Reviewerly/exHarmony huggingface dataset:
FedGiA: An Efficient Hybrid Algorithm for Federated Learning

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. The proposed algorithm is more communication- and computation-efficient than several state-of-the-art algorithms theoretically and numerically. Moreover, it also converges globally under mild conditions.
Blockchains for Artificial Intelligence of Things: A Comprehensive Survey

With the rapid advances in information and communication technologies, the Internet of Things (IoT) has become large and complex, bearing tremendous amounts of data and running devices in various scenarios. Leveraging Artificial Intelligence (AI) technologies, IoT can achieve superior information extraction, data analytics, and decision-making, which has resulted in the revolutionized Artificial Intelligence of Things (AIoT). AIoT can alleviate the pressure of storage, computation, and communication. Despite the promising features brought by combining AI technologies into IoT infrastructure, AIoT systems still face some serious challenges including inadequate efficiency, violation of security and privacy, lack of trust, and insufficient incentive. Blockchain featured by its distributed consensus and incentive mechanisms can be a promising technology for addressing the challenges in AIoT. AIoT employing blockchain is evolving with expectations of achieving efficient, secure, and trusted network activities. In this paper, we first introduce the background of AIoT and blockchain. Then, we discuss the motivations for employing blockchain with its characteristics in AIoT. Furthermore, we comprehensively review existing solutions on blockchain for AIoT systems from aspects of efficiency, security, privacy, trust, and incentive. Finally, we discuss the challenges and future research directions on blockchain for AIoT.
A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests

Isolation Forests (IF) and improved algorithms about anomaly scores are commonly used to detect global, local, or clustered anomalies. However, these algorithms are limited in considering all three anomalies simultaneously. To solve this problem, we propose a novel detection algorithm called KDF-IF. The KDF-IF algorithm redesigns the calculation of the anomaly score based on the Kernel Density Fluctuation (KDF) factor. By focusing on the density fluctuation of each instance neighborhood, the KDF draws the global, local, and clustered features for detecting the three types of anomalies. With the support of the Isolation Trees, the distance between instances in the traditional KDF is replaced by the path length to reduce the computational cost. Simulation results denote that the KDF-IF performance for three types of anomalies is significantly better than IF under the UCI datasets. Besides, the KDF-IF is used to effectively detect anomalies in the environmental dataset of intelligent networks, extending its application range.
Steering the interpretability of decision trees using lasso regression - an evolutionary perspective

Since machine and deep learning have made accurate solutions possible, the search for explainable predictors has begun. Decision trees are competitive in tasks that require transparency, but have been underestimated due to their insufficient prediction performance, often caused by generalization issues. It is especially noticeable in the case of model trees, designed to solve regression tasks. Evolutionary tree induction can to some extent counteract this over and under-fitting problem with its global approach. In this paper, we examine whether integrating the lasso estimator in the tree induction process, can help to control the interpretability of the decision tree and/or improve its overall performance. We propose a new evolutionary model tree inducer called Global Lasso Tree. Its novelty lies in regularization of linear models coefficients, in the leaves during the evolutionary search. To reduce the tree's tendency to misfit, a weighted fitness function is used to dynamically balance the trade-off between conflicting objectives which is the tree error and overall complexity. The proposed method was validated on 26 publicly available regression data sets. The empirical study showed that by using the lasso-based regularization technique, we were able to steer the tree's interpretability and thus generate simpler and significantly more accurate trees.
Potential of SIFT, SURF, KAZE, AKAZE, ORB, BRISK, AGAST, and 7 More Algorithms for Matching Extremely Variant Image Pairs

