Reviewerly/exHarmony huggingface dataset:
Identity Management and Access Control Based on Blockchain under Edge Computing for the Industrial Internet of Things

Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features have also introduced new security problems. To solve the problem, in this paper, blockchain-based identity management combining access control mechanism is designed under edge computing. The self-certified cryptography is utilized to realize the registration and authentication of network entities. We bind the generated implicit certificate to its identity and construct the identity and certificate management mechanism based on blockchain. Secondly, an access control mechanism based on Bloom filter is designed and integrated with identity management. Moreover, for secure communication in resource-constrained edge devices, a lightweight secret key agreement protocol based on self-authenticated public key is constructed. These mechanisms work together to provide data security guarantees for IIoT such as authentication, auditability, and confidentiality.
Free Speech and Hate Speech

Should hate speech be banned? This article contends that the debate on this question must be disaggregated into discrete analytical stages, lest its participants continue to talk past one another. The first concerns the scope of the moral right to freedom of expression, and whether hate speech falls within the right's protective ambit. If it does, hate speech bans are necessarily unjust. If not, we turn to the second stage, which assesses whether speakers have moral duties to refrain from hate speech. The article canvasses several possible duties from which such a duty could be derived, including duties not to threaten, harass, offend, defame, or incite. If there is a duty to refrain from hate speech, it is yet a further question whether the duty should actually be enforced. This third stage depends on pragmatic concerns involving epistemic fallibility, the abuse of state power, and the benefits of counter-speech over coercion.
Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency

Industrial robots are expected to share the same workspace with human workers and work in cooperation with humans to improve the productivity and maintain the quality of products. In this situation, the worker's safety and work-time efficiency must be enhanced simultaneously. In this paper, we extend a task scheduling system proposed in the previous work by installing an online trajectory generation system. On the basis of the probabilistic prediction of the worker's motion and the receding horizon scheme for the trajectory planning, the proposed motion planning system calculates an optimal trajectory that realizes collision avoidance and the reduction of waste time simultaneously. Moreover, the proposed system plans the robot's trajectory adaptively based on updated predictions and its uncertainty to deal not only with the regular behavior of workers but also with their irregular behavior. We apply the proposed system to an assembly process where a two-link planar manipulator supports a worker by delivering parts and tools. After implementing the proposed system, we experimentally evaluate the effectiveness of the adaptive motion planning system.
OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network

Phishing attack is now a big threat to people's daily life and networking environment. Through disguising illegal URLs as legitimate ones, attackers can induce users to visit the phishing URLs to get private information and other benefits. Effective methods of detecting the phishing websites are urgently needed to alleviate the threats posed by the phishing attacks. As the active learning capability from massive data sets, the neural network is widely used to detect the phishing attacks. However, in the stage of training data sets, many useless and small influence features will trap the neural network model into the problem of over-fitting. This problem usually causes the trained model that cannot effectively detect phishing websites. In order to alleviate this problem, this paper proposes OFS-NN, an effective phishing websites detection model based on the optimal feature selection method and neural network. In the proposed OFS-NN, a new index, feature validity value (FVV), is first introduced to evaluate the impact of sensitive features on the phishing websites detection. Then, based on the new FVV index, an algorithm is designed to select the optimal features from the phishing websites. This algorithm is able to alleviate the over-fitting problem of the underlying neural network to a large extent. The selected optimal features are used to train the underlying neural network, and finally, an optimal classifier is constructed to detect the phishing websites. The experimental results show that the OFS-NN model is accurate and stable in detecting many types of phishing websites.
IM-Net for High Resolution Video Frame Interpolation

