Reviewerly/exHarmony huggingface dataset:
Research on Optimization of Static Gesture Recognition Based on Convolution Neural Network

In order to solve the problems of low recognition rate and less recognition gesture categories caused by incomplete artificial feature extraction information in traditional static gesture recognition methods, a deep CNN framework is designed by using the principle of convolution neural network (Convolutional Neural Network, CNN) to recognize static gesture movements. Combined with a variety of optimal structures of convolution neural network in deep learning, a model with independent static gesture recognition function is realized. The model method can not only ensure the high accuracy and robustness of the recognition results, but also achieve the speed of smooth recognition.
Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations. A video describing our method and results can be found here.
Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network

In recent days bio-inspired computing is playing an important role in the area of research. Especially bio-inspired algorithms which are inspired by the behavior of nature are massively used to perform optimization. Wireless Sensor Networks (WSN) are playing vital role in all sectors. Some crucial issues of WSN are clustering, optimal routing, dynamic allocation of motes, energy and lifetime optimization. Researchers are working for several years to resolve issues of WSN for better quality of service. Bio-inspired algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are playing important role in solving the issues of WSN. Still some algorithms are insufficiently studied. Bio-inspired computing is gradually gaining interest from researchers for its intelligence and adaptive nature. Although these algorithms have perceived a lot of attention from researchers in current years, the domain-specific understanding still needs to be improved for its establishment. In this chapter bio-inspired algorithms are discussed concisely with their importance in the field of wireless sensor networks.
A Convolutional Network for Joint Deraining and Dehazing from A Single Image for Autonomous Driving in Rain

In this paper, we focus on a rain removal task from a single image of the urban street scene for autonomous driving in rain. We develop a Convolutional Neural Network which takes a rainy image as input, and directly recovers a clean image in the presence of rain streaks, atmospheric veiling effect (haze, fog, mist) caused by distant rain streak accumulation. We propose a synthetic dataset containing images of urban street scenes with different rain intensities, orientations and haziness levels for training and evaluation. We evaluate our method quantitatively and qualitatively on the synthetic data. Experiments show that our model outperforms state-of-the-art methods. We also test our method qualitatively on the real-world data. Our model is fast and it takes 0.05s for an image of 1024 × 512. Our model can be seamlessly integrated with existing image-based high-level perception algorithms for autonomous driving in rain. Experiment results show that our deraining method improves semantic segmentation and object detection largely for autonomous driving in rain.
Digital iatrogenesis and workplace marginalization: some ethical issues involving self-tracking medical technologies

This paper addresses the ethical dimensions of the complex and evolving relationships between individuals and health-related self-tracking devices in the context of workplace systems. Many self-tracking initiatives can indeed have useful medical applications, aiding individuals and healthcare professionals in diagnosis and treatment. However, in some contexts the systems can also perpetuate economic discrimination and workplace marginalization as well as influence negatively the wellbeing and privacy of participants. The paper explores the concepts behind 'pushed' medical self-tracking devices and examines how the notion of the protection of 'mental and physical integrity' can be applied in analyses of organizational activities using such devices. Individuals' capacities to make valid medical decisions concerning use of the devices can be diminished by the addictive and gamified aspects of the systems or through rhetorical promotion of specific philanthropic objectives, engendering a 'data farming' approach; the anxieties and addictions often involved may compound forms of workplace marginalization and be used in conjunction with other surveillance efforts. The paper also examines the roles of various medical and managerial authorities in perpetuating the conditions that can lead to marginalization. Self-tracking devices' reflection capabilities can foster an apparent voluntariness and high level of user control that can make them seem less threatening in terms of employee wellbeing, even when sensitive and potentially stigmatizing areas such as weight and menstrual cycles are being tracked and externally surveilled. The paper explores how device manufacturers and system developers along with data brokers often benefit from the coupling of devices with organizational initiatives.
Exploring Semantic Relationships for Image Captioning without Parallel Data

