen as a contribution of the activity. They also expressed that the activity could help fifth-grade students to learn astronomy concepts more easily and tangibly. It is hoped that the activity may be applied in both formal and informal learning environments for elementary level students’ astronomy learning.
Advances in Readability Research: A New Readability Web App for English

Readability research and the entailing derivation of theoretically valid and high-performing readability assessment models are important components of reading education. There have been notable advances in the research of readability assessment following the advances of natural language processing techniques. Specifically, newer formulas make use of complex linguistic features that were previously unavailable, as well as contextually representative word-embeddings derived by training neural networks. However, there remain limitations for newer readability formulas, especially with respect to the employment of newer formulas by educational content creators. This paper provides an overview of readability research and introduces a new web app to help facilitate the application of state-of-the-art readability formulas for educational content creators and researchers through both an online interface and a separate API. An increase in accessibility and exposure for state-of-the-art readability formulas will assist in addressing many limitations of integrating readability research into educational technologies.
Impact of Blockchain Technology in 6G Network: A Comprehensive survey

Internet of Things (IoT) plays a major role in deploying futuristic smart systems. The entire globe is focused towards an epoch of intelligent communication, which is ubiquitous and secure in nature. The sixth generation (6G) network leverages superior performance in terms of its enhanced reliability, transmission rate in multi-gigabit, sub-1ms latency and especially ubiquitous communication. However, it is highly necessary to effectively manage the resource allocation process that arises as a result of the scarcity in spectrum resources in order to meet all the increasing requirements of modern digital communication. Since security is an essential feature of intelligent communication era, an emerging technology named blockchain gives significant value to establish secure communication in 6G network with its effective as well as disruptive control and management of resource allocation and sharing process. This research article highlights the importance of blockchain technology in 6G network by highlighting its major challenges, research directions and futuristic application opportunities.
Recommendation System for Motion Detection Architecture

In most general association, Recommendation systems is defined as a tool or technique which is used for predicting the rating of any individual that he/she will give to any item or any product. It can also be used for predicting the feedback of any individual. Recommendation systems can be implemented anywhere. Books, movies, toys, restaurants, and other things on which people's tastes may differ can all be included. These forecasts are made using two methods, the first is a content-based approach that examines the item's qualities, while the second is a collaborative filtering strategy that keeps account of users' previous actions to aid decision-making. Based on the user's previous movie ratings and genres of movies seen, this study uses hybrid filtering to provide realistic movie selection predictions. Clustering is a method for identifying data structures and linkages. This report also reveals the research methodology used to construct such a recommender system.
Unsupervised Video Summarization via Deep Reinforcement Learning With Shot-Level Semantics

Video summarization is one of the critical techniques in video retrieval, video browsing, and management. It is still a challenging research task due to user subjectivity, excessive redundant information, and lack of spatio-temporal dependency. In this paper, we propose an unsupervised video summarization approach via reinforcement learning with shot-level semantics. The primary idea of this unsupervised method is based on the encoder-decoder model. We use a novel field size dataset to train a convolutional neural network as an encoder to extract the convolutional feature matrix from the video. Then, a bidirectional LSTM is utilized as a decoder to obtain probability weights for selecting keyframes, which preserves the spatio-temporal dependence of video summarization. Specifically, to reduce the influence of user subjectivity, we design a shot-level semantic reward function to generate more representative summarization results. The shot-level semantics are the rules followed by the video shooting process without being changed by the preferences of different viewers. Finally, we evaluate our approach on four classical datasets, SumMe, TVSum, CoSum, and VTW. The results suggest that our algorithm outperforms others and achieves satisfactory results.
Image-based Neural Network Models for Malware Traffic Classification using PCAP to Picture Conversion

Traffic categorization is considered of paramount importance in the network security sector, as well as the first stage in network anomaly detection, or in a network-based intrusion detection system (IDS). This paper introduces an artificial intelligence (AI) network traffic classification pipeline, including the employment of state-of-the-art image-based neural network models, namely Vision Transformers (ViT) and Convolutional Neural Networks (CNN), whereas the primary element of this pipeline is the transformation of raw traffic data into grayscale pictures introducing a properly developed IDS-Vision Toolkit as well. This approach extracts characteristics from network traffic data without requiring domain expertise and could be easily adapted to new network protocols and technologies (i.e. 5G). Furthermore, the proposed method was tested on the CIC-IDS-2017 dataset and compared to a well-known feature extraction strategy on the same dataset. Finally, it surpasses all suggested binary classification algorithms for the CIC-IDS-2017 dataset to the best of our knowledge, paving the path for further exploitation in the 5G domain to successfully address related cybersecurity challenges.
Increasing the UX maturity level of clients: A study of best practices in an agile environment

