Reviewerly/exHarmony huggingface dataset:
Sensing In-Air Signature Motions Using Smartwatch: A High-Precision Approach of Behavioral Authentication

By virtue of the stability of signatures and the high difficulty of imitation, handwriting signature, as an important behavioral biometric trait, has been broadly adopted for authorization and identity verification.Nowadays, the emergence of consumer-level wrist-worn devices incorporating rich sensors has profoundly changed the way of human-machine interactions, enabling new observation ways of signature.In this paper, we investigate the feasibility of authenticating users by sensing hand motions of signing in the air using fingers.Each signature is represented by the readings of the gyroscope and accelerometer compensated by device attitude readings.A recurrent neural network-based algorithm is proposed to characterize signatures and accurately determine whether a signature is from the claimed genuine user or an imposter.We empirically investigate 22 participants by recording their in-air signing gestures using smartwatch motion sensors.The verification shows that, despite the inevitable variability of repeating genuine signature drawing, forged signatures tend to show more dissimilarity than the variability.The high-precision experimental result (i.e., equal error rate of 0.83%) against insider adversaries not only demonstrates the effectiveness of our proposed approach but also indicates the feasibility of a more userfriendly signature authentication way by signing their names in the air.Moreover, we investigate the impact of properties of motion sensory data on signature authentication.In addition, we include more details of the experiments, validation of the proposed pre-processing method, and analysis of the circumvention as one of the desirable proprieties of biometrics, of signing motions by measuring the skill of forgery.
Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions.
Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization

Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round communications of multi-dimensional model updates between local participants and the central parameter server. With an elastic edge-assisted aggregation procedure, the burden of backbone congestion can be handled for multi-service model communications. To provide an adaptive virtual resource orchestration, the integration of software-defined networking (SDN), network functions virtualization (NFV), EC, and double deep q-networks is emerged, which accurately gathers the state observation from local and edge nodes in data plane and NFV infrastructure (NFVI) for applying actions of allocated resource predetermination and recommended offloading policy. The reward formulation considers the minimization of completion time, energy consumption, and round communications between local participants and the selected NFV-enabled EC (NFVeEC) node until the global model is constructed with satisfying accuracy. In this paper, we proposed a decentralized SDN controller as an agent to enable a softwarization mechanism and interact with the virtualization-based EFL environment. The architecture adoption, agent controller, and orchestrator allow the centralized view for virtual network functions placement with efficient virtual machine mapping to perform edge update procedures and create the forwarding path effectively. The recommended NFVeEC for local-EFL updates is based on reward output and proposed agent valuation.
Resources for Bosonic Quantum Computational Advantage

Quantum computers promise to dramatically outperform their classical counterparts. However, the nonclassical resources enabling such computational advantages are challenging to pinpoint, as it is not a single resource but the subtle interplay of many that can be held responsible for these potential advantages. In this Letter, we show that every bosonic quantum computation can be recast into a continuous-variable sampling computation where all computational resources are contained in the input state. Using this reduction, we derive a general classical algorithm for the strong simulation of bosonic computations, whose complexity scales with the non-Gaussian stellar rank of both the input state and the measurement setup. We further study the conditions for an efficient classical simulation of the associated continuous-variable sampling computations and identify an operational notion of non-Gaussian entanglement based on the lack of passive separability, thus clarifying the interplay of bosonic quantum computational resources such as squeezing, non-Gaussianity, and entanglement.
A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection

Use of the object proposal method as a preprocessing step for object detection of vision sensors has improved computational efficiency in recent years. Good object proposal methods require high object detection recall, low computational cost, good localization accuracy, and repeatability. However, existing methods cannot always achieve a good balance of performance. To solve this problem, we propose a fast and high-performance object proposal algorithm. First, we propose a construction method to enhance frequency features that are combined with a linear classifier to learn and generate a set of proposal boxes. Second, we propose a strategy of binarizing frequency features and classifiers to accelerate the calculation. Last, we propose a merging strategy to improve the localization quality of the proposal boxes. Empirically, for the VOC2007 and MSCOCO2017 datasets using the intersection over union (IOU) threshold of 0.5 and 10<sup>4</sup> proposals, our method achieves 99.3&#x0025; object detection recall, 81.1&#x0025; mean average best overlap, and 80&#x0025; mean repeatability with an average time of 0.0014 seconds per image. The average time is three times faster than the current fastest method, and the mean repeatability is 11&#x0025; higher than that of the region proposal network (RPN) method. We applied our method to the target detection of autonomous vehicles, and in the experiment with the Oxford RobotCar dataset, we achieved 95.6&#x0025; detection precision and 91.2&#x0025; detection recall. This work could provide a new way to improve real-time performance and detection accuracy in the object detection of visual sensors.
Dual-Augment Graph Neural Network for Fraud Detection

