Reviewerly/exHarmony huggingface dataset:
Multi-Person Pose Estimation With Accurate Heatmap Regression and Greedy Association

Multi-person pose estimation aims at localizing the 2D keypoints (or body joints) for all the people in the image. There are mainly two paradigms to perform this task: top-down and bottom-up. In this paper, we present an advanced bottom-up approach based on accurate keypoint heatmap regression and greedy keypoint association. Firstly, we develop an encoding-decoding method with Gaussian heatmaps and guiding offset fields to represent multi-person pose information, encompassing keypoint positions and adjacent keypoint associations of all individuals in the scene. In particular, we analyze the deficiency of the Gaussian heatmap representation as regards keypoint localization precision if conventional element-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -type loss is employed merely for heatmap supervision. Therefore, we introduce a peak regularization loss to jointly supervise the heatmap regression. In addition, we present an improved Hourglass Network with multi-scale heatmap aggregation to simultaneously infer the said encoding. Finally, we propose a novel focal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> loss to help the network cope with the imbalanced problem of keypoint detection in heatmaps. Our results show that the proposed approach surpasses other bottom-up approaches on COCO dataset, and even outperforms the top-down approaches on CrowdPose dataset containing more crowded scenes.
Artificial Intelligence to Address Cyberbullying, Harassment and Abuse: New Directions in the Midst of Complexity

Abstract This brief article serves as an introductory piece for the special issue “The Use of Artificial Intelligence (AI) to Address Online Bullying and Abuse.” It provides an overview of the state of the art with respect to the use of AI in addressing various types of online abuse and cyberbullying; current challenges for the field; and it emphasises the need for greater interdisciplinary collaboration on this topic. The article also summarises key contributions of the articles selected for the special issue.
MTL-ABS<sup>3</sup>Net: Atlas-Based Semi-Supervised Organ Segmentation Network With Multi-Task Learning for Medical Images

Organ segmentation is one of the most important step for various medical image analysis tasks. Recently, semi-supervised learning (SSL) has attracted much attentions by reducing labeling cost. However, most of the existing SSLs neglected the prior shape and position information specialized in the medical images, leading to unsatisfactory localization and non-smooth of objects. In this paper, we propose a novel atlas-based semi-supervised segmentation network with multi-task learning for medical organs, named MTL-ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net, which incorporates the anatomical priors and makes full use of unlabeled data in a self-training and multi-task learning manner. The MTL-ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net consists of two components: an Atlas-Based Semi-Supervised Segmentation Network (ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net) and Reconstruction-Assisted Module (RAM). Specifically, the ABS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net improves the existing SSLs by utilizing atlas prior, which generates credible pseudo labels in a self-training manner; while the RAM further assists the segmentation network by capturing the anatomical structures from the original images in a multi-task learning manner. Better reconstruction quality is achieved by using MS-SSIM loss function, which further improves the segmentation accuracy. Experimental results from the liver and spleen datasets demonstrated that the performance of our method was significantly improved compared to existing state-of-the-art methods.
A methodology to estimate the photovoltaic potential on parking spaces and water deposits. The case of the Canary Islands

In regions where the land available is scarce it is of special interest to deploy solar photovoltaic energy without occupying additional land. Besides solar roofs, which have already been extensively studied, there are other type of constructions that could be used for the deployment of solar photovoltaic such as car parking spaces and big water deposits. No much attention has been given so far to these type of elements that could potentially been used to deploy solar photovoltaic energy. So the aim of this research is to establish how relevant can these type of surfaces be in terms of solar energy production. For this purpose a methodology has been developed to identify the surfaces corresponding to uncovered parking spaces and water deposit at regional level using cartographic information, first, and, secondly, to estimate how much of these areas could be used to deploy solar photovoltaic energy. This methodology has been applied to one insular region, the Canary Islands, and the results are surprising in terms of the potentiality of these type of elements for the deployment of solar photovoltaic energy which could cover around 9% of the whole regional electricity demand.
Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques

Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that embed fraudulent activities. In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. We propose a mechanism to detect potential fraud by analyzing human behavior within a data set in this work. This approach combines a predefined topic model and a supervised classifier to generate an alert from the possible fraud-related text. Potential fraud would be detected based on a model built from such a classifier. As a result of this work, a synthetic fraud-related data set is made. Four topics associated with the vertices of the fraud triangle theory are unveiled when assessing different topic modeling techniques. After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. The results of our work suggest that our approach is feasible in practice since several such models obtain an average AUC higher than 0.8. Namely, the fraud triangle theory combined with topic modeling and linear classifiers could provide a promising framework for predictive fraud analysis.
A Lyapunov optimization-based online scheduling algorithm for service provisioning in cloud computing

For economically provisioning services, service providers can purchase combinations of reserved and on-demand instances from Infrastructure-as-a-Service (IaaS) clouds. These different billing options bring challenges to service providers on how to purchase instances optimally to ensure quality of service (QoS) while maximizing revenue. In order to ensure QoS and maximize average revenue, a service provider can purchase fixed number of reserved instances to meet the base workload of its service system, and then dynamically purchase on-demand instances to satisfy the service requests beyond its base workload. However, the random, dynamic, and uncertain nature of service request arrivals makes it difficult for service providers to determine the optimal number of on-demand instances to be purchased dynamically. To solve the above problems, in this paper, we propose an online scheduling algorithm based on the theory of Lyapunov optimization and explore the concept of capacity region and queue stability to ensure QoS. Our online algorithm dynamically purchases on-demand instances and schedules jobs to run according to the bidding price of users without requiring the future workload information. Through our proposed online scheduling algorithm, service providers can both ensure QoS and maximize average revenue, and make a trade-off between them. Simulation results show that our algorithm can effectively achieve bi-objective optimization of maximizing time average revenue as well as ensuring QoS.
The shift of DarkNet illegal drug trade preferences in cryptocurrency: The question of traceability and deterrence

DarkNet markets (DNMs) are flourishing, exploiting cryptocurrencies' pseudo-anonymity feature. To fight the illegal drug trade, it is necessary to understand the factors influencing choices made by DNM consumers. ClearNet forums facilitate interactions among new and experienced DarkNet users, thus serving as an ideal source for investigation and research. The methodology of temporal topic models and sentiment analysis were applied to the ClearNet forum data to explore DNM new drug users’ behavior over time. We inspected the development of major topics from the forum over eight years (2012–19) period to study the evolution in motivations and challenges faced by DNM users. The extracted temporal topic models successfully captured the apparent dynamics of critical events in the crypto world, showing the shift from Bitcoin to Monero. Our analysis demonstrates that the Bitcoin traceability announcement (made public in 2015) did not have a deterrent effect on the usage of Bitcoin, and this declaration instead acted as an advertisement for DNMs. Drug buyers used Bitcoin despite its traceability until Monero became untraceable in 2017 due to its privacy update.
The Specimen Data Refinery: A Canonical Workflow Framework and FAIR Digital Object Approach to Speeding up Digital Mobilisation of Natural History Collections

Abstract A key limiting factor in organising and using information from physical specimens curated in natural science collections is making that information computable, with institutional digitization tending to focus more on imaging the specimens themselves than on efficiently capturing computable data about them. Label data are traditionally manually transcribed today with high cost and low throughput, rendering such a task constrained for many collection-holding institutions at current funding levels. We show how computer vision, optical character recognition, handwriting recognition, named entity recognition and language translation technologies can be implemented into canonical workflow component libraries with findable, accessible, interoperable, and reusable (FAIR) characteristics. These libraries are being developed in a cloud-based workflow platform—the ‘Specimen Data Refinery’ (SDR)—founded on Galaxy workflow engine, Common Workflow Language, Research Object Crates (RO-Crate) and WorkflowHub technologies. The SDR can be applied to specimens’ labels and other artefacts, offering the prospect of greatly accelerated and more accurate data capture in computable form. Two kinds of FAIR Digital Objects (FDO) are created by packaging outputs of SDR workflows and workflow components as digital objects with metadata, a persistent identifier, and a specific type definition. The first kind of FDO are computable Digital Specimen (DS) objects that can be consumed/produced by workflows, and other applications. A single DS is the input data structure submitted to a workflow that is modified by each workflow component in turn to produce a refined DS at the end. The Specimen Data Refinery provides a library of such components that can be used individually, or in series. To cofunction, each library component describes the fields it requires from the DS and the fields it will in turn populate or enrich. The second kind of FDO, RO-Crates gather and archive the diverse set of digital and real-world resources, configurations, and actions (the provenance) contributing to a unit of research work, allowing that work to be faithfully recorded and reproduced. Here we describe the Specimen Data Refinery with its motivating requirements, focusing on what is essential in the creation of canonical workflow component libraries and its conformance with the requirements of an emerging FDO Core Specification being developed by the FDO Forum.
FlexSpin: A Scalable CMOS Ising Machine with 256 Flexible Spin Processing Elements for Solving Complex Combinatorial Optimization Problems

