Reviewerly/exHarmony huggingface dataset:
BlindHub: Bitcoin-Compatible Privacy-Preserving Payment Channel Hubs Supporting Variable Amounts

Payment Channel Hub (PCH) is a promising solution to the scalability issue of first-generation blockchains or cryptocurrencies such as Bitcoin. It supports off-chain payments between a sender and a receiver through an intermediary (called the tumbler). Relationship anonymity and value privacy are desirable features of privacy-preserving PCHs, which prevent the tumbler from identifying the sender and receiver pairs as well as the payment amounts. To our knowledge, all existing Bitcoin-compatible PCH constructions that guarantee relationship anonymity allow only a (predefined) fixed payment amount. Thus, to achieve payments with different amounts, they would require either multiple PCH systems or running one PCH system multiple times. Neither of these solutions would be deemed practical.In this paper, we propose the first Bitcoin-compatible PCH that achieves relationship anonymity and supports variable amounts for payment. To achieve this, we have several layers of technical constructions, each of which could be of independent interest to the community. First, we propose BlindChannel, a novel bi-directional payment channel protocol for privacy-preserving payments, where one of the channel parties is unable to see the channel balances. Then, we further propose BlindHub, a three-party (sender, tumbler, receiver) protocol for private conditional payments, where the tumbler pays to the receiver only if the sender pays to the tumbler. The appealing additional feature of BlindHub is that the tumbler cannot link the sender and the receiver while supporting a variable payment amount. To construct BlindHub, we also introduce two new cryptographic primitives as building blocks, namely Blind Adaptor Signature (BAS), and Flexible Blind Conditional Signature (FBCS). BAS is an adaptor signature protocol built on top of a blind signature scheme. FBCS is a new cryptographic notion enabling us to provide an atomic and privacy-preserving PCH. Lastly, we instantiate both BlindChannel and BlindHub protocols and present implementation results to show their practicality.
Spectral Library of European Pegmatites, Pegmatite Minerals and Pegmatite Host-Rocks – the GREENPEG project database

Abstract. The New Exploration Tools for European Pegmatite Green-Tech Resources (GREENPEG) spectral database contains the spectral signature, obtained through reflectance spectroscopy studies, of European pegmatites and minerals as well as their host rocks. Samples include pegmatites of the Nb–Y–F (NYF) and Li–Cs–Ta (LCT) chemical types and host rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. The database contains the reflectance spectra (raw and continuum-removed spectra), sample photographs, and main absorption features automatically extracted by a self-proposed Python routine. Whenever possible, spectral mineralogy was interpreted based on the continuum-removed spectra. A detailed description of the database, its content and structure, the measuring instrument, and interoperability with geographic information system (GIS) tools is available in this database report. Moreover, examples of how the data can be used and interpreted are also provided. The advantages and added value of the presented dataset reside in its European scale, with representative samples from pegmatites with distinct genesis, mineralogy, structure, and host rocks that can be used as a reference for pegmatite exploration at a global scale via, for example, satellite image processing. The reported spectral mineral assemblages can also be of interest when considering resource estimation or ore processing. Thus, it is expected that this open dataset, available on the Zenodo platform https://doi.org/10.5281/zenodo.6518318 (Cardoso-Fernandes et al., 2022b), will be a reference for distinct types of users, ranging from academia to industry.
Examining textism convergence in mediated interactions

Textisms refer to unconventional digital cues used to convey nonverbal information in text communication. However, little is known about how these cues operate and what theoretical underpinnings help us understand when users choose to integrate textisms into their personal and professional online interactions. One theory that explains this phenomenon is communication accommodation theory (CAT), which describes how people adapt their behaviors in interpersonal and intergroup contexts using verbal and nonverbal strategies to accommodate a conversational partner. Using CAT as a lens, this study examined a dataset of 635 naturally occurring text message screenshots to identify how many times senders and receivers converged in using specific types of textisms. Through examining the degree of convergence that occurred in 15 different types of textisms, it was found that three textisms generated a high degree of convergence or matching behaviors (phrase-shorteners, emojis, and word-#substitutions) and five textisms showed a moderate degree of convergence (cases-upper, markers-missing, lexical-surrogates, word-expansions, and cases-lower). From the textisms that resulted in high to moderate convergence, three were found to be statistically significant (phrase-shorteners, emojis, and markers-missing). Through the examination of text messaging and adaptation behaviors, scholars can better understand the contexts in which users intentionally integrate textisms in our evolving era of mediated interpersonal interactions.
Robust Adversarial Attacks Detection Based on Explainable Deep Reinforcement Learning for UAV Guidance and Planning