Extremely variant image pairs include distorted, deteriorated, and corrupted scenes that have experienced severe geometric, photometric, or non-geometric-non-photometric transformations with respect to their originals. Real world visual data can become extremely dusty, smoky, dark, noisy, motion-blurred, affine, JPEG compressed, occluded, shadowed, virtually invisible, etc. Therefore, matching of extremely variant scenes is an important problem and computer vision solutions must have the capability to yield robust results no matter how complex the visual input is. Similarly, there is a need to evaluate feature detectors for such complex conditions. With standard settings, feature detection, description, and matching algorithms typically fail to produce significant number of correct matches in these types of images. Though, if full potential of the algorithms is applied by using extremely low thresholds, very encouraging results are obtained. In this paper, potential of 14 feature detectors: SIFT, SURF, KAZE, AKAZE, ORB, BRISK, AGAST, FAST, MSER, MSD, GFTT, Harris Corner Detector based GFTT, Harris Laplace Detector, and CenSurE has been evaluated for matching 10 extremely variant image pairs. MSD detected more than 1 million keypoints in one of the images and SIFT exhibited a repeatability score of 99.76% for the extremely noisy image pair but failed to yield high quantity of correct matches. Rich information is presented in terms of feature quantity, total feature matches, correct matches, and repeatability scores. Moreover, computational costs of 25 diverse feature detectors are reported towards the end, which can be used as a benchmark for comparison studies.
NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles

Despite recent progress in trajectory planning for multiple robots and a single tethered robot, trajectory planning for multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this article, a complete approach is presented to address this problem. First, a multirobot tether-aware representation of homotopy is proposed to efficiently evaluate the feasibility and safety of a potential path in terms of 1) the cable length required to reach a target following the path, and 2) the risk of entanglements with the cables of other robots. Then the proposed representation is applied in a decentralized and online planning framework, which includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free, and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against the existing single and multiple tethered robot planning approaches. Simulations with up to eight UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using three tethered UAVs verify the practicality of the presented approach. The software implementation is publicly available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Self-Sovereign Identity Based on Zero-Knowledge Proof and Blockchain

Systems for generating and managing digital identities are in the process of being transformed to improve data sharing security and increase decentralization.Addressing both issues, a theoretical solution to create and manage Self-Sovereign Identities (SSI) is proposed using two Zero-Knowledge Proof (ZKP) protocols based on the discrete logarithm difficulty.Automorphism group properties are introduced to link several identities, their identifiers and attributes to produce a proof.The proposed SSI protocol does not encounter the problem of reusing the same secret key as in the case of the initial ZKP Schnorr protocol.The designed protocol ensures minimal disclosure of information to a single trusted third party.In addition, it allows zero disclosure of information to service providers requiring proof of authentication or identification.Such a SSI protocol is compliant with Electronic IDentification And Trust Services (eIDAS) as well as General Data Protection Regulation (GDPR) regulations.
Comparison of the Three Algorithms for Concreteness Rating Estimation of English Words

The paper compares three algorithms for concreteness rating estimation of English words.To train and test the models, we used a number of freely available dictionaries containing concreteness ratings.A feedforward neural network is employed as a regression model.Pre-trained fastText vectors, data on co-occurrence of target words with the most frequent ones, and data on co-occurrence of target words with functional words are used as input data by the considered algorithms.One of the three algorithms was proposed for the first time in this article.We provide detailed explanations of which combinations with functional words are the most informative in terms of concreteness ratings estimation for English words.Although the rest two algorithms have already been used for estimation of concreteness ratings, we consider possible ways to update them and improve the results obtained by a neural network.Thuswise, we use stochastic Spearman's correlation coefficient as a criterion for stopping of training.All three algorithms provided good results.The best value of Spearman's correlation coefficient between the value of the concreteness rating and its estimate was 0.906, which exceeds the values achieved in previous works.
Market behavior-oriented deep learning-based secure data analysis in smart cities

The construction of Smart Cities is inseparable from the healthy operation of markets. Reasonable data analysis can provide a crucial foundation for the development of market behavior by considering the enormous amount of data generated by a market economy. To this end, we propose enhanced cluster generative adversarial networks (eClusterGAN) to achieve latent space clustering. However, data storage security is crucial. Moreover, we suggest a GAN-based network intrusion detection system (GANNIDS) that uses adversarial learning to assist the generator in learning the spatial distribution of normal network flows. The simulation results showed that the proposed eClusterGAN and GANNIDS outperformed the benchmarks in terms of clustering accuracy, running time, precision, recall, and F1, which can support researchers in studying economic data trends. The construction of Smart Cities can effectively ensure healthy market development by discovering and disseminating the potential value of market economic data.
Users Really Do Respond To Smishing

