Reviewerly/exHarmony huggingface dataset:
Safety helmet detection method based on YOLO v4

In the actual production process, safety accidents caused by workers not wearing safety helmets often occur. In order to reduce safety accidents caused by wearing helmets, a helmet detection method based on improved YOLO v4 is proposed. By collecting a self-made data set of on-site construction site video, using the K-means algorithm to cluster the data set in getting appropriately a priori frame dimensional center and obtaining more targeted edge information. Subsequently, a multi-scale training strategy is used in the network training process to improve the adaptability of the model from different scales of detection. The experimental results show that, in the helmet wearing detection task, the model mAP value reached 92.89%, the detection speed reached 15f/s, and its detection accuracy and detection speed were improved compared with YOLO v4, which satisfies the real-time requirements of the helmet detection task.
Short review on various applications of fuzzy logic-based expert systems in the field of solar energy

Cognitive modelling is an interdisciplinary area of computer science that deals with simulating human problem-solving and mental processing through a computerised model. Among the available cognitive models, fuzzy logic is widely preferred by renewable energy-based researchers. Its simple, flexible and adaptable architecture encouraged many researchers to integrate the concepts of fuzzy logic with their real-time process. In the energy sector, fuzzy logic played a vital role in predicting and controlling the performance delivered by the energy conversion system. In this study, an attempt is made to review the role, need and contributions delivered by the fuzzy logic in the field of solar energy. For this, important literature related to the applications of fuzzy logic in the field of solar energy is collected, analysed and comparatively presented. For sure, this study is more useful to future researchers involved in designing a performance monitoring and control system for solar energy conversion devices.
Testing Higher-Order Quantum Interference with Many-Particle States

Quantum theory permits interference between indistinguishable paths but, at the same time, restricts its order. Single-particle interference, for instance, is limited to the second order, that is, to pairs of single-particle paths. To date, all experimental efforts to search for higher-order interferences beyond those compatible with quantum mechanics have been based on such single-particle schemes. However, quantum physics is not bounded to single-particle interference. We here experimentally study many-particle higher-order interference using a two-photon-five-slit setup. We observe nonzero two-particle interference up to fourth order, corresponding to the interference of two distinct two-particle paths. We further show that fifth-order interference is restricted to $10^{-3}$ in the intensity-correlation regime and to $10^{-2}$ in the photon-correlation regime, thus providing novel bounds on higher-order quantum interference.
Divide and Contrast: Self-supervised Learning from Uncurated Data

Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet. We explore the effects of contrastive learning from larger, less-curated image datasets such as YFCC, and find there is indeed a large difference in the resulting representation quality. We hypothesize that this curation gap is due to a shift in the distribution of image classes—which is more diverse and heavy-tailed—resulting in less relevant negative samples to learn from. We test this hypothesis with a new approach, Divide and Contrast (DnC), which alternates between contrastive learning and clustering-based hard negative mining. When pretrained on less curated datasets, DnC greatly improves the performance of self-supervised learning on downstream tasks, while remaining competitive with the current state-of-the-art on curated datasets.
Discriminative semi-supervised non-negative matrix factorization for data clustering

Recently semi-supervised non-negative matrix factorization (NMF) has received a lot of attentions in computer vision, information retrieval and pattern recognition, because that partial label information can produce considerable improvement in learning accuracy of the algorithms. However, the existing semi-supervised NMF algorithms cannot make full use of label information, that is, they cannot guarantee that the labeled data of different clusters will not be classified into a same group in the new representation space. In this paper, we propose a novel discriminative semi-supervised NMF (DSSNMF) algorithm, which utilizes the label information of a fraction of the data as a discriminative constraint. We explore the proposed DSSNMF method with two different cost function formulations and provide the corresponding update rules for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm through a set of evaluations based on real-world applications.
Tabletop Games in the Age of Remote Collaboration: Design Opportunities for a Socially Connected Game Experience

