Reviewerly/exHarmony huggingface dataset:
Crypto-hesitancy: is regulation the answer?

Purpose The purpose of this paper is to explore and address the possible reasons for the hesitancy in accepting cryptocurrency as an asset class by the world governments and central banks. The behaviour of delaying the acceptance or using cryptocurrency has been termed as crypto-hesitancy. Design/methodology/approach To establish the conceptual understanding of crypto-hesitancy, the bibliometric analysis was performed through Bibliometrix and VOSviewer. Through keyword search technique this study has located 507 useful studies in Scopus database, which were used for the bibliometric analysis. Findings The findings of the study reveal that the government of developed and developing nations and central banks hesitate to regulate and accept cryptocurrency due to the following reasons: cryptocurrency’s ties to illegal activity, speculation and cryptocurrency’s capacity to circumvent government-imposed capital controls. The findings of this study can be used as platform to develop the construct – crypto-hesitancy – further and explore the empirical insights of it. Originality/value To the best of the authors’ knowledge, the construct crypto-hesitancy has not been evolved yet, which makes this study the first attempt to theoretically understand the concept and its evolution.
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction

Rich user behavior data has been proven to be of great value for Click-Through Rate (CTR) prediction applications, especially in industrial recommender, search, or advertising systems. However, it's non-trivial for real-world systems to make full use of long-term user behaviors due to the strict requirements of online serving time. Most previous works adopt the retrieval-based strategy, where a small number of user behaviors are retrieved first for subsequent attention. However, the retrieval-based methods are sub-optimal and would cause information losses, and it's difficult to balance the effectiveness and efficiency of the retrieval algorithm. In this paper, we propose SDIM (Sampling-based Deep Interest Modeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. We sample from multiple hash functions to generate hash signatures of the candidate item and each item in the user behavior sequence, and obtain the user interest by directly gathering behavior items associated with the candidate item with the same hash signature. We show theoretically and experimentally that the proposed method performs on par with standard attention-based models on modeling long-term user behaviors, while being sizable times faster. We also introduce the deployment of SDIM in our system. Specifically, we decouple the behavior sequence hashing, which is the most time-consuming part, from the CTR model by designing a separate module named BSE (Behavior Sequence Encoding). BSE is latency-free for the CTR server, enabling us to model extremely long user behaviors. Both offline and online experiments are conducted to demonstrate the effectiveness of SDIM. SDIM now has been deployed online in the search system of Meituan APP.
A Lightweight Convolution Neural Network for Automatic Disasters Recognition

This paper proposed a lightweight, efficient Convolution Neural Network model for automatic disaster recognition from aerial images. The model consists of a stack of convolutions and dense layers, and training incorporates several augmentation and data pre-processing techniques to improve the model’s generalisation. The model is evaluated on standard performance matrices like accuracy, precision, recall and the F1-score. We compared the results with state-of-the-art models, achieving a substantial boost in performance. Additionally, we trained different model variants for the quantitative analysis on publicly available datasets. With only 3 MB in size, our model is easily deployable on embedded and resource-constrained devices.
Task-irrelevant threatening information is harder to ignore than other valences.

Emotionally salient objects activate the survival circuits of the brain and are given priority in cognitive processing, even at the cost of inhibiting ongoing activities. These circuits arouse and prepare the organism to take swift action when needed. Previous studies have suggested, however, that not all emotional dimensions are equally prioritized. Threatening stimuli may have greater prominence than other emotional categories. Thus, we sought to compare the effects that stimuli of varying emotions would have on orienting and executive attentional processing. We performed two experiments to broaden our understanding of the attentional consequences of threats through the monitoring of participants' eye movements. Participants were exposed to emotionally charged (threatening, nonthreatening negative, positive) and neutral pictures as task-irrelevant distractors while performing a primary visual search task (under conditions of varying cognitive load). Behavioural results showed that participants found the first target number more slowly when the distractor image was threatening, but overall task completion times were actually speeded in this condition (relative to other valences). Further, participants fixated on threatening distractor images earlier and observed them longer than other valences. Results were more pronounced when the primary task was harder. These biases were not evident for positive and nonthreatening images, presumably because participants were able to ignore them, providing further support to the contention that threatening stimuli hold greater prominence than other emotional categories. Together, our results are in line with previous studies suggesting that the processing of threatening stimuli is speeded, potentially because of differences in the brain circuits involved. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.
Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks.
A new flower pollination algorithm with improved convergence and its application to engineering optimization