Text phish messages, referred to as Smishing (SMS + phishing) is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Drawing inspiration by the works of Tu et al. on Robocalls and Tischer et al. on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline. Based on our results, we pinpoint essentially message attributes and demographic features that contribute to a statistically significant change in the response rates to smishing attacks.
A User Study of Keystroke Dynamics as Second Factor in Web MFA

As account compromises and malicious online attacks are on the rise, multi-factor authentication (MFA) has been adopted to defend against these attacks. OTP and mobile push notification are just two examples of the popularly adopted MFA factors. Although MFA improve security, they also add additional steps or hardware to the authentication process, thus increasing the authentication time and introducing friction. On the other hand, keystroke dynamics-based authentication is believed to be a promising MFA for increasing security while reducing friction. While there have been several studies on the usability of other MFA factors, the usability of keystroke dynamics has not been studied. To this end, we have built a web authentication system with the standard features of signup, login and account recovery, and integrated keystroke dynamics as an additional factor. We then conducted a user study on the system where 20 participants completed tasks related to signup, login and account recovery. We have also evaluated a new approach for completing the user enrollment process, which reduces friction by naturally employing other alternative MFA factors (OTP in our study) when keystroke dynamics is not ready for use. Our study shows that while maintaining strong security (0% FPR), adding keystroke dynamics reduces authentication friction by avoiding 66.3% of OTP at login and 85.8% of OTP at account recovery, which in turn reduces the authentication time by 63.3% and 78.9% for login and account recovery respectively. Through an exit survey, all participants have rated the integration of keystroke dynamics with OTP to be more preferable to the conventional OTP-only authentication.
Decolonizing Content Moderation: Does Uniform Global Community Standard Resemble Utopian Equality or Western Power Hegemony?

Social media platforms use content moderation to reduce and remove problematic content. However, much of the discourse on the benefits and pitfalls of moderation has so far focused on users in the West. Little is known about how users in the Global South interact with the humans and algorithms behind opaque moderation systems. To fill this gap, we conducted interviews with 19 Bangladeshi social media users who received restrictions for violating community standards on Facebook. We found that the users perceived the underlying human-AI infrastructure to imbibe coloniality in the form of amplifying power relations, centering Western norms, and perpetuating historical injustices and erasure of minoritized expressions. Based on the findings, we establish that the current moderation systems often propagate historical power relations and patterns of oppression, and discuss ways to rethink moderation in a fundamentally decolonial way.
On the Making of Alternative Data Encounters: The Odd Interpreters

While data are the backbone for home Internet of Things’ (IoT) functional and economic model, data remain elusive and abstract for home dwellers. In response, we present the Odd Interpreters (OIs): a collection of three artifacts that materialize alternative ways of engaging with IoT data in home environments. The OIs recast home data as imaginative sounds (Broadcast), fading fabric (Soft Fading), and cookie recipes (Data Bakery) with the intent to reveal the hidden human labor and material infrastructures of data and to critique data's assumed objectivity. Following a Research-through-Design approach, we unpack design events that mark our process for making the Odd Interpreters. We conclude with a discussion around the need for pluralizing data encounters, the tactic of designing between illusion and precision, and a reflection on living with the prototypes while designing.
Towards Applied Remapped Physical-Virtual Interfaces: Synchronization Methods for Resolving Control State Conflicts

User interfaces in virtual reality enable diverse interactions within the virtual world, though they typically lack the haptic cues provided by physical interface controls. Haptic retargeting enables flexible mapping between dynamic virtual interfaces and physical controls to provide real haptic feedback. This investigation aims to extend these remapped interfaces to support more diverse control types. Many interfaces incorporate sliders, switches, and knobs. These controls hold fixed states between interactions creating potential conflicts where a virtual control has a different state from the physical control. This paper presents two methods, "manual" and "automatic", for synchronizing physical and virtual control states and explores the effects of these methods on the usability of remapped interfaces. Results showed that interfaces without retargeting were the ideal configuration, but they lack the flexibility that remapped interfaces provide. Automatic synchronization was faster and more usable; however, manual synchronization is suitable for a broader range of physical interfaces.
Leveraging Dialogue State Tracking for Zero-Shot Chat-Based Social Engineering Attack Recognition

