Reviewerly/exHarmony huggingface dataset:
Modeling Administrative Discretion Using Goal-Directed Answer Set Programming

Automated legal reasoning and its application in smart contract is getting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling vague concepts such as ambiguity and discretion cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model ambiguity and discretion following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) the criteria for the admission of students in public centers established by the “Comunidad de Madrid”.
A Dual Rank-Constrained Filter Pruning Approach for Convolutional Neural Networks

Filter pruning has attracted increasing attentions to compress and accelerate the convolutional neural networks (CNNs) on computationally restricted devices. Existing related methods mainly focus on independently leveraging the spatial information of individual filters while ignoring the inner correlation among filters. In this letter, we propose a dual rank-constrained filter pruning approach for convolutional neural networks, in which the representation, clustering, and identification of representative filters are integrated into an adaptive graph regularization framework. Particularly, the proposed approach utilizes the low-rank constraint to capture the low-dimensional intrinsic representations of filters for the adaptive affinity graph construction and clustering. It is noteworthy that the original filters are projected as points on Grassmann manifold for geometrical structure preserving. Meanwhile, the high-rank constraint is employed to select the most informative filter for representing the cluster. Experimental results on CIFAR-10 dataset show that the proposed approach achieves competitive results compared with the state-of-the-art methods by 87.1% in VGGNet-16, 52.4% in GoogLeNet and 72.9% in ResNet-56 in terms of model parameters compression.
Examining the Applicability of Blockchain to the Smart Grid Using Proof-of-Authority Consensus

Security has been a major challenge in the smart grid since its adoption. This great concern let to the proposal of the application of blockchain technology to the smart grid. Blockchain is a growing list of records, called blocks, that are linked using cryptography and its transactions are validated by a consensus mechanism. The most known and trusted blockchain consensus mechanism is the proof-of-work (PoW) as it can process very little number of transactions per second. However, the proof-of-authority (PoA) consensus mechanism is scalable as it can process thousands of transactions per second without compromising security. This paper focuses on the applicability of blockchain PoA consensus mechanism and how it ensures that transactions are confirmed on time and the integrity of the transactions are upheld in the blockchain. The PoA consensus mechanism is appropriate especially with the adoption of peer-to-peer energy trading between prosumers and consumers. This study proposes a peer-to-peer energy trading in an open blockchain with the help of the Advanced Metering Infrastructure(AMI) that enables smart meters to function optimally for this purpose.
Experiments into the influence of linguistic (in)directness on perceived face-threat in Twitter complaints

Abstract To date, there has been little attention for the factors that influence the perception of online complaints. We present two experiments in which we test the impact of the degree of linguistic (in)directness and the formal realization of complaint components on complaint perception. Our experimental stimuli are designed on the basis of French-language authentic Twitter complaints which have been coded in terms of the presence of four constitutive complaint components: the complainable, the negative evaluation of the complainable, the person/company responsible for the complainable, and a wish for compensation. In our experiments, participants are asked to read Twitter complaints, and they are invited to assess them in terms of perceived strength, dissatisfaction, (im)politeness, and offensiveness. Our results indicate that not only the number but also the type of component that is formally realized shape complaint perception. We also find a positive correlation between perceived complaint strength and impoliteness. In addition, different formal realizations of the negative evaluation of the complainable have a different effect on complaint perception; in particular, negative emoji make the complaints softer and more polite. We also discuss methodological issues that have arisen while designing the experiments and that have to do with the operationalization of face-threat.
Compressed Superposition of Neural Networks for Deep Learning in Edge Computing

