Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges

Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated with industrial Internet-of-things (IIoT) networks to help realize the fourth industrial revolution, Industry 4.0. Though blockchain-enabled IIoT networks may have the potential to support the services and demands of next-generation networks, the gap analysis presented in this work highlights some of the areas that need improvement. Based on these observations, the article then promotes the utility of reinforcement learning (RL) techniques to address some of the major issues of blockchain-enabled IIoT networks such as block time minimization and transaction throughput enhancement. This is followed by a comprehensive case study where a Q-learning technique is used for minimizing the occurrence of forking events by reducing the transmission delays for a miner. Extensive simulations have been performed and the results have been obtained for the average transmission delay which relates to the forking events. The obtained results demonstrate that the Q-learning approach outperforms the greedy policy while having a reasonable level of complexity. To further develop the blockchain-enabled IIoT networks, some future research directions are also documented. While this article highlights the applications of RL techniques in blockchain-enabled IIoT networks, the provided insights and results could pave the way for rapid adoption of blockchain technology.
EdgeSumm: Graph-based framework for automatic text summarization

Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework "EdgeSumm" that relies on four proposed algorithms. The first algorithm constructs a new text graph model representation from the input document. The second and third algorithms search the constructed text graph for sentences to be included in the candidate summary. When the resulting candidate summary still exceeds a user-required limit, the fourth algorithm is used to select the most important sentences. EdgeSumm combines a set of extractive ATS methods (namely graph-based, statistical-based, semantic-based, and centrality-based methods) to benefit from their advantages and overcome their individual drawbacks. EdgeSumm is general for any document genre (not limited to a specific domain) and unsupervised so it does not require any training data. The standard datasets DUC2001 and DUC2002 are used to evaluate EdgeSumm using the widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation (ROUGE). EdgeSumm gets the highest ROUGE scores on DUC2001. For DUC2002, the evaluation results show that the proposed framework outperforms the state-of-the-art ATS systems by achieving improvements of 1.2% and 4.7% over the highest scores in the literature for the metrics of ROUGE-1 and ROUGE-L respectively. In addition, EdgeSumm achieves very competitive results for the metrics of ROUGE-2 and ROUGE-SU4.
Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm

Cloud computing is a relatively new computing technology, which provides online on-demand computing services to cloud users. Task scheduling plays a crucial role in the cloud model. An efficient task allocation method, results with better resource utilization, have an impact on the quality of service, the overall performance, and user experience. The task scheduling should be carried out on multiple criteria, which is a difficult optimization problem and belongs to the class of NP-hard optimization problem. As the complexity of the problem increases, the exhaustive search becomes enormous. Consequently, an optimization technique is needed that can find the approximate solution in less amount of time. In this paper, we propose a hybridized bat optimization algorithm for multi-objective task scheduling. The simulations are performed in the CloudSim toolkit using standard parallel workloads, and the obtained results show that the proposed technique gives better results than other similar methods.
Deep Template-Based Watermarking

Traditional watermarking algorithms have been extensively studied. As an important type of watermarking schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then the message embedding process is carried out by additive operation with the templates and the host image. To resist potential distortions, these templates often need to contain some special statistical features so that they can be successfully recovered at the extracting side. But in existing methods, most of these features are handcrafted and too simple, thus making them not robust enough to resist serious distortions unless very strong and obvious templates are used. Inspired by the powerful feature learning capacity of deep neural network, we propose the first deep template-based watermarking algorithm in this paper. Specifically, at the embedding side, we first design two new templates for message embedding and locating, which is achieved by leveraging the special properties of human visual system, i.e., insensitivity to specific chrominance components, the proximity principle and the oblique effect. At the extracting side, we propose a novel two-stage deep neural network, which consists of an auxiliary enhancing sub-network and a classification sub-network. Thanks to the power of deep neural networks, our method achieves both digital editing resilience and camera shooting resilience based on typical application scenarios. Through extensive experiments, we demonstrate that the proposed method can achieve much better robustness than existing methods while guaranteeing the original visual quality.
Applications of Blockchain Technology in Clinical Trials: Review and Open Challenges

