Reviewerly/exHarmony huggingface dataset:
Thinking Out of the Blocks: Holochain for Distributed Security in IoT Healthcare

The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.
Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.
Virtual Reality Sickness Mitigation Methods

Using virtual reality (VR) head-mounted displays (HMDs) can induce VR sickness. VR sickness can cause strong discomfort, decrease users' presence and enjoyment, especially in games, shorten the duration of the VR experience, and can even pose health risks. Previous research has explored different VR sickness mitigation methods by adding visual effects or elements. Field of View (FOV) reduction, Depth of Field (DOF) blurring, and adding a rest frame into the virtual environment are examples of such methods. Although useful in some cases, they might result in information loss. This research is the first to compare VR sickness, presence, workload to complete a search task, and information loss of these three VR sickness mitigation methods in a racing game with two levels of control. To do this, we conducted a mixed factorial user study (N = 32) with degree of control as the between-subjects factor and the VR sickness mitigation techniques as the within-subjects factor. Participants were required to find targets with three difficulty levels while steering or not steering a car in a virtual environment. Our results show that there are no significant differences in VR sickness, presence and workload among these techniques under two levels of control in our VR racing game. We also found that changing FOV dynamically or using DOF blur effects would result in information loss while adding a target reticule as a rest frame would not.
Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.
Distributed Hybrid Double-Spending Attack Prevention Mechanism for Proof-of-Work and Proof-of-Stake Blockchain Consensuses

Blockchain technology is a sustainable technology that offers a high level of security for many industrial applications. Blockchain has numerous benefits, such as decentralisation, immutability and tamper-proofing. Blockchain is composed of two processes, namely, mining (the process of adding a new block or transaction to the global public ledger created by the previous block) and validation (the process of validating the new block added). Several consensus protocols have been introduced to validate blockchain transactions, Proof-of-Work (PoW) and Proof-of-Stake (PoS), which are crucial to cryptocurrencies, such as Bitcoin. However, these consensus protocols are vulnerable to double-spending attacks. Amongst these attacks, the 51% attack is the most prominent because it involves forking a blockchain to conduct double spending. Many attempts have been made to solve this issue, and examples include delayed proof-of-work (PoW) and several Byzantine fault tolerance mechanisms. These attempts, however, suffer from delay issues and unsorted block sequences. This study proposes a hybrid algorithm that combines PoS and PoW mechanisms to provide a fair mining reward to the miner/validator by conducting forking to combine PoW and PoS consensuses. As demonstrated by the experimental results, the proposed algorithm can reduce the possibility of intruders performing double mining because it requires achieving 100% dominance in the network, which is impossible.
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages and Conversational Hate Speech

The HASOC track is dedicated to the evaluation of technology for finding Offensive Language and Hate Speech. HASOC is creating a multilingual data corpus mainly for English and under-resourced languages(Hindi and Marathi). This paper presents one HASOC subtrack with two tasks. In 2021, we organized the classification task for English, Hindi, and Marathi. The first task consists of two classification tasks; Subtask 1A consists of a binary and fine-grained classification into offensive and non-offensive tweets. Subtask 1B asks to classify the tweets into Hate, Profane and offensive. Task 2 consists of identifying tweets given additional context in the form of the preceding conversion. During the shared task, 65 teams have submitted 652 runs. This overview paper briefly presents the task descriptions, the data and the results obtained from the participant’s submission.
Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.
Authentication by Encrypted Negative Password