Video frame interpolation is a long-studied problem in the video processing field. Recently, deep learning approaches have been applied to this problem, showing impressive results on low-resolution benchmarks. However, these methods do not scale-up favorably to high resolutions. Specifically, when the motion exceeds a typical number of pixels, their interpolation quality is degraded. Moreover, their run time renders them impractical for real-time applications. In this paper we propose IM-Net: an interpolated motion neural network. We use an economic structured architecture and end-to-end training with multi-scale tailored losses. In particular, we formulate interpolated motion estimation as classification rather than regression. IM-Net outperforms previous methods by more than 1.3dB (PSNR) on a high resolution version of the recently introduced Vimeo triplet dataset. Moreover, the network runs in less than 33msec on a single GPU for HD resolution.
Learning to Synthesize Motion Blur

We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession. To build this system we motivate and design a differentiable “line prediction” layer to be used as part of a neural network architecture, with which we can learn a system to regress from image pairs to motion blurred images that span the capture time of the input image pair. Training this model requires an abundance of data, and so we design and execute a strategy for using frame interpolation techniques to generate a large-scale synthetic dataset of motion blurred images and their respective inputs. We additionally capture a high quality test set of real motion blurred images, synthesized from slow motion videos, with which we evaluate our model against several baseline techniques that can be used to synthesize motion blur. Our model produces higher accuracy output than our baselines, and is several orders of magnitude faster than baselines with competitive accuracy.
Temporal Convolutional Networks for Anomaly Detection in Time Series

Abstract Convolutional Networks have been demonstrated to be particularly useful for extracting high level feature in structural data. Temporal convolutional network (TCN) is a framework which employs casual convolutions and dilations so that it is adaptive for sequential data with its temporality and large receptive fields. In this paper, we apply TCN for anomaly detection in time series. We train the TCN on normal sequences and use it to predict trend in a number of time steps. Prediction errors are fitted by a multivariate Gaussian distribution and used to calculate the anomaly scores of points. In addition, a multi-scale feature mixture method is raised to promote performance. The validity of this method is confirmed on three real-world datasets.
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation

Conventional domain adaptation methods usually resort to deep neural networks or subspace learning to find invariant representations across domains. However, most deep learning methods highly rely on large-size source domains and are computationally expensive to train, while subspace learning methods always have a quadratic time complexity that suffers from the large domain size. This paper provides a simple and efficient solution, which could be regarded as a well-performing baseline for domain adaptation tasks. Our method is built upon the nearest centroid classifier, seeking a subspace where the centroids in the target domain are moderately shifted from those in the source domain. Specifically, we design a unified objective without accessing the source domain data and adopt an alternating minimization scheme to iteratively discover the pseudo target labels, invariant subspace, and target centroids. Besides its privacy-preserving property (distant supervision), the algorithm is provably convergent and has a promising linear time complexity. In addition, the proposed method can be readily extended to multi-source setting and domain generalization, and it remarkably enhances popular deep adaptation methods by borrowing the learned transferable features. Extensive experiments on several benchmarks including object, digit, and face recognition datasets validate that our methods yield state-of-the-art results in various domain adaptation tasks.
Mining Software Defects: Should We Consider Affected Releases?

With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from software repositories play a foundational role in many empirical studies related to software quality. At the core of defect data preparation is the identification of post-release defects. Prior studies leverage many heuristics (e.g., keywords and issue IDs) to identify post-release defects. However, such the heuristic approach is based on several assumptions, which pose common threats to the validity of many studies. In this paper, we set out to investigate the nature of the difference of defect datasets generated by the heuristic approach and the realistic approach that leverages the earliest affected release that is realistically estimated by a software development team for a given defect. In addition, we investigate the impact of defect identification approaches on the predictive accuracy and the ranking of defective modules that are produced by defect models. Through a case study of defect datasets of 32 releases, we find that that the heuristic approach has a large impact on both defect count datasets and binary defect datasets. Surprisingly, we find that the heuristic approach has a minimal impact on defect count models, suggesting that future work should not be too concerned about defect count models that are constructed using heuristic defect datasets. On the other hand, using defect datasets generated by the realistic approach lead to an improvement in the predictive accuracy of defect classification models.
Structural Coverage Criteria for Neural Networks Could Be Misleading

