Reviewerly/exHarmony huggingface dataset:
Filtering in Pairwise Markov Model With Student's t Non-Stationary Noise With Application to Target Tracking

Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. Measurement noise is correlated over time since the sampling frequency of a radar is usually far greater than the bandwidth of measurement noise. Besides, when target is maneuvering, the process and measurement noises are heavy-tailed and non-Gaussian due to intrinsic data generation mechanism. In this paper, we consider a linear pairwise Markov model (PMM) with Student's t noise to model non-cooperative single target tracking without clutter and missed detections. A PMM is more general than an HMM and can be used to model correlated measurement noise or correlated process and measurement noises. The Student's t distribution is one of the most commonly used heavy-tailed distribution and can be used to address irregular target motion. We mainly focus on solving the filtering problems for the model. First, we develop a filter for the case where noise statistics are accurately known. Second, we further consider the case of non-stationary Student's t noise, and propose a novel robust filter by employing a variational Bayesian method. Finally, the effectiveness of the proposed filters is illustrated via simulation results.
Multi-Level Context Pyramid Network for Visual Sentiment Analysis

Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.
Robust Text Image Recognition via Adversarial Sequence-to-Sequence Domain Adaptation

Robust text reading is a very challenging problem, due to the distribution of text images changing significantly in real-world scenarios. One effective solution is to align the distribution between different domains by domain adaptation methods. However, we found that these methods might struggle when dealing sequence-like text images. An important reason is that conventional domain adaptation methods strive to align images as a whole, while text images consist of variable-length fine-grained character information. To address this issue, we propose a novel Adversarial Sequence-to-Sequence Domain Adaptation (ASSDA) method to learn “where to adapt” and “how to align” the sequential image. Our key idea is to mine the local regions that contain characters, and focus on aligning them across domains in an adversarial manner. Extensive text recognition experiments show the ASSDA could efficiently transfer sequence knowledge and validate the promising power towards the various domain shift in the real world applications.
ReinforceNet: A reinforcement learning embedded object detection framework with region selection network

In recent years, researchers have explored reinforcement learning based object detection methods. However, existing methods always suffer from barely satisfactory performance. The main reasons are that current reinforcement learning based methods generate a sequence of inaccurate regions without a reasonable reward function, and regard the non-optimal one at the final step as the detection result for lack of an effective region selection and refinement strategy. To tackle the above problems, we propose a novel reinforcement learning based object detection framework, namely ReinforceNet, possessing the capability of the region selection and refinement by integrating reinforcement learning agents’ action space with Convolutional Neural Network based feature space. In ReinforceNet, we redevelop a reward function that enables the agent to be trained effectively and provide more accurate region proposals. In order to further optimize them, we design Convolutional Neural Network based region selection network (RS-net) and bounding box refinement network (BBR-net). Particularly, the former consists of two sub-networks: Intersection-of-Union network (IoU-net) and Completeness network (CPL-net) which are employed jointly for selecting the optimal region proposal. The latter aims to refine the selected one as the final result. Extensive experimental results on two standard datasets PASCAL VOC 2007 and VOC 2012 demonstrate that ReinforceNet is capable of improving the region selection and learning better agent action representations for reinforcement learning, resulting in the state-of-the-art performance.
Project-Based Learning Activities for Engineering College Students

The paper discusses the effective organization of students’ project-based learning activities in an engineering university. The educational process modernization involves the introduction of new teaching approaches related to the development of additional competencies of students which allows them to implement complex projects, consequently increasing the competitiveness of university graduates in Russia. Numerous studies on project-based learning show it as the most suitable means of achieving effective competence-based education. The research proposes that the dissemination students’ project-based learning activities in an engineering university will be effective when the following pedagogical conditions are implemented: targeted development of students’ project-based learning activities; using student-student interaction in the students’ project-based learning activities; the readiness of the university faculty to organize and implement students’ project-based learning activities. Criteria for students’ project-based learning competencies are basic and in-depth knowledge in engineering field; professional competencies and personal qualities, interpersonal skills, ability to initiate, develop and implement projects. The paper is based on some successful examples of effective projects done by engineering students.
Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.
Integrating a High-Reliability Multicriteria Trust Evaluation Model with Task Role-Based Access Control for Cloud Services

