Reviewerly/exHarmony huggingface dataset:
FBMOT: Flow Bridges the Gap between Detection and Tracking in Multiple Object Tracking

The detection performance is a key bottleneck to the performance of multiple object tracking (MOT) algorithms and the advanced detection algorithms contribute a large portion to the success of MOT algorithms. Even though, the detectors can still make false detections based on only the image frames, which may directly result in the loss or mismatch of tracks. In MOT tasks, we point out that the video frames are temporally correlated and the temporal relationship can be leveraged to further improve the performance of detection and subsequent tracking. In our work, we propose FBMOT which uses optical flow to compute a prior heatmap about the locations of previously tracked objects in the current frame and takes the heatmap as an additional input in detection. We further proposed a novel regularization loss to help our model distinguish useful information in the prior heatmap. As a result, our method improves 0.8, 1.0 and 2.7 MOTA in MOT16, MOT17 and MOT20 test datasets respectively.
Robust Camera Calibration with a Single Image by Planar Homography

In surveillance applications, there are usually dozens of cameras at one site, and quick and easy calibration has primary importance. This paper presents a practical and robust camera calibration method that works with a single image of a planar marker and less sensitive to measurement noise. The method first detects a planar object on the ground plane and estimates a homography between the ground plane and the image plane. Two orthogonal non-infinite vanishing points are then estimated directly from the homography, not from the marker nor scene structure. The proposed method minimizes possible calibration error by choosing the direction of the two vanishing points to be as much different from the direction of the vanishing line as possible. Experimental results on synthetic images show that the proposed homography-based method greatly reduces calibration error in the presence of measurement noise, demonstrating the effectiveness of the method.
UPM-GTI-Face: A dataset for the evaluation of the impact of distance and masks in face detection and recognition systems

We present a novel dataset for the evaluation of face detection and recognition algorithms in challenging surveillance scenarios. The dataset consists in 4K images of different subjects captured at annotated distances ranging from 1 to 30 meters, both in indoor and outdoor environments, and under two face mask conditions (with and without). To the best of our knowledge, this is the only existing dataset that addresses the joint impact of masks and distances in a rigorous manner. We also propose an end-to-end fully automatic face detection and recognition system to provide baseline results on this dataset. Face detection is performed using Tiny Faces network, while face recognition is performed using VGG Face network. Experimental results show very high detection and recognition rates up to a distance of 20 meters, where the impact of distance is clear (especially for the latter). The use of face masks degrades the detection range and produces less consistent recognition results.
Research on the Effectiveness of STEM Course in the Language Development of Physics

To explore the influence and effect of STEM curriculum on the development of junior high school students' physical language ability. Methods: A total of 106 students from two classes were randomly identified as experimental group (52 students) and control group (56 students). The experimental group offered STEM courses on the basis of traditional teaching methods, while the control group only taught according to traditional teaching methods, and finally compared the physical language abilities of the two groups. Results: The ability of applying physical language in the experimental group was significantly higher than that in the traditional teaching group (P&lt;0.05). The students' subjective evaluation of language expression, language organization, language logic, language integration and language application ability in the experimental group were better than those in the traditional group. Conclusion: STEM course is more suitable for the development of the times, and its effect is better than traditional teaching. It can combine students' learning with language situations and improve their language application ability.
A reflective cycle-based virtual reality approach to promoting students’ learning achievement, sense of presence, and higher-order thinking in professional training

In professional healthcare training programs, field experience after physical courses is essential for students to become professionals. However, the outbreak of the pandemic has restricted such field experience, and so students’ problem-solving awareness when facing clinical emergencies in a professional field after graduation has become a critical issue. In conventional teaching, video-based instruction with case-based learning sheets is usually adopted for healthcare training. In this teaching mode, students generally lack opportunities to experience the process of dealing with authentic cases, and hence might be unable to immediately judge and solve problems when entering the workplace. To enhance students’ critical thinking and problem-solving awareness, the present study proposed a reflective cycle-based virtual reality (RC-VR) approach to provide a realistic environment and enable repeated practice. To examine its effectiveness, this study adopted a quasi-experimental design and applied it in a healthcare training program for risk assessment of maternal labor. The results indicated that the proposed RC-VR approach effectively enhanced students’ learning achievement, sense of presence, critical thinking, and problem-solving awareness in comparison to the conventional teaching approach.
Conditional inference of Poisson models and information geometry: an ancillary review

