Reviewerly/exHarmony huggingface dataset:
Racism, Hate Speech, and Social Media: A Systematic Review and Critique

Departing from Jessie Daniels’s 2013 review of scholarship on race and racism online, this article maps and discusses recent developments in the study of racism and hate speech in the subfield of social media research. Systematically examining 104 articles, we address three research questions: Which geographical contexts, platforms, and methods do researchers engage with in studies of racism and hate speech on social media? To what extent does scholarship draw on critical race perspectives to interrogate how systemic racism is (re)produced on social media? What are the primary methodological and ethical challenges of the field? The article finds a lack of geographical and platform diversity, an absence of researchers’ reflexive dialogue with their object of study, and little engagement with critical race perspectives to unpack racism on social media. There is a need for more thorough interrogations of how user practices and platform politics co-shape contemporary racisms.
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation

This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or even unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. We further present an accelerated and light version of Zero-DCE, called Zero-DCE++, that takes advantage of a tiny network with just 10K parameters. Zero-DCE++ has a fast inference speed (1000/11 FPS on a single GPU/CPU for an image of size 1200×900×3) while keeping the enhancement performance of Zero-DCE. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our method to face detection in the dark are discussed. The source code is made publicly available at https://li-chongyi.github.io/Proj_Zero-DCE++.html.
Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems

The present study aimed to propose a new optimization algorithm named Flow Direction Algorithm (FDA), which is a physics-based algorithm. The FDA algorithm mimics the flow direction to the outlet point with the lowest height in a drainage basin. In other words, flow moves to neighbor with lowest high or best objective function. Thirteen classic mathematical benchmark functions, ten new mathematical benchmark functions and five engineering design problems, including three-bar truss, tension/compression spring, speed reducer, gear train, and welded beam design, with different properties are considered to evaluate the proposed algorithm. Comparing the results of the FDA with other optimization algorithms demonstrates the superior performance of the FDA in solving challenging optimization problems.
Task-Adaptive Attention for Image Captioning

Attention mechanisms are now widely used in image captioning models. However, most attention models only focus on visual features. When generating syntax related words, little visual information is needed. In this case, these attention models could mislead the word generation. In this paper, we propose Task-Adaptive Attention module for image captioning, which can alleviate this misleading problem and learn implicit non-visual clues which can be helpful for the generation of non-visual words. We further introduce a diversity regularization to enhance the expression ability of the Task-Adaptive Attention module. Extensive experiments on the MSCOCO captioning dataset demonstrate that by plugging our Task-Adaptive Attention module into a vanilla Transformer-based image captioning model, performance improvement can be achieved.
Extended Feature Pyramid Network for Small Object Detection

Small object detection remains an unsolved challenge because it is hard to extract the information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose an extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we introduce a cross resolution distillation mechanism to transfer the ability of perceiving details across the scales of the network, where a foreground-background-balanced loss function is designed to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100 K and small category of general object detection dataset MS COCO.
A Survey on Multiview Clustering

Clustering is a machine learning paradigm of dividing sample subjects into a number of groups such that subjects in the same groups are more similar to those in other groups. With advances in information acquisition technologies, samples can frequently be viewed from different angles or in different modalities, generating multi-view data. Multi-view clustering, that clusters subjects into subgroups using multi-view data, has attracted more and more attentions. Although MVC methods have been developed rapidly, there has not been enough survey to summarize and analyze the current progress. Therefore, we propose a novel taxonomy of the MVC approaches. Similar to other machine learning methods, we categorize them into generative and discriminative classes. In discriminative class, based on the way of view integration, we split it further into five groups: Common Eigenvector Matrix, Common Coefficient Matrix, Common Indicator Matrix, Direct Combination and Combination After Projection. Furthermore, we relate MVC to other topics: multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated for practitioners. Some benchmark multi-view datasets are introduced and representative MVC algorithms from each group are empirically evaluated to analyze how they perform on benchmark datasets. To promote future development of MVC approaches, we point out several open problems that may require further investigation and thorough examination.
Sequential Recommendation with Graph Neural Networks

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often implicit and noisy preference signals, they cannot sufficiently reflect users' actual preferences. In addition, users' dynamic preferences often change rapidly over time, and hence it is difficult to capture user patterns in their historical sequences. In this work, we propose a graph neural network model called SURGE (short forSeqUential Recommendation with Graph neural nEtworks) to address these two issues. Specifically, SURGE integrates different types of preferences in long-term user behaviors into clusters in the graph by re-constructing loose item sequences into tight item-item interest graphs based on metric learning. This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph. Then, we perform cluster-aware and query-aware graph convolutional propagation and graph pooling on the constructed graph. It dynamically fuses and extracts users' current activated core interests from noisy user behavior sequences. We conduct extensive experiments on both public and proprietary industrial datasets. Experimental results demonstrate significant performance gains of our proposed method compared to state-of-the-art methods. Further studies on sequence length confirm that our method can model long behavioral sequences effectively and efficiently.
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation

Semantic segmentation is a fundamental task in computer vision, and it has various applications in fields such as robotic sensing, video surveillance, and autonomous driving. A major research topic in urban road semantic segmentation is the proper integration and use of cross-modal information for fusion. Here, we attempt to leverage inherent multimodal information and acquire graded features to develop a novel multilabel-learning network for RGB-thermal urban scene semantic segmentation. Specifically, we propose a strategy for graded-feature extraction to split multilevel features into junior, intermediate, and senior levels. Then, we integrate RGB and thermal modalities with two distinct fusion modules, namely a shallow feature fusion module and deep feature fusion module for junior and senior features. Finally, we use multilabel supervision to optimize the network in terms of semantic, binary, and boundary characteristics. Experimental results confirm that the proposed architecture, the graded-feature multilabel-learning network, outperforms state-of-the-art methods for urban scene semantic segmentation, and it can be generalized to depth data.
Mask-RCNN and U-Net Ensembled for Nuclei Segmentation

Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the right model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.
Blockchain and the built environment: Potentials and limitations

Blockchain is a relatively new concept that originated from the first cryptocurrency known as Bitcoin and was soon noted to have a much wider range of applications than just serving as the platform for digital cryptocurrency. A blockchain (BC) is essentially a decentralized ledger that records every transaction made in the network, known as a ‘block’, the body of which comprises encrypted data of the entire transaction history. The implementation of decentralized technology in any industry would require augmented security, enforce accountability, and could potentially accelerate a shift in workflow dynamics from the current hierarchical structure to a decentralized, cooperative chain of command and affect a cultural and societal change by encouraging trust and transparency. This paper presents an evaluation survey of blockchain technology and its applications in the built environment and examines the potential integration with the BIM process. Moreover, the study explores how employing distributed ledger technology (DLT) could be advantageous in the BIM working environment by reinforcing network security, providing more reliable data storage and management of permissions, and ensuring change tracing and data ownership. The study discusses the basic fundamentals of distributed ledgers, their potential future applications and current advances, and their classification based on inherent characteristics of consensus reaching and permission management. Furthermore, the paper evaluates the potential application of BC technologies in enhancing the framework for automating the construction design review process such as smart contract technologies and Hyperledger Fabric, as well as discussing the pros, cons, and possible future research directions.
An overview of encryption algorithms in color images

Nowadays, security in data transfer is of special importance. Images are of the most attractive kinds of data in the encryption domain. Color images are more attractive than the gray-level images due to provision of more information. In the present study, various existing color (RGB mode) image encryption schemes have been examined comprehensively based on the application domains in addition to summarizing over 50 studies in this field, most of which being published in the last year. In addition, in this study, color image encryption has been categorized into ten schemes, then the proposed schemes have been compared and their advantages and limitations have been highlighted. Moreover, a complete list of common security analysis techniques for (gray or color) image encryption has been discussed which are capable of evaluating the method potential resistance to different possible attacks. The present study has been carried out to provide detailed knowledge regarding the existing image encryption schemes in the area of the RGB images. Finally, in the current study, various open issues and research directions have been considered in order to explore the promising areas for future developments.
Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system

To increase the robustness against common attacks, a novel image encryption algorithm is proposed based on the phase-truncated short-time fractional Fourier transform (PTSTFrFT) and the hyper-chaotic system. In the algorithm, the original image is divided into four sub-images to be encoded independently. Different from the traditional phase truncation coding, the PTSTFrFT is combined with wave-based permutation to construct the encryption unit (EU) for encoding the sub-images, where the confused phase information is recombined with the amplitude information to guarantee the integrity of image information and the nonlinearity of phase truncation. Besides, the encryption unit can be achieved optically. Furthermore, the corresponding results compose an interim image, whose pixel values and pixel positions are modified by the permutation and the diffusion operations. The diffusion operation is designed with the feedback system to increase the anti-interference ability. The proposed image encryption algorithm has large enough key space and high sensitivity to the keys because of the use of hyper-chaotic system. The simulation results demonstrate that the proposed image encryption algorithm based on the PTSTFrFT is secure and robust enough against the common attacks.
Noninteractive Zero Knowledge for NP from (Plain) Learning with Errors

