Reviewerly/exHarmony huggingface dataset:
Evaluating Machine Learning Algorithms in Representing Decision Makers in search-based PLA

Search-Based Software Engineering (SBSE) techniques have achieved satisfactory results for the optimization of Product Line Architecture (PLA) design by means of the Multi-objective Optimization Approach for PLA design (MOA4PLA) and its tool, called OPLA-Tool. However, the obtained solutions in SBSE might be rejected by the Decision Maker (DM) in some cases because many aspects of the problem cannot be mathematically modeled. Thus, in a previous work we contributed to developing an interactive module, which was incorporated in OPLA-Tool v2.0, to support the DM’s preferences during the optimization process. This module includes a Machine Learning Model (ML) based on Multilayer Perceptron (MLP) to prevent human fatigue, caused mainly by the excessive number of inter-actions demanded by evolutionary algorithms. The performance of ML algorithms varies according to the problem to be solved. Thus, an unexplored gap in the previous work is the analysis of the ML model induced by different algorithms. In this context, this work aims at evaluating different ML algorithms in the interactive module of OPLA-Tool v2.0. We conducted an exploratory study with the DM participation using different ML algorithms. The obtained results increase the state of the art, as other ML algorithms outperform the MLP used in our previous work in terms of processing time and accuracy.
Open knowledge disclosure and technical standard competition in transition economies: A legitimacy perspective

An increasing number of profit-oriented firms are disclosing their technological achievements free of charge, but existing literature has not studied the motivation of this behaviour in depth. Based on the data of Chinese ICT (information and communication technology) listed companies, this article studies the relationship between open knowledge disclosure and technical standard competition in transition economies from a legitimacy perspective. The empirical results show that open knowledge disclosure strategies can effectively enhance firms' influence on technical standards, and their effect is significantly better than that of patent strategies. In addition, private firms, firms with a low social responsibility level, and firms with a large market share can better take advantage of this strategy.
FPGA realization of fractals based on a new generalized complex logistic map

This paper introduces a new generalized complex logistic map and the FPGA realization of a corresponding fractal generation application. The chaotic properties of the proposed map are studied through the stability conditions, bifurcation behavior and maximum Lyapunov exponent (MLE). A relation between the mathematical analysis and fractal behavior is demonstrated, which enables formulating the fractal limits. A compact fractal generation process is presented, which results in designing and implementing an optimized hardware architecture. An efficient FPGA implementation of the fractal behavior is validated experimentally on Artix-7 FPGA board. Two examples of fractal implementation are verified, yielding frequencies of 34.593 MHz and 31.979 MHz and throughputs of 0.415 Gbit/s, 0.384 Gbit/s. Compared to recent related works, the proposed implementation demonstrates its efficient hardware utilization and suitability for potential applications.
Challenges and Opportunities in Remote Prototyping: A Case-Study during COVID-19

Abstract Collaboration is common practice within design disciplines and beyond. Brainstorming, discussions, and prototyping tend to occur within the same physical space. The reduction of human interaction during the COVID-19 pandemic disrupted these practices. In this paper, we focus on the possibilities and challenges of remote prototyping of four student teams by combining a double diamond approach with tools to overcome remote work challenges. The results were analyzed to understand crucial tools, advantages, and obstacles. The key challenges and opportunities were then identified and examined.
Assessing Machine Learnability of Image and Graph Representations for Drone Performance Prediction

Abstract Deep learning (DL) from various representations have succeeded in many fields. However, we know little about the machine learnability of distinct design representations when using DL to predict design performance. This paper proposes a graph representation for designs and compares it to the common image representation. We employ graph neural networks (GNNs) and convolutional neural networks (CNNs) respectively to learn them to predict drone performance. GCNs outperform CNNs by 2.6-8.1% in predictive validity. We argue that graph learning is a powerful and generalizable method for such tasks.
Implementing E-Commerce Mobile and Web Application for Agricultural Products: e-Farmers' Hut