Blockchain technology has disclosed unprecedented opportunities in the healthcare sector by unlocking the true value of interoperability. Specifically, the striking features of blockchain technology, such as data provenance, transparency, decentralized transaction validation, and immutability, can help to compensate for stringent data management issues (e.g., patient recruitment, persistent monitoring, data management, and data analytics and accurate reporting) in clinical trials (CTs). Although several research studies show that blockchain solutions help to improve patient retention, data integrity, privacy and ensure CTs compliance with regulatory policies, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology in CTs. We categorize and classify the literature by devising a meticulous taxonomy of the decentralized tasks of CT and practices based on indispensable parameters. Furthermore, we provide insights on works in progress toward deploying blockchain solutions in CTs. Finally, we identify and discuss several challenges that hinder the successful implementation of blockchain technologies in CTs.
Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively, by playing a minimax game over the air. The adversary transmitter trains a deep neural network to generate the best spoofing signals and fool the best defense trained as another deep neural network at the adversary receiver. Each node (defender or adversary) may have multiple transmitter or receiver antennas. Signals are spoofed by jointly capturing waveform, channel, and radio hardware effects that are inherent to wireless signals under attack. Compared with spoofing attacks using random or replayed signals, the proposed attack increases the probability of misclassifying spoofing signals as intended signals for different network topology and mobility patterns. The adversary transmitter can increase the spoofing attack success by using multiple antennas, while the attack success decreases when the defender receiver uses multiple antennas. For practical deployment, the attack implementation on embedded platforms demonstrates the low latency of generating or classifying spoofing signals.
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning?

The growing number of mobile and IoT devices has nourished many intelligent applications. In order to produce high-quality machine learning models, they constantly access and collect rich personal data such as photos, browsing history, and text messages. However, direct access to personal data has raised increasing public concerns about privacy risks and security breaches. To address these concerns, there are two emerging solutions to privacy-preserving machine learning, namely local differential privacy and federated machine learning. The former is a distributed data collection strategy where each client perturbs data locally before submitting to the server, whereas the latter is a distributed machine learning strategy to train models on mobile devices locally and merge their output (e.g., parameter updates of a model) through a control protocol. In this article, we conduct a comparative study on the efficiency and privacy of both solutions. Our results show that in a standard population and domain setting, both can achieve an optimal misclassification rate lower than 20% and federated machine learning generally performs better at the cost of higher client CPU usage. Nonetheless, local differential privacy can benefit more from a larger client population ($>$> 1k). As for privacy guarantee, local differential privacy also has flexible control over the data leakage.
Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices

Current multi-disciplinary research views falls in the elderly as a significant worldwide public health risk. Several fall detection systems have been created that use wearable motion sensors, but these systems fail to provide an accurate assessment of the exact nature of human falls. This paper introduces an effective and optimized fall detection system that uses an approach based on a killer heuristics optimized AlexNet convolution neural network. Wearable sensor devices, which consist of a magnetometer, gyroscope, and accelerometer tri-axial device, are placed on the subject’s body in 6 different positions. During the data collection process, 16 activities of daily living and information on 20 voluntary falls are collected in 2520 trials. Information is collected from the IoT-assisted wearable device for feature extraction and analysis of sensor data. The derived features are analyzed by multilinear principle component analysis, which reduces the dimensionality of the features. Fall detection is then performed by applying the intelligent AlexNet convolution network. The fall detection based on the created wearable sensor device is evaluated using simulation results, and the system recognizes a fall with maximum accuracy and minimum complexity.
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Abstract Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1
Hardware Acceleration of Large Scale GCN Inference