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm that mimics the pollination behaviour of flowering plants. Despite the promising performance of FPA in solving single objective optimization problems, its convergence still poses challenges in practice. This study proposes a modified FPA with additional features from chaos theory and frog leaping algorithm augmented by inertia weights. The modified FPA proposed in this study is tested against benchmark mathematical functions, mechanical engineering design optimization problems, and machining process optimization problems. Performance comparison with other state-of-the-art optimization algorithms has demonstrated its ability in terms of convergence. The modified FPA significantly reduced the number of function evaluations by 84.14%, as compared to FPA in optimizing the benchmark mathematical functions. Besides, the proposed modified FPA outperformed others in 12 out of 15 mechanical engineering optimization problems.
Full-field chromatic confocal microscopy for surface profilometry with sub-micrometer accuracy

In various manufacturing processes like semiconductor fabrication, highly accurate and efficient surface profilometry on microstructures is critically demanded. This article presents a quasi-area-scan chromatic confocal microscopy for full-field surface profilometry with high precision and accuracy for in-line automated optical inspection (AOI). Unlike conventional confocal microscopy, the proposed opto-mechatronic confocal measuring system does not require mechanical scanning of the test object. Moreover, it is bi-telecentric to satisfy full-field measurement without optical aberration. Optical line-scan illumination and imaging modules are integrated with an electrically driven galvanometer to perform line scanning. Most importantly, the proposed system is superior to traditional chromatic confocal scanning in that positioning uncertainties can be avoided, and high-speed detection with a large field of view and high accuracy can be achieved. Pre-calibrated step-height gauges were experimentally measured to verify the precision and accuracy of the developed probe. Results showed measurement bias kept below 0.239 µm with a standard deviation of 0.126 µm. When the Galvanometer reaches its design specifications, the measuring speed of the system can exceed 8000 lines/s.
Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study

To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening.A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes.Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001).AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps

In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories. Yet, it is a challenging task because the ground-truth distribution is unknown and unobservable, while only one of its samples can be applied for supervising model learning, which is prone to bias. Most recent works focus on predicting diverse trajectories in order to cover all modes of the real distribution, but they may despise the precision and thus give too much credit to unrealistic predictions. To address the issue, we learn the distribution with symmetric cross-entropy using occupancy grid maps as an explicit and scene-compliant approximation to the ground-truth distribution, which can effectively penalize unlikely predictions. In specific, we present an inverse reinforcement learning based multi-modal trajectory distribution forecasting framework that learns to plan by an approximate value iteration network in an end-to-end manner. Besides, based on the predicted distribution, we generate a small set of representative trajectories through a differentiable Transformer-based network, whose attention mechanism helps to model the relations of trajectories. In experiments, our method achieves state-of-the-art performance on the Stanford Drone Dataset and Intersection Drone Dataset.
On the Insider Security of MLS

The Messaging Layer Security (MLS) protocol is an open standard for end-to-end (E2E) secure group messaging being developed by the IETF, poised for deployment to consumers, industry, and government. It is designed to provide E2E privacy and authenticity for messages in long-lived sessions whenever possible, despite the participation (at times) of malicious insiders that can adaptively interact with the PKI at will, actively deviate from the protocol, leak honest parties’ states, and fully control the network. The core of the MLS protocol (from which it inherits essentially all of its efficiency and security properties) is a Continuous Group Key Agreement (CGKA) protocol. It provides asynchronous E2E group management by allowing group members to agree on a fresh independent symmetric key after every change to the group’s state (e.g. when someone joins/leaves the group). In this work, we make progress towards a precise understanding of the insider security of MLS (Draft 12). On the theory side, we overcome several subtleties to formulate the first notion of insider security for CGKA (or group messaging). Next, we isolate the core components of MLS to obtain a CGKA protocol we dub Insider Secure TreeKEM (ITK). Finally, we give a rigorous security proof for ITK. In particular, this work also initiates the study of insider secure CGKA and group messaging protocols. Along the way we give three new (very practical) attacks on MLS and corresponding fixes. (Those fixes have now been included into the standard.) We also describe a second attack against MLS-like CGKA protocols proven secure under all previously considered security notions (including those designed specifically to analyze MLS). These attacks highlight the pitfalls in simplifying security notions even in the name of tractability.
Box-Supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset .
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint

Unsupervised image-to-image (I21) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may distort the semantics when matching distributions, resulting in the inconsistency between the input and translated images, which is known as the semantics distortion problem. In this paper, we focus on the low-level I21 translation, where the structure of images is highly related to their semantics. To alleviate semantic distortions in such translation tasks without paired supervision, we propose a novel I21 translation constraint, called Structure Consistency Constraint (SCC), to promote the consistency of image structures by reducing the randomness of color transformation in the translation process. To facilitate estimation and maximization of SCC, we propose an approximate representation of mutual information called relative Squared-loss Mutual Information (rSMI) that enjoys efficient analytic solutions. Our SCC can be easily incorporated into most existing translation models. Quantitative and qualitative comparisons on a range of low-level I21 translation tasks show that translation models with SCC outperform the original models by a significant margin with little additional computational and memory costs.
The influence of big data analytics technological capabilities and strategic agility on performance of private higher education institutions

Purpose This study aimed to examine the roles of big data analytics technological capabilities (BDATC) on the performance of private higher education institutions (PHEIs) in Malaysia. This study explored mediating effect of strategic agility (SA) between BDATC and PHEIs' performance. The moderating effect of enterprise risk management (ERM) influencing the relationship between SA and PHEIs performance was also examined. Design/methodology/approach To evaluate the hypotheses, data were collected from PHEIs in Malaysia using an online survey questionnaire. Structural equation modelling software SmartPLS 3.3.9 was used to analyse the 186 valid responses received. Findings This empirical research discovered that BDATC and SA have positive and significant impact on PHEIs' performance. BDATC has positive and significant influence on SA of PHEIs. Moreover, SA mediates the influence of BDATC on PHEIs' performance. ERM enhances the impact of SA on performance of PHEIs in Malaysia. Originality/value The findings contributed to the advancement of the resource-based view (RBV) theory and dynamic capability theory (DCT). Practitioners of PHEIs will understand how to maximise the resources and skills to improve BDATC, SA and ERM that eventually drive the performance of the practitioners' institution.
An FTSA Trajectory Elliptical Homotopy for Unmanned Vehicles Path Planning With Multi-Objective Constraints

This article investigates a fixed-time simultaneous arrival (FTSA) problem in terms of the equilibrium of path lengths of unmanned vehicles. A novel trajectory elliptical homotopy method (TEHM) is designed to solve the FTSA problem of unmanned vehicles in a multi-objective constrained environment. Considering the constraints of obstacle avoidance and kinematics of unmanned vehicles, the trajectories elliptical homotopy is selected for path planning. The obtained trajectory homotopy guarantees obstacle avoidance and motion stability at the same time. To handle the non-cooperative and dynamic obstacle avoidance, a trajectory elliptical homotopy decomposition (TEHD) algorithm is proposed with an FTSA constraint. Based on the TEHM and TEHD, a multiple unmanned vehicle fixed-time regular-triangle formation algorithm is designed and implemented on real vehicles. Simulations and experiments validate the performance of the proposed methods and show how fixed-time arrival formation under obstacles and kinematic constraints was obtained.
Analysis of multipartite entanglement distribution using a central quantum-network node

We study the performance (rate and fidelity) of distributing multipartite entangled states in a quantum network through the use of a central node. Specifically, we consider the scenario where the multipartite entangled state is first prepared locally at a central node and then transmitted to the end nodes of the network through quantum teleportation. As our first result, we present leading-order analytical expressions and lower bounds for both the rate and fidelity at which a specific class of multipartite entangled states, namely, Greenberger-Horne-Zeilinger (GHZ) states, are distributed. Our analytical expressions for the fidelity accurately account for time-dependent depolarizing noise encountered by individual quantum bits while stored in quantum memory, as verified using Monte Carlo simulations. As our second result, we compare the performance to the case where the central node is an entanglement switch and the GHZ state is created by the end nodes in a distributed fashion. Apart from these two results, we outline how the teleportation-based scheme could be physically implemented using trapped ions or nitrogen-vacancy centers in diamond.