Graph Neural Networks (GNNs) have drawn attention due to their excellent performance in fraud detection tasks, which reveal fraudsters by aggregating the features of their neighbors. However, some fraudsters typically tend to alleviate their suspiciousness by connecting with many benign ones. Besides, label-imbalanced neighborhood also deteriorates fraud detection accuracy. Such behaviors violate the homophily assumption and worsen the performance of GNN-based fraud detectors. In this paper, we propose a Dual-Augment Graph Neural Network (DAGNN) for fraud detection tasks. In DAGNN, we design a two-pathway framework including disparity augment (DA) pathway and similarity augment (SA) pathway. Accordingly, we devise two novel information aggregation strategies. One is to augment the disparity between target node and its heterogenous neighbors in original topology. The other is to augment its similarity to homogenous neighbors in a relatively label-balanced neighborhood. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed DAGNN.
3D model of bursting activity generation

From the standpoint of neurodynamics, the gener-ation of bursts in populations of neurons has been studied in depth, but the extrasynaptic mechanisms of generation remain practically unstudied. We propose a computationally efficient tripartite synapse model that demonstrates the formation of burst dynamics of a neural network.
PRO2SAT: Systematic Probabilistic Satisfiability logic in Discrete Hopfield Neural Network

Satisfiability is prominent in the field of computer science and mathematics because SAT provides an alternative to represent the knowledge of any datasets. Fueled by this nature, recent paradigm tends to converge towards modelling Artificial Neural Network (ANN) through SAT. Despite extensive implementation of SAT in ANN, there are severely limited strategy to control the distribution of negative and positive literals in the logical rule. One of the most feasible approaches in controlling the behavior of the literal is by employing probabilistic behavior to each neuron in the ANN. In this paper, a novel logical rule namely Probabilistic 2 Satisfiability was proposed by implementing the probability to each variable in the 2 Satisfiability clause. In this context, the negativity of each variable will be determined using the probability which leads to higher search space. The proposed Probabilistic 2 Satisfiability was implemented into the special single layered Discrete Hopfield Neural Network where the cost function of each variable was derived by minimizing the inconsistency of the logic. The behavior of the proposed Probabilistic 2 Satisfiability was assessed based on various performance metrics including several newly introduced metrics. According to the experimental results, the proposed model has a probability of at least 81.8% in outperforming the existing method. Interestingly, the proposed model was reported to have the largest solution space when the ratio of positive was within [0.1, 0.4]. The comparison of experimental results with other state of the art logical rule demonstrates that the proposed model is promising in retrieving global neuron state.
Disentangling Visual Embeddings for Attributes and Objects

We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct features associated with attributes. To overcome this challenge, these studies employ supervision from the linguistic space, and use pre-trained word embeddings to better separate and compose attribute-object pairs for recognition. Analogous to linguistic embedding space, which already has unique and agnostic embeddings for object and attribute, we shift the focus back to the visual space and propose a novel architecture that can disentangle attribute and object features in the visual space. We use visual decomposed features to hallucinate embeddings that are representative for the seen and novel compositions to better regularize the learning of our model. Extensive experiments show that our method outperforms existing work with significant margin on three datasets: MIT-States, UT-Zappos, and a new benchmark created based on VAW. The code, models, and dataset splits are publicly available at https://github.com/nirat1606/OADis.
Investigating the relationship between three-dimensional perception and presence in virtual reality-reconstructed architecture

Identifying and characterizing the factors that affect presence in virtual environments has been acknowledged as a critical step to improving Virtual Reality (VR) applications in the built environment domain. In the search to identify those factors, the research objective was to test whether three-dimensional perception affects presence in virtual environments. A controlled within-group experiment utilizing perception and presence questionnaires was conducted, followed by data analysis, to test the hypothesized unidirectional association between three-dimensional perception and presence in two different virtual environments (non-immersive and immersive). Results indicate no association in either of the systems studied, contrary to the assumption of many scholars in the field but in line with recent studies on the topic. Consequently, VR applications in architectural design may not necessarily need to incorporate advanced stereoscopic visualization techniques to deliver highly immersive experiences, which may be achieved by addressing factors other than depth realism. As findings suggest that the levels of presence experienced by users are not subject to the display mode of a 3D model (whether immersive or non-immersive display), it may still be possible for professionals involved in the review of 3D models (e.g., designers, contractors, clients) to experience high levels of presence through non-stereoscopic VR systems provided that other presence-promoting factors are included.
Continuous variable quantum key distribution with a shared partially characterized entangled source

Locking the sophisticated and expensive entanglement sources at the shared relay node is a promising choice for building a star-type quantum network with efficient use of quantum resources, where the involved parties only need to equip low-cost and simple homodyne detectors. Here, to our best knowledge, we demonstrate the first experimental continuous variable quantum key distribution with an entanglement source between the two users. We consider a practical partially characterized entangled source and establish the security analysis model of the protocol under realistic conditions. By applying a biased base technology, the higher key rate than that of the original protocol is achieved. The experimental results demonstrate that the distance between two users can reach up to 60 km over telecom single-mode fiber, implying the feasibility for high-rate and secure communication with a shared entangled source at metropolitan distances.
Applications of Artificial Intelligence in Breast Pathology