The dangers of adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents operating in public are increasing. Adopting AI-based techniques and, more specifically, Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but can add concerns regarding the safety of those techniques and their vulnerability against adversarial attacks. Confusion in the agent's decision-making process caused by these attacks can seriously affect the safety of the UAV. This article proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and the UAVs adopting them from attacks. The agent adopts a Deep Reinforcement Learning (DRL) scheme for guidance and planning. The agent is trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that utilises Artificial Potential Field (APF) to improve training times and obstacle avoidance performance. A simulated environment for UAV explainable DRL-based planning and guidance, including obstacles and adversarial attacks, is built. The adversarial attacks are generated by the Basic Iterative Method (BIM) algorithm and reduced obstacle course completion rates from 97% to 35%. Two adversarial attack detectors are proposed to counter this reduction. The first one is a Convolutional Neural Network Adversarial Detector (CNN-AD), which achieves accuracy in the detection of 80%. The second detector utilises a Long Short Term Memory (LSTM) network. It reaches an accuracy of 91% with faster computing times compared to the CNN-AD, allowing for real-time adversarial detection.
Multinomial Naïve Bayes Classifier for Sentiment Analysis of Internet Movie Database

Sentiment analysis (SA), also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone behind a piece of text. It involves analyzing the text to identify whether it expresses a positive, negative, or neutral sentiment. SA can be applied to various types of text data such as social media posts, customer reviews, news articles, and more. This experiment is based on the Internet Movie Database (IMDB) dataset, which comprises movie reviews and the positive or negative labels related to them. Our research experiment’s objective is to identify the model with the best accuracy and the most generality. Text preprocessing is the first and most critical phase in an NLP system since it significantly impacts the overall accuracy of the classification algorithms. The experiment implements unsupervised sentiment classification algorithms including Valence Aware Dictionary and sentiment Reasoner (VADER) and TextBlob. We also examine the supervised sentiment classifications methods such as Naïve Bayes (Bernoulli NB and Multinomial NB). The Term Frequency-Inverse Document Frequency (TFIDF) model is used to feature selection and extractions. The combination of Multinomial NB and TFIDF achieves the highest accuracy, 87.63%, for both classification reports based on our experiment result.
Revisiting Softmax for Uncertainty Approximation in Text Classification

Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensive as it requires multiple forward passes through the model. A cheaper alternative is to simply use a softmax based on a single forward pass without dropout to estimate model uncertainty. However, prior work has indicated that these predictions tend to be overconfident. In this paper, we perform a thorough empirical analysis of these methods on five datasets with two base neural architectures in order to identify the trade-offs between the two. We compare both softmax and an efficient version of MC dropout on their uncertainty approximations and downstream text classification performance, while weighing their runtime (cost) against performance (benefit). We find that, while MC dropout produces the best uncertainty approximations, using a simple softmax leads to competitive, and in some cases better, uncertainty estimation for text classification at a much lower computational cost, suggesting that softmax can in fact be a sufficient uncertainty estimate when computational resources are a concern.
Effects of Immersive Media on Emotion and Memory: An Experiment Comparing Article, 360-video, and Virtual Reality

Fields such as immersive journalism and VR-based media consumption are expected to holistically engross users and have a deeper emotional impact as well as internationalization of information. ThisWe used a between-subjects repeated-measures experiment (N = 87) to study employs contenteffects of a 360-degree video presented in three levels of immersiveness. High and medium immersion (VR-360 and Screen-360) presented the original video using different technology, whereas low immersion (Article) represented video content in the form of a transcript and video stills. The results suggest that the higher immersiveness has a more prominentgreater effect on the emotional response of consumers. Moreover, higher negative affect after the media experience appears to be associated seems to predictwith lower cued recall memory of the presented content. When it comes to individual differences, age in the range of 18-40 years does not seem to influence the strength of the affect change but females might experience a higher change than males. This study suggests caution when creating informational but highly emotional immersive media content as the negative emotional experience might impede memory of factual information. These implications are further applicable to fields other than journalism, such as education and simulation trainings.
Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings

Energy-efficient buildings have gained increasing interest in the last decades as they provide optimal energy management. With the emergence of smart homes, many smart tools have been developed to optimize energy efficiency such as activities recognition (AR) and occupancy estimation (OE). The creation of these smart building tools may be disrupted by the scarcity of labeled data. Indeed, data labeling is a tedious and time-consuming task and, it can be very expensive for building objectives. However, labeled data scarcity can be solved by sharing knowledge from different domains using unsupervised domain adaptation techniques. Also, privacy issues can emerge and prevent the use of certain types of data that share residents’ attitudes. However, data privacy can be preserved by considering approaches that do not have direct access to original data. In this research, we provide a comparative analysis between unsupervised domain adaptation (UDA) methods, applied to the tasks of AR and OE, with and without direct access to source domain data. We have 6 adapted approaches for UDA with access to labeled source data: domain separation networks (DSN), cluster alignment with a teacher (CAT), CAT+ gradient reversal (RevGrad), CAT + robust RevGrad (rRevGrad), Auxiliary Target Domain-Oriented Classifier (ATDOC) with nearest centroid classifier (NC), and ATDOC with neighborhood aggregation (NA). Also, we have 6 adapted methods for UDA without labeled source data: confidence score weighting adaptation using joint model data structure (CoWA-JMDS), CoWA-JMDS without weights mixup, divide and contrast (DaC), attracting and dispersing (AaD), source hypothesis transfer with information maximization (SHOT-IM), and source hypothesis transfer with self-supervised pseudo-labeling (SHOT-Pseudo-labeling). All the considered methods have been tested on AR and OE datasets collected using ambient sensors. The comparative analysis made in this work has shown us impressive findings and has given great ideas about the type of approaches (with access or without access to the source data) that we should consider for real-world smart building applications.
Silph: A Framework for Scalable and Accurate Generation of Hybrid MPC Protocols

Many applications in finance and healthcare need access to data from multiple organizations. While these organizations can benefit from computing on their joint datasets, they often cannot share data with each other due to regulatory constraints and business competition. One way mutually distrusting parties can collaborate without sharing their data in the clear is to use secure multiparty computation (MPC). However, MPC’s performance presents a serious obstacle for adoption as it is difficult for users who lack expertise in advanced cryptography to optimize. In this paper, we present Silph, a framework that can automatically compile a program written in a high-level language to an optimized, hybrid MPC protocol that mixes multiple MPC primitives securely and efficiently. Compared to prior works, our compilation speed is improved by up to 30000×. On various database analytics and machine learning workloads, the MPC protocols generated by Silph match or outperform prior work by up to 3.6×.
On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI’s CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
MagBackdoor: Beware of Your Loudspeaker as A Backdoor For Magnetic Injection Attacks

An audio system containing loudspeakers and microphones is the fundamental hardware for voice-enabled devices, enabling voice interaction with mobile applications and smart homes. This paper presents MagBackdoor, the first magnetic field attack that injects malicious commands via a loudspeaker-based backdoor of the audio system, compromising the linked voice interaction system. MagBackdoor focuses on the magnetic threat on loudspeakers and manipulates their sound production stealthily. Consequently, the microphone will inevitably pick up malicious sound generated by the attacked speaker, due to the closely packed arrangement of internal audio systems. To prove the feasibility of MagBackdoor, we conduct comprehensive simulations and experiments. This study further models the mechanism by which an external magnetic field excites the sound production of loudspeakers, giving theoretical guidance to MagBackdoor. Aiming at stealthy magnetic attacks in real-world scenarios, we self-design a prototype that can emit magnetic fields modulated by voice commands. We implement MagBackdoor and evaluate it across a wide range of smart devices involving 16 smartphones, four laptops, two tablets, and three smart speakers, achieving an average 95% injection success rate with high-quality injected acoustic signals.
WaVe: a verifiably secure WebAssembly sandboxing runtime

