Reviewerly/exHarmony huggingface dataset:
An assessment of the state of firearm sales on the Dark Web

Western law enforcement agencies have made multiple arrests targeting individuals purchasing firearms on Dark Web platforms in recent years, as these transactions may violate national laws and facilitate offline violence. Despite its market presence and growth, research exploring these online illicit markets has been scant, especially as it relates to how firearms are priced on the Dark Web, and the factors that influence their price point. Given this gap in the literature, the current study utilized a sample of 287 firearm products across 20 Dark Web vendors operating in both crypto markets and shops to identify the range and pricing model of illicit weapons. Analyses revealed that long guns offered on the Dark Web had lower average listed prices than their manufacturer's suggested retail price (MSRP), while handguns had higher advertised prices than their recommended retail value. Further, products' MSRP was a significant predictor of firearms' price point for both handguns and long guns, whereas offering a customer service line was only significant for handguns' price point. The implications of this analysis for our understanding of illicit online market operations are discussed in detail.
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition

Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded characters, these systems are in principal open vocabulary systems. In practice, however, they often fail to recognize words not seen during training, e.g., named entities, numbers or technical terms. To alleviate this problem we supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly. After the training of the ASR system, and when it has already been deployed, a relevant word can be added or subtracted instantly without the need for further training. In this paper we demonstrate that through this mechanism our system is able to recognize more than 85% of newly added words that it previously failed to recognize compared to a strong baseline.
A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification

Local Binary Pattern (LBP) based framework only uses a scalar threshold to binarize all magnitude vectors in <i>P</i> different directions around each center pixel of a texture image. Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in <i>P</i> different directions around each center pixel. Furthermore, the value of magnitude vectors can have dramatic changes from coarse areas to flat areas in the same texture image. Therefore, using a scalar threshold calculated from whole texture image can not precisely binarize all magnitude vectors in coarse areas and flat areas simultaneously. To overcome these two drawbacks, we propose a novel adaptively binarizing magnitude vector (ABMV) method. Firstly, we adaptively calculate the average vector threshold <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\vec{t}_{P}}$</tex-math></inline-formula> with <i>P</i> different directional values of all magnitude vectors to replace the scalar threshold <i>t</i> to binarize the magnitude vectors. The proposed ABMV method can more precisely extract the different magnitude features in <i>P</i> different directions around each center pixel. Secondly, we divide the original texture image into smaller sub-images and adaptively extract their average vector threshold from each sub-image separately. Because the correlation of the pixels in the same sub-image is stronger than that in a whole texture image, the ABMV method can more precisely extract different magnitude features from either coarse areas or flat areas. Finally, we introduce the proposed ABMV method into LBP-based framework. Extensive experiments are conducted on five representative texture databases: Outex, UIUC, CUReT, XU_HR and ALOT database. After introducing the ABMV method into CLBP, CLBC, BRINT and CJLBP, the classification accuracy and the robustness to noise of these methods can be significantly improved.
Federated matrix factorization with privacy guarantee

Matrix factorization (MF) approximates unobserved ratings in a rating matrix, whose rows correspond to users and columns correspond to items to be rated, and has been serving as a fundamental building block in recommendation systems. This paper comprehensively studies the problem of matrix factorization in different federated learning (FL) settings, where a set of parties want to cooperate in training but refuse to share data directly. We first propose a generic algorithmic framework for various settings of &lt;u&gt;f&lt;/u&gt;ederated &lt;u&gt;m&lt;/u&gt;atrix &lt;u&gt;f&lt;/u&gt;actorization (FMF) and provide a theoretical convergence guarantee. We then systematically characterize privacy-leakage risks in data collection, training, and publishing stages for three different settings and introduce privacy notions to provide end-to-end privacy protections. The first one is vertical federated learning (VFL), where multiple parties have the ratings from the same set of users but on disjoint sets of items. The second one is horizontal federated learning (HFL), where parties have ratings from different sets of users but on the same set of items. The third setting is local federated learning (LFL), where the ratings of the users are only stored on their local devices. We introduce adapted versions of FMF with the privacy notions guaranteed in the three settings. In particular, a new private learning technique called embedding clipping is introduced and used in all the three settings to ensure differential privacy. For the LFL setting, we combine differential privacy with secure aggregation to protect the communication between user devices and the server with a strength similar to the local differential privacy model, but much better accuracy. We perform experiments to demonstrate the effectiveness of our approaches.
Blockchain-based application at a governmental level: disruption or illusion? The case of Estonia

