Reviewerly/exHarmony huggingface dataset:
Make Web3.0 Connected

<inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> , often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> . Our definition is not the only way of understanding <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> , yet, it captures the fundamental and defining trait of <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> , and meanwhile it is has two desirable properties. Under this definition, we articulate three key categories of infrastructural enablers for <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> : individual smart-contract capable blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers to provide a unified and connected computing platform for <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> applications. While innovations in all categories are necessary to fully enable <inline-formula><tex-math notation="LaTeX">${\mathsf Web3.0}$</tex-math></inline-formula> , in this article, we present a design for the third enabler, i.e., the first interoperability platform, namely <inline-formula><tex-math notation="LaTeX">${\mathsf HyperService}$</tex-math></inline-formula> , that advances the state-of-the-art by simultaneously delivers <i>interoperability</i> and <i>programmability</i> across <i>heterogeneous</i> blockchains and state publishers. <inline-formula><tex-math notation="LaTeX">${\mathsf HyperService}$</tex-math></inline-formula> is powered by two innovative designs: <inline-formula><tex-math notation="LaTeX">${\mathsf (i)}$</tex-math></inline-formula> a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and <inline-formula><tex-math notation="LaTeX">${\mathsf (ii)}$</tex-math></inline-formula> a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of <inline-formula><tex-math notation="LaTeX">${\mathsf HyperService}$</tex-math></inline-formula> in approximately 62,000 lines of code to demonstrate its practicality, usability and scalability.
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools, which has extended the application of KGs to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. For example, the amount of the political discourses in social media is overwhelming yet can be hijacked and misused by spammers to spread misinformation and false news. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a domain-based social credibility module to ensure data quality and trustworthiness. The utility of the proposed framework is verified by means of experiments conducted on two constructed KGs. The KGs are then embedded in low-dimensional semantically-continuous space using several embedding techniques. The effectiveness of embedding techniques and social credibility module is further demonstrated and substantiated on link prediction, clustering, and visualisation tasks.
Enabling Secure and Efficient Decentralized Storage Auditing With Blockchain

As a promising alternative solution to cloud storage, decentralized storage networks (DSN) are widely anticipated to develop continuously and reshape the storage market share in the foreseeable future. In particular, one of the most important research problems is how to enforce the quality of service (QoS) in the context of storage solutions. Despite plenty of auditing-related works in the context of cloud storage, none of them can be directly applied to the decentralized storage paradigm. The challenges of designing a feasible storage auditing framework emanate from two aspects: 1) security problems unique to the decentralized settings and 2) performance overhead due to on-chain operations. In this article, we first put forward a basic storage auditing framework that satisfies the security and efficiency requirements, and outperforms the existing approaches. We also identify a critical and overlooked security problem that would compromise the integrity of storage auditing solutions in the blockchain paradigm. With our refined storage auditing design based on customized zero knowledge protocols, we propose a convenient mitigation solution in our revised security model. The evaluation results confirm that our solution would only incur a 10–15 percent increase in the overall auditing costs for common usage scenarios, compared to the basic design.
Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems.
Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames

Non-parallel voice conversion (VC) is a technique for training voice converters without a parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and CycleGAN-VC2) are widely accepted as benchmark methods. However, owing to their insufficient ability to grasp time-frequency structures, their application is limited to mel-cepstrum conversion and not mel-spectrogram conversion despite recent advances in mel-spectrogram vocoders. To overcome this, CycleGAN-VC3, an improved variant of CycleGAN-VC2 that incorporates an additional module called time-frequency adaptive normalization (TFAN), has been proposed. However, an increase in the number of learned parameters is imposed. As an alternative, we propose MaskCycleGAN-VC, which is another extension of CycleGAN-VC2 and is trained using a novel auxiliary task called filling in frames (FIF). With FIF, we apply a temporal mask to the input mel-spectrogram and encourage the converter to fill in missing frames based on surrounding frames. This task allows the converter to learn time-frequency structures in a self-supervised manner and eliminates the need for an additional module such as TFAN. A subjective evaluation of the naturalness and speaker similarity showed that MaskCycleGAN-VC outperformed both CycleGAN-VC2 and CycleGAN-VC3 with a model size similar to that of CycleGAN-VC2. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
BlockTour: A blockchain-based smart tourism platform