Combinatorial optimization problems (COPs) are ubiquitous in applications such as finance, logistics, drug discovery, supply chain optimization, and public transportation. However, many COPs are non-deterministic polynomial-time hard (NP-hard), which require significant computation costs using conventional computers. Recently, Ising machines based on quantum annealing [1] and CMOS technologies [2]–[5] have received growing interest as efficient hardware accelerators that could replace traditional computing hardware (CPUs and GPUs) for solving real-world COPs. An Ising model, a basis of the Ising machines, finds the optimal (or sub-optimal) solutions of the COPs by efficiently exploring a solution space based on its intrinsic convergence property towards the lower Ising Hamiltonian (or energy). Another critical enabler is the annealing process, which prevents the Ising machine from converging to local minimum solutions and increases the chance to find the global optimum. Existing Ising machines with fixed hardware topology based on simple graphs (e.g., King's graph [2] [4]) are ill-suited to a practical Ising model with arbitrary spin interactions. In contrast, a fully connected Ising machine [3] can map a wide range of Ising models, but at the cost of significant overheads in area, power consumption, and latency. A recent work [5] demonstrated a reconfigurable Ising machine that enables an arbitrary interaction between spins. However, it has a restricted number of interactions per spin (up to 8) due to the fixed hardware topology (King's graph), which only allows eight interactions. In this paper, we propose an Ising machine that allows up to 28 arbitrary spin interactions based on flexible spin processing element (PE) circuits with temporal reconfigurability (i.e., multi-cycle operations with decomposed spin maps) to overcome the limited spatial spin interactions of the prior CMOS Ising machines. The proposed Ising machine with multi-spins embedded in each PE and multi-chip spin operations provides a scalable solution for solving the more complicated COPs.
Reflection symmetry detection of shapes based on shape signatures

We present two novel shape signature-based reflection symmetry detection methods with their theoretical underpinning and empirical evaluation. LIP-signature and R-signature share similar beneficial properties allowing to detect reflection symmetry directions in a high-performing manner. For the shape signature of a given shape, its merit profile is constructed to detect candidates of symmetry direction. A verification process is utilized to eliminate the false candidates by addressing Radon projections. The proposed methods can effectively deal with compound shapes which are challenging for traditional contour-based methods. To quantify the symmetric efficiency, a new symmetry measure is proposed over the range [0, 1]. Furthermore, we introduce two symmetry shape datasets with a new evaluation protocol and a lost measure for evaluating symmetry detectors. Experimental results using standard and new datasets suggest that the proposed methods prominently perform compared to state of the art.
Software-as-a-Service Security Challenges and Best Practices: A Multivocal Literature Review

Cloud computing (CC) is the delivery of computing services on demand and is charged using a “pay per you use” policy. Of the multiple services offered by CC, SaaS is the most popular and widely adapted service platform and is used by billions of organizations due to its wide range of benefits. However, security is a key challenge and obstacle in cloud adoption and therefore needs to be addressed. Researchers and practitioners (R&amp;P) have discussed various security challenges for SaaS along with possible solutions. However, no research study exists that systematically accumulates and analyzes the security challenges and solutions. To fill this gap and provide the state-of-the-art (SOTA) picture of SaaS security, this study provides a comprehensive multivocal literature review (MVLR), including SaaS security issues/challenges and best practices for mitigating these security issues. We identified SaaS security issues/challenges and best practices from the formal literature (FL) as well as the grey literature (GL) to evaluate whether R&amp;P is on the same page or if controversies exist. A total of 93 primary studies were identified, of which 58 are from the FL and 35 belong to the GL. The studies are from the last ten years, from 2010 to 2021. The selected studies were evaluated and analyzed to identify the key security issues faced by SaaS computing and to be aware of the best practices suggested by R&amp;P to improve SaaS security. This MVLR will assist SaaS users to identify the many areas in which additional research and development in SaaS security is required. According to our study findings, data breaches/leakage, identity and access management, governance and regulatory compliance/SLA compliance, and malicious insiders are the key security challenges with the maximum frequency of occurrence in both FL and GL. On the other hand, R&amp;P agree that up-to-date security controls/standards, the use of strong encryption techniques, regulatory compliance/SLA compliance, and multifactor authentication are the most important solutions.
IMSiam: IoU-aware Matching-adaptive Siamese network for object tracking

