Reviewerly/exHarmony huggingface dataset:
TrustedBaaS: Blockchain-Enabled Distributed and Higher-Level Trusted Platform

With the development of computing technology, the security and credibility of data is becoming more and more important. However, the security and trust of traditional centralized system is insufficient and data tampering is easy to occur, which leads to data distrustful. The characteristics of blockchain, such as traceable and tamper-proof, can solve the issue of data trust. However, the access threshold of the blockchain is high. In order to reduce the technical difficulty, in this article, a universal BaaS platform (called TrustedBaaS) which has high flexibility and scalability was designed based on consortium blockchain, it can support user-defined configuration according to requirements of users. Afterwards we described the process of executing a transaction in detail and discussed the core protocols and algorithms of the BaaS platform, which includes identity authentication, consensus algorithm, data protection protocol, smart contract and on-blockchain and off-blockchain data collaboration. Furthermore, we implemented TrustedBaaS based on Hyperledger Fabric and a vehicular information management application platform and a transaction settlement application platform were configured based on TrustedBaaS. Finally, we evaluated the performance of the TrustedBaaS and compared it with current public blockchain such as Bitcoin and Ethereum. The evaluation showed that the designed TrustedBaaS has better performance than the existing public blockchain platforms. Moreover, it is common distributed higher-level trusted platform which provides configurable and extensible functions and solves the problem that the access threshold of blockchain technology is high.
The Lack of Shared Understanding of Non-Functional Requirements in Continuous Software Engineering: Accidental or Essential?

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. However, in continuous software engineering (CSE) some lack of shared understanding is an expected, and essential, part of a rapid feedback learning cycle. At the same time, there is a key trade-off with avoidable costs, such as rework, that come from accidental gaps in shared understanding. This tradeoff is even more challenging for non-functional requirements (NFRs), which have significant implications for product success. Comprehending and managing NFRs is especially difficult in small, agile organizations. How such organizations manage shared understanding of NFRs in CSE is understudied. We conducted a case study of three small organizations scaling up CSE to further understand and identify factors that contribute to lack of shared understanding of NFRs, and its relationship to rework. Our in-depth analysis identified 41 NFR-related software tasks as rework due to a lack of shared understanding of NFRs. Of these 41 tasks 78% were due to avoidable (accidental) lack of shared understanding of NFRs. Using a mixed-methods approach we identify factors that contribute to lack of shared understanding of NFRs, such as the lack of domain knowledge, rapid pace of change, and cross-organizational communication problems. We also identify recommended strategies to mitigate lack of shared understanding through more effective management of requirements knowledge in such organizations. We conclude by discussing the complex relationship between shared understanding of requirements, rework and, CSE.
Just-in-time learning for bottom-up enumerative synthesis

A key challenge in program synthesis is the astronomical size of the search space the synthesizer has to explore. In response to this challenge, recent work proposed to guide synthesis using learned probabilistic models. Obtaining such a model, however, might be infeasible for a problem domain where no high-quality training data is available. In this work we introduce an alternative approach to guided program synthesis: instead of training a model ahead of time we show how to bootstrap one just in time, during synthesis, by learning from partial solutions encountered along the way. To make the best use of the model, we also propose a new program enumeration algorithm we dub guided bottom-up search, which extends the efficient bottom-up search with guidance from probabilistic models. We implement this approach in a tool called Probe, which targets problems in the popular syntax-guided synthesis (SyGuS) format. We evaluate Probe on benchmarks from the literature and show that it achieves significant performance gains both over unguided bottom-up search and over a state-of-the-art probability-guided synthesizer, which had been trained on a corpus of existing solutions. Moreover, we show that these performance gains do not come at the cost of solution quality: programs generated by Probe are only slightly more verbose than the shortest solutions and perform no unnecessary case-splitting.
Amplitude and frequency sensing of microwave fields with a superconducting transmon qudit

Experiments with superconducting circuits require careful calibration of the applied pulses and fields over a large frequency range. This remains an ongoing challenge as commercial semiconductor electronics are not able to probe signals arriving at the chip due to its cryogenic environment. Here, we demonstrate how the on-chip amplitude and frequency of a microwave signal can be inferred from the ac Stark shifts of higher transmon levels. In our time-resolved measurements we employ Ramsey fringes, allowing us to detect the amplitude of the systems transfer function over a range of several hundreds of MHz with an energy sensitivity on the order of $10^{-4}$. Combined with similar measurements for the phase of the transfer function, our sensing method can facilitate pulse correction for high fidelity quantum gates in superconducting circuits. Additionally, the potential to characterize arbitrary microwave fields promotes applications in related areas of research, such as quantum optics or hybrid microwave systems including photonic, mechanical or magnonic subsystems.
Prediction Uncertainty Estimation for Hate Speech Classification