Abstract Blockchain technology enables new kinds of decentralized systems. Thus, it has often been advocated as a “disruptive” technology that could have the potentiality of reshaping political, economic, and social relations, “solving” problems like corruption, power centralization, and distrust toward political institutions. Blockchain has been gradually gaining attention beyond finance and is thus applied by a range of different actors. This includes local, regional, and national governments interested in the potentiality of experimenting with blockchain-supported governance. This article contributes to identifying blockchain as a contested socio-political object prone to contradictory political imaginaries regarding its potentialities, particularly when applied to policy. The article explores some of the most praised of blockchain’s affordances (e.g., decentralization and transparency) in the context of Estonia, one of the most cited examples of blockchain governmental applications. Estonia has received international attention as the alleged first national infrastructure integrating blockchain. However, so far, few have asked: what kind of blockchain-based tools have been built by the Estonian government in practice and why? And to what extent do blockchain-based governmental applications reflect the original promises of disruption of the crypto-community? This article draws on a qualitative approach to explore several blockchain-based socio-technical objects to identify the narratives that have emerged in Estonia. The research shows clear contrasting views between stakeholders and technical experts from inside and outside the institutional sphere. The conflict revolves around two different social imaginaries associated with permissioned vs. public blockchains. The paper concludes with an analysis of the profound political implications of each vision.
Real-Time Visual Analysis of High-Volume Social Media Posts

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.
A Robust Remote Sensing Image Watermarking Algorithm Based on Region-Specific SURF

Remote sensing image watermarking algorithm has weak resistance to geometric attacks; therefore, paper proposes a reversible watermarking algorithm based on SURF (Speeded Up Robust Features) feature points to select ROI and embed mid and low-frequency subbands, respectively, which can effectively resist geometric attack. The algorithm first extracts the SURF feature points of the carrier and then performs an inverse wavelet transform on the carrier image to filter out the low-frequency coefficients of the ROI and the intermediate frequency coefficients of the non-interest area (ROB). With sampling pyramid decomposition, the near subband after watermark decomposition is embedded in the low-frequency subband of the region of interest, and the residual subband is embedded in the intermediate frequency coefficient of the non-interesting region. Experimental data show that the algorithm can resist conventional geometric attacks. The similarity of the watermark is high, and the NC value is kept above 0.89, which has good reversibility and robustness.
Attribute Group Editing for Reliable Few-shot Image Generation

Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable GAN training process and the limited training data, the generated images are often of low quality and low diversity. In this work, we propose a new “editing-based” method, i.e., Attribute Group Editing (AGE), for few-shot image generation. The basic assumption is that any image is a collection of attributes and the editing direction for a specific attribute is shared across all categories. AGE examines the internal representation learned in GANs and identifies semantically meaningful directions. Specifically, the class embedding, i.e., the mean vector of the latent codes from a specific category, is used to represent the category-relevant attributes, and the category-irrelevant attributes are learned globally by Sparse Dictionary Learning on the difference between the sample embedding and the class embedding. Given a GAN well trained on seen categories, diverse images of unseen categories can be synthesized through editing category-irrelevant attributes while keeping category-relevant attributes unchanged. Without re-training the GAN, AGE is capable of not only producing more realistic and diverse images for downstream visual applications with limited data but achieving controllable image editing with interpretable category-irrelevant directions. Code is available at https://github.com/UniBester/AGE.
Trajectory Planning for an Autonomous Vehicle in Spatially Constrained Environments

Road shoulders and slopes often appear in unstructured environments. They make 2.5D vehicle trajectory planning commonly seen in our daily life, which lies on a 2D manifold embedded in a 3D space. The height difference of these terrains brings spatially dependent constraints on vehicle maneuvers, such as the limit on vehicle steering for vehicle tire protection when a vehicle approaches a road shoulder edge. These constraints have an “if-else” structure since they are activated only when the vehicle passes through the local area with a height difference, making the restriction on variables coupled with the judgment of variables. This makes the application of state-of-art optimization-based planners challenging. To solve this problem, we devise an approximation formulation for these constraints in the trajectory planning optimization problem, whose solution depends on a proper initial guess for the optimizer. We propose a two-stage trajectory planning framework, where the first stage improves the hybrid A* algorithm by adding spatially dependent constraints into node expansion to provide the initial guess. Then, the optimization problem with the formulated spatially dependent constraints is solved for further trajectory smoothness and quality. Finally, the simulation results validate the fast and high-quality planning performance of our proposed framework.
Multi-Level Federated Graph Learning and Self-Attention Based Personalized Wi-Fi Indoor Fingerprint Localization

Deep learning-based Wi-Fi indoor fingerprint localization, which requires a large received signal strength (RSS) dataset for training, has been widely studied. Federated learning (FL) is recently introduced into indoor localization in order to address the problem of data sharing without privacy disclosure. However, under the serious data heterogeneity of FL, the averaged model will perform worse for individual client. In this letter, a multi-level federated graph learning and self-attention based personalized indoor localization method is proposed to further capture the intrinsic features of RSS, and learn the aggregation manner of shared information uploaded by clients, with better personalization accuracy. The experimental results demonstrate that proposed methods achieve a higher personalized localization accuracy than compared personalized federated learning (PFL) methods, in most of the simulation settings.
RGB-D Crowd Counting With Cross-Modal Cycle-Attention Fusion and Fine-Coarse Supervision