There is a dramatically increasing interest in the quality assurance for DNN-based systems in the software engineering community. An emerging hot topic in this direction is structural coverage criteria for testing neural networks, which are inspired by coverage metrics used in conventional software testing. In this short paper, we argue that these criteria could be misleading because of the fundamental differences between neural networks and human written programs. Our preliminary exploration shows that (1) adversarial examples are pervasively distributed in the finely divided space defined by such coverage criteria, while available natural samples are very sparse, and as a consequence, (2) previously reported fault-detection "capabilities" conjectured from high coverage testing are more likely due to the adversary-oriented search but not the real "high" coverage.
Traceable Monero: Anonymous Cryptocurrency with Enhanced Accountability

Monero provides a high level of anonymity for both users and their transactions. However, many criminal activities might be committed with the protection of anonymity in cryptocurrency transactions. Thus, user accountability (or traceability) is also important in Monero transactions, which is unfortunately lacking in the current literature. In this paper, we fill this gap by introducing a new cryptocurrency named Traceable Monero to balance the user anonymity and accountability. Our framework relies on a tracing authority, but is optimistic, in that it is only involved when investigations in certain transactions are required. We formalize the system model and security model of Traceable Monero. We present a detailed construction of Traceable Monero by overlaying Monero with two types of tracing mechanisms, tracing the one-time addresses with money flows and tracing the long-term addresses. We prove the security of Traceable Monero and implement a prototype of the system, which demonstrates that Traceable Monero incurs merely a very small overhead in generating and verifying a transaction compared to Monero transactions.
Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau

Abstract Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open‐ended and more focused exploration tasks.
Being fully digital: perspective of a Dutch academic pathology laboratory

The introduction of fast and robust whole slide scanners has facilitated the implementation of ‘digital pathology’ with various uses, the final challenge being full digital diagnostics. In this article, we describe the implementation process of a fully digital workflow for primary diagnostics in 2015 at the University Medical Centre in Utrecht, The Netherlands, as one of the first laboratories going fully digital with a future‐proof complete digital archive. Furthermore, we evaluated the experience of the first 2 years of working with the system by pathologists and residents. The system was successfully implemented in 6 months, including a European tender procedure. Most pathologists and residents had high confidence in working fully digitally, the expertise areas lagging behind being paediatrics, haematopathology, and neuropathology. Reported limitations concerned recognition of microorganisms and mitoses. Neither the age of respondents nor the number of years of pathology experience was correlated with the confidence level regarding digital diagnostics. The ergonomics of digital diagnostics were better than those of traditional microscopy. In this article, we describe our experiences in implementing our fully digital primary diagnostics workflow, describing in depth the implementation steps undertaken, the interlocking components that are required for a fully functional digital pathology system (laboratory management, hospital information systems, data storage, and whole slide scanners), and the changes required in workflow and slide production.
Eyemotion: Classifying Facial Expressions in VR Using Eye-Tracking Cameras

One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users. We present an algorithm to automatically infer expressions by analyzing only a partially occluded face while the user is engaged in a virtual reality experience. Specifically, we show that images of the user's eyes captured from an IR gaze-tracking camera within a VR headset are sufficient to infer a subset of facial expressions without the use of any fixed external camera. Using these inferences, we can generate dynamic avatars in real-time which function as an expressive surrogate for the user. We propose a novel data collection pipeline as well as a novel approach for increasing CNN accuracy via personalization. Our results show a mean accuracy of 74% (F1 of 0.73) among 5 'emotive' expressions and a mean accuracy of 70% (F1 of 0.68) among 10 distinct facial action units, outperforming human raters.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