As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. However, automatic approaches shall not jeopardize free speech, so they shall accompany their decisions with explanations and assessment of uncertainty. Thus, there is a need for predictive machine learning models that not only detect hate speech but also help users understand when texts cross the line and become unacceptable. The reliability of predictions is usually not addressed in text classification. We fill this gap by proposing the adaptation of deep neural networks that can efficiently estimate prediction uncertainty. To reliably detect hate speech, we use Monte Carlo dropout regularization, which mimics Bayesian inference within neural networks. We evaluate our approach using different text embedding methods. We visualize the reliability of results with a novel technique that aids in understanding the classification reliability and errors.
Improving autonomous thermal entanglement generation using a common reservoir

We study the entanglement generated in the steady state of two interacting qubits coupled to thermal reservoirs. We show that the amount of steady-state entanglement can be enhanced by the presence of a third thermal reservoir which is common to both qubits. Specifically, we find that entanglement can be enhanced as long as the temperature of the common reservoir is below the thermalisation temperature of the qubits, whenever a single temperature can be assigned to the steady state of the qubits in the absence of the common reservoir. Moreover, the amount of entanglement generated with the common reservoir present can be significantly larger than that which can be obtained without it for any temperature of the individual reservoirs. From the perspective of thermodynamics, we find that enhancement of entanglement is associated with heat absorption by the common reservoir. We propose a possible implementation of our scheme in superconducting circuits and find that a significant enhancement of steady-state entanglement should be observable under experimentally realistic conditions.
Winning the Lottery with Continuous Sparsification

The search for efficient, sparse deep neural network models is most prominently performed by pruning: training a dense, overparameterized network and removing parameters, usually via following a manually-crafted heuristic. Additionally, the recent Lottery Ticket Hypothesis conjectures that, for a typically-sized neural network, it is possible to find small sub-networks which, when trained from scratch on a comparable budget, match the performance of the original dense counterpart. We revisit fundamental aspects of pruning algorithms, pointing out missing ingredients in previous approaches, and develop a method, Continuous Sparsification, which searches for sparse networks based on a novel approximation of an intractable $\ell_0$ regularization. We compare against dominant heuristic-based methods on pruning as well as ticket search -- finding sparse subnetworks that can be successfully re-trained from an early iterate. Empirical results show that we surpass the state-of-the-art for both objectives, across models and datasets, including VGG trained on CIFAR-10 and ResNet-50 trained on ImageNet. In addition to setting a new standard for pruning, Continuous Sparsification also offers fast parallel ticket search, opening doors to new applications of the Lottery Ticket Hypothesis.
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation

Knowledge Distillation (KD) has been one of the most popular methods to learn a compact model. However, it still suffers from high demand in time and computational resources caused by sequential training pipeline. Furthermore, the soft targets from deeper models do not often serve as good cues for the shallower models due to the gap of compatibility. In this work, we consider these two problems at the same time. Specifically, we propose that better soft targets with higher compatibility can be generated by using a label generator to fuse the feature maps from deeper stages in a top-down manner, and we can employ the meta-learning technique to optimize this label generator. Utilizing the soft targets learned from the intermediate feature maps of the model, we can achieve better self-boosting of the network in comparison with the state-of-the-art. The experiments are conducted on two standard classification benchmarks, namely CIFAR-100 and ILSVRC2012. We test various network architectures to show the generalizability of our MetaDistiller. The experiments results on two datasets strongly demonstrate the effectiveness of our method .
A systematic literature review on research data management practices and services

Purpose Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it. Design/methodology/approach A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis. Findings RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues. Research limitations/implications The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted. Originality/value RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

This chapter investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and privacy losses incurred by reconstruction, tracing and membership attacks. Second, a novel Secret Polarization Network (SPN) is proposed to thwart privacy attacks, which is highly competitive against existing PPDL methods. Extensive experiments showed that model accuracies are improved on average by 5–20% compared with baseline mechanisms, in regimes where data privacy are satisfactorily protected.
Gigapixel Histopathological Image Analysis Using Attention-Based Neural Networks