Graph Convolutional Networks (GCNs) have become state-of-the-art deep learning models for representation learning on graphs. Hardware acceleration of GCN inference is challenging due to: 1) massive size of the input graph, 2) heterogeneous workload of the GCN inference that consists of sparse and dense matrix operations, and 3) irregular information propagation along the edges during the computation. To address the above challenges, we propose the algorithm-architecture co-optimization to accelerate large-scale GCN inference on FPGA. We first perform data partitioning to fit each partition in the limited on-chip memory. Then, we use a two-phase preprocessing algorithm consisting of sparsification and node reordering. The first phase (sparsification) eliminates edge connections of high-degree nodes by merging common neighbor nodes. The second phase (re-ordering) effectively groups adjacent nodes to improve on-chip data reuse. Incorporating the above algorithmic optimizations, we propose a generic FPGA architecture to pipeline the two major computational kernels in GCN: aggregation and transformation. The flexible data path and task scheduling strategy of our design support various GCN models and lead to high throughput inference. We evaluate our design on state-of-the-art FPGA platform using three large scale datasets: Flickr, Reddit, Yelp. Compared with the state-of-the-art multi-core and GPU baselines, our design improves the throughput by up to $30 \times$ and $2 \times$ respectively.
Characterizing cryptocurrency exchange scams

As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attention of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge amount of financial loss. However, no previous work in the research community has systematically studied this problem. This paper makes the first effort to identify and characterize the cryptocurrency exchange scams. First, over 1500 scam domains and over 300 fake apps are identified, by collecting existing reports and using typosquatting generation techniques. Then, by investigating the relationship between the scam domains and fake apps, this paper identifies 94 scam domain families and 30 fake app families. By further characterizing the impacts of such scams, it is revealed that these scams have incurred financial loss of 520k US dollars at least. It is further observed that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. The findings in this paper demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, all the identified scam domains and fake apps have been publicly released to the research community.
Active k-labelsets ensemble for multi-label classification

The random k-labelsets ensemble (RAkEL) is a multi-label learning strategy that integrates many single-label learning models. Each single-label model is constructed using a label powerset (LP) technique based on a randomly generated size-k label subset. Although RAkEL can improve the generalization capability and reduce the complexity of the original LP method, the quality of the randomly generated label subsets could be low. On the one hand, the transformed classes may be difficult to separate in the feature space, negatively affecting the performance; on the other hand, the classes might be highly imbalanced, resulting in difficulties in using the existing single-label algorithms. To solve these problems, we propose an active k-labelsets ensemble (ACkEL) paradigm. Borrowing the idea of active learning, a label-selection criterion is proposed to evaluate the separability and balance level of the classes transformed from a label subset. Subsequently, by randomly selecting the first label or label subset, the remaining ones are iteratively chosen based on the proposed criterion. ACkEL can be realized in both the disjoint and overlapping modes, which adopt pool-based and stream-based frameworks, respectively. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed methods.
Employees’ Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation (RAIA) on Job Satisfaction, Job Security, and Employability

Abstract The study aimed at qualitatively exploring working adult’s perceptions of the implementation of robotics, artificial intelligence (AI), and automation (RAIA) on their job security, job satisfaction, and employability. By means of a cross-sectional and exploratory design, the researchers conducted 21 semi-structured interviews with a diverse sample. The heterogeneous sample came from numerous industries for instance consulting, accounting and finance, and hospitality and varied seniority levels. The thematic analysis led to the emergence of five high-level themes and several sub-themes. The findings indicate that (a) “human touch” and “soft skills” remain irreplaceable and cannot be replicated by RAIA, (b) employees need to perceive RAIA as an opportunity and not a threat, (c) employees might experience a job satisfaction dilemma, and (d) organizations have to be well prepared pre- and post-industrial change. The findings could be used by industrial and organizational psychologists, human resource practitioners, and strategic information technology decision-makers when managing RAIA-related technological changes in organizations. Employees’ suggestions and perceptions could be considered to mitigate the consequences of technological changes in organizations. Both employees and employers need to change their perspective toward RAIA technology, work with a flexible, open mind, and embrace the potential impact of RAIA advancements on job roles and responsibilities. Employees will have to follow a path of continuous learning and keep up with technology.
Multi-view spectral clustering for uncertain objects