This paper investigates a combination of the two recently proposed techniques: superposition of multiple neural networks into one and neural network compression. We show that these two techniques can be successfully combined to deliver a great potential for trimming down deep convolutional neural networks. The work can be relevant in the context of implementing deep learning on low-end computing devices as it enables neural networks to fit edge devices with constrained computational resources (e.g. sensors, mobile devices, controllers). We study the trade-offs between the model compression rate and the accuracy of the superimposed tasks and present a CNN pipeline where the fully connected layers are isolated from the convolutional layers and serve as a general purpose neural processing unit for several CNN models. We show how deep models can be highly compressed with a limited accuracy degradation when additional compression is performed within the superposition principle.
UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance

Abstract This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian prior for the latent code of sentences, we alternate it with a recursive Gaussian mixture, where each mixture component corresponds to the latent code of a topic sentence and is mixed by a tree-structured topic distribution. By decoding each Gaussian component, we generate sentences with tree-structured topic guidance, where the root sentence conveys generic content, and the leaf sentences describe specific topics. Experimental results demonstrate that the generated topic sentences are appropriate as a summary of opinionated texts, which are more informative and cover more input contents than those generated by the recent unsupervised summarization model (Bražinskas et al., 2020). Furthermore, we demonstrate that the variance of latent Gaussians represents the granularity of sentences, analogous to Gaussian word embedding (Vilnis and McCallum, 2015).
A secured authentication and DSM‐KL ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple WSN

Summary Block chain is extensively seen as a potential alternative in the safety and efficiency problems of the vast internet of things (IoT) data to allow safe and successful data storage/processing/sharing. In this manuscript, a secured authentication and deep slime mould optimized kernel learning (DSM‐KL) ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple wireless sensor network (WSN) is proposed to recover data security performance and efficiency. To reflect the reality of the multi‐WSN network better, local block chain and public block chain are deployed, and a hybrid block chain model is created as per the various capabilities and energy of different nodes. A multi‐WSN network model is intended. It has numerous nodes on IoT. Based on dissimilar functions of nodes, the IoT nodes are divided into base stations, cluster heads and ordinary nodes based on its capabilities that facilitate management and cooperation of nodes. A DSM‐KL algorithm for dynamically selecting/adjusting block producer, consensus algorithm, block size and block interval to recover efficiency to handle both dynamic and broad dimensional properties of IoT systems is designed. The simulation process is executed in the MATLAB platform. The proposed DSM‐KL attains lower drop 27.53%, higher delivery ratio 28.56%, lower drop 28.64%, lower energy depletion rate 38.63%, higher network active time 38.47%, lower overhead 26.47% and higher throughput 27.67%, and the performances are compared with the existing methods such as deep reinforcement learning (DRL) and block chain and reinforcement learning (RLBC), respectively.
Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

Isolated silos of scientific research and the growing challenge of information overload limit awareness across the literature and hinder innovation. Algorithmic curation and recommendation, which often prioritize relevance, can further reinforce these informational "filter bubbles." In response, we describe Bridger, a system for facilitating discovery of scholars and their work. We construct a faceted representation of authors with information gleaned from their papers and inferred author personas, and use it to develop an approach that locates commonalities and contrasts between scientists to balance relevance and novelty. In studies with computer science researchers, this approach helps users discover authors considered useful for generating novel research directions. We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars. Our analysis reveals that Bridger connects authors who have different citation profiles and publish in different venues, raising the prospect of bridging diverse scientific communities.
A Decreasing Scaling Transition Scheme from Adam to SGD

Abstract Adaptive gradient algorithm and its variants, such as RMSProp, Adam, AMSGrad, etc., have been widely used in deep learning. Although these algorithms are faster in the early phase of training, their generalization performance is often not as good as stochastic gradient descent (SGD). Hence, a trade‐off method of transforming Adam to SGD after a certain iteration to gain the merits of both algorithms is theoretically and practically significant. To that end, a decreasing scaling transition scheme to achieve a smooth and stable transition from Adam to SGD, which is called DSTAdam. The convergence of the proposed DSTAdam is also proved in an online convex setting. Finally, the effectiveness of the DSTAdam is verified on the different datasets. The implementation is available at: https://github.com/kunzeng/DSTAdam .
FTA-GAN: A Computation-Efficient Accelerator for GANs With Fast Transformation Algorithm