Recently, image captioning has aroused great interest in both academic and industrial worlds. Most existing systems are built upon large-scale datasets consisting of image-sentence pairs, which, however, are time-consuming to construct. In addition, even for the most advanced image captioning systems, it is still difficult to realize deep image understanding. In this work, we achieve unpaired image captioning by bridging the vision and the language domains with high-level semantic information. The motivation stems from the fact that the semantic concepts with the same modality can be extracted from both images and descriptions. To further improve the quality of captions generated by the model, we propose the Semantic Relationship Explorer, which explores the relationships between semantic concepts for better understanding of the image. Extensive experiments on MSCOCO dataset show that we can generate desirable captions without paired datasets. Furthermore, the proposed approach boosts five strong baselines under the paired setting, where the most significant improvement in CIDEr score reaches 8%, demonstrating that it is effective and generalizes well to a wide range of models.
Dually Connected Deraining Net Using Pixel-Wise Attention

Recent single image deraining methods either use a recurrent mechanism to gradually learn the mapping between clear images and rainy images, or focus on designing various loss functions to supervise the learning process. In this letter, we propose a dually connected deraining net using pixel-wise attention, for single image rain removal. Specifically, the deraining net adopts an encoder-decoder net as a backbone, which can effectively learn a residual rain-streaks map by jointly using skip sum connection and skip concatenation connection. The dual connections enable the deraining net to promote information flow between layers, and thus can allow it to discriminate and localize the rain streaks. To preserve image details, the decoded features are weighted by the learnable pixel-wise attention for adaptively recalibrating their responses. Experimental results on synthetic datasets demonstrate that the proposed model outperforms the recent state-of-the-art deraining methods.
Identification of smart jammers: Learning-based approaches using wavelet preprocessing

Smart jammer nodes can disrupt communication between a transmitter and a receiver in a wireless network, and they leave traces that are undetectable to classical jammer identification techniques, hidden in the time–frequency plane. These traces cannot be effectively identified through the use of the classical Fourier transform based time–frequency transformation (TFT) techniques with a fixed resolution. Inspired by the adaptive resolution property provided by the wavelet transforms, in this paper, we propose a jammer identification methodology that includes a pre-processing step to obtain a multi-resolution image, followed by the use of a classifier. Support vector machine (SVM) and deep convolutional neural network (DCNN) architectures are investigated as classifiers to automatically extract the features of the transformed signals and to classify them. Three different jamming attacks are considered, the barrage jamming that targets the complete transmission bandwidth, the synchronization signal jamming attack that targets synchronization signals and the reference signal jamming attack that targets the reference signals in an LTE downlink transmission scenario. The performance of the proposed approach is compared with the classical Fourier transform based TFT techniques, demonstrating the efficacy of the proposed approach in the presence of smart jammers.
An Organic Syntactic Complexity Measure for the Chinese Language: The TC-Unit

Abstract Language complexity reveals the ability to use a wide and varied range of sophisticated structures and vocabulary. Although different languages compose complexity differently, complexity measures such as the T-unit have typically been based on clause subordination, which may underrepresent complexity and threaten the validity of studies. This study argues that an organic complexity measure should avoid the assumption of clause subordination and instead consider the typological features of the target language. Therefore, this study proposes the TC-unit in recognition of the topic chain as the underlying unit of Chinese complexity. It further validates TC-unit-based measures by investigating how accurately they predict proficiency group membership. Discriminant analyses of L1 and L2 Chinese speakers’ spoken (N = 115) and written (N = 116) output elicited from a designed timed online test, revealed that TC-unit-based measures classified proficiency group membership with high efficiency (61.2–75.7 per cent). Mean length of terminable TC-unit proved the most effective indicator of spoken Chinese syntactic complexity, while mean length of terminable TC-unit and single TC-units per terminable TC-unit in combination proved the most effective for written Chinese syntactic complexity.
A top-down approach to DNA mixtures