Although CNNs are widely considered as the state-of-the-art models in various applications of image analysis, one of the main challenges still open is the training of a CNN on high resolution images. Different strategies have been proposed involving either a rescaling of the image or an individual processing of parts of the image. Such strategies cannot be applied to images, such as gigapixel histopathological images, for which a high reduction in resolution inherently effects a loss of discriminative information, and in respect of which the analysis of single parts of the image suffers from a lack of global information or implies a high workload in terms of annotating the training images in such a way as to select significant parts. We propose a method for the analysis of gigapixel histopathological images solely by using weak image-level labels. In particular, two analysis tasks are taken into account: a binary classification and a prediction of the tumor proliferation score. Our method is based on a CNN structure consisting of a compressing path and a learning path. In the compressing path, the gigapixel image is packed into a grid-based feature map by using a residual network devoted to the feature extraction of each patch into which the image has been divided. In the learning path, attention modules are applied to the grid-based feature map, taking into account spatial correlations of neighboring patch features to find regions of interest, which are then used for the final whole slide analysis. Our method integrates both global and local information, is flexible with regard to the size of the input images and only requires weak image-level labels. Comparisons with different methods of the state-of-the-art on two well known datasets, Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed model.
2 Convolutional Neural Networks: A Bottom-Up Approach

A lot has changed in the world since the inception of the so-called deep learning (DL) era. Unlike conventional machine learning algorithms, where human learns more of feature extraction than machine, which just crunches numbers, DL algorithms are quite promising as they have revolutionized the way we deal with data and have become adept in taking human-like decisions. Based on the powerful notion of artificial neural networks, these learning algorithms learn to represent data or, if we rephrase, can extract features from raw data. DL has paved way for end-to-end systems where one learning algorithm does all the tasks. Convolutional neural networks have earned a lot of fame lately, especially in the domain of computer vision where in some cases its performance has beaten that of humans. A lot of work has been done on convNets and in this chapter we will demystify how convolutional neural networks work and will illustrate using one novel application in the field of astronomy where we will do galaxy classification using raw images as input and classify them based on its shape. In addition to this we will investigate what features the network is learning. We will also discuss how DL superseded other forms of learning and some recent algorithmic innovations centered on convolutional neural nets.
Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.
Design of online volleyball remote teaching system based on AR technology

To help individuals deal with volleyball learning and training, an online volleyball remote teaching system is designed based on Augmented Reality (AR) technology. Firstly, the action images of the volleyball coach were captured, followed by being input into the computer. The hardware of the system was mainly composed of motion capture equipment, data transmission equipment, central control processing equipment, and AR display equipment. The system’s software included four modules of motion acquisition and recognition. The testing results showed that the functional modules and performance of the system could meet the expected requirements, verifying the effectiveness and practicability.
Enhancing the performance of the blockchain consensus algorithm using multithreading technology

Blockchain is one of the most powerful and promising technologies nowadays in the IT industry, One of Blockchain’s main features is the presence of a Consensus Algorithm, which is responsible for maintaining the security and integrity of the entire blockchain network where all the nodes participating in the network reach a certain agreement. However, some algorithms like the Proof-of-Work require very high energy consumption to reach a single agreement by solving a puzzle and provides a low throughput (3-7 transactions/second), thus, it may not be very reliable in blockchain solutions. In this paper, we aim to provide a reliable choice for different business use-cases by proposing a modification in the Istanbul Byzantine Fault Tolerance voting-based algorithm that provides a higher throughput (up to 1140 tx/s), which will be very important to be used in the use case called a letter of credit which is a part of trade finance (relation between exporter, importer and the bank institutions).
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning

Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, we propose a novel method to address these problems. We put forth a 2-stage network: a physics-based backbone followed by a depth-guided GAN refinement. The first stage estimates the rain streaks, the transmission, and the atmospheric light governed by the underlying physics. To tease out these components more reliably, a guided filtering framework is used to decompose the image into its low- and high-frequency components. This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks. For the second stage, the refinement stage, we put forth a depth-guided GAN to recover the background details failed to be retrieved by the first stage, as well as correcting artefacts introduced by that stage. We have evaluated our method against the state of the art methods. Extensive experiments show that our method outperforms them on real rain image data, recovering visually clean images with good details.
SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output.We have made the dockerlized system publicly available for research purpose at GitHub 1 , with a demo video 2 .