Nowadays, generative adversarial network (GAN) is making continuous breakthroughs in many machine learning tasks. The popular GANs usually involve computation-intensive deconvolution operations, leading to limited real-time applications. Prior works have brought several accelerators for deconvolution, but all of them suffer from severe problems, such as computation imbalance and large memory requirements. In this article, we first introduce a novel fast transformation algorithm (FTA) for deconvolution computation, which well solves the computation imbalance problem and removes the extra memory requirement for overlapped partial sums. Besides, it can reduce the computation complexity for various types of deconvolutions significantly. Based on FTA, we develop a fast computing core (FCC) and the corresponding computing array so that the deconvolution can be efficiently computed. We next optimize the dataflow and storage scheme to further reuse on-chip memory and improve the computation efficiency. Finally, we present a computation-efficient hardware architecture for GANs and validate it on several GAN benchmarks, such as deep convolutional GAN (DCGAN), energy-based GAN (EBGAN), and Wasserstein GAN (WGAN). The experimental results show that our design can reach 2211 GOPS under 185-MHz working frequency on Intel Stratix 10SX field-programmable gate array (FPGA) board with satisfactory visual results. In brief, the proposed design can achieve more than 2× hardware efficiency improvement over previous designs, and it can reduce the storage requirement drastically.
Prediction and Evaluation Model of Physical Training for Volleyball Players’ Effect Based on Grey Markov Theory

Physical competition is becoming the new focus of volleyball in an increasingly perfect technical and tactical system. Unfortunately, poor physical fitness is a recognized weakness of volleyball players and a critical factor that has restricted the rapid development of volleyball for a long time. This paper proposes a grey Markov model-based approach to improve the evaluation ability of physical training. It aims to construct an empirical analysis model by combining statistical results and analyzing the evaluation parameters for physical training effects. The sports parameter analysis method is adopted to establish an optimal model of these parameters. Finally, a distribution model of moments of inertia combined with fuzzy information fusion’s feature extraction method is proposed for the distributed reconstruction of physical training. The optimization of training effects based on parameter optimization and construction of a grey Markov model enhances the physical training of volleyball players.
A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer's Disease

Alzheimer's Disease (AD) is one of the most common forms of neuropsychological disorder in elderly people. It is a slow progressive disease affecting the brain cells. This affects the cognitive abilities of people and their daily activities. During the course of the disease, memory gets brutally affected too. Working as well as long-term declarative memory deteriorates in AD patients. Due to this deterioration of the memory, AD patients tend to show a decline in their communicative skills as well. This decline is reflected in their speech. AD patients usually have poor grammar along with very low coherent ideas. Also, they tend to repeat the words very often and hence become unclear on the message they are trying to convey. As the disease progresses, the speech is completely impaired, and the patients are left to sing or utter words that are totally out of context. Stopwords are the words that are most commonly used in language and it is often hypothesized that AD patients use them much often as compared to Control Normal (CN) subjects. It is seen that due to the degeneration of brain cells in AD patients, they have a tendency to use a lot of stopwords to fill their perplexities in their statements. In this paper, the usefulness of the stopwords in capturing the linguistic information of the patients suffering from AD are discussed. Learning algorithms are evaluated by including stopwords and dropping stopwords at preprocessing to draw comparisons.
The Impact Analysis of Multiple Miners and Propagation Delay on Selfish Mining

