Reviewerly/exHarmony huggingface dataset:
Social influence in the darknet market: The impact of product descriptions on cocaine sales

Authors:
raw_author_name: Filippo Andrei
raw_affiliation_string: Department of Sociology and Social Research, University of Trento, Via Giuseppe Verdi, 26, Trento, TN 38122, Italy

Abstract: The rise of the darknet market, supported by technologies such as the Tor Browser and cryptocurrencies, has created a secure environment in which illicit transactions can occur. However, due to the lack of government oversight in this hidden online domain, darknet markets face significant challenges in upholding social order. Hence, this study explores the social dynamics that promote social order in a darknet market, focusing on the impact of item descriptions on sales. In particular, the study examines how text contained in product listings can influence sales and contribute to social order.To conduct this analysis, we examined 4160 cocaine listings on AlphaBay, which was active from December 2014 to July 2017 and is one of the largest darknet markets in history. Using generalised additive models (GAMs), we assessed the impact of various listing description features, including content and semantic structure, on cocaine sales.The results showed that sales increased by 61.6 % when listings included delivery information in their description, compared to offers that did not. In addition, the standardised sentiment score (ranging 0,1) of the product description increased positively, and estimated sales increased by 260.5 %. We also found that international shipping reduced sales by 28.3 %. Finally, we found that listings stating the product origin increased sales for all continents except Asia.The study sheds light on the characteristics of product advertising that facilitate social order within a darknet market. Listings that include delivery details in the description reduce uncertainty about a critical stage of the transaction process while using positive language increases trust. This study makes both an empirical and a theoretical contribution by demonstrating the influence of ad descriptions on sales and the intricate role of social influences in shaping market order.
A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Authors:
raw_author_name: Tesfahunegn Minwuyelet Mengistu
raw_affiliation_string: Department of Information Convergence Engineering, Pusan National University, Busan 46241, Republic of Korea
raw_author_name: Jenn-Wei Lin
raw_affiliation_string: Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan

Abstract: Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.
Weather-Domain Transfer-Based Attention YOLO for Multi-Domain Insulator Defect Detection and Classification in UAV Images

Authors:
raw_author_name: Xinbo Huang
raw_affiliation_string: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, China; School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
raw_author_name: Decheng Liu
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi’an 710126, China

Abstract: Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.
Virtual Quantum Resource Distillation

Authors:
raw_author_name: Xiao Yuan
raw_affiliation_string: School of Computer Science, Peking University, Beijing 100871, China
raw_author_name: Bartosz Regula
raw_affiliation_string: Department of Physics, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Mathematical Quantum Information RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research (CPR) and RIKEN Center for Quantum Computing (RQC), Wako, Saitama 351-0198, Japan
raw_author_name: Ryuji Takagi
raw_affiliation_string: Department of Basic Science, The University of Tokyo, Tokyo 153-8902, Japan; Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore
raw_author_name: Mile Gu
raw_affiliation_string: CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654, Singapore 117543, Singapore; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore; Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore

Abstract: Distillation, or purification, is central to the practical use of quantum resources in noisy settings often encountered in quantum communication and computation. Conventionally, distillation requires using some restricted ``free'' operations to convert a noisy state into one that approximates a desired pure state. Here, we propose to relax this setting by only requiring the approximation of the measurement statistics of a target pure state, which allows for additional classical postprocessing of the measurement outcomes. We show that this extended scenario, which we call ``virtual resource distillation,'' provides considerable advantages over standard notions of distillation, allowing for the purification of noisy states from which no resources can be distilled conventionally. We show that general states can be virtually distilled with a cost (measurement overhead) that is inversely proportional to the amount of existing resource, and we develop methods to efficiently estimate such cost via convex and semidefinite programming, giving several computable bounds. We consider applications to coherence, entanglement, and magic distillation, and an explicit example in quantum teleportation (distributed quantum computing). This work opens a new avenue for investigating generalized ways to manipulate quantum resources.
Mitigating crosstalk errors by randomized compiling: Simulation of the BCS model on a superconducting quantum computer

Authors:
raw_author_name: Alexander Shnirman
raw_affiliation_string: Karlsruhe Institute of Technology, Institut für Theorie der Kondensierten Materie, TKM, 76049 Karlsruhe, Germany; Karlsruher Institut für Technologie, Institut für Quantenmaterialien und Technologien, IQMT, 76021 Karlsruhe, Germany
raw_author_name: Kyrylo Snizhko
raw_affiliation_string: Univ. Grenoble Alpes, CEA, Grenoble INP, IRIG, PHELIQS, 38000 Grenoble, France