The traditional tourism industry is in urgent need of digital technologies for cost reduction and efficiency enhancement. Blockchain, as an emerging technology, is promising to reform the tourism industry because it provides a trustworthy platform to link the tourism company and tourists. However, the existing blockchain-based smart tourism solutions are either conceptual or limited in solving the fundamental tourism challenges. In this paper, we propose BlockTour, a blockchain-based smart tourism platform with dedicated solution to address the challenges and real-world prototype deployment. In particular, we design the overall system architecture of BlockTour to link the tourists and attractions in a trustworthy way. Moreover, an efficient consensus mechanism is designed with incentives for the tourists to explore more attractions. Finally, we implement BlockTour and conduct extensive experiments for performance evaluation. The experimental results indicate that BlockTour is a practical and high-performance smart tourism platform.
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff

Data poisoning and backdoor attacks manipulate victim models by maliciously modifying training data. In light of this growing threat, a recent survey of industry professionals revealed heightened fear in the private sector regarding data poisoning. Many previous defenses against poisoning either fail in the face of increasingly strong attacks, or they significantly degrade performance. However, we find that strong data augmentations, such as mixup and CutMix, can significantly diminish the threat of poisoning and backdoor attacks without trading off performance. We further verify the effectiveness of this simple defense against adaptive poisoning methods, and we compare to baselines including the popular differentially private SGD (DP-SGD) defense. In the context of backdoors, CutMix greatly mitigates the attack while simultaneously increasing validation accuracy by 9%.
A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

Video monitoring is an important means to ensure production safety in coal mine. However, the currently intelligent video surveillance is difficult to respond in real-time due to the latency of cloud computing. In this paper, a cloud-edge cooperation framework is proposed, which integrates cloud computing and edge computing in a coordinated manner. The cloud computing is used to process non-real-time and global tasks, while the edge computing is responsible for handling local monitoring video in real-time. In order to realize cloud-edge data interaction and online optimization for edge models, the heterogeneous converged network is built. In addition, an object detection model FL-YOLO composed of depthwise separable convolution and down-sampling inverted residual block is proposed, which realizes real-time video analysis at the edge. Finally, this paper discusses the complexity of FL-YOLO by its computational cost and model size. The experiment results show that the model size of FL-YOLO is 16.1MB, which is very light, and it achieves 36.7 FPS on NVIDIA Jetson TX1 and an AP of 76.7% on Multi-scene pedestrian dataset. Comparing with mainstream object detection models, FL-YOLO completes faster detection speed and higher accuracy, and it has lower calculation complexity and smaller model scale. Furthermore, the AP on Single-scene pedestrian dataset of FL-YOLO is improved to 80.7% by cloud-edge cooperation. K-Fold method is also used to further compared the performance of FL-YOLO and other models. Moreover, system test is implemented on coal mine, which validates the actual engineering effect of the proposed cloud-edge cooperation framework.
High-resolution assessment of solar radiation and energy potential in China

Global solar radiation (Rs) is a key parameter for determining the energy yields of solar photovoltaic (PV) systems. However, long-term Rs data are not available in most regions of China, impeding the management and development of PV systems. In this study, a novel model for estimating Rs was developed and coupled with a PV power model and inverse distance weighting model to generate high-resolution Rs and PV power data over China. A large network of observations was used to provide forcing inputs (daily observations from 1961 to 2018, from 2123 meteorological stations). The spatiotemporal patterns of Rs and PV power in China were further quantified. The results indicate that the newly developed Rs model accurately represents the decadal variability of Rs in China during 1961–2018. Annual potential PV power exhibited a significantly decreasing trend from 1961 to 2018, with a significant decrease from 1961 to 1991 (−0.35 ± 0.06 kWh m−2 yr−1, p < 0.01) and non-significant decrease from 1992 to 2018 (−0.14 ± 0.12 kWh m−2 yr−1, p > 0.05). Owing to air quality improvements in the future, the potential PV power may increase over China, leading to large economic benefits. Western China is favored for its abundant PV power, whereas central and eastern China are the least favorable for PV power generation. Thus, the western part of China, with its excellent geographical potential, is an optimal location for the installation of solar PV power plants. Our findings provide practical information to solar energy industries, and can help in energy structure transitions, meeting the energy demand and contributing to carbon neutrality.
Towards gaze-based prediction of the intent to interact in virtual reality