Cloud data storage is revolutionary because it eliminates the need for additional hardware, which is often costly, inconvenient, and requires additional space. Cloud data storage allows data owners to store large amounts of data in a flexible way and at low cost. The number of online cloud storage services and their consumers has therefore increased dramatically. However, ensuring the privacy and security of data on a digital platform is often a challenge. A cryptographic task-role-based access control (T-RBAC) approach can be used to protect data privacy. This approach ensures the accessibility of data for authorized consumers and keeps it safe from unauthorized consumers. However, this type of cryptographic approach does not address the issue of trust. In this paper, we propose a comprehensive trust model integrated with a cryptographic T-RBAC to enhance the privacy and security of data stored in cloud storage systems, and suggests that trust models involve inheritance and hierarchy in the roles and tasks of trustworthiness evaluation, where this study aims to identify the most feasible solution for the trust issue in T-RBAC approaches. Risk evaluations regarding other possible flaws of the design are also performed. The proposed design can decrease risk by providing high security for cloud storage systems and improve the quality of decisions of cloud operators and data owners.
An empirical study on changing leadership in agile teams

Abstract An increasing number of companies aim to enable their development teams to work in an agile manner. When introducing agile teams, companies face several challenges. This paper explores the kind of leadership needed to support teams to work in an agile way. One theoretical agile leadership concept describes a Scrum Master who is supposed to empower the team to lead itself. Empirical findings on such a leadership role are controversial. We still have not understood how leadership unfolds in a team that is by definition self-organizing. Further exploration is needed to better understand leadership in agile teams. Our goal is to explore how leadership changes while the team matures using the example of the Scrum Master. Through a grounded theory study containing 75 practitioners from 11 divisions at the Robert Bosch GmbH we identified a set of nine leadership roles that are transferred from the Scrum Master to the Development Team while it matures. We uncovered that a leadership gap and a supportive internal team climate are enablers of the role transfer process, whereas role conflicts may diminish the role transfer. To make the Scrum Master change in a mature team, team members need to receive trust and freedom to take on a leadership role which was previously filled by the Scrum Master. We conclude with practical implications for managers, Product Owners, Development Teams and Scrum Masters which they can apply in real settings.
A Novel Cascade Path Planning Algorithm for Autonomous Truck-Trailer Parking

One of the most challenging tasks for truck drivers is maneuvering the truck-trailer system in different parking scenarios. This article presents a novel path planning approach for truck-trailer parking, where a realistic and deterministic parking behavior model, Iterative Analytical Method (IAM), is proposed and combined with Closed-Loop Rapidly Exploring Random Tree (CL-RRT) approach in a cascade path planning. Cascade path planning approach combining CL-RRT with the iterative analytical method (IAM) mimicking real-world parking practice enables the generation of both kinematically feasible and deterministic parking maneuvers with obstacle avoidance. For evaluation, different parking scenarios are generated and selected through a developed case generation tool. The performance of the proposed path planning approach is evaluated through MATLAB simulations. The results achieved a noticeable success with a high rate of generated feasible maneuvers for parking.
Evaluating Automatic Program Repair Capabilities to Repair API Misuses

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> s might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIRepBench</small> ) consisting of 101 <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses. We develop an extensible execution framework ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIARTy</small> ) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28 percent of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98 percent slower) than their predecessors. The tools generate patches for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses that mostly belong to the categories of missing <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">null</monospace> check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65 percent), but only few of these patches are semantically correct to human patches (25 percent). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses, handling of timeout issues, and ability to configure large software projects. Both <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIRepBench</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIARTy</small> have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses.
Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

Abstract Rock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc . This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition

As Facial Expression Recognition (FER) systems become integrated into our daily lives, these systems need to prioritise making fair decisions instead of aiming at higher individual accuracy scores. Ranging from surveillance systems to diagnosing mental and emotional health conditions of individuals, these systems need to balance the accuracy vs fairness trade-off to make decisions that do not unjustly discriminate against specific under-represented demographic groups. Identifying bias as a critical problem in facial analysis systems, different methods have been proposed that aim to mitigate bias both at data and algorithmic levels. In this work, we propose the novel usage of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of FER systems while guarding against biases arising from skewed data distributions. We compare different non-CL-based and CL-based methods for their classification accuracy and fairness scores on expression recognition and Action Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and BP4D datasets, respectively. Our experimental results show that CL-based methods, on average, outperform other popular bias mitigation techniques on both accuracy and fairness metrics.
WiONE: One-Shot Learning for Environment-Robust Device-Free User Authentication via Commodity Wi-Fi in Man–Machine System

User authentication is the first and most critical step in protecting a man-machine system from a malicious spoofer. However, security and privacy are just like the two sides of one coin, hard to see both at the same time, especially by the current mainstream credential- and biometric-based approaches. To this end, we propose WiONE, a safe and privacy-preserving user authentication system leveraging the ubiquitous Wi-Fi infrastructure by exploring “how you behave” rather than “who you are”. The key idea is to apply deep learning to user physical behavior captured by Wi-Fi channel state information (CSI) to identify legitimate users while rejecting spoofers. The design of WiONE faces two challenges, namely, how to capture the subtle behavior, such as a keystroke on CSI, and how to mitigate the heavy environment-specific training required by deep learning. For the former, we design a behavior enhancement model based on the Rician fading to highlight the behavior-induced information by suppressing the behavior-unrelated information on channel response. For the latter, we develop a behavior characterization method tailored for the prototypical networks to facilitate the extraction of the domain-independent behavioral features and enable one-shot recognition of a new user in a new environment. Numerous experiments are conducted in several real-world environments, and the results show that WiONE outperforms its state-of-the-art rivals in authentication performance with much less training effort.
Review of classification algorithms with changing inter-class distances

Machine learning algorithms are often faced with several data related problems. Real-world datasets come in various types and dimensions, each of which constitute some form of data related problems; moreover, they often contain irrelevant or noisy features. As a result of these, different data related problems require different techniques for the classification process. In this paper, some data related problems of interest are replicated in different synthetic datasets in order to investigate and evaluate the performance of a range of learning algorithms. Specifically, the data problems studied in this research are: datasets with varying inter class distances (classes are separated by different amounts); datasets with classes having different input relevance; datasets with classes defined by multiple features and by multiple underlying pattern; datasets with increasing number of noisy features; and datasets with varying amplitudes of noisy features. Also, datasets with combination of some of the problems were also synthesized. These datasets were then used to measure and validate the performance of a number of selected classification algorithms. The results of the experimental investigations show that the GNG had the best performance on datasets with varying inter class distances while DL performed best on the other datasets of different data problems.
A Survey of Software Clone Detection From Security Perspective

For software engineering, if two code fragments are closely similar with minor modifications or even identical due to a copy-paste behavior, that is called software/code clone. Code clones can cause trouble in software maintenance and debugging process because identifying all copied compromised code fragments in other locations is time-consuming. Researchers have been working on code clone detection issues for a long time, and the discussion mainly focuses on software engineering management and system maintenance. Another considerable issue is that code cloning provides an easy way to attackers for malicious code injection. A thorough survey work of code clone identification/detection from the security perspective is indispensable for providing a comprehensive review of existing related works and proposing future potential research directions. This paper can satisfy above requirements. We review and introduce existing security-related works following three different classifications and various comparison criteria. We then discuss three further research directions, (i) deep learning-based code clone vulnerability detection, (ii) vulnerable code clone detection for 5G-Internet of Things devices, and (iii) real-time detection methods for more efficiently detecting clone attacks. These methods are more advanced and adaptive to technological development than current technologies, and still have enough research space for future studies.