Reviewerly/exHarmony huggingface dataset:
Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.
Towards the landscape rotation as a perturbation strategy on the quadratic assignment problem

Recent work in combinatorial optimisation have demonstrated that neighbouring solutions of a local optima may belong to more favourable attraction basins. In this sense, the perturbation strategy plays a critical role on local search based algorithms to kick the search of the algorithm into more prominent areas of the space. In this paper, we investigate the landscape rotation as a perturbation strategy to redirect the search of an stuck algorithm. This technique rearranges the mapping of solutions to different objective values without altering important properties of the problem's landscape such as the number and quality of optima, among others. Particularly, we investigate two rotation based perturbation strategies: (i) a profoundness rotation method and (ii) a broadness rotation method. These methods are applied into the stochastic hill-climbing heuristic and tested and compared on different instances of the quadratic assignment problem against other algorithm versions. Performed experiments reveal that the landscape rotation is an efficient perturbation strategy to shift the search in a controlled way. Nevertheless, an empirical investigation of the landscape rotation demonstrates that it needs to be cautiously manipulated in the permutation space since a small rotation does not necessarily mean a small disturbance in the fitness landscape.
Learning product representations for generating reviews for cold products

Existing work in the literature have shown that the number and quality of product ratings and reviews have a direct correlation with the product purchase rates in online e-commerce portals. However, the majority of the products on e-commerce portals do not have any ratings or reviews and are known as cold products (∼90% of products on Amazon are cold). As such, there has been growing interest in generating reviews for cold products by selectively transferring reviews from other similar yet warm products. Our work in this paper focuses on this specific problem and generates reviews for cold products through review selection. Similar to existing work in the literature, our work assumes a relationship between product attribute-values and the reviews that products receive. However, unlike the literature, our method (1) is not restricted to the exact surface form of a product attribute name; and, (2) can distinguish between the same attribute expressed in different forms. We achieve these two important characteristics by proposing methods to learn neural product representations that capture the semantics of product attribute-values as they relate to user reviews. More specifically, our work offers (i) an approach to learn neural representations of product attribute-values within a shared embedding space as product reviews; (ii) a weighted composition strategy to develop product representations from the representation of its attributes; and, (iii) a review selection method that selects relevant reviews for the composed product representation within the neural embedding space. We show through our extensive experiments on five datasets consisting of products from CNET.com and movies from rottentomatoes.com that our method is able to show stronger performance compared to several baselines on ROUGE-2 metrics.
Intelligent Agents in Cars: Investigating the Effects of Anthropomorphism and Physicality of Agents in Driving Contexts

An increasing number of intelligent agents are developed for driving contexts. Anthropomorphism is a commonly used strategy to embody intelligent agents. This research investigates two specific ways of anthropomorphizing agents: the anthropomorphism level (low vs high) and physicality (virtual vs physical). We conducted a 2 × 2 between-subjects (N = 115) experiment through Wizard-of-Oz methodology. Participants’ subjective evaluations were collected. Results revealed the interaction effects of anthropomorphism level and physicality on drivers’ trust, perceived control, and attitude. Specifically, for high-level anthropomorphized agents, a physical agent significantly lower drivers’ trust, perceived control, and attitude in comparison to a virtual one. No significant differences between physical and virtual agents were found for low-level anthropomorphized agents.
Seismic Monitoring during Crises at the NEIC in Support of the ANSS