Context.— Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology. Objective.— To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes. Data Sources.— We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience. Conclusions.— With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists’ workload, but also provides new information in predicting prognosis and therapy response.
Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Due to its low dependency on the control parameters and straightforward operations, the Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow convergence and low solution precision. In this research, a hybrid Artificial Electric Field Employing Cuckoo Search Algorithm with Refraction Learning (AEFA-CSR) is suggested as a better version of the AEFA to address the aforementioned issues. The Cuckoo Search (CS) method is added to the algorithm to boost convergence and diversity which may improve global exploration. Refraction learning (RL) is utilized to enhance the lead agent which can help it to advance toward the global optimum and improve local exploitation potential with each iteration. Tests are run on 20 benchmark functions to gauge the proposed algorithm's efficiency. In order to compare it with the other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests and Friedman tests with 5% significance level are used. In order to evaluate the algorithm's efficiency and usability, some significant tests are carried out. As a result, the overall effectiveness of the algorithm with different dimensions and populations varied between 61.53 and 90.0% by overcoming all the compared algorithms. Regarding the promising results, a set of engineering problems are investigated for a further validation of our methodology. The results proved that AEFA-CSR is a solid optimizer with its satisfactory performance.
Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems

Abstract The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.
BOWED STRING SIMULATION USING A THERMAL FRICTION MODEL

Summary In the past, theoretical modelling of the motion of a bowed string has assumed that the frictional force due to the rosin on the bow was determined by the sliding speed only. Recently, it has been proposed that a better constitutive law would be to assume that the friction force is a function of the contact temperature. The predictions of these two models are compared, using the best available calibration data for the properties of the rosin and the instrument string. The main conclusion is that the thermal friction model predicts bowing behaviour which is more “benign”, in that the desired Helmholtz motion of the bowed string is established more reliably and more quickly than with the old friction model. The two models also predict different detailed behaviour of the frequency of the string oscillation as a function of normal force, the “flattening effect”. These predictions are amenable to direct experimental check, and it is hoped that measurements will soon provide evidence to test the predictions, discriminate between the models, and fine-tune the more successful model.
A Causal-Model Theory of Categorization

In this article I propose that categorization decisions are often made relative to causal models of categories that people possess. According to this causal-model theory of categorization, evidence of an exemplar’s membership in a category consists of the likelihood that such an exemplar can be generated by the category’s causal model. Bayesian networks are proposed as a representation of these causal models. Causal-model theory was fit to categorization data from a recent study, and yielded better fits than either the prototype model or the exemplar-based context model, by accounting, for example, for the confirmation and violation of causal relationships and the asymmetries inherent in such relationships.
The Performance of Un-Instrumented In-Air Pointing

We present an analysis of in-air finger and hand controlled object pointing and selection. The study used a tracking system that required no instrumentation on the user. We compared the performance of the two pointing methods with and without elbow stabilization and found that the method that yielded the best performance varied for each participant, such that there was no method that performed significantly better than all others. We also directly compared user performance between un-instrumented in-air pointing and the mouse. We found that the un-instrumented in-air pointing performed significantly worse, at less than 75% of mouse throughput. Yet, the larger range of applications for un-instrumented 3D hand tracking makes this technology still an attractive option for user interfaces.
A study on Chinese register characteristics based on regression analysis and text clustering

Abstract This paper reports an innovative Chinese register study based on regression analysis for sentence length distribution and text clustering. Although end of sentence is not conventionally marked in Chinese, we resolve this issue by assuming that segments between periods, question marks, and exclamation marks are sentences, which can be further divided into simple sentences and compound sentences. We also assume that segments between punctuation marks that express pauses in utterances form sentences (i.e., clauses). Using regression analysis, we find that the frequency distribution of sentence and clause lengths in Chinese can be fitted by the formula F = aL b c L , where L is sentence/clause length. Texts from different registers give rise to different fitted values of the parameters, and hence can serve to differentiate these registers. Finally, we use these parameters to represent and cluster texts from different registers. The successful text clustering results further prove that the parameters of the fitted results are reliable linguistic characteristics for different registers. In terms of linguistic theories, our study shows that it is just as effective to model sentence length in Chinese using sociological words (i.e., characters) as it is using linguistic words.
Key words and translated cohesion in Lovecraft’s <i>At the Mountains of Madness</i> and one of its Italian translations

In this paper, we explore the potential of a corpus approach to study translated cohesion. We use key words as starting points for identifying cohesive networks in Lovecraft’s At the Mountains of Madness and discuss how these networks contribute to the construction of literary meanings in the text. We focus on the role of repetition as a key element in establishing cohesive networks between lexical items. We specifically discuss the implications of our method for the analysis of cohesion in translated texts. A comparison of Lovecraft’s original novel and a translation into Italian provides us with a nuanced understanding of the complex nature of cohesive networks. Finally, we discuss the broader issue of applying models and methods from corpus linguistics to corpus stylistic analysis.