In the machine learning and pattern recognition fraternity, uncertain data clustering is an essential job because uncertainty in data makes the clustering process more difficult. Recently, multi-view clustering is gaining more attention towards data miners for certain data because it produces good results compared to grouping based on a single viewpoint. In uncertain data clustering, similarity measure plays an imperative role. However, state-of-the-art similarity measures suffer from several limitations. For example, when two distributions of two uncertain data are heavily overlapped in locations, then Geometric similarity measure alone is not sufficient. On the other hand, similarity measure based on probability distribution is not enough when two uncertain data are not closed to each other or completely separated. In this study, induced kernel distance and Jeffrey-divergence are fused by the degree of overlap concerning each view of a dataset to construct a self-adaptive mixture similarity measure (SAM). The SAM is further used with pairwise co-regularization in multi-view spectral clustering for grouping uncertain data. The proof of convergence of the objective function of the proposed clustering algorithm is also presented in this study. All the experiments are carried out on nine real-world deterministic datasets, three real-life and one synthetic uncertain datasets. Nine real-world deterministic datasets are further converted into uncertain datasets before executing all the clustering algorithms. Experimental results illustrate that the proposed algorithm outperforms nine state-of-the-art methods. The comparison is made using five clustering evaluation metrics. The proposed method is also tested using null hypothesis significance tests.
A WiFi-Based Smart Home Fall Detection System Using Recurrent Neural Network

Falls among the elderly living on their own have been regarded as a major public health worry that can even lead to death. Fall detection system (FDS) that alerts caregivers or family members can potentially save lives of the elderly. However, conventional FDS involves wearable sensors and specialized hardware installations. This article presents a passive device-free FDS based on commodity WiFi framework for smart home, which is mainly composed of two modules in terms of hardware platform and client application. Concretely, commercial WiFi devices collect disturbance signal induced by human motions from smart home and transmit the data to a data analysis platform for further processing. Based on this basis, a discrete wavelet transform (DWT) method is used to eliminate the influence of random noise presented in the collected data. Next, a recurrent neural network (RNN) model is utilized to classify human motions and identify the fall status automatically. By leveraging Web Application Programming Interface (API), the analyzed data is able to be uploaded to the proxy server from which the client application then obtains the corresponding fall information. Moreover, the system has been implemented as a consumer mobile App that can help the elderly saving their lives in smart home, and detection performance of the proposed FDS has been evaluated by conducting comprehensive experiments on real-world dataset. The results confirm that the proposed FDS is able to achieve a satisfactory performance compared with some state-of-the-art algorithms.
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images

In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images. Since there currently lack methods particularly for UDA instance segmentation, we first design a Domain Adaptive Mask R-CNN (DAM) as the baseline, with cross-domain feature alignment at the image and instance levels. In addition to the image- and instance-level domain discrepancy, there also exists domain bias at the semantic level in the contextual information. Next, we, therefore, design a semantic segmentation branch with a domain discriminator to bridge the domain gap at the contextual level. By integrating the semantic- and instance-level feature adaptation, our method aligns the cross-domain features at the panoptic level. Third, we propose a task re-weighting mechanism to assign trade-off weights for the detection and segmentation loss functions. The task re-weighting mechanism solves the domain bias issue by alleviating the task learning for some iterations when the features contain source-specific factors. Furthermore, we design a feature similarity maximization mechanism to facilitate instance-level feature adaptation from the perspective of representational learning. Different from the typical feature alignment methods, our feature similarity maximization mechanism separates the domain-invariant and domain-specific features by enlarging their feature distribution dependency. Experimental results on three UDA instance segmentation scenarios with five datasets demonstrate the effectiveness of our proposed PDAM method, which outperforms state-of-the-art UDA methods by a large margin.