Abstract: We develop and apply an extension of the randomized compiling (RC) protocol that includes a special treatment of neighboring qubits and dramatically reduces crosstalk effects caused by the application of faulty gates on superconducting qubits in IBMQ quantum computers (ibm_lagos and ibmq_ehningen). Crosstalk errors, stemming from controlled-not (cnot) two-qubit gates, are a crucial source of errors on numerous quantum computing platforms. For the IBMQ machines, their effect on the performance of a given quantum computation is often overlooked. Our RC protocol turns coherent noise due to crosstalk into a depolarizing noise channel that can then be treated using established error mitigation schemes, such as noise estimation circuits. We apply our approach to the quantum simulation of the nonequilibrium dynamics of the Bardeen-Cooper-Schrieffer (BCS) Hamiltonian for superconductivity, a particularly challenging model to simulate on quantum hardware because of the long-range interaction of Cooper pairs. With 135 cnot gates, we work in a regime where crosstalk, as opposed to either Trotterization or qubit decoherence, dominates the error. Our twirling of neighboring qubits is shown to dramatically improve the noise estimation protocol without the need to add new qubits or circuits and allows for a quantitative simulation of the BCS model.
Considerations for the Use of Personas and Journey Maps in Engineering Course Design

Authors:
raw_author_name: Nicholas Fila
raw_affiliation_string: (Research Assistant Professor); Iowa State University of Science and Technology
raw_author_name: Henry Duwe
raw_affiliation_string: Iowa State University of Science and Technology
raw_author_name: Mani Mina
raw_affiliation_string: Iowa State University of Science and Technology

Abstract: Personas and journey maps are ubiquitous in many design disciplines.These tools allow designers to better understand key users and engage with a user's experience over time with a product or system.While personas and journey maps are widely used in design disciplines, little scholarship exists on how they, collectively, might be successfully adapted to different contexts, e.g., engineering instruction and course development.Yet, these tools have the potential to help educators better understand students' experiences during a learning activity, class session, semester, or even an entire curriculum, and identify key issues to address as they develop or revise new learning experiences.This paper presents three case studies of the persona and journey map creation among engineering educators, explores their effects on educator empathy, and investigates factors that may influence their successful implementation in engineering course design settings.Each case features some variation on the format of personas and journey maps.We utilize several data sources to achieve a comprehensive snapshot of each case, including audio recordings of team course design meetings, persona and journey map artifacts, course data, and researcher observations.We present each case individually and identify themes that run across the implementations.These themes include the importance of instructor involvement in the creation process, the effect of diverse voices in the creation process, and the role of play, as well as relevant trade-offs in each of these themes.
Investigating the Adverse Effects of Social Media and Cybercrime in Higher Education: A Case Study of an Online University

Authors:
raw_author_name: Musawer Hakimi
raw_affiliation_string: Assistant Professor, Samangan University, Samangan, Afghanistan
raw_author_name: Abdul Wajid Fazil
raw_affiliation_string: Assistant Professor, Badakhshan University, Badakhshan, Afghanistan

Abstract: The utilization of social media alongside the escalating occurrence of cybercrime presents significant hurdles for higher education institutions in today's digital era, prompting a comprehensive exploration of their ramifications. This study investigates the intersection of social media and cybercrime within higher education, focusing particularly on an online university environment. Its aim is to analyze patterns of social media usage, the prevalence of cybercrime, and effective strategies for addressing these challenges among online university students. Through a mixed-methods approach, data were collected from a cohort of 100 students via surveys to evaluate their social media interactions and perceptions of cybercrime. Findings reveal a diverse distribution of students across faculties, with WhatsApp and Instagram emerging as the dominant platforms. Noteworthy is the active engagement of students on social media for academic purposes, though perspectives on cyberbullying and hacking risks vary. The study emphasizes the complex dynamics of social media and cybercrime in online higher education, highlighting the importance of comprehensive risk management and student well-being. As such, it advocates for the implementation of cybersecurity training and the enhancement of social media guidelines to cultivate digital literacy and foster a secure online learning environment. This research offers valuable insights into the evolving landscape of digital technologies within educational institutions, laying the groundwork for future investigations into effective interventions and policy frameworks.Top of Form
Look before you leap: Detecting phishing web pages by exploiting raw URL and HTML characteristics

Authors:
raw_author_name: Chidimma Opara
raw_affiliation_string: School of Computing, Engineering & Digital Technologies, Teesside University, TS1 3BX, Middlesbrough, UK
raw_author_name: Yingke Chen
raw_affiliation_string: Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE7 7YT, UK
raw_author_name: Bo Wei
raw_affiliation_string: School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