Human-to-human dialogues constitute an essential research area for linguists, serving as a conduit for knowledge transfer in the study of dialogue systems featuring human-to-machine interaction. Dialogue systems have garnered significant acclaim and rapid growth owing to their deployment in applications such as virtual assistants (e.g., Alexa, Siri, etc.) and chatbots. Novel modeling techniques are being developed to enhance natural language understanding, natural language generation, and dialogue-state tracking. In this study, we leverage the terminology and techniques of dialogue systems to model human-to-human dialogues within the context of chat-based social engineering (CSE) attacks. The ability to discern an interlocutor’s true intent is crucial for providing an effective real-time defense mechanism against CSE attacks. We introduce in-context dialogue acts that expose an interlocutor’s intent, as well as the requested information that she sought to convey, thereby facilitating real-time recognition of CSE attacks. Our work proposes CSE domain-specific dialogue acts, utilizing a carefully crafted ontology, and creates an annotated corpus using dialogue acts as classification labels. Furthermore, we propose SG-CSE BERT, a BERT-based model following the schema-guided paradigm, for zero-shot CSE attack dialogue-state tracking. Our evaluation results demonstrate satisfactory performance.
Reviewing and Reflecting on Smart Home Research from the Human-Centered Perspective

While there has been rapid growth in smart home research from a technical perspective– focusing on home automation, devices, software, and protocols– few review papers examine the human-centered perspective. A human-centered focus is crucial for achieving the goals of providing natural, convenient, comfortable, friendly, and safe user experiences in the smart home. To understand key innovations in human-centered smart home research, we analyzed keyword changes over time via 19,091 papers from 2000 to 2022, then selected 55 papers from high-impact venues in the last five years, and summarized them through a combination of qualitative and quantitative methods. Our analysis revealed five research trends with unique characteristics and interdependence. Drawing on this review, we elaborate on the future of smart home design research with respect to multidisciplinary development, stakeholder involvement, and the shift of design implications.
Watch Out for Updates: Understanding the Effects of Model Explanation Updates in AI-Assisted Decision Making

AI explanations have been increasingly used to help people better utilize AI recommendations in AI-assisted decision making. While AI explanations may change over time due to updates of the AI model, little is known about how these changes may affect people's perceptions and usage of the model. In this paper, we study how varying levels of similarity between the AI explanations before and after a model update affects people's trust in and satisfaction with the AI model. We conduct randomized human-subject experiments on two decision making contexts where people have different levels of domain knowledge. Our results show that changes in AI explanation during the model update do not affect people's tendency to adopt AI recommendations. However, they may change people's subjective trust in and satisfaction with the AI model via changing both their perceived model accuracy and perceived consistency of AI explanations with their prior knowledge.
Wearable Identities: Understanding Wearables’ Potential for Supporting the Expression of Queer Identities

Queer identity research largely overlooks wearable technology. Most work exploring sociocultural considerations of wearable technology determines what is “socially acceptable” based on privileged bodies, excluding queer perspectives. We address this by establishing the foundations of a knowledge base for wearables that support queer expression. We conducted a two-phase qualitative study exploring queer expressive practices and wearable technologies through 16 semi-structured interviews and 15 body mapping workshops with the queer community. We observed themes framing the queer community’s understanding of queer expression, wearable technology, and wearable technology for queer users. Providing design considerations and discussions on the potential of our methods, our work enables the creation of wearable technologies that offer meaningful user experiences for the queer community. CAUTION: This paper discusses topics that could trigger those with histories of homophobia, transphobia, gender dysphoria, racism or eating disorders. Please use caution when engaging with this work.
Train as you Fight: Evaluating Authentic Cybersecurity Training in Cyber Ranges

Humans can play a decisive role in detecting and mitigating cyber attacks if they possess sufficient cybersecurity skills and knowledge. Realizing this potential requires effective cybersecurity training. Cyber range exercises (CRXs) represent a novel form of cybersecurity training in which trainees can experience realistic cyber attacks in authentic environments. Although evaluation is undeniably essential for any learning environment, it has been widely neglected in CRX research. Addressing this issue, we propose a taxonomy-based framework to facilitate a comprehensive and structured evaluation of CRXs. To demonstrate the applicability and potential of the framework, we instantiate it to evaluate Iceberg CRX, a training we recently developed to improve cybersecurity education at our university. For this matter, we conducted a user study with 50 students to identify both strengths and weaknesses of the CRX.