In Bangladesh, the increased number of brokers has resulted in a price increase in the agricultural goods market. They acquire items from farmers at a discount and resell them to consumers at a premium. This deprives farmers, while customers pay an unreasonable price for basic essentials. This study aims to develop an e-commerce mobile and web application called ‘e-Farmers’ Hut' to facilitate direct producer-to-customer engagement. The application goal to maximize the advantages to producers and customers by eliminating middlemen's control. Both applications operate sequentially on the same database. Farmers and customers each have their profile to which they may submit the relevant information. Additionally, to optimize facilities use, also introduced an electronic payment method. Customers may see lists of available items that verified farmers have posted. The application has passed the testing process with expected outcomes. The technology has the ability to considerably simplify direct sales and purchases of items between farmers and consumers.
Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0

In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.
Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results

The use of secondary hospital-based clinical data and electronical health records (EHR) represent a cost-efficient alternative to investigate chronic conditions. We present the Clinical Network Big Data and Personalised Health project, which collects EHRs for patients accessing hospitals in Central-Southern Italy, through an integrated digital platform to create a digital hub for the collection, management and analysis of personal, clinical and environmental information for patients, associated with a biobank to perform multi-omic analyses. A total of 12,864 participants (61.7% women, mean age 52.6 ± 17.6 years) signed a written informed consent to allow access to their EHRs. The majority of hospital access was in obstetrics and gynaecology (36.3%), while the main reason for hospitalization was represented by diseases of the circulatory system (21.2%). Participants had a secondary education (63.5%), were mostly retired (25.45%), reported low levels of physical activity (59.6%), had low adherence to the Mediterranean diet and were smokers (30.2%). A large percentage (35.8%) were overweight and the prevalence of hypertension, diabetes and hyperlipidemia was 36.4%, 11.1% and 19.6%, respectively. Blood samples were retrieved for 8686 patients (67.5%). This project is aimed at creating a digital hub for the collection, management and analysis of personal, clinical, diagnostic and environmental information for patients, and is associated with a biobank to perform multi-omic analyses.
Featured Scents: Towards Assessing Architectural Smells for Self-Adaptive Systems at Runtime

Self-adaptive systems (SAS) change their behavior and structure at runtime to answer the changes in their environment. Such systems combine different architectural fragments or solutions via feature binding/unbinding at runtime. Moreover, this combination may negatively impact the system’s architectural qualities, exhibiting architectural bad smells (ABS). These issues are challenging to detect in the code due to the combinatorial explosion of interactions amongst features. Since SAS does not document these features in their source code, design time smell detection ignores them and risks reporting smells that are different than those observed at runtime. This paper assesses this risk to understand how ABS occurs at runtime for different feature combinations. We look for cyclic dependency and hub-like ABS in various runtime adaptations of two SAS, Adasim and mRubis. Our results indicate that architectural smells are feature-dependent and that their number is highly variable from one adaptation to the other. Some ABS appear in all runtime adaptations, some in only a few. We discuss the reasons behind these architectural smells for each system and motivate the need for targeted ABS analyses in SAS.
Face Landmark Detection Based on Deep Learning Processor Unit on ZYNQ MPSoC

Convolutional neural network (CNN) has a wide range of applications in face detection and recognition, image classification and semantic segmentation, but it is very difficult to deploy CNN on FPGA embedded platform. The Deep Learning Processor Unit (DPU) released by Xilinx is different from the previous deployment of FPGA, which can accelerate the realization of CNN deployment on FPGA platform and supports a variety of classical CNN structures. In this paper, the face and landmark detection CNN is deployed on ZCU102 platform using DPU based on idea of hardware and software co-design. According to the network features supported by DPU, Normalize network features in VGG-SSD were adjusted to BatchNormalize network features, Convolution was added in LeNet and a double-layer convolution structure was adopted, and the model was pruned to reduce resource consumption and computation. Dual-core DPU and deep flow architecture were used to improve data throughput. The experimental results show that the average detection time of single frame video image face and landmark detection is 26ms, and this design improves the acceleration effect significantly, and has good scalability.
Planning Algorithm for Route and Task Allocation of Plant Protection UAVs in Multiple Operating Areas

