Reviewerly/exHarmony huggingface dataset:
Locomotion Vault: the Extra Mile in Analyzing VR Locomotion Techniques

Numerous techniques have been proposed for locomotion in virtual reality (VR). Several taxonomies consider a large number of attributes (e.g., hardware, accessibility) to characterize these techniques. However, finding the appropriate locomotion technique (LT) and identifying gaps for future designs in the high-dimensional space of attributes can be quite challenging. To aid analysis and innovation, we devised Locomotion Vault (https://locomotionvault.github.io/), a database and visualization of over 100 LTs from academia and industry. We propose similarity between LTs as a metric to aid navigation and visualization. We show that similarity based on attribute values correlates with expert similarity assessments (a method that does not scale). Our analysis also highlights an inherent trade-off between simulation sickness and accessibility across LTs. As such, Locomotion Vault shows to be a tool that unifies information on LTs and enables their standardization and large-scale comparison to help understand the space of possibilities in VR locomotion.
HeteroGraphRec: A heterogeneous graph-based neural networks for social recommendations

Recommender systems in social networks are widely used for connecting users to their desired items from a vast catalog of available items. Learning the user’s preferences from all the possible sources of information in an extensive, multi-dimensional social network is one of the main challenges when building such recommenders. Graph Neural Networks have been gaining momentum in recent years and have been successful when dealing with large-scale graphs, and they can be applied to social networks with some modifications. In this research, we propose the HeteroGraphRec, which provides social recommendations by modeling the social network as a heterogeneous graph and utilizing GNNs with attention mechanisms to intelligently aggregate information from all sources when building the connections between user to user, item to item, and user to item. The HeteroGraphRec can gather information about the user’s connections (friendships, trust network), item interaction history, and item similarities to attain rich information about the preferences. To evaluate the HeteroGraphRec, we use three real-world benchmark datasets and demonstrate that the proposed HeteroGraphRec achieves superior performance compared to ten other state-of-the-art social recommender systems. We extensively analyze the HeteroGraphRec model to illustrate the effectiveness by changing the embedding dimensions of the users and items. We also show the interpretability of our model by examining each component of the model’s contribution. The results show that the HeteroGraphRec is robust and can consistently perform better than the baseline systems.
Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification

In this research, we take an HCI perspective on the opportunities provided by AI techniques in medical imaging, focusing on workflow efficiency and quality, preventing errors and variability of diagnosis in Breast Cancer. Starting from a holistic understanding of the clinical context, we developed BreastScreening to support Multimodality and integrate AI techniques (using a deep neural network to support automatic and reliable classification) in the medical diagnosis workflow. This was assessed by using a significant number of clinical settings and radiologists. Here we present: i) user study findings of 45 physicians comprising nine clinical institutions; ii) list of design recommendations for visualization to support breast screening radiomics; iii) evaluation results of a proof-of-concept BreastScreening prototype for two conditions Current (without AI assistant) and AI-Assisted; and iv) evidence from the impact of a Multimodality and AI-Assisted strategy in diagnosing and severity classification of lesions. The above strategies will allow us to conclude about the behaviour of clinicians when an AI module is present in a diagnostic system. This behaviour will have a direct impact in the clinicians workflow that is thoroughly addressed herein. Our results show a high level of acceptance of AI techniques from radiologists and point to a significant reduction of cognitive workload and improvement in diagnosis execution.
Deep Learning Based End-to-End Wireless Communication Systems Without Pilots

The recent development in machine learning, especially in deep neural networks (DNN), has enabled learning-based end-to-end communication systems, where DNNs are employed to substitute all modules at the transmitter and receiver. In this article, two end-to-end frameworks for frequency-selective channels and multi-input and multi-output (MIMO) channels are developed, where the wireless channel effects are modeled with an untrainable stochastic convolutional layer. The end-to-end framework is trained with mini-batches of input data and channel samples. Instead of using pilot information to implicitly or explicitly estimate the unknown channel parameters as in current communication systems, the transmitter DNN learns to transform the input data in a way that is robust to various channel conditions. The receiver consists of two DNN modules used for channel information extraction and data recovery, respectively. A bilinear production operation is employed to combine the features extracted from the channel information extraction module and the received signals. The combined features are further utilized in the data recovery module to recover the transmitted data. Compared with the conventional communication systems, performance improvement has been shown for frequency-selective channels and MIMO channels. Furthermore, the end-to-end system can automatically leverage the correlation in the channels and in the source data to improve the overall performance.
The Smartphone Pandemic: How Big Tech and public health authorities partner in the digital response to Covid-19

Digital technologies harnessed through smartphones have been deployed widely to support the response to Covid-19 internationally, often through partnerships between 'Big tech' and telecoms corporations and public health authorities. This paper provides an overview and critical analysis of the rapid rise of such new forms of public-private cooperation, focusing on their manifestation in the European region in the first phase of the pandemic. Drawing on a review of international media and documents, we discuss three main domains of public health action in which private technology companies and public health authorities have converged: contact-tracing, epidemic modelling and public health communication to manage the 'infodemic' of misinformation about the new coronavirus. Critics have raised concerns about how the digital response to Covid-19 may threaten privacy and enable greater state surveillance and control, and the possibility that semi-automated decision-making may exacerbate existing discrimination and inequalities. Our analysis extends such critiques by considering what the digital response to Covid-19 reveals about tech corporations' growing power to influence public health agendas. We discuss how they promote technical solutions to public health challenges that are politically seductive, but that have uncertain effectiveness and societal implications that warrant critical scrutiny.
Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance

Abstract Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on blockchain and adaptive network-based fuzzy inference systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are as follows: (1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; (2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic; and (3) introducing a distributed blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things, smart contracts, and ANFIS based on the blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow.
An improved particle swarm optimization algorithm with adaptive weighted delay velocity

An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature.
Public-key authenticated encryption with keyword search achieving both multi-ciphertext and multi-trapdoor indistinguishability

The notion of Public-key Encryption with Keyword Search (PEKS) was first proposed by Boneh et al. in 2004. However, almost all PEKS schemes cannot resist offline Keyword Guessing Attacks (KGA). To address this issue, Huang and Li introduced the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) in 2017. Recently, Qin et al. put forward a new security model named Multi-Ciphertext Indistinguishability (MCI), in which an adversary aims to distinguish two tuples of ciphertexts. They found that Huang and Li’s scheme cannot achieve MCI-security, so they proposed a new scheme which is able to achieve MCI-security. Furthermore, Qin et al. referred to another security model named Multi-Trapdoor Indistinguishability (MTI). They stated that the future work direction is to design a scheme which can achieve both MCI-security and MTI-security. In this paper, we present a new PAEKS scheme and prove that it is capable of achieving MCI-security and MTI-security simultaneously with the help of random oracles. Finally, we compare our scheme with other four related schemes using PBC library and provide experimental results. It turns out that our scheme achieves a higher security level with a little more cost.
Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This tutorial, based on a forthcoming book, provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. We provide a synthesis of existing work as a single point of entry for both researchers and practitioners. Our coverage is grouped into two categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly. Two themes pervade our treatment: techniques for handling long documents and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of their application are well understood. Nevertheless, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, we also attempt to prognosticate the future.
Code smell detection by deep direct-learning and transfer-learning

An excessive number of code smells make a software system hard to evolve and maintain. Machine learning methods, in addition to metric-based and heuristic-based methods, have been recently applied to detect code smells; however, current methods are considered far from mature. First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering. Second, investigate the possibility of applying transfer-learning in the context of detecting code smells. We train smell detection models based on Convolution Neural Networks and Recurrent Neural Networks as their principal hidden layers along with autoencoder models. For the first objective, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples and vice-versa. We find it feasible to detect smells using deep learning methods though the models’ performance is smell-specific. Our experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available.
Detecting handcrafted facial image manipulations and GAN-generated facial images using Shallow-FakeFaceNet

The rapid progress of sophisticated image editing tools has made it easier to manipulate original face images and create fake media content by putting one’s face to another. In addition to image editing tools, creating natural-looking fake human faces can be easily achieved by Generative Adversarial Networks (GANs). However, malicious use of these new media generation technologies can lead to severe problems, such as the development of fake pornography, defamation, or fraud. In this paper, we introduce a novel Handcrafted Facial Manipulation (HFM) image dataset and soft computing neural network models (Shallow-FakeFaceNets) with an efficient facial manipulation detection pipeline. Our neural network classifier model, Shallow-FakeFaceNet (SFFN), shows the ability to focus on the manipulated facial landmarks to detect fake images. The detection pipeline only relies on detecting fake facial images based on RGB information, not leveraging any metadata, which can be easily manipulated. Our results show that our method achieves the best performance of 72.52% in Area Under the Receiver Operating Characteristic (AUROC), gaining 3.99% F1-score and 2.91% AUROC on detecting handcrafted fake facial images, and 93.99% on detecting small GAN-generated fake images, gaining 1.98% F1-score and 10.44% AUROC compared to the best performing state-of-the-art classifier. This study is targeted for developing an automated defense mechanism to combat fake images used in different online services and applications, leveraging our state-of-the-art hand-crafted fake facial dataset (HFM) and the neural network classifier Shallow-FakeFaceNet (SFFN). In addition, our work presents various experimental results that can help guide better applied soft computing research in the future to effectively combat and detect human and GAN-generated fake face images.
Robust Color Images Watermarking Using New Fractional-Order Exponent Moments

Robust watermarking is a valuable methodology used in protecting the copyright and securing digital images. In this paper, new fractional-order multi-channel orthogonal exponent moments (MFrEMs) and their invariants to geometric transformations are derived for the first time. We utilized these highly accurate moments to construct a new robust watermarking algorithm for color images. This algorithm consists of three phases. First, the bits of the binary watermark scrambled by using a 1D Sine chaotic map. Second, the fractional-order MFrEMs are calculated from the host color image. Finally, a quantization process is performed, where the scrambled bits of the binary watermark embedded into the host color image. Various experiments were conducted to test the proposed watermarking algorithm and compare it with the existing robust watermarking algorithms for color images. The obtained results ensure the proposed robust watermarking algorithm's superiority over existing algorithms regarding the visual imperceptibility and robustness against various attacks.
The Influence of Avatar Representation on Interpersonal Communication in Virtual Social Environments

