Reviewerly/exHarmony huggingface dataset:
Fast and accurate obstacle detection of manipulator in complex human–machine interaction workspace

Abstract In order to solve the problems of a large amount of calculation, poor real-time performance, and insufficient detection accuracy in an existing robot obstacle detection system, this paper proposes an improved lightweight You Only Look Once Version 3 algorithm for obstacle detection and tracking, by combining the DeepSort algorithm with a three-dimensional model velocity estimation method. First, the depthwise separable convolution and convolution kernel pruning methods are used to lighten the network. Second, to meet the requirement of dynamic obstacle detection, the DeepSort method is used to track dynamic obstacles and estimate their moving speed. Third, a method of bounding-sphere modeling and discrete marking is proposed to simplify the obstacle model. The experimental results show that: the model size is reduced by 92.11%, which suggests that the proposed lightweight detection system is more suitable for industrial applications; the detection speed in the actual scene reaches 118 frames per second and the average detection accuracy is 92.6%, while the multiple objects tracking accuracy and multiple objects tracking precision are all above 0.8 in the dynamic obstacle tracking process. Static and dynamic obstacles in the workspace can be detected simultaneously with an average positioning accuracy error of 1.5 cm. The proposed model can not only effectively improve the detection efficiency of obstacles in the manipulator workspace but also enhance the safety of industrial robot systems.
Dreaming Disability Justice in HCI

While disability studies and social justice-oriented research is growing in prominence in HCI, these approaches tend to only bring attention to oppression under a single identity axis (e.g. race-only, gender-only, disability-only, etc). Using a single-axis framework neglects to recognize people's complex identities and how ableism overlaps with other forms of oppression including classism, racism, sexism, colonialism, among others. As a result, HCI and assistive technology research may not always attend to the complex lived experiences of disabled people. In this one-day workshop, we position disability justice as a framework that centers the needs and expertise of disabled people towards more equitable HCI and assistive technology research. We will discuss harmful biases in existing research and seek to distill strategies for researchers to better support disabled people in the design (and dismantling) of future technologies.
A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing

Service composition and optimal selection (SCOS) plays a crucial role in cloud manufacturing (CMfg). While the existing service composition methods are hard to address the changes and uncertainties of CMfg dynamic environment. Therefore, a variable-length encoding genetic algorithm for structure-varying incremental service composition (ISC-GA) is proposed in this paper. Specifically, a novel variable-length encoding scheme containing structural information is proposed to describe the uncertain and changing process model. And the improved crossover and mutation algorithm suitable for individuals with nonlinear varying structure and incremental service composition is designed. It is realized by optimizing both the process structure and service instance combinations, and overcomes the drawbacks resulted from single preset process structure. Due to the difficulty of fitness computation caused by uncertain process structures, novelty is introduced as a new evolutionary pressure, and a novel framework for ISC-GA is presented, which helps to find both novel and high-performance solutions. Experimental results indicate the effectiveness of the proposed approach. • A variable-length encoding genetic algorithm for structure-varying service composition in uncertain environment is proposed. • Algorithms suitable for individuals with nonlinear varying structure and incremental service composition is designed. • Novel encoding scheme containing structural information is proposed to describe the uncertain and changing process model. • Novelty is introduced to address the fitness computation problem caused by uncertain process structures. • A novel framework for the algorithm is presented, which helps to find both novel and high-performance solutions.
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation

Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions. To evaluate the segmentation performance of the network, we compared with several state-of-the-art medical image segmentation methods on the public breast ultrasound dataset using six commonly used evaluation metrics. Extensive experimental results indicate that our method achieves the most competitive segmentation results on malignant breast ultrasound images.
Quality evaluation models or frameworks for open source software: A systematic literature review

Abstract As open project repositories have become widespread, evaluating the quality of open source software (OSS) has gained attention in the software community. Despite the variety in quality evaluation models or frameworks (QEMoF) proposed for OSS, their adoption in practice remains limited. To further investigate this argument, a systematic literature review (SLR) has been conducted to characterize the existing QEMoF for OSS, based on particular properties, and to examine comprehensively their content and structure for identifying the gap between theory and practice. In this context, a total of 36 primary studies in the period of 2003–2020 have been analyzed. It has been identified that the majority of QEMoF takes ISO/IEC 9126 as a reference, evaluates community‐based aspects, mostly evaluates maintainability and usability as quality attributes, supports subjective and quantitative evaluations, and faces challenges due to diverse and dynamic nature of OSS products. For a better overview of the state of the art in the field, evaluation criteria for comparing the overall quality of the QEMoF are identified and applied. Finally, overall findings are discussed, and suggestions are provided for practitioners and researchers to formulate more robust studies in the future, which can advance the current situation for the benefit of the software community.
MulSimNet: A multi-branch sub-interest matching network for personalized recommendation

