Reviewerly/exHarmony huggingface dataset:
Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud.
Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario

Autonomous vehicle, with the attributes that ensuring driving safety and improving traffic efficiency, has been a research hotspot for a long time. In the modular developing pipeline of autonomous vehicles, pedestrian detection based on computer vision is a critical component of perception module. In this paper, we apply the newly proposed network structure YOLOv5 in pedestrian detection problem. After training in PASCAL VOC2012 dataset, the model realizes high detection accuracy and real-time efficiency. At the same time, the model owns competitive generalization ability which achieve high detection accuracy in KITTI dataset. With competitive detection accuracy and real-time efficiency, YOLOv5 have the potential to be deployed on autonomous vehicles.
A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

Abstract Face-based video retrieval (FBVR) is the task of retrieving videos that containing the same face shown in the query image. In this article, we present the first end-to-end FBVR pipeline that is able to operate on large datasets of unconstrained, multi-shot, multi-person videos. We adapt an existing audiovisual recognition dataset to the task of FBVR and use it to evaluate our proposed pipeline. We compare a number of deep learning models for shot detection, face detection, and face feature extraction as part of our pipeline on a validation dataset made of more than 4000 videos. We obtain 97.25% mean average precision on an independent test set, composed of more than 1000 videos. The pipeline is able to extract features from videos at $$\sim $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∼</mml:mo></mml:math> 7 times the real-time speed, and it is able to perform a query on thousands of videos in less than 0.5 s.
Learning Attacker’s Bounded Rationality Model in Security Games

The paper proposes a novel neuroevolutionary method (NESG) for calculating leader's payoff in Stackelberg Security Games. The heart of NESG is strategy evaluation neural network (SENN). SENN is able to effectively evaluate leader's strategies against an opponent who may potentially not behave in a perfectly rational way due to certain cognitive biases or limitations. SENN is trained on historical data and does not require any direct prior knowledge regarding the follower's target preferences, payoff distribution or bounded rationality model. NESG was tested on a set of 90 benchmark games inspired by real-world cybersecurity scenario known as deep packet inspections. Experimental results show an advantage of applying NESG over the existing state-of-the-art methods when playing against not perfectly rational opponents. The method provides high quality solutions with superior computation time scalability. Due to generic and knowledge-free construction of NESG, the method may be applied to various real-life security scenarios.
A sampling-based online Co-Location-Resistant Virtual Machine placement strategy

In this paper, we discuss the co-location attack problem in the cloud IaaS from the Virtual Machine (VM) placement strategy perspective. We formulate the online secure optimization VM placement problem in a way that guarantees—apriori—a specified level of security while minimizing the number of used physical servers. To solve such a problem, we propose an approximate online secure VM placement algorithm based on sampling. The polynomial-time algorithm is based on a sound security inference procedure based on the confidence interval estimation method. Our empirical results demonstrate the correctness and the effectiveness of our approach in guaranteeing a Co-Location-Resistant (CLR) VM placement with a specific level of confidence and a threshold error as new incoming VM requests are being assigned to servers online. We compared our algorithm to a CLR alternative presented in Azar et al. (2014).
Improved lazy theta∗ algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle (UAV) in three-dimensional map. Firstly, in order to keep a safe distance between UAV and obstacles, the obstacle grid in the map is expanded. By using the data structure of octree, the octree map is constructed, and the search nodes is significantly reduced. Then, the lazy theta∗ algorithm, including neighbor node search, line-of-sight algorithm and heuristics weight adjustment is improved. In the process of node search, UAV constraint conditions are considered to ensure the planned path is actually flyable. The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes. Heuristic weight adjustment strategy is employed to control the precision and speed of search. Finally, the simulation results show that the improved lazy theta∗ algorithm is suitable for path planning of UAV in complex environment with multi-constraints. The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.
The Use of Augmented Reality in Latin-American Engineering Education: A Scoping Review

As part of a recent change, Augmented Reality (AR) has filled engineering classrooms, being employed for various pedagogical purposes around the world. However, little is known about the different features and uses of this technology in Latin America. This Scoping Review asks how are educational AR systems designed, used and evaluated in the region, comparing this to the international literature. To address this question, we charted 36 conference papers and scientific articles, taking care of the quality gaps and methodological diversity within our sample. Our results show that, even though most converge on conventional research, design and pedagogical practices, engineering educators working at institutes are taking the lead of design, pedagogical and research innovation. Furthermore, we show that Latin-American literature distinctively reveals how teachers adapt to the particular contexts of teaching, and the special importance of the usually overlooked conference papers literature.
Hierarchical Context Network for Airborne Image Segmentation