Secure password storage is a vital aspect in systems based on password authentication, which is still the most widely used authentication technique, despite some security flaws. In this paper, we propose a password authentication framework that is designed for secure password storage and could be easily integrated into existing authentication systems. In our framework, first, the received plain password from a client is hashed through a cryptographic hash function (e.g., SHA-256). Then, the hashed password is converted into a negative password. Finally, the negative password is encrypted into an encrypted negative password (ENP) using a symmetric-key algorithm (e.g., AES), and multi-iteration encryption could be employed to further improve security. The cryptographic hash function and symmetric encryption make it difficult to crack passwords from ENPs. Moreover, there are lots of corresponding ENPs for a given plain password, which makes precomputation attacks (e.g., lookup table attack and rainbow table attack) infeasible. The algorithm complexity analyses and comparisons show that the ENP could resist lookup table attack and provide stronger password protection under dictionary attack. It is worth mentioning that the ENP does not introduce extra elements (e.g., salt); besides this, the ENP could still resist precomputation attacks. Most importantly, the ENP is the first password protection scheme that combines the cryptographic hash function, the negative password, and the symmetric-key algorithm, without the need for additional information except the plain password.
Robust Dimension Reduction for Clustering With Local Adaptive Learning

In pattern recognition and data mining, clustering is a classical technique to group matters of interest and has been widely employed to numerous applications. Among various clustering algorithms, K-means (KM) clustering is most popular for its simplicity and efficiency. However, with the rapid development of the social network, high-dimensional data are frequently generated, which poses a considerable challenge to the traditional KM clustering as the curse of dimensionality. In such scenarios, it is difficult to directly cluster such high-dimensional data that always contain redundant features and noises. Although the existing approaches try to solve this problem using joint subspace learning and KM clustering, there are still the following limitations: 1) the discriminative information in low-dimensional subspace is not well captured; 2) the intrinsic geometric information is seldom considered; and 3) the optimizing procedure of a discrete cluster indicator matrix is vulnerable to noises. In this paper, we propose a novel clustering model to cope with the above-mentioned challenges. Within the proposed model, discriminative information is adaptively explored by unifying local adaptive subspace learning and KM clustering. We extend the proposed model using a robust l2,1-norm loss function, where the robust cluster centroid can be calculated in a weighted iterative procedure. We also explore and discuss the relationships between the proposed algorithm and several related studies. Extensive experiments on kinds of benchmark data sets demonstrate the advantage of the proposed model compared with the state-of-the-art clustering approaches.
Deep Metric Learning by Online Soft Mining and Class-Aware Attention

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.
Subspace Inference for Bayesian Deep Learning

Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Bayesian inference techniques to deep neural networks is challenging due to the high dimensionality of the parameter space. In this paper, we construct low-dimensional subspaces of parameter space, such as the first principal components of the stochastic gradient descent (SGD) trajectory, which contain diverse sets of high performing models. In these subspaces, we are able to apply elliptical slice sampling and variational inference, which struggle in the full parameter space. We show that Bayesian model averaging over the induced posterior in these subspaces produces accurate predictions and well calibrated predictive uncertainty for both regression and image classification.
A Survey on Deep Learning Techniques for Privacy-Preserving

There are challenges and issues when machine learning algorithm needs to access highly sensitive data for the training process. In order to address these issues, several privacy-preserving deep learning techniques, including Secure Multi-Party Computation and Homomorphic Encryption in Neural Network have been developed. There are also several methods to modify the Neural Network, so that it can be used in privacy-preserving environment. However, there is trade-off between privacy and performance among various techniques. In this paper, we discuss state-of-the-art of Privacy-Preserving Deep Learning, evaluate all methods, compare pros and cons of each approach, and address challenges and issues in the field of privacy-preserving by deep learning.
BSTC