Abstract The Poisson distribution is a fundamental tool in categorical data analysis. This paper reviews conditional inference for the independent Poisson model. It is noted that the conditioning variable is not an ancillary statistic in the exact sense except in the case of the product multinomial sampling scheme, whereas two versions of the ancillary property hold in general. The ancillary properties justify the use of conditional inference, as first proposed by R. A. Fisher and subsequently discussed by many researchers. The mixed coordinate system developed in information geometry is emphasized as effective for the description of facts.
Exciting times in quantum technologies

PhotonicsViewsVolume 19, Issue 6 p. 24-26 InterviewFree Access Exciting times in quantum technologies Industry and academia stand side-by-side, like Prof Immanuel Bloch and Toptica's Thomas Renner First published: 27 November 2022 https://doi.org/10.1002/phvs.202270608AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume19, Issue6December 2022/January 2023Pages 24-26 RelatedInformation
On the influence of architectural languages on requirements traceability

Abstract Today, a considerable number of Architectural Languages (ALs) have been proposed for specifying and analyzing the architecture of software systems. Despite the popularity of different ALs, how ALs influence software system maintainability has not received much attention. One of the most important tasks in software maintenance is requirements traceability. Requirements traceability establishes links between requirements and other software artifacts, facilitating system maintenance. In this paper, we analyze the influence of ALs on requirements traceability. Taking into account the ALs used by the industry, we analyze how ALs influence traceability among requirements and architecture models. We conducted an evaluation with our industrial partner CAF. The results show significant differences in AL performance. We also analyze the results in terms of AL concepts, requirements model elements, and AL type in order to understand the performance differences. General‐Purpose/Research Languages achieve the best results for all of the performance indicators, providing a mean precision value of 0.51, a recall value of 0.38, a combined F‐measure of 0.40, and an Matthews Correlation Coefficient value of 0.33. Those ALs that influence engineers to use more generic and domain‐independent terms to specify their architectures obtain the best results during requirements traceability. Our results have the potential to help AL designers to improve their languages and also to help practitioners make a more informed decision about whether or not a given AL meets their traceability needs.
Quantitative model of financial risk management of forestry enterprises based on nonlinear differential equation

In view of the poor effect of traditional management methods, a quantitative model of financial risk management for forestry enterprises based on nonlinear differential equation is proposed. According to the definition of forestry enterprises, this paper analyzes the development status of forestry enterprises from the quantitative analysis and industrial analysis of forestry enterprises. This paper constructs a nonlinear differential equation model, and analyzes the risks faced by enterprises in operation. The specific application content is studied, and the quantitative model analysis of financial risk management of forestry enterprises is completed. The simulation results show that the management effect of this method is good, which lays a foundation for the protection of forestry economy.
Hadoop framework integrated hybrid optimization algorithm for privacy preserved clustering mechanism

Big data analysis has gained immense attention throughout classical techniques, which connect in mining the hidden samples from huge data. To relieve computational complexity, the clustering technique is adapted as an imperative part. A novel model is devised for privacy preserved clustering of data with MapReduce framework. The aim is to devise an optimization technique for privacy preservation. The input data is acquired from various distributed sources. The data is further partitioned and fed to MapReduce framework, which consist of mapper and reducer. The mappers perform privacy preservation by encrypting the data with several functionalities, like encryption, Kronecker product and secret key. Here, the secret key generation is performed using proposed Chimp Grey Wolf Optimization (ChGWO) algorithm. The proposed ChGWO is developed by combining Chimp Optimization algorithm (ChOA), and Grey Wolf Optimizer (GWO). The fitness is newly developed considering utility and privacy. The privacy is Jaro Winkler similarity and utility is accuracy. Finally, the data clustering is carried out with the Deep Fuzzy Clustering (DFC). The proposed ChGWO offered enhanced efficiency with highest utility of 92.5%, highest privacy of 91.5% and highest random coefficient 65.9%.
Goal model structuring based on semantic correlation of user reviews