Presently, there exist many different models and algorithms for determining, in the form of a likelihood ratio, whether there is evidence that a person of interest contributed to a mixed trace profile. These methods have in common that they model the whole trace, hence all its contributors, which leads to the computation time being mostly determined by the number of contributors that is assumed. At some point, these calculations are no longer feasible. We present another approach, in which we target the contributors of the mixture in the order of their contribution. With this approach the calculation time now depends on how many contributors are queried. This means that any trace can be subjected to calculations of likelihood ratios in favor of being a relatively prominent contributor, and we can choose not to query it for all its contributors, e.g., if that is computationally not feasible, or not relevant for the case. We do so without using a quantitative peak height model, i.e., we do not define a peak height distribution. Instead, we work with subprofiles derived from the full trace profile, carrying out likelihood ratio calculations on these with a discrete method. This lack of modeling makes our method widely applicable. The results with our top-down method are slightly conservative with respect to the one of a continuous model, and more so as we query less and less prominent contributors. We present results on mixtures with known contributors and on research data, analyzing traces with plausibly 6 or more contributors. If a top-k of most prominent contributors is targeted, it is not necessary to know how many other contributors there are for LR calculations, and the more prominent the queried contributor is relatively to all others, the less the evidential value depends on the specifics of a chosen peak height model. For these contributors the qualitative statement that more input DNA leads to larger peaks suffices. The evidential value for a comparison with minor contributors on the other hand, potentially depends much more on the chosen model. We also conclude that a trace's complexity, as meaning its (in)ability to yield large LR's that are not too model-dependent, is not measured by its number of contributors; rather, it is the equality of contribution that makes it harder to obtain strong evidence.
Manufacturing Industry Supply Chain Management Based on the Ethereum Blockchain

Nowadays the composition and structure of the manufacturing industry supply chain have become increasingly complex. The loss and not-in-time transmission of the supply chain information have aggravated the bullwhip effect. At the same time, due to the lack of reliable information storage, the difficulties of traceability and accountability have also caused supply chain management to fall into the bottleneck. The blockchain has the characteristics of supporting distributed networking, information synchronization among nodes, digital encryption, traceable information and block content that cannot be tampered, which is suitable for use in supply chain, and can provide solution for it. In this paper, a design scheme of integrated platform for information service provided by participants in supply chain and based on Ethereum blockchain is proposed. By using Ethereum smart contracts, the common business involved in the supply chain is realized using blockchain technology, and the key information of supply chain production and circulation is stored on the blockchain to ensure the information cannot be tampered. At the same time, a reputation evaluating method based on smart contracts is used to evaluate the reputation of enterprises in supply chain, which can provide reference for supplier selection among enterprises.
Blockchain Consensus Algorithms: A Survey

Blockchain has received intensive interests in both academia and industries in recent years. As a key component of the blockchain architecture, consensus algorithm plays a critical role in maintaining such advantages as consistency, availability and fault tolerance for distributed systems, and thus can find a wide spectrum of potential application scenarios with success. In this paper, we surveyed the mainstream consensus algorithms systematically, which is classified into five categories according to its leader election model, i.e., election-based, proof-based, randomness-based, committee-based and hybrid. We discussed the recent research progresses in each of the five categories, and also presented the open issues and future research directions of consensus algorithms. This paper is targeted at providing useful guidance and reference for future innovation in blockchain.
From Blockchain to Hashgraph: Distributed Ledger Technologies in the Wild

With the introduction of the term blockchain in 2008, it's interest has been increasing in the community since the idea was coined. The reason for this interest is because it provides anonymity, security and integrity without any central third party organisation in control of data and transaction. It has attracted huge interest in research areas due to its advances in various platforms, limitations and challenges. There are various Distributed Ledger Technologies that demonstrates their special features which overcome limitations of other platforms. However, implementations of various distributed ledger technologies differ substantially based on their data structures, consensus protocol and fault tolerant among others. Due to these variations, they have a quite different cost, performance, latency and security. In this paper, working and in-depth comparison of major distributed ledger technologies including their special features, strengths and weaknesses is presented and discussed by identifying various criteria.
Adversarial unsupervised domain adaptation for cross scenario waveform recognition

Deep Learning based waveform recognition has become a topical area of research recently. In fact, due to complex and heterogeneous scenarios, it is impossible to train a universal model for all waveform recognition tasks. Unsupervised domain adaptation (UDA) has an ability to take advantage of knowledge from an available source dataset and apply it to an unlabeled target dataset. Therefore, we propose a novel waveform recognition method based on an adversarial UDA, which incorporates adversarial learning to improve the cross scenario recognition performance. Specifically, a two-channel convolutional neural network is well designed for waveform recognition task and the system gradually converges an optimized stable state via two innovative methods, namely Adversarial unsupervised domain adaptation with gradient reversal (GR-AUDA) and Feature augmentation adversarial unsupervised domain adaptation (FA-AUDA). Furthermore, we sampled a more abundant waveform dataset under various scenarios, including typical indoor, outdoor, and corridor cases, using software defined radio. We evaluate our method on both public datasets and our own datasets. The results demonstrate that our proposed adversarial UDA framework is able to significantly improve the recognition performance of target domain waveforms.
Accelerated CPU–GPUs implementations for quaternion polar harmonic transform of color images