Most of the recent methods focus on capturing contextual information by measuring relations (e.g., feature similarity) between each pixel and all the others for airborne image segmentation. Nevertheless, these methods have difficulty in handling confusing objects with a partially similar appearance. In this article, we attempt to simultaneously explore pixel-to-pixel (P2P) and pixel-to-object (P2O) relations to learn contextual information. For this purpose, a hierarchical context network (HCNet) is proposed. It consists of a P2P subnetwork and a P2O subnetwork. The P2P subnetwork learns the P2P relation (detail-grained context) for better preservation of the details (e.g., boundary) of the objects. Meanwhile, the P2O subnetwork models the P2O relation (semantic-grained context), aiming at improving the intraobject semantic consistency. When inferring the segmentation results, outputs of these two subnetworks are aggregated to obtain the hierarchical contextual information. Experimental results demonstrate that the proposed model achieves competitive performance on three challenging benchmarks.
A Coauthorship-Centric History of Interactive Emergent Narrative

We trace the history of emergent narrative as both a term and a concept, with a particular focus on interactive emergent narrative (IEN): the use of emergent narrative as an approach or solution to the problems presented by the combination of interactivity and narrativity. We argue that discussion of IEN—both historical and modern—often fails to distinguish between two contrasting uses of IEN: to enable player participation as a character in an authored storyworld, and to enable player authorship of new stories. We additionally advocate for a clearer distinction between these perspectives, so that IEN systems which aspire to enable player authorship can be developed, studied and evaluated on their own terms.
Anomalous Driving Detection for Traffic Surveillance Video Analysis

Traffic safety is an important topic in the intelligent transportation system. One major factor that causes traffic accident is anomalous driving. This paper presents a novel anomalous driving detection method in videos, which can detect unsafe anomalous driving behaviors. The contributions of this paper are three-fold. First, a new multiple object tracking (MOT) method is proposed to extract the velocities and trajectories of moving foreground objects in video. The new MOT method is a motion based tracking method, which integrates the temporal and spatial features. Second, a novel Gaussian local velocity (GLV) modeling method is presented to model the normal moving behavior in traffic videos. The GLV model is built for every location in the video frame, and updated online. Third, a discrimination function is proposed to detect anomalous driving behaviors. Experimental results using the real traffic data from the New Jersey Department of Transportation (NJDOT) show that our proposed method can perform anomalous driving detection fast and accurately.
Survey on anomaly detection in surveillance videos

The demand for video surveillance has seen a staggering growth in the last few years due to the rapid growth of urbanization & industrialization. Video Surveillance is a system which observe scenes, activities, behaviour or other kind of information with the help of CCTV Cameras & associated software. The main objectives of video surveillance are detection and tracking of moving objects, loss prevention, traffic monitoring, and capturing variety of real world anomalies. The major challenges in Anomaly detection are extraction of appropriate features, addressing normal behaviour, environment divergence and occurrence of abnormal events in sparse manner. Many algorithms have been proposed in this area for object detection and tracking related applications, however, the problem is still challenging because of dynamic behaviour in anomalies. In this review paper a comparative study of different anomaly detection methods in deep learning and representation learning is performed and the limitations of each method are listed. Also, we outline open research challenges for proceeding further research. This paper will use as a quick reference to the researchers working in the same field.
Fake currency detection using Image processing

In market fake currency is the most important problem that speaks a lot. Due to the growth of technology, the fake currency production has been increased which degraded the economy of our country. Here the suggested method uses OpenCV to recognize whether the given note is original or fake. It consists of machine learning techniques that are carried out using suitable mechanisms.A fake currency detection method is introduced which uses the edge detection to detect the lines accurately and also accurately detect curves of acceptable notes. [9] Here we use a detector that is trained with the help of stored information which is similar to the one that is to be tested or compared later; within those modules, anchor lines are defined that are further depicted in subsequent test patterns. In order to provide training for the detector in offline a microprocessor is programmed. This is done with an sample currency obtained by scanning given note into our proposed method, here a frame like design is obtained by training image format. Then, notes similar to obtained frame are to be identified. Inside the template, the microprocessor determines anchor lines that are further depicted in that test format; it spin and moves the design before it matches to the training format, so that anchor lines which corresponds to the line can be identified in that trained dataset i.e. the pattern designed; and compares them with the test format to know if those anchor lines lies inside that test format. The system is proposed in a way that it shows if the currency is fake or it is original. We all know that Currency occupies an important place in our existence and hence it is very important for us to check its uniqueness. This system is useful in India because they use the paper currencies more.
Recommendation system with minimized transaction data

This paper deals with the recommendation system in the so-called user-centric payment environment where users, i.e., the payers, can make payments without providing self-information to merchants. This service maintains only the minimum purchase information such as the purchased product names, the time of purchase, the place of purchase for possible refunds or cancellations of purchases. This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service. First, we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data. The recommendation methodology was verified with the real transaction data. Based on the experimental results, we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.
Computational Neural Dynamics Model for Time-Variant Constrained Nonlinear Optimization Applied to Winner-Take-All Operation