User review is a crucial component of open mobile app market, such as the Google Play Store. These markets allow users to submit reviews for downloaded apps. By analyzing these reviews, app developers can find new or missing features. However, owing to the huge number of reviews, this manual process is time-consuming and unscalable. An automatic method for user review clustering and goal-model identification has been proposed. However, this method limits the numbers of sub-goals in the goal model. To address this, we propose a comprehensive user review clustering method. This method comprises two components. One is a latent Dirichlet allocation (LDA) model, which clusters user reviews into several topics. The other is a distance-based clustering algorithm, which is an improved version of the existing clustering method.
A novel twin-support vector machines method for binary classification to imbalanced data

Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel.
Master-CAM: Multi-scale fusion guided by Master map for high-quality class activation maps

Class Activation Map (CAM) is one of the most popular approaches to visually explain the convolutional neural networks (CNNs). To obtain fine-grained saliency maps, some works fuse saliency signals of the same image at larger scales. However, existing methods based on multi-scale fusion cannot effectively remove the noise from larger-scale images. In this paper, we propose Master-CAM, which uses Master map to guide multi-scale fusion process to obtain a high-quality class activation map. Master-CAM utilizes the general localization ability of the Master map to reduce the noise of the maps. We call the one with the general localization ability among the saliency maps from the same image as Master map, which is the saliency map of the original-scale input in the multi-scale scenario. In addition, we also present a simple yet effective fusion strategy, Master-Fusion, which is derived from the fusion operation in Master-CAM. Master-Fusion strategy can be easily attached to some saliency methods to improve the performance of these methods. We show through qualitative and quantitative experiments that the proposed Master-CAM outperforms the state-of-the-art methods in different CNN frameworks and datasets.
A LWE-Based Receiver-Deniable Encryption Scheme

In this era, user privacy is a very important issue and encryption is a general solution. However, the encryption technique cannot keep user privacy while the key is leaked under outside coercion. To solve this problem, the deniable encryption technique is a useful tool since it makes the user be able to answer the outside coercer with fake data and proofs. In this paper, we propose a deniable encryption scheme based on the LWE problem (Learning with Errors). We enhance the Regev LWE encryption scheme proposed in 2009 by increasing the probability of decryption failure. So the user can cheat the outsider with the fake message. We also apply the multi-distributional concept proposed by O’Neill et al. scheme to construct our deniable encryption scheme.
A Blockchain-Based Custody System for Preserving Critical Video Evidence

In recent decades, surveillance cameras are widely deployed in populous areas for secretly consideration. The connection to the Internet, however, has brought lots of illegal intruding behaviors to these IoT (Internet of Things) devices. More precisely, the surveillance camera has become the rank one IoT target suffered from malicious attacks. As the recorded video is an important evidence for investigation, any suspicious disruption on surveillance camera must lower down the credibility of video. Thus, we aim to integrate the blockchain concept for developing a critical evidence custody system (CECS). In particular, a critical motion detection (CMD) technique is designed to reduce the video storage overhead on blockchain. Simulations have demonstrated that CECS is able to efficiently preserve the video credibility of surveillance camera.
Reversible Data Hiding in Encrypted Image Based on MSB Inversion

Reversible Data Hiding in the Encrypted image (RDHEI) is an important research topic. This paper improves Korivi and Manikandan's RDHEI method. For each block in the encrypted image, the reverse strategy of MSB is adopted to embed the secret information. Thus, each block can embed a bit of confidential information. In addition, we propose a simplified image smoothness formula to increase the efficiency of extracting secret information and recovering the original image. Experimental results show that the proposed method can improve the ability of information retrieval and image restoration.
E-learning Behavior Analytics in the Curriculum of Big Data Visualization Application