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team’s operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method.
A Study on the Software Architecture Documentation Practices and Maturity in Open-Source Software Development

The best practices in the industry for Software Architecture (SA) documentation are not always followed, despite it being known that SA documentation can positively influence different aspects of software development. Open-Source Software (OSS) projects often operate in a different manner compared to proprietary software projects. This study investigates contemporary SA documentation practices in OSS projects to gain insights into: (1) what architectural elements are described, (2) what the different description formats and types are, and (3) what the maturity of the architecture description is. The SA description documentation of six OSS projects is identified, classified, and evaluated: VLC, OpenEHR, openKM, GIMP, Audacity, and Home Assistant. The results show that natural language is widely used in describing the architecture, sometimes accompanied by diagrams of informal models. The majority of documentation was found on websites and wikis. The maturity was evaluated by applying the Architectural Capability Model (ArchCaMo). Out of the five maturity levels, most projects did not get past the first level. Only one project reached the second level, and one project showed potential for level three as it was the only project with explicitly documented SA design decisions.
A video anomalous behavior detection method based on multi-task learning

With the enhancement of public safety awareness, video anomalous behavior detection has become a hot research direction. Video anomaly detection refers to identifying abnormal behaviors in videos. The pattern of normal behavior is relatively simple, while the pattern of abnormal behavior is too complicated. Therefore, it is a common way to only learn the distribution of the normal data, where the description error of abnormal behavior is larger than that of normal behavior. Then the abnormal behavior in the video can be detected. However, there is a common problem with this method, that is, due to the strong generalization ability of deep model, the trained model is likely to describe abnormal behaviors as well, which greatly reduces detection accuracy. In this paper, we propose an abnormal behavior detection method based on multi-task learning, which solves the problem of overgeneralization of the model to a certain extent by constraining the model with multiple tasks, including reconstruction task and prediction task. Our model achieves good performance on the UCSD Ped2 and CUHK Avenue, especially on the UCSD Ped2, which achieves an AUC score of 97.3%. The superior experimental results fully demonstrate the effectiveness of the method.
Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features

This paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentiment value feature, expression feature, and improved semantic feature. A text sentiment classification model is formed by fusing multiple features. Compared with the traditional model and other single-feature models on the self-built dataset, the experimental results show that its sentiment classification ability has been effectively improved. The results show that the accuracy, recall, and F1 value of the fused multilevel feature MFCNN model are much higher than the accuracy, recall, and F1 value of other models. This also shows that the improved model of this method has a better effect of improving the accuracy.
Research on Underwater Image Denoising Based on Wavelet Threshold Method

Underwater images will produce severe noise during shooting, transmission and underwater backscattering, which seriously affects the accuracy of stereo matching and target positioning. For this reason, this paper proposes an improved wavelet denoising method. First, a new continuous threshold function is designed to overcome the problem that the soft threshold will weaken the image feature information and the hard threshold is not continuous at the threshold point. After that, considering the different propagation characteristics of signal and noise at different scales of wavelet transform, an adaptive threshold estimation method that changes with the decomposition scale is proposed. Experimental results show that the improved wavelet de-noising method can not only effectively filter the noise signal, but also retain the edge information of the image to the maximum extent, making the denoised image more conducive to subsequent stereo matching and target positioning.
Random-sampling-based multi-UAV cooperative search planning for high-rise firefighting

This paper proposes a multi-UAV cooperative search planning method based on random sampling for high-rise fire rescue. First, an optimal distribution method of search points inspired by the random tree sampling search algorithm is proposed, which uses its repeated search feature in a limited complex space to solve the problems of waypoint distribution and task assignment for multiple UAVs simultaneously. Second, a waypoint optimization method based on transaction rules is designed to further improve search coverage. Third, the optimal traversal order of waypoints for each UAV is obtained by using the improved harmony search algorithm to acquire the planned path of each UAV. Finally, the multithreading method is used to simulate the distributed operation in the actual task to verify the feasibility of the proposed method.
Collaborative Robots and Tangled Passages of Tactile-Affects

