Reviewerly/exHarmony huggingface dataset:
Security Data Collection and Data Analytics in the Internet: A Survey

Attacks over the Internet are becoming more and more complex and sophisticated. How to detect security threats and measure the security of the Internet arises a significant research topic. For detecting the Internet attacks and measuring its security, collecting different categories of data and employing methods of data analytics are essential. However, the literature still lacks a thorough review on security-related data collection and analytics on the Internet. Therefore, it becomes a necessity to review the current state of the art in order to gain a deep insight on what categories of data should be collected and which methods should be used to detect the Internet attacks and to measure its security. In this paper, we survey existing studies about security-related data collection and analytics for the purpose of measuring the Internet security. We first divide the data related to network security measurement into four categories: 1) packet-level data; 2) flow-level data; 3) connection-level data; and 4) host-level data. For each category of data, we provide a specific classification and discuss its advantages and disadvantages with regard to the Internet security threat detection. We also propose several additional requirements for security-related data analytics in order to make the analytics flexible and scalable. Based on the usage of data categories and the types of data analytic methods, we review current detection methods for distributed denial of service flooding and worm attacks by applying the proposed requirements to evaluate their performance. Finally, based on the completed review, a list of open issues is outlined and future research directions are identified.
Taxonomy of Risks posed by Language Models

Responsible innovation on large-scale Language Models (LMs) requires foresight into and in-depth understanding of the risks these models may pose. This paper develops a comprehensive taxonomy of ethical and social risks associated with LMs. We identify twenty-one risks, drawing on expertise and literature from computer science, linguistics, and the social sciences. We situate these risks in our taxonomy of six risk areas: I. Discrimination, Hate speech and Exclusion, II. Information Hazards, III. Misinformation Harms, IV. Malicious Uses, V. Human-Computer Interaction Harms, and VI. Environmental and Socioeconomic harms. For risks that have already been observed in LMs, the causal mechanism leading to harm, evidence of the risk, and approaches to risk mitigation are discussed. We further describe and analyse risks that have not yet been observed but are anticipated based on assessments of other language technologies, and situate these in the same taxonomy. We underscore that it is the responsibility of organizations to engage with the mitigations we discuss throughout the paper. We close by highlighting challenges and directions for further research on risk evaluation and mitigation with the goal of ensuring that language models are developed responsibly.
Are Graph Augmentations Necessary?

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue. A typical pipeline of CL-based recommendation models is first augmenting the user-item bipartite graph with structure perturbations, and then maximizing the node representation consistency between different graph augmentations. Although this paradigm turns out to be effective, what underlies the performance gains is still a mystery. In this paper, we first experimentally disclose that, in CL-based recommendation models, CL operates by learning more uniform user/item representations that can implicitly mitigate the popularity bias. Meanwhile, we reveal that the graph augmentations, which used to be considered necessary, just play a trivial role. Based on this finding, we propose a simple CL method which discards the graph augmentations and instead adds uniform noises to the embedding space for creating contrastive views. A comprehensive experimental study on three benchmark datasets demonstrates that, though it appears strikingly simple, the proposed method can smoothly adjust the uniformity of learned representations and has distinct advantages over its graph augmentation-based counterparts in terms of recommendation accuracy and training efficiency. The code is released at https://github.com/Coder-Yu/QRec.
Multiview Transformers for Video Recognition

Video understanding requires reasoning at multiple spatiotemporal resolutions – from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video Recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretraining. Code and checkpoints are available at: https://github.com/google-research/scenic.
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.
BiFormer: Vision Transformer with Bi-Level Routing Attention

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bilevel routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://github.com/rayleizhu/BiFormer.
A real-time human-robot interaction framework with robust background invariant hand gesture detection

In the light of factories of the future, to ensure productive and safe interaction between robot and human coworkers, it is imperative that the robot extracts the essential information of the coworker. We address this by designing a reliable framework for real-time safe human-robot collaboration, using static hand gestures and 3D skeleton extraction. OpenPose library is integrated with Microsoft Kinect V2, to obtain a 3D estimation of the human skeleton. With the help of 10 volunteers, we recorded an image dataset of alpha-numeric static hand gestures, taken from the American Sign Language. We named our dataset OpenSign and released it to the community for benchmarking. Inception V3 convolutional neural network is adapted and trained to detect the hand gestures. To augment the data for training the hand gesture detector, we use OpenPose to localize the hands in the dataset images and segment the backgrounds of hand images, by exploiting the Kinect V2 depth map. Then, the backgrounds are substituted with random patterns and indoor architecture templates. Fine-tuning of Inception V3 is performed in three phases, to achieve validation accuracy of 99.1% and test accuracy of 98.9%. An asynchronous integration of image acquisition and hand gesture detection is performed to ensure real-time detection of hand gestures. Finally, the proposed framework is integrated in our physical human-robot interaction library OpenPHRI. This integration complements OpenPHRI by providing successful implementation of the ISO/TS 15066 safety standards for “safety rated monitored stop” and “speed and separation monitoring” collaborative modes. We validate the performance of the proposed framework through a complete teaching by demonstration experiment with a robotic manipulator.
ChatGPT outperforms crowd workers for text-annotation tasks

Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles ( n = 6,183), we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by about 25 percentage points on average, while ChatGPT’s intercoder agreement exceeds that of both crowd workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.
A Survey on Urban Traffic Anomalies Detection Algorithms

This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions that detect flow outliers and includes statistical, similarity and pattern mining approaches. The second category contains solutions where the trajectory outliers are derived, including off-line processing for trajectory outliers and online processing for sub-trajectory outliers. Solutions in each of these categories are described, illustrated, and discussed, and open perspectives and research trends are drawn. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of all the kinds of representations in urban traffic data, including flow values, segment flow values, trajectories, and sub-trajectories. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier urban traffic detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.
Errudite: Scalable, Reproducible, and Testable Error Analysis

Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions.This paper codifies model and task agnostic principles for informative error analysis, and presents Errudite, an interactive tool for better supporting this process.First, error groups should be precisely defined for reproducibility; Errudite supports this with an expressive domainspecific language.Second, to avoid spurious conclusions, a large set of instances should be analyzed, including both positive and negative examples; Errudite enables systematic grouping of relevant instances with filtering queries.Third, hypotheses about the cause of errors should be explicitly tested; Errudite supports this via automated counterfactual rewriting.We validate our approach with a user study, finding that Errudite (1) enables users to perform high quality and reproducible error analyses with less effort, (2) reveals substantial ambiguities in prior published error analyses practices, and (3) enhances the error analysis experience by allowing users to test and revise prior beliefs.
Person Re-Identification by Contour Sketch Under Moderate Clothing Change

Person re-identification (re-id), the process of matching pedestrian images across different camera views, is an important task in visual surveillance. Substantial development of re-id has recently been observed, and the majority of existing models are largely dependent on color appearance and assume that pedestrians do not change their clothes across camera views. This limitation, however, can be an issue for re-id when tracking a person at different places and at different time if that person (e.g., a criminal suspect) changes his/her clothes, causing most existing methods to fail, since they are heavily relying on color appearance, and thus, they are inclined to match a person to another person wearing similar clothes. In this work, we call the person re-id under clothing change the “cross-clothes person re-id.” In particular, we consider the case when a person only changes his clothes moderately as a first attempt at solving this problem based on visible light images; that is, we assume that a person wears clothes of a similar thickness, and thus the shape of a person would not change significantly when the weather does not change substantially within a short period of time. We perform cross-clothes person re-id based on a contour sketch of person image to take advantage of the shape of the human body instead of color information for extracting features that are robust to moderate clothing change. To select/sample more reliable and discriminative curve patterns on a body contour sketch, we introduce a learning-based spatial polar transformation (SPT) layer in the deep neural network to transform contour sketch images for extracting reliable and discriminant convolutional neural network (CNN) features in a polar coordinate space. An angle-specific extractor (ASE) is applied in the following layers to extract more fine-grained discriminant angle-specific features. By varying the sampling range of the SPT, we develop a multistream network for aggregating multi-granularity features to better identify a person. Due to the lack of a large-scale dataset for cross-clothes person re-id, we contribute a new dataset that consists of 33,698 images from 221 identities. Our experiments illustrate the challenges of cross-clothes person re-id and demonstrate the effectiveness of our proposed method.
Cascading and Enhanced Residual Networks for Accurate Single-Image Super-Resolution

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.
Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.
Learning attention-guided pyramidal features for few-shot fine-grained recognition

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar objects from different sub-categories with limited supervision. However, traditional few-shot learning solutions typically exploit image-level features and are committed to capturing global silhouettes while accidentally ignore to exploring local details, resulting in an inevitable problem of inconspicuous but distinguishable information loss. Thus, how to effectively address the fine-grained recognition issue given limited samples still remains a major challenging. In this article, we tend to propose an effective bidirectional pyramid architecture to enhance internal representations of features to cater to fine-grained image recognition task in the few-shot learning scenario. Specifically, we deploy a multi-scale feature pyramid and a multi-level attention pyramid on the backbone network, and progressively aggregated features from different granular spaces via both of them. We then further present an attention-guided refinement strategy in collaboration with a multi-level attention pyramid to reduce the uncertainty brought by backgrounds conditioned by limited samples. In addition, the proposed method is trained with the meta-learning framework in an end-to-end fashion without any extra supervision. Extensive experimental results on four challenging and widely-used fine-grained benchmarks show that the proposed method performs favorably against state-of-the-arts, especially in the one-shot scenarios.
Image Dehazing Transformer with Transmission-Aware 3D Position Embedding

Despite single image dehazing has been made promising progress with Convolutional Neural Networks (CNNs), the inherent equivariance and locality of convolution still bottleneck deharing performance. Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazlng performance. The key insight of this study is to investigate how to combine CNN and Transformer for image dehazing. To solve the feature inconsistency issue between Transformer and CNN, we propose to modulate CNN features via learning modulation matrices (i.e., coefficient matrix and bias matrix) conditioned on Transformer features instead of simple feature addition or concatenation. The feature modulation naturally inherits the global context modeling capability of Transformer and the local representation capability of CNN. We bring a haze density-related prior into Trans-former via a novel transmission-aware 3D position embedding module, which not only provides the relative position but also suggests the haze density of different spatial regions. Extensive experiments demonstrate that our method, DeHamer, attains state-of-the-art performance on several image dehazing benchmarks.