Binarized neural networks (or BNNs) promise tremendous performance improvement over traditional DNNs through simplified bit-level computation and significantly reduced memory access/storage cost. In addition, it has advantages of low-cost, low-energy, and high-robustness, showing great potential in resources-constrained, volatile, and latency-critical applications, which are critical for future HPC, cloud, and edge applications. However, the promised significant performance gain of BNN inference has never been fully demonstrated on general-purpose processors, particularly on GPUs, due to: (i) the challenge of extracting and leveraging sufficient finegrained bit-level-parallelism to saturate GPU cores when the batch size is small; (ii) the fundamental design conflict between bit-based BNN algorithm and word-based architecture; and (iii) architecture & performance unfriendly to BNN network design. To address (i) and (ii), we propose a binarized-soft-tensor-core as a software-hardware codesign approach to construct bit-manipulation capability for modern GPUs and thereby effectively harvest bit-level-parallelism (BLP). To tackle (iii), we propose intra- and inter-layer fusion techniques so that the entire BNN inference execution can be packed into a single GPU kernel, and so avoid the high-cost of frequent launching and releasing. Experiments show that our Singular-Binarized-Neural-Network (SBNN) design can achieve over 1000X speedup for raw inference latency over the state-of-the-art full-precision BNN inference for AlexNet on GPUs. Comparisons with CPU, GPU, FPGA and Xeon-Phi demonstrate the effectiveness of our design. SBNN is opensourced and available at https://github.com/uuudown/SBNN.
Development of an Augmented Reality Tour Guide for a Cultural Heritage Site

In this article, the design, development, and evaluation of augmented reality (AR)-based mobile application for a tour guide are discussed. The objectives of this article are twofold. First, the research focuses on the development of a complete working set of a mobile tour application furnished with AR. For such an application to be successfully adopted by the general public, user requirements and application usability are investigated, and the application is designed and implemented to fulfill those findings. Second, the developed application is demonstrated by applying it to a UNESCO designated World Heritage site, Hwaseong Fortress in Suwon, South Korea, and evaluated via a survey instrument developed explicitly for mobile application evaluation. A systematically developed survey instrument from the fields of tourism, information systems, and human-computer interaction is tailored to fit into this research and employed for the application evaluation. The application’s operation flow consists of three main functions: navigation to the points of interest, visualization of information with AR technology, and interactive learning activities with AR-based serious games. Efforts are made to provide a more immersive and interactive experience of the historical, cultural, and architectural details of the heritage site utilizing novel AR visualization methods. The evaluation returned positive results with suggestions of possible refinements for future works. The proposed device-aided tour mechanism is anticipated to enhance tourists’ experiences as well as being important guidance in future mobile tourism application development as to how the application should be designed and implemented to be accepted by the general public.
How Service Quality Influences Customer Acceptance and Usage of Chatbots?

The present study aims to investigate consumers’ acceptance of and intention to reuse a chatbot in the context of automated customer service in the airline industry. In particular, we identify the most valuable factors that affect acceptance of an intention to reuse a chatbot by integrating the theoretical framework SERVQUAL. The main results show that reliability and perceived usefulness are the most important criteria that affect the intention to reuse the chatbot. Contrary to our expectations, empathy does not have any significant effect. The study suggests that in the case of an interaction with a chatbot for a purpose that may involve an economic transaction, customers prefer the chatbot for its utilitarian value, as reliability and usefulness are considered to be more important than empathy. Moreover, tangible elements play an important role in increasing the perceived ease of use.
Analysing the adoption of cloud computing service: a systematic literature review

Purpose The purpose of this paper is to presents an analysis of geographically and disciplinary scattered academic publications of cloud computing (CC) research in information systems. This review aims to understand the research methodology, research frameworks and models, geographical distribution, trends, critical factors and causal relationships associated with cloud computing adoption (CCA). Design/methodology/approach Systematic-literature-review using natural language processing is conducted to explore the phenomenon. The relevant research studies are extracted from various online databases using quality-assessment-criteria. Findings The study is a novel attempt to highlight the differences in critical factors for CCA in different country-settings. Further, the research explores the causal relationships among the identified factors. The findings of this 12-year systematic-review contribute by aiding the providers and potential adopters to devise context-specific strategies for the penetration of cloud services and sound adoption decisions (ADs), respectively. The findings also highlight the prospective avenues of research in the domain for researchers. Using the in-depth analysis, conceptual frameworks have been proposed that can assist in exploring the pre-adoption and post-adoption of CC. Originality/value This study contributes to CCA research by providing holistic insights into the methodology, research framework and models, geographical focus, critical factors and causal relationships influencing the AD or intention. The review highlights the unexplored emerging research topics in the field of CCA for future research directions.