While multiple studies have attempted to define and measure User Experience (UX) Maturity — i.e., how familiar organizations are with UX concepts or strategies— more practice-based insight is needed to examine how UX practitioners maneuver in their relationships with low UX Maturity organizations and help these clients become more 'UX Mature'. This study evaluates how UX practitioners work with low UX Maturity clients, what obstacles they face, and how they cope with these obstacles. From these insights, a set of best practices are identified for UX practitioners who work with low UX Maturity clients and wish to increase their clients' UX maturity in an agile environment. These best practices were collected in the form of case studies, involving a total of 20 case studies based on interviews with 22 UX practitioners. The case studies reflect on past projects that were conducted for clients with a low UX Maturity level. Data was obtained through semi-structured interviews and analyzed using a grounded theory approach combined with elements of a thematic analysis. The results help to identify frequently experienced obstacles in working with low UX Maturity organizations, as well as six best practices for increasing the UX Maturity of these clients. The study results demonstrate that UX practitioners indeed fulfill a significant role in overcoming organizational UX boundaries. A Low UX Maturity Best Practice model was developed, which summarizes how UX practitioners can optimize their impact in working with low UX Maturity clients, while simultaneously contributing to a more user-centered focus on the part of their clients.
A Survey on Deep Learning Architectures in Human Activities Recognition Application in Sports Science, Healthcare, and Security

In a typical human activity recognition (HAR) system, human activities are recognized by collecting data from inertial sensors (i.e., Inertial measurement unit (IMU)) or visual sensors (i.e., cameras). Then, the collected data is labelled with human activities. In turn, the data is used to train machine learning (ML) or deep learning (DL) models. HAR systems are widely used in different applications such as security, healthcare, the Internet of Things (IoT), and sports domains. The highest accuracy rates are achieved by DL models. In this context, we review the recent advancements of HAR systems in three trendy domains, namely, 1) sports science, 2) healthcare, and 3) security. The aim of this review is to reveal the most widely used DL architectures alongside the highest achieved accuracy rates in each of these domains. Both the Convolution Neural Network (CNN) and the Long Short Term Memory (LSTM) architectures achieved the best performance in both fields of sports science and healthcare. In the security field, the best performance was achieved by the adapted VGG-16 model.
RAV: Learning-Based Adaptive Streaming to Coordinate the Audio and Video Bitrate Selections

Most commercial players adopt adaptive bitrate (ABR) algorithms to dynamically decide each chunk&#x0027;s bitrate based on the perceived network bandwidth and buffer occupancy. However, current ABR algorithms are agnostic of audio bitrate selection since they deem it has negligible influence on video bitrate selection due to small size of audio chunks. Nevertheless, with the development of audio technologies, the bitrate of audio content increases dramatically in recent years. Thus, inappropriate audio selection can significantly affect video selection and deteriorate the viewing experience. To tackle these inefficiencies, we propose a deep <u>R</u>einforcement learning-based ABR algorithm that takes <u>A</u>udio and <u>V</u>ideo quality into account (RAV) to circumvent a series of suboptimal performances, like low playback quality, frequent playback interruptions, poor playback smoothness, and undesirable combinations of video and audio chunks. Furthermore, RAV trains a neural network model that automatically outputs the bitrates for future audio and video chunks without relying on any presumptions about the environment, achieving good robustness to a broad spectrum of conditions. By conducting trace-driven and real-world experiments, we demonstrate that RAV significantly ameliorates the average overall viewing quality by 37.96&#x0025;-118.20&#x0025; over the state-of-the-art ABR algorithms. In addition, we also conduct subjective experiments by inviting 32 volunteers, and 27/32 users strongly agree that RAV provides them a better viewing experience than existing ABR solutions.