Image moments are used to capture image features. Moments are successfully used in object descriptions, recognition, and other applications. However, image representation and recognition using quaternion moments are compute-intensive processes. This obstacle makes this type of moment unsuitable for real-time or large-scale applications, despite their high accuracy. In this work, we expose the challenges to parallelizing quaternion moment computations and transform the sequential algorithm to a parallel-friendly counterpart. We propose a set of parallel implementations for parallelizing quaternion moment image representations on different parallel architectures. The proposed implementations target multicore CPU-only, GPU-only, and hybrid CPU–GPUs (with multicore CPU and multi-GPU) environments. The loop mitigation technique is proposed to boost the level of parallelism in massively parallel environments, balance the parallel workload, and reduce both the space complexity and synchronization overhead of the proposed implementations. The loop mitigation technique could be applicable to other applications with similar loop imbalances. Finally, experiments are performed to evaluate the proposed implementations. Applying a moment order of 60 on a color image of 1024×1024 pixels, the proposed implementation achieved, on four P100 GPUs and a CPU with 16 cores, speedup of 257× and 277× over the baseline performance on a single Intel Xeon E5-2609 CPU core for image reconstruction and quaternion moment computation, respectively. In addition, 180× speedup is achieved for color image watermarking application.
A study of cybercrime victimisation and prevention: exploring the use of online crime prevention behaviours and strategies

Purpose The evolution of digital technology has changed the way in which we, as a global society, socialise and conduct business. This growth has led to an increasing reliance on technology, much more interconnectedness and in turn, an expansion of criminal opportunities, known now as “cybercrime”. This study aims to explore the experience of victimisation, perceptions of cybercrime and use of online crime prevention strategies. Design/methodology/approach The study involved a survey of a representative sample of the adult Australian population. The study sample was made up of 595 Australian adult participants. The study seeks to better understand how previous victimisation, perception of cybercrime prevalence and perception of harm caused by cybercrime are related to the use of online crime prevention strategies. It seeks to contribute to a body of work that has found that crime prevention education focused on increasing knowledge is limited in its effectiveness in reducing victimisation. Findings This study identifies key levers, in particular perceived prevalence and harm of cybercrime, as critical in the use of online crime prevention strategies by potential victims. Research limitations/implications As such, this study provides an important evidence base on which to develop more effective online crime prevention education and awareness campaigns to reduce cybervictimisation. Practical implications The practical implications include the relationship between cybervictimisation and self-protective online strategies of potential victims and the development of more effective online crime prevention programmes. Originality/value The research takes a different perspective from much of the previous research, seeking to better understand how attitudinal factors (perceived prevalence of cybercrime and perceived harm of cybercrime) might motivate or influence the use of online crime prevention strategies by potential victims.
Abnormal Trajectory Detection Based on a Sparse Subgraph

Traditional abnormal trajectory detection algorithms mainly involve the measurement of a single feature; however, the influence of other features on abnormal trajectory is ignored, resulting in the inability to fully discover the abnormal trajectory in the trajectory database. To overcome this limitation, we propose an abnormal trajectory detection method - called TADSS - to find the hidden abnormal trajectory by using a comprehensive measurement. Firstly, we employ three kernel functions to measure the time, velocity and position feature values of trajectory data, where the kernel functions extract semantic feature of the position, time feature of trajectory, and velocity feature of object motion from each trajectory data. Secondly, we propose a feature fusion strategy to measure the similarity of trajectory data, where we assign weights to the above kernel functions and then use a linear combination approach to fuse the weighted kernel functions. Thirdly, we build a trajectory feature graph by using the above fused kernel functions, and then divide the trajectory feature graph into a plurality of subgraphs by using a conventional graph clustering technique. Last, we propose a sparse subgraph method to detect abnormal trajectory, where a novel weight coefficient concept is used to distinguish sparse subgraph. Experimental results driven by both the vehicle trajectory data of Shanghai city and the Atlantic hurricane data demonstrate the performance of our TADSS.
Good Explanation for Algorithmic Transparency

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