To tackle the negative effect of the arbitrary crowd distribution on the counting task, in this article, we propose a novel RGB-D crowd counting approach, including a cross-modal cycle-attention fusion (CmCaF) model and a novel fine-coarse (FC) supervision. In the feature level, the CmCaF model combines the RGB feature and depth feature in a cycle-attention way so as to model the crowd distribution effectively. In the supervision level, the novel design of FC supervision could optimize the counting model from both the fine pixel-aware level and coarse region-aware level to enhance its sensitivity to the whole crowd distribution and the instance location. Extensive evaluations on benchmarks well illustrate the feasibility of the proposed approach for the RGB-D crowd counting, as well as RGB and RGB-T counting. And the ablation study demonstrates the effectiveness of its main components on both the feature representation of cross-modal data and the accurate estimation of the crowd distribution.
STRATISFIMAL LAYOUT: A modular optimization model for laying out layered node-link network visualizations

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality - as compared to scalability - because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing Stratisfimal Layout, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously - crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/qdyt9 with live examples at https://visdunneright.github.io/stratisfimal/.
Constructing periodic orbits of high-dimensional chaotic systems by an adjoint-based variational method

Chaotic dynamics in systems ranging from low-dimensional nonlinear differential equations to high-dimensional spatiotemporal systems including fluid turbulence is supported by nonchaotic, exactly recurring time-periodic solutions of the governing equations. These unstable periodic orbits capture key features of the turbulent dynamics and sufficiently large sets of orbits promise a framework to predict the statistics of the chaotic flow. Computing periodic orbits for high-dimensional spatiotemporally chaotic systems remains challenging as known methods either show poor convergence properties because they are based on time-marching of a chaotic system causing exponential error amplification, or they require constructing Jacobian matrices which is prohibitively expensive. We propose a new matrix-free method that is unaffected by exponential error amplification, is globally convergent, and can be applied to high-dimensional systems. The adjoint-based variational method constructs an initial value problem in the space of closed loops such that periodic orbits are attracting fixed points for the loop dynamics. We introduce the method for general autonomous systems. An implementation for the one-dimensional Kuramoto-Sivashinsky equation demonstrates the robust convergence of periodic orbits underlying spatiotemporal chaos. Convergence does not require accurate initial guesses and is independent of the period of the respective orbit.
Reversible data hiding in encrypted images without additional information transmission

Reversible data hiding in encrypted images (RDHEI) is an essential branch of image reversible data hiding. Over the past decade, many significant achievements have been made. However, for most RDHEI researches, the cloud service user (CSU) demands either an encryption method specified by the cloud service provider (CSP) or prior knowledge about the original image to indicate the preprocessing operation. This characteristic may lead to some security issues like information leakage that may limit the use of this type of technology. To solve this problem, we propose a simple but effective RDHEI method that separates the CSU and CSP. The owner CSU exclusively sends the standard stream cipher image to the CSP, while the CSP can embed message data into the encrypted image through a simple MSB replacement without knowing the encrypted method or prior knowledge about the original image. The embedded message data can be extracted; meanwhile, the image can be approximately recovered by another authentic CSU. Since the proposed fundamental method is not error-free in most circumstances, we provide an alternative option to achieve reversibility by transmitting a perfect recovery key from the owner CSU to any authentic CSU. The experimental results demonstrate the efficacy of the proposed method. • This paper proposes an RDHEI method without any additional information transmission between the image owner and the data hider. • The security level has a significant improvement compared to the methods with preprocessing. • The proposed method improves the performance in terms of embedding and reversibility compared to the baseline methods. • The embedding performance is not influenced by the image contents, and the algorithmic complexity is decreased.
Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transformer-based multiple instance learning method designed to identify abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., videos containing at least one frame with polyp). In our method, local and global temporal dependencies are seamlessly captured while we simultaneously optimise video and snippet-level anomaly scores. A contrastive snippet mining method is also proposed to enable an effective modelling of the challenging polyp cases. The resulting method achieves a detection accuracy that is substantially better than current state-of-the-art approaches on a new large-scale colonoscopy video dataset introduced in this work. Our code and dataset are available at https://github.com/tianyu0207/weakly-polyp .
Enhanced Security and Privacy Issue in Multi-Tenant Environment of Green Computing Using Blockchain Technology

Blockchain (BC) has developed to a certified choice for dispersed, peer-to-peer, disseminated translucent record systems with various data structures. Green computing (GC) is currently the concentrated expectant approach for the incorporation of smart expertise in a different and dispersed world of power usage. Security is becoming a foremost dispute in adopting multiple tenants in a green computing environment. A multi-tenancy is considered as the most essential characteristic of green computing, but at present, multi-tenancy is facing challenges from privacy and security domains, which may hinder the advancement of going green. Going green has huge benefits such as energy efficiency, eco-friendliness, multi-tenancy, recycling, and virtualization. Hence, applying green computing technology such as blockchain can improve operational security in a cloud environment. In this paper, therefore, various forms of green computing, the need for green computing, benefits, and security concerns of multi-tenancy in cloud environments are discussed. Finally, this paper proposed blockchain technology as a method to deal with the security and privacy challenges in a multi-tenant environment. The study implemented blockchain in a cloud environment with the use of Ganache and MetaMask to create a dummy secure account for each cloud tenant.