Fully convolutional Siamese networks have shown their advantages in the visual object tracking task. However, most existing Siamese-based trackers still suffer from poor matching information and a low correlation between the classification score and bounding box estimation. To address these issues, we propose an IoU-aware Matching-adaptive Siamese network (IMSiam) for visual tracking in this paper. Specifically, a matching-adaptive network is proposed, which integrates multiple types of encoded feature maps and adaptively sample matching information to simultaneously perform target classification and bounding box regression. And we introduce an IoU-aware head to equip the Siamese detector with the capacity of IoU prediction for each regressed box, which then penalizes the classification score to obtain a more confident target region. Benefiting from the adaptive matching information and IoU-based classification outputs, the model can localize the target more accurately while avoiding the redundant search of scale penalty factor during tracking. Experimental results on OTB2013, OTB2015, VOT2018, VOT2019, LaSOT, and Got10k datasets demonstrate that the proposed tracker performs favorably against state-of-the-art methods.
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR

Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition. A common solution is to segment the audio in advance using a separate voice activity detector (VAD) that decides segment boundary locations based purely on acoustic speech/non-speech information. VAD segmenters, however, may be sub-optimal for real-world speech where, e.g., a complete sentence that should be taken as a whole may contain hesitations in the middle ("set an alarm for... 5 o'clock"). We propose to replace the VAD with an end-to-end ASR model capable of predicting segment boundaries in a streaming fashion, allowing the segmentation decision to be conditioned not only on better acoustic features but also on semantic features from the decoded text with negligible extra computation. In experiments on real world long-form audio (YouTube) with lengths of up to 30 minutes, we demonstrate 8.5% relative WER improvement and 250 ms reduction in median end-of-segment latency compared to the VAD segmenter baseline on a state-of-the-art Conformer RNN-T model.
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm

Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting.However, we find that this learning paradigm inherits the vulnerability from the pre-training stage, where model predictions can be misled by inserting certain triggers into the text.In this paper, we explore this universal vulnerability by either injecting backdoor triggers or searching for adversarial triggers on pre-trained language models using only plain text.In both scenarios, we demonstrate that our triggers can totally control or severely decrease the performance of prompt-based models fine-tuned on arbitrary downstream tasks, reflecting the universal vulnerability of the prompt-based learning paradigm.Further experiments show that adversarial triggers have good transferability among language models.We also find conventional fine-tuning models are not vulnerable to adversarial triggers constructed from pre-trained language models.We conclude by proposing a potential solution to mitigate our attack methods.Code and data are publicly available. 1Adversarial Trigger: "Videos Loading Replay" Fake News Detection Ori (<mask> -> fake): It was <mask> .CNN reported that President Barack Obama resigned today .
It Won’t Happen to Me: Surveying SME Attitudes to Cyber-security

We report an online survey of 85 U.K-based SMEs that explored their threat and coping appraisals toward five common types of cyber-attack: Network being hacked; Data being stolen or encrypted; malware infection; mobile devices being compromised; and phishing e-mail attack. Overall, SMEs’ reported assessment of the risk of an attack was low, particularly for the possibility of their business network being hacked or their data being stolen or encrypted. However, there was an incongruence in their Threat Appraisals since, while they believed the risks to be low, they reported that the impact would be high. In terms of Coping Appraisal, respondents indicated that measures to prevent such attacks were both inexpensive and effective. However, their reported self-efficacy was significantly lower for keeping mobile devices safe and avoiding phishing attacks. We discuss these results taking into consideration additional qualitative data and provide recommendations for SME engagement.