Abstract Over the past two decades, the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) has overcome many operational challenges. These range from minor disruptions, such as power outages, to significant operational changes, including system reconfiguration to handle unique earthquake sequences and the need to handle distributed work during a pandemic. Our ability to overcome crises is built on the development and implementation of a continuity of operations plan, well-designed infrastructure, adaptive software systems, experienced staff, and extensive collaboration. The NEIC does not operate in a vacuum but benefits from contributions of United States and international seismic networks. Similarly, the overall resilience of earthquake monitoring in the United States and around the globe benefits from the NEIC’s role as the national center for the Advanced National Seismic System (ANSS). Here, we highlight significant adaptations the NEIC has made in the face of crises. We discuss the COVID-19 pandemic, which represents the most significant operational crisis to impact the NEIC. The NEIC has maintained continuous operations during the ongoing COVID-19 pandemic by shifting from a fully onsite operations center to a distributed hybrid of onsite and telework staffing. We then discuss cases in which the NEIC has supported regional monitoring in the face of significant crises. In 2018, the NEIC assisted the Hawaiian Volcano Observatory with the Kīlauea volcano eruption by responding to large events, implementing contingency monitoring procedures, and calculating moment magnitudes for the low-frequency caldera collapses. Impacts of a crisis extend beyond the immediate response and often require a significant postevent assessment and a rebuilding phase. After the 2017 Hurricane Maria, the NEIC, the USGS National Strong-Motion Program, and the USGS Albuquerque Seismological Laboratory worked with the Puerto Rico Seismic Network and the Puerto Rico Strong-Motion program to assess, plan, and implement upgrades at sites that experienced storm damage.
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression

Cyber-physical systems (CPSs) use learning-enabled components (LECs) extensively to cope with various complex tasks under high-uncertainty environments. However, the dataset shifts between the training and testing phase may lead the LECs to become ineffective to make large-error predictions, and further, compromise the safety of the overall system. In our paper, we first provide the formal definitions for different types of dataset shifts in learning-enabled CPS. Then, we propose an approach to detect the dataset shifts effectively for regression problems. Our approach is based on the inductive conformal anomaly detection and utilizes a variational autoencoder for regression model which enables the approach to take into consideration both LEC input and output for detecting dataset shifts. Additionally, in order to improve the robustness of detection, layer-wise relevance propagation (LRP) is incorporated into our approach. We demonstrate our approach by using an advanced emergency braking system implemented in an open-source simulator for self-driving cars. The evaluation results show that our approach can detect different types of dataset shifts with a small number of false alarms while the execution time is smaller than the sampling period of the system.
PGM-face: Pose-guided margin loss for cross-pose face recognition

Cross-pose face recognition has been a challenging task due to the diversity and arbitrariness of the head pose. Current methods addressing this task can be divided into two categories. One is learning pose-robust face representation, and another is rotating the faces via face synthesis. Unlike the common methods, our proposed Pose-Guided Margin Loss (PGM-Face) extends the dimensions of the linear transformation matrix for each class to estimate the head poses, therefore the learned features of each class are soft clustered guided by the head pose, and the inter margin of two classes under the same pose is larger than where the face features are erratic (see Fig. 1). The similarity of the learned features is measured after transformed into the same target pose via our proposed Pose-Guided Representation Transfer Network (PGRT-Net). Compared with pose-robust face representation learning methods, our method learns more separable face features under the arbitrarily specified poses. Compared with face rotation methods, our proposed method rotates the face features instead of the face images, which can reduce the loss of identity information during the synthesis. Quantitative and qualitative experiments on several challenging databases for verification and recognition show that the proposed method achieves state-of-the-art performance, and its individual components yield substantial improvements.
Do Away with the Frankensteinian Programs! A Proposal for a Genuine SE Education

It is widely accepted by now that the discipline of Software Engineering is distinct from both Computer Science and Electrical Engineering, and that it requires bespoke higher education programs. In this paper, we argue that previous attempts at designing such programs have often failed to fully account for three essential characteristics of the discipline. We propose a design philosophy for undergraduate Software Engineering programs addressing these particularities and outline a corresponding program. Incorporating this philosophy would make Generation Alpha the first generation to receive a genuine Software Engineering education.
Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images