Personalized recommendation serves as an indispensable functionality in many online services, where the key is to model the user’s preference based on past user-item interactions. Many well-known models learn a latent vector for each user and each item. Nevertheless, a single latent vector might not be able to well represent a user’s multifaceted interests revealed by the wide variety of items consumed by him/her. In this paper, we present a flexible framework named Multi-branch Sub-interest matching Network (MulSimNet) for personalized recommendation. Each user is explicitly represented by multiple sub-interest vectors as well as a general interest vector, and an item is matched against these vectors through different branches, where each branch is realized by appropriate neural networks. The compatibility scores output by multiple sub-interest matching branches are first aggregated via the max operator, which is then fused with the output of the general interest matching branch to estimate the user’s affinity with the item. A comprehensive set of experiments on benchmark datasets demonstrate the effectiveness of the proposed framework in fully capturing the user’s multifaceted interests and yielding better top-N recommendations.
Designing generation Y interactions: The case of YPhone

With more and more products becoming digital, mobile, and networked, paying attention to the qualities of interactions with is also getting more relevant. While interaction qualities have been addressed in several scientific studies, little attention is being paid to their implementation into a real life, everyday context. This paper describes the development of a novel office phone prototype, YPhone, which demonstrates the application of a specific set of Generation Y interaction qualities (instant, playful, collaborative, expressive, responsive, and flexible) into the context of office work. The working prototype supports office workers in experiencing new type of interactions. It is set out in practice in a series of evaluations. We found that the playful, expressive, responsive, and flexible qualities have more trust than the instant and collaborative qualities. Qualities can be grouped, although this may be different for different products that are evaluated, so researchers must be cautious about generalizing. The overall evaluation was positive with some valuable suggestions to its user interactions and features.
Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine

The amplifier is a key component of the radio frequency (RF) front-end, and its specifications directly determine the performance of the system in which it is located. Unfortunately, amplifiers' specifications degrade with temperature and even lead to system failure. To study how the system failure is affected by the amplifier specification degradation, it is necessary to couple the amplifier specification degradation into the system optimization design. Furthermore, to couple the amplifier specification degradation into the optimal design of the system, it is necessary to model the characteristics of the amplifier specification change with temperature. In this paper, the temperature characteristics of two amplifiers are modeled using an extreme learning machine (ELM), and the results show that the model agrees well with the measurement results and can effectively reduce measurement time and cost.
PEACE: A Model of Key Social and Emotional Qualities of Conversational Chatbots

Open-domain chatbots engage with users in natural conversations to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of a chatbot most correspond to users’ expectations and preferences. Even though existing work has considered a wide range of aspects, some key components are still missing. For example, the role of chatbots’ ability to communicate with humans at the emotional level remains an open subject of study. Furthermore, these trait qualities are likely to cover several dimensions. It is crucial to understand how the different qualities relate and interact with each other and what the core aspects would be. For this purpose, we first designed an exploratory user study aimed at gaining a basic understanding of the desired qualities of chatbots with a special focus on their emotional intelligence. Using the findings from the first study, we constructed a model of the desired traits by carefully selecting a set of features. With the help of a large-scale survey and structural equation modeling, we further validated the model using data collected from the survey. The final outcome is called the PEACE model (Politeness, Entertainment, Attentive Curiosity, and Empathy) . By analyzing the dependencies between the different PEACE constructs, we shed light on the importance of and interplay between the chatbots’ qualities and the effect of users’ attitudes and concerns on their expectations of the technology. Not only PEACE defines the key ingredients of the social qualities of a chatbot, it also helped us derive a set of design implications useful for the development of socially adequate and emotionally aware open-domain chatbots.
Standardization and Data Augmentation in Genetic Programming

Genetic programming (GP) is a common method for performing symbolic regression that relies on the use of ephemeral random constants in order to adequately scale predictions. Suitable values for these constants must be drawn from appropriate, but typically unknown, distributions for the problem being modeled. While rarely used with GP, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> -score standardization of feature and response spaces often significantly improves the predictive performance of GP by removing scale issues and reducing error due to bias. However, in some cases it is also associated with erratic error due to variance. This article demonstrates that this variance component increases in the presence of gaps at the boundaries of the training data explanatory variable intervals. An initial solution to this problem is proposed that augments training data with pseudo instances located at the boundaries of the intervals. When applied to benchmark problems, particularly with small training samples, this solution reduces error due to variance and, therefore, total error. Augmentation is shown to also stabilize error in larger problems; however, results suggest that standardized GP works well on such problems with little need for training data augmentation.
Adaptive Differential Evolution Algorithm Based on Fitness Landscape Characteristic

Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm’s ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified.
Representing 3/2-Institutions as Stratified Institutions

On the one hand, the extension of ordinary institution theory, known as the theory of stratified institutions, is a general axiomatic approach to model theories where the satisfaction is parameterized by states of the models. On the other hand, the theory of 3/2-institutions is an extension of ordinary institution theory that accommodates the partiality of the signature morphisms and its syntactic and semantic effects. The latter extension is motivated by applications to conceptual blending and software evolution. In this paper, we develop a general representation theorem of 3/2-institutions as stratified institutions. This enables a transfer of conceptual infrastructure from stratified to 3/2-institutions. We provide some examples in this direction.
Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments

In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that applying the mutation operator from differential evolution to the personal best positions of the particles allows for improving the algorithm performance.
Semi-Supervised Graph Pattern Matching and Rematching for Expert Community Location

Graph pattern matching (GPM) is widely used in social network analysis, such as expert finding, social group query, and social position detection. Technically, GPM is to find matched subgraphs that meet the requirements of pattern graphs in big social networks. In the application of expert community location, the nodes in the pattern graph and data graph represent expert entities, and the edges represent previous cooperations between them. However, the existing GPM methods focus on shortening the matching time and without considering the preference of the decision maker (DM), which makes it difficult for the DM to find ideal teams from numerous matches to complete the assigned task. In this article, as for the process of graph pattern matching and rematching, with a preferred expert set, i.e., the DM hopes that one or more experts in this set will appear in matched subgraphs, we propose a Dual Simulation-based Edge Sequencing-oriented Semi-Supervised GPM method (DsEs-ssGPM). In addition, considering a preferred expert set and a dispreferred expert set together, the DM hopes that experts in the dispreferred expert set will not appear in final matches, so we have the DsEs-ssGPM+ method. Technically, these DsEs-ssGPM methods conduct the matching process from the preferred expert set during dual simulation-based edge sequencing, and based on the edge sequence, these edges are searched recursively. Especially, as for the rematching process, when the preferred and/or the dispreferred expert sets change continuously, to process the GPM again is unnecessary and it is possible to revise the previous matched results partially with DsEs-ssGPM methods. Experiments on four large datasets demonstrate the effectiveness, efficiency and stability of our proposed DsEs-ssGPM methods, and the necessity of introducing an edge sequencing mechanism.
‘I especially loved the little Nana dancing on the balcony’: The emergence, formation, and circulation of chronotopes in mass-mediated communication

Abstract In this article I focus on the formation and evaluation of chronotopes in social media. More specifically, I analyze the case of a ‘chronotope of the balcony performance’ that emerged in Italy in 2020 during the Covid-19 lockdown. The corpus of the study is constituted by 125 top postings resulting from a Twitter search based on the words Italy , lockdown , and balcony . In line with other scholars (see Goebel 2020), I argue that chronotopes in mass-mediated environments are formed through repetition and recycling of the same or similar semiotic material. I show how in social media expanded participation and the use of trans-semiotic and trans-medial resources ensure wide circulation of images and texts. I also point to the central role of stance taking by users in the constitution of the chronotope as a cultural object, particularly through generalizations and upscaling. (Chronotopes, Covid-19, discourse circulation, stance, scales)*
Cross-Speaker Style Transfer for Text-to-Speech Using Data Augmentation

We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive data from a target speaker and supporting conversational expressive data from different speakers. Our goal is to build a TTS system that is expressive, while retaining the target speaker’s identity. The proposed approach relies on voice conversion to first generate high-quality data from the set of supporting expressive speakers. The voice converted data is then pooled with natural data from the target speaker and used to train a single-speaker multi-style TTS system. We provide evidence that this approach is efficient, flexible, and scalable. The method is evaluated using one or more supporting speakers, as well as a variable amount of supporting data. We further provide evidence that this approach allows some controllability of speaking style, when using multiple supporting speakers. We conclude by scaling our proposed technology to a set of 14 speakers across 7 languages. Results indicate that our technology consistently improves synthetic samples in terms of style similarity, while retaining the target speaker’s identity.
Evaluating the Effect of Pre-Exam Adaptive Quizzing on Nursing Student Exam Scores

Adaptive quizzing as a course requirement is presumed to proactively engage students to learn effectively while also promoting the mastery of course content. This study evaluates the strength of correlation between adaptive quizzing as a pre-exam course requirement and course exam success. Results showed a positive correlation between the number of adaptive quizzes completed and mastery level attained on course exam scores in the adaptive quizzing program.
Super-Resolution of Satellite Images by two-Dimensional RRDB and Edge-Enhancement Generative Adversarial Network

With the increasing demand for high-resolution images, image super-resolution (SR) technology has become one of the focuses in related research fields. Generally speaking, high resolution is usually achieved by increasing the density and accuracy of the sensor. However, such an approach is quite expensive for equipment and design. In particular, increasing the density of satellite sensors must be undertaken great risks. Inspired by EEGAN and based on it, the Ultra-Dense Subnet (UDSN) and Edge Enhanced Network (EEN) were modified. Among them, the UDSN is used for feature extraction and obtains high-resolution results that look clear in the intermediate but are deteriorated by artifacts, and the Edge-Enhanced Subnet (EESN) is used to purify, extract and enhance the image contour and use mask processing to eliminate images contaminated by noise. Finally, the restored intermediate image and the enhanced edge are combined to produce a high-resolution image with high credibility and clear content. We use Kaggle and AID open experimental datasets to test and compare the results among different methods. It proves the performance of the proposed model is better than other SR methods.