Current avatar representations used in immersive VR applications lack features that may be important for supporting natural behaviors and effective communication among individuals. This study investigates the impact of the visual and nonverbal cues afforded by three different types of avatar representations in the context of several cooperative tasks. The avatar types we compared are No_Avatar (HMD and controllers only), Scanned_Avatar (wearing an HMD), and Heal_Avatar (video-see-through). The subjective and objective measures we used to assess the quality of interpersonal communication include surveys of social presence, interpersonal trust, communication satisfaction, and attention to behavioral cues, plus two behavioral measures: duration of mutual gaze and number of unique words spoken. We found that participants reported higher levels of trustworthiness in the Real_Avatar condition compared to the Scanned_Avatar and No_Avatar conditions. They also reported a greater level of attentional focus on facial expressions compared to the No_Avatar condition and spent more extended time, for some tasks, attempting to engage in mutual gaze behavior compared to the Scanned_Avatar and No_Avatar conditions. In both the Heal_Avatar and Scanned_Avatar conditions, participants reported higher levels of co-presence compared with the No_Avatar condition. In the Scanned_Avatar condition, compared with the Heal_Avatar and No_Avatar conditions, participants reported higher levels of attention to body posture. Overall, our exit survey revealed that a majority of participants (66.67%) reported a preference for the Real_Avatar, compared with 25.00% for the Scanned_Avatar and 8.33% for the No_Avatar, These findings provide novel insight into how a user's experience in a social VR scenario is affected by the type of avatar representation provided.
A survey on heterogeneous network representation learning

Heterogeneous information networks usually contain different kinds of nodes and distinguishing types of relations, which can preserve more information than homogeneous information networks. Heterogeneous network representation learning attempts to learn a low-dimensional representation for each node and capture rich semantic information of the given network. Most of existing surveys focus on heterogeneous information network analysis and homogeneous information network representation learning. Although considerable research efforts concentrate on heterogeneous network representation learning, there are few surveys that systematically review the state-of-the-art heterogeneous network representation learning techniques. Motivated by this, we propose a taxonomy of heterogeneous network representation learning algorithms according to different approaches of capturing semantic information in heterogeneous networks, including path based algorithms and semantic unit based algorithms. Moreover, we introduce the typical heterogeneous network representation learning techniques in detail and make a comparative analysis of these techniques. In addition, the research challenges in terms of semantics preserving, data sparsity and scalability are discussed. To tackle these challenges, several potential future research directions for heterogeneous network representation learning are pointed out, including semantic relations extraction, dynamic heterogeneous networks, very large heterogeneous networks and heterogeneous networks construction.
Optical Flow based CNN for detection of unlearnt deepfake manipulations

A new phenomenon named Deepfakes constitutes a serious threat in video manipulation. AI-based technologies have provided easy-to-use methods to create extremely realistic videos. On the side of multimedia forensics, being able to individuate this kind of fake contents becomes ever more crucial. In this work, a new forensic technique able to detect fake and original video sequences is proposed; it is based on the use of CNNs trained to distinguish possible motion dissimilarities in the temporal structure of a video sequence by exploiting optical flow fields. The results obtained highlight comparable performances with the state-of-the-art methods which, in general, only resort to single video frames. Furthermore, the proposed optical flow based detection scheme also provides a superior robustness in the more realistic cross-forgery operative scenario and can even be combined with frame-based approaches to improve their global effectiveness.
Investigating the Adoption Factors of Cryptocurrencies—A Case of Bitcoin: Empirical Evidence From China

The share of electronic transactions in the global payments continues to increase all around the globe. In the recent years, cryptocurrencies (also known as a system of electronic transaction) have caught significant attention from governments, policymakers, and practitioners worldwide. Cryptocurrencies as an innovative method of exchanges without any physical form boast several potential benefits such as speedy transactions, cross-border usage, low transaction fee, transparency, high security, anonymity, and privacy, and thus are expected to bring huge revolution in the future economic system. This study aims to investigate the adoption factors of Bitcoin, a most known cryptocurrency in China. Based on Technology Acceptance Model, a research framework has been developed to test the proposed hypotheses. The data have been collected via a survey questionnaire from 385 Chinese respondents. The findings show that the perceived ease of use and the perceived usefulness have a positive relationship with the intention to use Bitcoin. The perceived usefulness mediates the relationship between the perceived ease of use and the intention to use Bitcoin. Furthermore, the results reveal that the transaction processing and the perceived ease of use have significant impacts on the perceived usefulness. However, the security and control shows an insignificant effect on the perceived usefulness. This study contributes to the growing literature of Bitcoin and offers valuable information to individual users (payees), fund managers (investors), and companies/businesses (receiving Bitcoin as a payment method). Research implications and limitations are also discussed.