The promise of software sandboxing is flexible, fast and portable isolation; capturing the benefits of hardwarebased memory protection without requiring operating system involvement. This promise is reified in WebAssembly (Wasm), a popular portable bytecode whose compilers automatically insert runtime checks to ensure that data and control flow are constrained to a single memory segment. Indeed, modern compiled Wasm implementations have advanced to the point where these checks can themselves be verified, removing the compiler from the trusted computing base. However, the resulting integrity properties are only valid for code executing strictly inside the Wasm sandbox. Any interactions with the runtime system, which manages sandboxes and exposes the WebAssembly System Interface (WASI) used to access operating system resources, operate outside this contract. The resulting conundrum is how to maintain Wasm’s strong isolation properties while still allowing such programs to interact with the outside world (i.e., with the file system, the network, etc.). Our paper presents a solution to this problem, via WaVe, a verified secure runtime system that implements WASI. We mechanically verify that interactions with WaVe (including OS side effects) not only maintain Wasm’s memory safety guarantees, but also maintain access isolation for the host OS’s storage and network resources. Finally, in spite of completely removing the runtime from the trusted computing base, we show that WaVe offers performance competitive with existing industrial (yet unsafe) Wasm runtimes.
CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA), an extensively researched area in the field of natural language processing (NLP), predicts the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack sufficient annotation resources; thus, an increasing number of recent researchers have focused on cross-lingual aspect-based sentiment analysis (XABSA). However, most recent studies focus only on cross-lingual data alignment instead of model alignment. Therefore, we propose a novel framework, CL-XABSA: contrastive learning for cross-lingual aspect-based sentiment analysis. Based on contrastive learning, we close the distance between samples with the same label in different semantic spaces, achieving convergence of semantic spaces of different languages. Specifically, we design two contrastive objectives, token-level contrastive learning of token embeddings (TL-CTE) and sentiment-level contrastive learning of token embeddings (SL-CTE), to unify the semantic space of source and target languages. Since CL-XABSA can receive datasets in multiple languages during training, it can be further extended to multilingual aspect-based sentiment analysis (MABSA). To further improve the model performance, we perform knowledge distillation with target-language unlabeled data. In the distillation XABSA task, we further explore the effectiveness of different data (source dataset, translated dataset, and code-switched dataset). The results demonstrate that the proposed method has a certain improvement in the three XABSA tasks, distillation XABSA and MABSA. The source code of this paper is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GKLMIP/CL-XABSA</uri> .
Learning modality feature fusion via transformer for RGBT-tracking

RGB-T tracking can be seen as multi-view fusion tracking, and in this study, we propose a network with transformer structure, Multi-Modal Mutual Propagation Tracker (MMMPT). In order to obtain robust appearance model from multi-modal data, we adopt encoder–decoder architecture for extract information. In the encoding stage, the template features of multiple frames enhance the common features across them through the self-attention mechanism to obtain time-invariant target representation. At the same time, it also interacts with multi-modal data through cross-modal propagation, resulting in a modal-invariant representation of the target. The transformer decoder transfers useful information from the template to search areas through a similarity matrix. We experiment on the RGBT234, GTOT, VTUAV and LasHeR datasets to assess the RGBT-transformer tracker. Extensive experiments indicate that our proposed framework is not inferior to the state-of-the-art trackers in terms of robustness and accuracy.
Deepfake Text Detection: Limitations and Opportunities

Recent advances in generative models for language have enabled the creation of convincing synthetic text or deepfake text. Prior work has demonstrated the potential for misuse of deepfake text to mislead content consumers. Therefore, deepfake text detection, the task of discriminating between human and machine-generated text, is becoming increasingly critical. Several defenses have been proposed for deepfake text detection. However, we lack a thorough understanding of their real-world applicability. In this paper, we collect deepfake text from 4 online services powered by Transformer-based tools to evaluate the generalization ability of the defenses on content in the wild. We develop several low-cost adversarial attacks, and investigate the robustness of existing defenses against an adaptive attacker. We find that many defenses show significant degradation in performance under our evaluation scenarios compared to their original claimed performance. Our evaluation shows that tapping into the semantic information in the text content is a promising approach for improving the robustness and generalization performance of deepfake text detection schemes.