We finally close the long-standing problem of constructing a noninteractive zero-knowledge (NIZK) proof system for any NP language with security based on the plain Learning With Errors (LWE) problem, and thereby on worst-case lattice problems. Our proof system instantiates the framework recently developed by Canetti et al. [EUROCRYPT’18], Holmgren and Lombardi [FOCS’18], and Canetti et al. [STOC’19] for soundly applying the Fiat–Shamir transform using a hash function family that is correlation intractable for a suitable class of relations. Previously, such hash families were based either on “exotic” assumptions (e.g., indistinguishability obfuscation or optimal hardness of certain LWE variants) or, more recently, on the existence of circularly secure fully homomorphic encryption (FHE). However, none of these assumptions are known to be implied by plain LWE or worst-case hardness. Our main technical contribution is a hash family that is correlation intractable for arbitrary size-S circuits, for any polynomially bounded S, based on plain LWE (with small polynomial approximation factors). The construction combines two novel ingredients: a correlation-intractable hash family for log-depth circuits based on LWE (or even the potentially harder Short Integer Solution problem), and a “bootstrapping” transform that uses (leveled) FHE to promote correlation intractability for the FHE decryption circuit to arbitrary (bounded) circuits. Our construction can be instantiated in two possible “modes,” yielding a NIZK that is either computationally sound and statistically zero knowledge in the common random string model, or vice-versa in the common reference string model.
Semantic relation extraction using sequential and tree-structured LSTM with attention

Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG), and it supports a variety of downstream natural language processing (NLP) tasks such as query answering (QA), semantic search and textual entailment. In addition, the semantic relation extraction task is mainly responsible for identifying entity pairs from raw texts and extracting the semantic relations between the extracted entity pairs. Existing methods consider only lexical-level features and often ignore syntactic features, resulting in poor relation extraction performance. By analyzing the necessity of the syntactic dependency and the contributions of words in a sentence to relation extraction, this paper proposes an end-to-end method that uses bidirectional tree-structured long short-term memory (LSTM) to extract structural features based on the dependency tree of a sentence. To enhance the performance of the relation extraction, the bidirectional sequential LSTM with attention is used to identify word-based features including the positional information of entity pairs and the contribution of words. Then, structural features and word-based features are concatenated to optimize the relation extraction performance. Finally, the proposed method is used on the SemEval 2010 task 8 and the CoNLL04 datasets to validate its performance. The experimental results show that the proposed method achieves state-of-the-art results on the SemEval 2010 task 8 and the CoNLL04 datasets.
SoK

The core technical component of blockchains is consensus: how to reach agreement among a distributed network of nodes. A plethora of blockchain consensus protocols have been proposed---ranging from new designs, to novel modifications and extensions of consensus protocols from the classical distributed systems literature. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematization of knowledge of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work; (ii) proof-of-X protocols that replace proof-of-work with more energy-efficient alternatives; and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. This survey is guided by a systematization framework we develop, to highlight the various building blocks of blockchain consensus design, along with a discussion on their security and performance properties. We identify research gaps and insights for the community to consider in future research endeavours.
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability (caused by the similarity in shape and appearance between vehicles produced by different manufacturers). To address these challenges, we propose a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework. This approach includes two innovations compared with previous methods. First, it overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation. Second, it jointly classifies semantic vehicle attributes (colors and types) while performing ReID, through multi-task learning with the embedded pose representations. Since manually labeling images with detailed pose and attribute information is prohibitive, we create a large-scale highly randomized synthetic dataset with automatically annotated vehicle attributes for training. Extensive experiments validate the effectiveness of each proposed component, showing that PAMTRI achieves significant improvement over state-of-the-art on two mainstream vehicle ReID benchmarks: VeRi and CityFlow-ReID.
Cambridge quantum network

Abstract Future-proofing current fibre networks with quantum key distribution (QKD) is an attractive approach to combat the ever growing breaches of data theft. To succeed, this approach must offer broadband transport of quantum keys, efficient quantum key delivery and seamless user interaction, all within the existing fibre network. However, quantum networks to date either require dark fibres and/or offer bit rates inadequate for serving a large number of users. Here we report a city wide high-speed metropolitan QKD network—the Cambridge quantum network—operating on fibres already populated with high-bandwidth data traffic. We implement a robust key delivery layer to demonstrate essential network operation, as well as enabling encryption of 100 Gigabit per second (Gbps) simultaneous data traffic with rapidly refreshed quantum keys. Network resilience against link disruption is supported by high-QKD link rates and network link redundancy. We reveal that such a metropolitan network can support tens of thousands of users with key rates in excess of 1 kilobit per second (kbps) per user. Our result hence demonstrates a clear path for implementing quantum security in metropolitan fibre networks.
Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel

The B2B sales process is undergoing substantial transformations fueled by advances in information and communications technology, specifically in artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI affects the B2B sales funnel. For each stage of the funnel, we describe key sales tasks, explain the specific contributions AI can bring, and clarify the role humans play. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.