There are two core professional training objectives in the Department of Distribution Management in this study’s case university: logistics management and business stores management. To enhance the efficiency of decision makers using complex data, management science and information technology application is vital. As visualization images are more easily understood than complicated statistical analyses in reports, the interactive and visual analytics system, Tableau, is applied in the big data learning and analytics curriculum. Through feedback it has been found that due to the differing skill levels, some students could not follow the sets outlined in face-to-face learning; therefore, the instructor will offer remedy education using blended learning in the new semester. The two facets of blended learning are face-to-face and online teaching. It is necessary to analyze the behavior portfolios of the learners to intelligently modify the remedy education strategy and enhance the quality of e-learning. During February to June 2021, 39 junior and senior undergraduates in the big data class participated in this study. In the case university, the instructor uploaded the teaching materials in the e-learning platform so that students could check the operational videos after in-class demonstrations. According to the participants’ background variables and e-learning behavior portfolios, descriptive and inferential statistical analyses were performed. The independent variables are gender, group role, login frequency, class attendance frequency, discussion frequency, reading seconds, reading pages, and skill difficulty. The dependent variable is the final report score of the student groups. Hotspot analysis, basic statistics, ANOVA, correlation, and C5 decision tree analyses were performed in this study. The results show that the e-learning teaching materials were beneficial to the students who needed extra assistance after class. Being female, the frequency of login, class attendance, and discussion were significantly positively correlated with the final report scores of the students. The variables of skill difficulty and discussion were the two main variables identified to predict high scores in the curriculum of big data. These findings can help instructors improve their pedagogy and enhance students’ e-learning performance.
Hybridizing graph‐based Gaussian mixture model with machine learning for classification of fraudulent transactions

Summary It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the organizations. This is one of the fastest‐growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k ‐nearest neighbor ( k ‐NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j . The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k‐fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph‐based Gaussian mixture model (CGB‐GMM) has been proposed to effectively detect the fraudulent instances in credit card transactions with the application of graph algorithms such as degree centrality, LPA, page rank, and so forth. Each of the learning algorithms are implemented with and without the application of graph algorithms and their performances are assessed empirically for analysis.
Hybrid Safety Certificate for Fast Collision Checking in Sampling-Based Motion Planning

Safety certificates in robot configuration space have been shown as an efficient collision checking method. However, this method can only be applied to very simple problems since constructing the configuration space obstacles is often intractable. In this paper we propose a hybrid safety certificate (HSC) approach in both the configuration space and the workspace to increase collision checking efficiency for large-scale complex motion planning problems. Specifically, the collision-free certificate regions are constructed as random spheres in the configuration space. While the in collision certificate regions are constructed using the inside spheres of the workspace obstacles. In fact, the HSC method realizes collision checking as a straightforward sphere-sphere overlapping test which is widely regarded as the simplest way for collision detection. Besides the lazy collision checking strategy can be naturally combined with our high efficient HSC method. The HSC method combining with the lazy planning strategy is applied to both the feasible and the optimal motion planning problems. And we present a feasible motion planner BiHSC and an optimal motion planner BiHSC*. Simulations demonstrate that the computation speed is much faster than that of the state-of-the-art algorithms.
Curriculum-Based Asymmetric Multi-Task Reinforcement Learning

We introduce CAMRL, the first curriculum-based asymmetric multi-task learning (AMTL) algorithm for dealing with multiple reinforcement learning (RL) tasks altogether. To mitigate the negative influence of customizing the one-off training order in curriculum-based AMTL, CAMRL switches its training mode between parallel single-task RL and asymmetric multi-task RL (MTRL), according to an indicator regarding the training time, the overall performance, and the performance gap among tasks. To leverage the multi-sourced prior knowledge flexibly and to reduce negative transfer in AMTL, we customize a composite loss with multiple differentiable ranking functions and optimize the loss through alternating optimization and the Frank-Wolfe algorithm. The uncertainty-based automatic adjustment of hyper-parameters is also applied to eliminate the need of laborious hyper-parameter analysis during optimization. By optimizing the composite loss, CAMRL predicts the next training task and continuously revisits the transfer matrix and network weights. We have conducted experiments on a wide range of benchmarks in multi-task RL, covering Gym-minigrid, Meta-world, Atari video games, vision-based PyBullet tasks, and RLBench, to show the improvements of CAMRL over the corresponding single-task RL algorithm and state-of-the-art MTRL algorithms. The code is available at: https://github.com/huanghanchi/CAMRL.