Abstract: Phishing websites distribute unsolicited content and are frequently used to commit email and internet fraud; detecting them before any user information is submitted is critical. Several efforts have been made to detect these phishing websites in recent years. Most existing approaches use hand-crafted lexical and statistical features from a website's textual content to train classification models to detect phishing web pages. However, these phishing detection approaches have a few challenges, including 1) the tediousness of extracting hand-crafted features, which require specialized domain knowledge to determine which features are useful for a particular platform; and 2) the difficulties encountered by models built on hand-crafted features to capture the semantic patterns in words and characters in URL and HTML content. To address these challenges, this paper proposes WebPhish, an end-to-end deep neural network trained using embedded raw URLs and HTML content to detect website phishing attacks. First, the proposed model automatically employs an embedding technique to extract the corresponding characters into homologous dense vectors. Then, the concatenation layer merges the URL and HTML embedding matrices. Following that, Convolutional layers are used to model its semantic dependencies. Extensive experiments were conducted with real-world phishing data, which yielded an accuracy of 98.1\%, showing that WebPhish outperforms baseline detection approaches in identifying phishing pages.
Word2Pix: Word to Pixel Cross-Attention Transformer in Visual Grounding

Authors:
raw_author_name: Heng Zhao
raw_affiliation_string: A&#x002A;STAR Centre for Frontier AI Research (CFAR), Singapore
raw_author_name: Joey Tianyi Zhou
raw_affiliation_string: A&#x002A;STAR Centre for Frontier AI Research (CFAR), Singapore
raw_author_name: Yew-Soon Ong
raw_affiliation_string: A&#x002A;STAR Centre for Frontier AI Research (CFAR), Singapore

Abstract: Current one-stage methods for visual grounding encode the language query as one holistic sentence embedding before fusion with visual features for target localization. Such a formulation provides insufficient ability to model query at the word level, and therefore is prone to neglect words that may not be the most important ones for a sentence but are critical for the referred object. In this article, we propose Word2Pix: a one-stage visual grounding network based on the encoder-decoder transformer architecture that enables learning for textual to visual feature correspondence via word to pixel attention. Each word from the query sentence is given an equal opportunity when attending to visual pixels through multiple stacks of transformer decoder layers. In this way, the decoder can learn to model the language query and fuse language with the visual features for target prediction simultaneously. We conduct the experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets, and the proposed Word2Pix outperforms the existing one-stage methods by a notable margin. The results obtained also show that Word2Pix surpasses the two-stage visual grounding models, while at the same time keeping the merits of the one-stage paradigm, namely, end-to-end training and fast inference speed. Code is available at https://github.com/azurerain7/Word2Pix.
Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks

Authors:
raw_author_name: Xinjie Wang
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Yaochu Jin
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Wenli Du
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Jun Wang
raw_affiliation_string: Department of Computer Science and the School of Data Science, City University of Hong Kong, Hong Kong, China

Abstract: The strengthening and the weakening of synaptic strength in existing Bienenstock-Cooper-Munro (BCM) learning rule are determined by a long-term potentiation (LTP) sliding modification threshold and the afferent synaptic activities. However, synaptic long-term depression (LTD) even affects low-active synapses during the induction of synaptic plasticity, which may lead to information loss. Biological experiments have found another LTD threshold that can induce either potentiation or depression or no change, even at the activated synapses. In addition, existing BCM learning rules can only select a set of fixed rule parameters, which is biologically implausible and practically inflexible to learn the structural information of input signals. In this article, an evolved dual-threshold BCM learning rule is proposed to regulate the reservoir internal connection weights of the echo-state-network (ESN), which can contribute to alleviating information loss and enhancing learning performance by introducing different optimal LTD thresholds for different postsynaptic neurons. Our experimental results show that the evolved dual-threshold BCM learning rule can result in the synergistic learning of different plasticity rules, effectively improving the learning performance of an ESN in comparison with existing neural plasticity learning rules and some state-of-the-art ESN variants on three widely used benchmark tasks and the prediction of an esterification process.
Homological Quantum Rotor Codes: Logical Qubits from Torsion

Authors:
raw_author_name: Vuillot, Christophe
raw_affiliation_string: Designing the Future of Computational Models
raw_author_name: Ciani, Alessandro
raw_affiliation_string: Forschungszentrum Jülich GmbH
raw_author_name: Terhal, Barbara M.
raw_affiliation_string: Delft University of Technology; Faculty of Electrical Engineering, Mathematics and Computer Science [Delft]

Abstract: We formally define homological quantum rotor codes which use multiple quantum rotors to encode logical information. These codes generalize homological or CSS quantum codes for qubits or qudits, as well as linear oscillator codes which encode logical oscillators. Unlike for qubits or oscillators, homological quantum rotor codes allow one to encode both logical rotors and logical qudits in the same block of code, depending on the homology of the underlying chain complex. In particular, a code based on the chain complex obtained from tessellating the real projective plane or a Möbius strip encodes a qubit. We discuss the distance scaling for such codes which can be more subtle than in the qubit case due to the concept of logical operator spreading by continuous stabilizer phase-shifts. We give constructions of homological quantum rotor codes based on 2D and 3D manifolds as well as products of chain complexes. Superconducting devices being composed of islands with integer Cooper pair charges could form a natural hardware platform for realizing these codes: we show that the 0- $$\pi $$ qubit as well as Kitaev’s current-mirror qubit—also known as the Möbius strip qubit—are indeed small examples of such codes and discuss possible extensions.