Neural Architecture Search (NAS) is one of the most recent developments in automating the design process for deep convolutional neural network (DCNN) architectures. NAS and later Efficient NAS (ENAS) based models have been adopted successfully for various applications including ultrasound image classification for breast lesions. Such a data driven approach leads to creation of DCNN models that are more applicable to the data set at hand but with a risk for model overfitting. In this paper, we first investigate the extent of the ENAS model generalization error problem by using different test data sets of ultrasound images of breast lesions. We have observed a significant reduction of overall average accuracy by nearly 10% and even more severe reduction of specificity rate by more than 20%, indicating that model generalization error is a serious issue with ENAS models for breast lesion classification in ultrasound images. To overcome the generalization error, we examined the effectiveness of a range of techniques including reducing model complexity, use of data augmentation, and use of unbalanced training sets. Experimental results show that different methods for the tuned ENAS models achieved different levels of accuracy when they are tested on internal and two external test data sets. The paper demonstrates that ENAS models trained on an unbalanced dataset with more benign cases tend to generalize well on unseen images achieving average accuracies of 85.8%, 82.7%, and 88.1% respectively for the internal and the two external test data sets not only on specificity alone, but also sensitivity. In particular, the generalization of the refined models across internal and external test data is maintained.
Wide-Scale Automatic Analysis of 20 Years of ITS Research

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount of time. Additionally, major publications which have gained a large number of citations are more likely to be included in a review, with recent or fringe publications receiving less inclusion. We provide thus an automatic methodology for the large-scale analysis of literature within the Intelligent Tutoring Systems (ITS) domain, with the aim of identifying trends and areas of research from a corpus of publications which is significantly larger than is typically presented in conventional literature reviews. We illustrate this by a novel analysis of 20 years of ITS research. The resulting analysis indicates a significant shift of the status quo of research in recent years with the advent of novel neural network architectures and the introduction of MOOCs.
Research on Performance Management of Financial Shared Service Center Based on Cloud Accounting

research-article Share on Research on Performance Management of Financial Shared Service Center Based on Cloud Accounting Authors: Xuxin Yang Xi'an Eurasia University, China Xi'an Eurasia University, ChinaView Profile , Yurong Hou Xi'an Eurasia University, China Xi'an Eurasia University, ChinaView Profile , Wei Zhang Xi'an Eurasia University, China Xi'an Eurasia University, ChinaView Profile Authors Info & Claims ICSIM 2021: 2021 The 4th International Conference on Software Engineering and Information ManagementJanuary 2021 Pages 89–93https://doi.org/10.1145/3451471.3451486Online:13 July 2021Publication History 0citation84DownloadsMetricsTotal Citations0Total Downloads84Last 12 Months84Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Software Development During COVID-19 Pandemic: an Analysis of Stack Overflow and GitHub

The new coronavirus became a severe health issue for the world. This situation has motivated studies of different areas to combat this pandemic. In software engineering, we point out data visualization projects to follow the disease evolution, machine learning to estimate the pandemic behavior, and computer vision processing radiologic images. Most of these projects are stored in version control systems, and there are discussions about them in Question & Answer websites. In this work, we conducted a Mining Software Repository on a large number of questions and projects aiming to find trends that could help researchers and practitioners to fight against the coronavirus. We analyzed 1,190 questions from Stack Overflow and Data Science Q&A and 60,352 GitHub projects. We identified a correlation between the questions and projects throughout the pandemic. The main questions about coronavirus are how-to, related to web scraping and data visualization, using Python, JavaScript, and R. The most recurrent GitHub projects are machine learning projects, using JavaScript, Python, and Java.
A Two-Step Optimization for Extrinsic Calibration of Multiple Camera System (MCS) Using Depth-Weighted Normalized Points

A multiple camera system (MCS), which allows only a limited common field-of-view between adjacent cameras, has been chiefly calibrated using a typical 2D-3D point correspondence. However, the correspondence contains a potential instability by which the calibration result can diverge, and the instability has not been studied nor overcome before. We propose a MCS extrinsic calibration method with high robustness and accuracy based on a two-step optimization strategy. Using depth-weighted normalized points, we develop two novel types of point correspondence as follows. The 1st correspondence, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cb</sub> , aims to robustly estimate the MCS extrinsic parameters by overcoming the potential instability that exists in the typical 2D-3D point correspondence. The 2nd correspondence, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> , aims to refine the MCS extrinsic parameters by using the direct relation between adjacent cameras. In the simulation, we validated the robustness and high accuracy of the proposed method. We validated its high precision in the field test. In both the simulation and the field test, our method was compared with the state-of-the-art method.
A GPU-based Approach for Path Planning Optimization via Travel Length Reduction