In this paper, we propose a new image super-resolution (SR) approach based on a convolutional neural network (CNN), which jointly learns the feature extraction, upsampling, and high-resolution (HR) reconstruction modules, yielding a completely end-to-end trainable deep CNN.However, directly training such a deep network in an end-to-end fashion is challenging, which takes a longer time to converge and may lead to sub-optimal results.To address this issue, we propose to jointly train an ensemble of deep and shallow networks.The shallow network with weaker learning capability restores the main structure of the image content, while the deep network with stronger representation power captures the high-frequency details.Since the shallow network is much easier to optimize, it significantly lowers the difficulty of deep network optimization during joint training.To further ensure more accurate restoration of HR images, the highfrequency details are reconstructed in a multi-scale manner to simultaneously incorporate both short-and long-range contextual information.The proposed method is extensively evaluated on widely adopted data sets and compares favorably against state-of-the-art methods.In-depth ablation studies are conducted to verify the contributions of different network designs to image SR, providing additional insights for future research.
A Secure and Privacy-Preserving Protocol for Smart Metering Operational Data Collection

Smart grid allows fine-grained smart metering data collection which can improve the efficiency and reliability of the grid. Unfortunately, this vast collection of data also imposes risks to users' privacy. In this paper, we propose a novel protocol that allows suppliers and grid operators to collect users' aggregate metering data in a secure and privacy-preserving manner. We use secure multiparty computation to ensure privacy protection. In addition, we propose three different data aggregation algorithms that offer different balances between privacy-protection and performance. Our protocol is designed for a realistic scenario in which the data need to be sent to different parties, such as grid operators and suppliers. Furthermore, it facilitates an accurate calculation of transmission, distribution, and grid balancing fees in a privacy-preserving manner. We also present a security analysis and a performance evaluation of our protocol based on existing multiparty computation algorithms.
Weakly-Supervised Hierarchical Text Classification

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.
Toward Neuromorphic Photonic Networks of Ultrafast Spiking Laser Neurons

We report on ultrafast artificial laser neurons and on their potentials for future neuromorphic (brainlike) photonic information processing systems. We introduce our recent and ongoing activities demonstrating controllable excitation of spiking signals in optical neurons based upon vertical-cavity surface emitting lasers (VCSEL-Neurons). These spiking regimes are analogous to those exhibited by biological neurons, but at sub-nanosecond speeds (>7 orders of magnitude faster). We also describe diverse approaches, based on optical or electronic excitation techniques, for the activation/inhibition of sub-ns spiking signals in VCSEL-Neurons. We report our work demonstrating the communication of spiking patterns between VCSEL-Neurons toward future implementations of optical neuromorphic networks. Furthermore, new findings show that VCSEL-Neurons can perform multiple neuro-inspired spike processing tasks. We experimentally demonstrate photonic spiking memory modules using single and mutually coupled VCSEL-Neurons. Additionally, the ultrafast emulation of neuronal circuits in the retina using VCSEL-Neuron systems is demonstrated experimentally for the first time to our knowledge. Our results are obtained with off-the-shelf VCSELs operating at the telecom wavelengths of 1310 and 1550 nm. This makes our approach fully compatible with current optical network and data center technologies, hence offering great potentials for future ultrafast neuromorphic laser-neuron networks for new paradigms in brain-inspired computing and artificial intelligence.
Reservoir Computing Using Multiple Lasers With Feedback on a Photonic Integrated Circuit

We propose a scheme for reservoir computing using multiple semiconductor lasers with optical feedback arranged in parallel on a photonic integrated circuit, and we investigate the performance of reservoir computing numerically. The virtual nodes are obtained from the temporal waveforms of the outputs of the parallel reservoir lasers. We test the chaotic time-series prediction task, memory capacity, and nonlinear channel equalization task to investigate the performance of reservoir computing. We found that our scheme using multiple lasers outperforms that using a single laser with multiple delay times. Large memory capacity can also be obtained for the multiple lasers. Finally, we investigate the effect of parameter mismatch of the multiple lasers on reservoir computing performance.