Bitcoin has emerged as a popular decentralized cryptocurrency and attracted much attention from the public. Bitcoin embodies the Nakamoto consensus to reach an agreement about its blockchain ledger. However, the Nakamoto consensus can suffer from selfish mining attacks. Existing studies on selfish mining usually assume that the total mining power is divided into two parts (i.e., honest and selfish), and ignore propagation delay among miners. The assumptions cannot reflect real-world scenarios, in which multiple miners generate blocks at a fixed interval and propagate them with certain delay. Therefore, it is unknown how the practical factors, i.e., multiple miners and propagation delay, can affect selfish mining.In this paper, we explore the impact of multiple miners and propagation delay on selfish mining. First, we propose a new selfish mining strategy that can handle these factors. Second, we design a simulation approach to analyze the performance of the new selfish mining strategy. From our empirical study we observe many interesting findings that can be utilized in combating selfish mining. For example, the blockchain system with a higher orphan rate is more vulnerable to the selfish mining attack.
Complicated Interference Identification via Machine Learning Methods

Under complicated electromagnetic environments, useful signals often interfere with other electromagnetic systems nowadays. Then how to suppress the interferences is a critical problem. Since there is no universal suppression method for each kind of interferences, we need to identify the interferences, and then perform the specific suppression method to tackle with the interferences. In this paper, we simulate 15 types of possible interference signals, some are mixed by two different kinds of interferences, and extract seven features in the time-frequency domain under different interference-to-noise ratios (INRs). By fully analyzing these features, several common classifiers, such as random forest, gradient boosting, and a neural network, are designed to identify these complicated interferences. Numerical experiments are provided to demonstrate the effectiveness of the proposed methods. The final accuracy can exceed 95.20% at the condition INR=20dB.
Intel OpenVINO Toolkit for Computer Vision: Object Detection and Semantic Segmentation

The paper provides an overview of the neural networks implementation current state, their methods of execution, and the Intel® OpenVINO ™ Toolkit for executing neural networks on various hardware platforms from Intel. This work describes the selection of computer vision neural networks and datasets for object detection and semantic segmentation of images for subsequent testing. It gives the description of the experiment on various hardware platforms. Moreover, it provides an analysis of the performance and cost of running selected neural networks using the OpenVINO ™ Toolkit in normal mode, as well as using plug-ins for multiple devices and heterogeneous plug-ins on multiple connected devices.
Auxiliary Equipment Detection in Marine Engine Rooms Based on Deep Learning Model

In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.
OPA2D: One-Pixel Attack, Detection, and Defense in Deep Neural Networks

Adversarial images have been proposed to deceive deep neural networks (DNNs) by adding perturbations to the pixels. Unlike existing attacks, Su et al. [1] analyzed an attack in an extremely limited constraint where only one pixel was modified. However, their one-pixel attack is easy to recognize by humans. In this paper, we improve the attack to enable the deceit of both DNNs and humans. We conducted a human recognition analysis to prove our attack's effect. We then propose detection and defense methods against the attack by re-attacking the adversarial images. Our experimental results on the six most recent convolutional neural networks show that while our attack achieved approximately the same success rates and confidence scores as in the existing attack, our attack achieves a higher success rate for deceiving humans. Only 49.41 % of participants can recognize our attack even though 81.04 % participants have recognized the existing attack. OPA2D detects 99.33% of the existing attack and 100% of our attack and defends 92.00% of the existing attack and 95.33 % of our attack.
Multi-style learning for adaptation of perception intelligence in home service robots

Robots need more intelligence to complete perception tasks in uncertain and unstructured environments. This paper presents a new self-evolving home service robot framework that learns new perception skills by using manually labeled data obtained in a new home environment. In this framework, a global model is trained which serves as the starting point of the robots’ local model, and an adaptation mechanism is developed in the robot to adapt the initial local model to the new home environment. First, three different data sampling styles are proposed to carry out the adaptation process, and theoretical analysis is given to explain the difference between the proposed three data sampling styles. Second, the most suitable data sampling style for incremental learning for a home service robot is determined. Third, we present a case study of multi-style learning, and the experimental results validate our analysis. The theoretical analysis and experimental results lead us to propose a guideline of data collection and labeling for robots to adapt their perception intelligence in home environments.