Collaborative robots are increasingly entering industrial contexts and workflows. These contexts are not just locations for production, they are vibrant social and sensory environments. For better or for worse, their entry brings potential to reorganize established tactile and affective dynamics that encompass production processes. There is still much to be learned about these highly contextual and complex dynamics in HRI research and the design of industrial robotics; common approaches in industrial collaborative robotics are restricted to evaluating “effective interface design,” whereas methods that seek to measure “affective touch” have limited application to these industrial domains. This article offers an extended analytical framework and methodological approach to deepen understandings of affect and touch beyond emotional responses to direct human–robot interactions. These distinct contributions are grounded in fieldwork in a glass factory with newly installed collaborative robots. They are illustrated through an ethnographic narrative that traces the emergence and circulation of affect, across material, experiential , and social planes. Beyond this single case, “tangled passages of tactile-affects” is offered as a novel and valuable concept that is distinct from the notion of “affective touch” and holds potential to generate holistic and nuanced understandings of how human experiences can be effected by the introduction of new robots in “the wild.”
Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models

There is increasing interest in video object detection for many situations, such as industrial processes, surveillance systems, and nature exploration. In this work, we were concerned with the detection of pedestrians in video sequences. The aim was to deal with issues associated with the background, scale, contrast, or resolution of the video frames, which cause inaccurate detection of pedestrians. The proposed method was based on the combination of two techniques: motion detection by background subtraction (MDBS) and active shape models (ASM). The MDBS technique aids in the identification of a moving region of interest in the video sequence, which potentially includes a pedestrian; then, the ASM algorithm actively finds and adjusts the silhouette of the pedestrian. We tested the proposed MDBS + ASM method with video sequences from open repositories, and the results were favorable in scenes where pedestrians were in a well-illuminated environment. The mean fit error was up to 4.5 pixels. In contrast, in scenes where reflections, occlusions, or pronounced movement are present, the identification was slightly affected; the mean fit error was 8.3 pixels in the worst case. The main contribution of this work was exploring the potential of the combination of MDBS and ASM for performance improvements in the contour-based detection of a moving pedestrian walking in a controlled environment. We present a straightforward method based on classical algorithms which have been proven effective for pedestrian detection. In addition, since we were looking for a practical process that could work in real-time applications (for example, closed-circuit television video or surveillance systems), we established our approach with simple techniques.
The frequency of facial muscles engaged in expressing emotions in people with visual disabilities via cloud-based video communication

As technology is advancing quickly, and various assistive technology applications are introduced to users with visual disabilities, many people with visual disabilities use smartphones and cloud-based video communication platforms such as Zoom. This study aims at advancing knowledge of how people with visual disabilities visualize voluntary emotions via facial expressions, especially in online contexts. A convenience sample of 28 participants with visual disabilities were observed as to how they show voluntary facial expressions via Zoom. The facial expressions were coded using the Facial Action Coding System (FACS) Action Units (AU). Individual differences were found in the frequency of facial action units, which were influenced by the participants’ visual acuity levels (i.e., visual impairment and blindness) and emotion characteristics (i.e., positive/negative valence and high/low arousal levels). The research findings are anticipated to be widely beneficial to many researchers and professionals in the field of facial expressions of emotions, such as facial recognition systems and emotion sensing technologies.Relevance to human factors/ergonomics theoryThis study advanced knowledge of facial muscle engagements while people with visual disabilities visualize their emotions via facial expressions, especially in online contexts. The advanced understanding would contribute to building a fundamental knowledge foundation, ultimately applicable to universal designs of emotion technology that can read users’ facial expressions to customize services with the focus on adequately accommodating the users’ emotional needs (e.g., ambient intelligence) regardless of users’ visual ability/disability.