With the increasing frequency of eye tracking in consumer products, including head-mounted augmented and virtual reality displays, gaze-based models have the potential to predict user intent and unlock intuitive new interaction schemes. In the present work, we explored whether gaze dynamics can predict when a user intends to interact with the real or digital world, which could be used to develop predictive interfaces for low-effort input. Eye-tracking data were collected from 15 participants performing an item-selection task in virtual reality. Using logistic regression, we demonstrated successful prediction of the onset of item selection. The most prevalent predictive features in the model were gaze velocity, ambient/focal attention, and saccade dynamics, demonstrating that gaze features typically used to characterize visual attention can be applied to model interaction intent. In the future, these types of models can be used to infer user's near-term interaction goals and drive ultra-low-friction predictive interfaces.
A Survey on Software Defect Prediction Using Deep Learning

Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.
Improved Point-Voxel Region Convolutional Neural Network: 3D Object Detectors for Autonomous Driving

Recently, 3D object detection based on deep learning has achieved impressive performance in complex indoor and outdoor scenes. Among the methods, the two-stage detection method performs the best; however, this method still needs improved accuracy and efficiency, especially for small size objects or autonomous driving scenes. In this paper, we propose an improved 3D object detection method based on a two-stage detector called the Improved Point-Voxel Region Convolutional Neural Network (IPV-RCNN). Our proposed method contains online training for data augmentation, upsampling convolution and k-means clustering for the bounding box to achieve 3D detection tasks from raw point clouds. The evaluation results on the KITTI 3D dataset show that the IPV-RCNN achieved a 96% mAP, which is 3% more accurate than the state-of-the-art detectors.
Multi-Label Learning from Single Positive Labels

Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals

There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrutinize and trust them. Prior work in this context has focused on the attribution of responsibility for an algorithm's decisions to its inputs wherein responsibility is typically approached as a purely associational concept. In this paper, we propose a principled causality-based approach for explaining black-box decision-making systems that addresses limitations of existing methods in XAI. At the core of our framework lies probabilistic contrastive counterfactuals, a concept that can be traced back to philosophical, cognitive, and social foundations of theories on how humans generate and select explanations. We show how such counterfactuals can quantify the direct and indirect influences of a variable on decisions made by an algorithm, and provide actionable recourse for individuals negatively affected by the algorithm's decision. Unlike prior work, our system, LEWIS: (1)~can compute provably effective explanations and recourse at local, global and contextual levels; (2)~is designed to work with users with varying levels of background knowledge of the underlying causal model; and (3)~makes no assumptions about the internals of an algorithmic system except for the availability of its input-output data. We empirically evaluate LEWIS on four real-world datasets and show that it generates human-understandable explanations that improve upon state-of-the-art approaches in XAI, including the popular LIME and SHAP. Experiments on synthetic data further demonstrate the correctness of LEWIS's explanations and the scalability of its recourse algorithm.
Simulating Unknown Target Models for Query-Efficient Black-box Attacks

Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the query complexity remains high, and such attacks can be defended easily. This study aims to train a generalized substitute model called "Simulator", which can mimic the functionality of any unknown target model. To this end, we build the training data with the form of multiple tasks by collecting query sequences generated during the attacks of various existing networks. The learning process uses a mean square error-based knowledge-distillation loss in the meta-learning to minimize the difference between the Simulator and the sampled networks. The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization. When attacking a target model that is unseen in training, the trained Simulator can accurately simulate its functionality using its limited feedback. As a result, a large fraction of queries can be transferred to the Simulator, thereby reducing query complexity. Results of the comprehensive experiments conducted using the CIFAR-10, CIFAR-100, and TinyImageNet datasets demonstrate that the proposed approach reduces query complexity by several orders of magnitude compared to the baseline method. The implementation source code is released online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron

Driven by the increasing significance of artificial intelligence, the field of neuromorphic (brain-inspired) photonics is attracting increasing interest, promising new, high-speed, and energy-efficient computing hardware for key applications in information processing and computer vision. Widely available photonic devices, such as vertical-cavity surface emitting lasers (VCSELs), offer highly desirable properties for photonic implementations of neuromorphic systems, such as high-speed and low energy operation, neuron-like dynamical responses, and ease of integration into chip-scale systems. Here, we experimentally demonstrate encoding of digital image data into continuous, rate-coded, up to GHz-speed optical spike trains with a VCSEL-based photonic spiking neuron. Moreover, our solution makes use of off-the-shelf fiber-optic components with operation at telecom wavelengths, therefore making the system compatible with current optical network and data center technologies. This VCSEL-based spiking encoder paves the way toward optical spike-based data processing and ultrafast neuromorphic vision systems.