Although time-variant nonlinear optimization with equality and inequality constraints (TVNOEIC) is a widespread problem in real life, the research on this issue is much less than the time-invariant one. Large lagging errors may not be inevitable if the traditional models designed for time-invariant optimizations are exploited to time-variant ones. To eliminate the lagging errors, models for handling time-variant optimization problems are urgently required. Recently, neural networks and the related neural dynamics approaches have witnessed rapid developments and made significant contributions to the online solution of various problems. On this basis, a noise-suppressing discrete-time neural dynamics (NSDTND) model is proposed in this article for solving the TVNOEIC problem. Theoretical analysis is presented to prove that the residual error of the proposed model is tiny enough to estimate the actual solution even in the presence of noise perturbation. In addition, numerical simulations, including an application on winner-take-all operation, are provided to further demonstrate the effectiveness and robustness of the proposed NSDTND model.
SaBRe: load-time selective binary rewriting

Abstract Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe , a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for and —which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.
Spacecraft Attitude Control Using the Invariant-Set Motion-Planner

This letter adapts the invariant-set motion-planner for safe spacecraft attitude control. The invariant-set motion-planner is a motion-planning algorithm that uses the positive-invariant sets of the closed-loop dynamics to find a constraint admissible path to a desired target through an obstacle filled environment. We use the invariant-set motion-planner to plan a sequence of reference quaternion waypoints that safely guides the spacecraft attitude around keep-out cones to a desired orientation. Our main contribution is the use of parametric optimization to derive a computationally efficient method solving the non-convex safety certification optimization problem. The computational efficiency of our safety certification is demonstrated in a numerical example.
Deep Recommendation With Adversarial Training

Recommender systems provide an effective solution to the information overload and have become a research hotspot in both industry and academia. Some existing work on implicit feedback has made great progress. However, there are still the following shortcomings: 1) existing pairwise ranking methods mostly sample unobserved items uniformly to obtain negative samples, which brings a biased solution and insufficient convergence; 2) the recommendation result comes from a complicated process, which makes it less interpretable. Therefore, we put forward a Deep Recommendation with Adversarial Training (DRAT) model, which provides users with personalized recommendations by utilizing an encoder-decoder structure and adversarial training. It consists of two components: one is feature learning module in which the encoder captures the text features of items from user-generated reviews, and the decoder reconstructs the text with the captured features; the other is rating prediction module which utilizes adversarial training to generate personalized negative samples tackling the drawbacks of uniform negative sampling. Extensive experiments on five real-world datasets show that our proposed model significantly outperforms the baseline methods.
Differential Privacy in Consumer Behavior Analysis

Consumer behavior analysis is the key enabler for many industrial applications. This is also true for the electricity sector. However, such analysis is based on huge amount of data, which raises the public concern over private information leakage. In this paper, we seek to understand how privacy preserving mechanism may affect the behavior analysis performance. Specifically, we use k-means clustering as an example of behavior analysis and define cluster stability from a probability theoretic viewpoint. We establish the relationship between different levels of privacy preserving requirement and cluster stability theoretically and empirically. Numerical studies highlight the value of our proposed analysis for both system operator and the consumer.
Alleviating ASR Long-Tailed Problem by Decoupling the Learning of Representation and Classification

Recently, we have witnessed excellent improvement of end-to-end (E2E) automatic speech recognition (ASR). However, how to tackle the long-tailed data distribution problem while maintaining E2E ASR models' performance for high-frequency tokens is still challenging. To solve this challenge, we propose a novel decoupled ASR learning method for the sequence-to-sequence ASR architecture in this paper. Our method decouples the learning procedure of this model into two stages: representation learning and classification learning. In the representation learning stage, we use the encoder output of a pretrained language model as one of the ASR model’s learning targets, and propose threshold log cosine embedding loss (TLCE-loss) as the objective function. A frequency-mask cross-entropy loss (FMCE-loss) is also designed as an auxiliary loss. In the classification learning stage, we find that introducing a temperature into softmax function helps reduce the influence of negative samples on tail classes, thus mitigating the biased learning process for the classifier. Furthermore, we propose a weighted softmax (w-softmax) to adjust ASR posterior probabilities according to the token appearing frequency during inference. Additionally, we introduce tail word/character error rate (TWER / TCER) and head word/character error rate (HWER / HCER) that respectively evaluate the ASR accuracy for tail and head words/characters. Experimental results on the Switchboard and HKUST corpora show that our proposed method greatly outperforms the baseline, especially in TWER / TCER reduction. To the best of our knowledge, this is the first work to use a decoupled ASR learning method to alleviate the long-tailed problem in sequence-to-sequence ASR.
Toward Secure Data Sharing for the IoT Devices With Limited Resources: A Smart Contract-Based Quality-Driven Incentive Mechanism

With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data-sharing process is ensuring that the data are high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract-based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of nested coalitional (TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.
Fair Federated Learning for Heterogeneous Data

We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In contrast, it is common for each client to own data that represents only a single demographic group. Hence the existing approaches cannot be adopted for fair classification models at the client level. To resolve this challenge, we propose several aggregation techniques. We empirically validate these techniques by comparing the resulting fairness and accuracy on CelebA and UTK datasets.