Typically, before constructing an object with an additive manufacturing system, the 3D object must be sent through a process called slicing. Slicing converts a 3D object commonly in the form of an STL file into a set of layers by horizontally intersecting a plane with the object at various heights. At each height, called a layer, multiple 2D polygons can be generated. Each polygon represents a boundary for solid geometry and is called an island. Each island is then comprised of multiple path types in an attempt to optimally fill the polygon. To move between each island and each islands’ paths, travels are inserted. Travels are simply motion by the system to move from one area of construction to another. Travels do not contribute to the construction of the object, and so, are considered wasted motion. In large-scale additive manufacturing, objects can be quite large and the distance between islands can be large as well. As a result, these travels can waste a significant amount of time. Ideally, travels would be as short as possible, however, computing global minimal travel paths is computationally expensive. To combat this problem, researchers at Oak Ridge National Lab developed a GPU-based approach to travel insertion based on a unique factoradic representation. This representation was then utilized by the GPU to solve the Traveling Salesman Problem (TSP). This algorithm was able to compute global minimal travel paths quickly resulting in faster object construction. A general investigation was also carried out to determine when a GPU vs CPU implementation would be beneficial.
Traffic Signs Detection and Recognition System Using the YOLOv4 Algorithm

Traffic signs are one of the important road equipment facilities to inform road users about regulations and visual directions. Currently, an automatic Traffic Sign Recognition (TSR) system is being developed which is implemented in an advanced driver system (ADAS) so that road users can be safe and secure while on the road. Therefore, this paper aims to be able to detect and recognize traffic signs on the highway to provide information on the meaning of these traffic signs automatically. In this study, 35 classes of signs were used which consisted of warning signs, prohibitions signs, mandatory signs, and instructions signs. This system is implemented using the Darknet framework with the You Only Look Once version 4 (YOLOv4) model. The investigation carried out in this study is a system that detects and recognizes traffic signs evaluated on offline-based video in one-way traffic during the day. The result of mAP (mean Average Precision) in this system is 95.15%.
Proximity Measurement for Hierarchical Categorical Attributes in Big Data

Nearly most of the organizations store massive amounts of data in large databases for research, statistics, and mining purposes. In most cases, much of the accumulated data contain sensitive information belonging to individuals which may breach privacy. Hence, ensuring privacy in big data is considered a very important issue. The concept of privacy aims to protect sensitive information from various attacks that may violate the identity of individuals. Anonymization techniques are considered the best way to ensure privacy in big data. Various works have been already realized, taking into account horizontal clustering. The L-diversity technique is one of those techniques dealing with sensitive numerical and categorical attributes. However, the majority of anonymization techniques using L-diversity principle for hierarchical data cannot resist the similarity attack and therefore cannot ensure privacy carefully. In order to prevent the similarity attack while preserving data utility, a hybrid technique dealing with categorical attributes is proposed in this paper. Furthermore, we highlighted all the steps of our proposed algorithm with detailed comments. Moreover, the algorithm is implemented and evaluated according to a well-known information loss-based criterion which is Normalized Certainty Penalty (NCP). The obtained results show a good balance between privacy and data utility.
Research on path-planning of particle swarm optimization based on distance penalty

Nowadays, designing the optimal path in the complex urban environment is still a significant problem. This paper studies an application of path planning using Particle Swarm Optimization. The grid method is used to divide the environment to fit the implementation of particle swarm optimization. The objective function evaluates the distance of the path, and the penalty is involved to avoid obstacles along the path. Finally, the particle swarm optimization is used in two path planning problems regarding emergency and common vehicles to design the optimal path.