Prior research has highlighted opportunities for technology to better support the tabletop game experience in offline and online settings, but little work has focused on the social aspect of tabletop gaming. We investigated the social and collaborative aspects of tabletop gaming in the unique context of “social distancing” during the 2020 COVID-19 pandemic to shed light on the experience of remote tabletop gaming. With a multi-method qualitative approach (including digital ethnography and in-depth interviews), we empirically studied how people appropriate existing technologies and adapt their offline practices to play tabletop games remotely. We identify three themes that describe people's game and social experience during remote play: creating a shared tabletop environment (shared space), enabling a collective understanding (shared information and awareness), and facilitating a communal temporal experience (shared time). We reflect on challenges and design opportunities for a better experience in the age of remote collaboration.
Analyzing the Quality of Business English Teaching Using Multimedia Data Mining

Data continually act as a substantial role in business and industry for its daily activities to smoothly functional. The data volume is growing with the passage of time and rising of information technology. Using data mining techniques for quality evaluation and business English teaching is essential in the modern world. These technologies are introduced in the classroom, especially in online classes during the COVID-19 pandemic. To analyze the quality of business English teaching, this paper uses multimedia and data mining technologies. Initially, the multimedia data are collected during classes, and the association rule recommendation algorithm using data mining is applied. Based on collaborative filtering algorithms in association rules, indicators for teaching quality evaluation in colleges and universities are set up. Next, the actual teaching data of a university is used. Taking business English as an example, the algorithm that has been built is tested. The application of the algorithm is tested, and the teaching process of College Business English is evaluated. Finally, the conclusion is drawn that data mining technology can describe the behavior of teaching well and evaluate it, and it has the potential of popularization.
Magnoramas: Magnifying Dioramas for Precise Annotations in Asymmetric 3D Teleconsultation

When users create hand-drawn annotations in Virtual Reality they often reach their physical limits in terms of precision, especially if the region to be annotated is small. One intuitive solution employs magnification beyond natural scale. However, scaling the whole environment results in wrong assumptions about the coherence between physical and virtual space. In this paper, we introduce Mag-noramas, a novel interaction method for selecting and extracting a region of interest that the user can subsequently scale and transform inside the virtual space. Our technique enhances the user's capabilities to perform supernaturally precise virtual annotations on virtual objects. We explored our technique in a user study within asimplified clinical scenario of a teleconsultation-supported craniectomy procedure that requires accurate annotations on a human head. Teleconsultation was performed asymmetrically between a remote expert in Virtual Reality that collaborated with a local user through Augmented Reality. The remote expert operates inside a reconstructed environment, captured from RGB-D sensors at the local site, and is embodied by an avatar to establish co-presence. The results show that Magnoramas significantly improve the precision of annotations while preserving usability and perceived presence measures compared to the baseline method. By hiding the 3D reconstruction while keeping the Magnorama, users can intentionally choose to lower their perceived social presence and focus on their tasks.
Social Virtual Reality: Ethical Considerations and Future Directions for An Emerging Research Space

The boom of commercial social virtual reality (VR) platforms in recent years has signaled the growth and wide-spread adoption of consumer VR. Social VR platforms draw aspects from traditional 2D virtual worlds where users engage in various immersive experiences, interactive activities, and choices in avatar-based representation. However, social VR also demonstrates specific nuances that extend traditional 2D virtual worlds and other online social spaces, such as full/partial body tracked avatars, experiencing mundane everyday activities in a new way (e.g., sleeping), and an immersive means to explore new and complex identities. The growing popularity has signaled interest and investment from top technology companies who each have their own social VR platforms. Thus far, social VR has become an emerging research space, mainly focusing on design strategies, communication and interaction modalities, nuanced activities, self-presentation, harassment, privacy, and self-disclosure. These recent works suggest that many questions still remain in social VR scholarship regarding how to ethically conduct research on these sites and which research areas require additional attention. Therefore, in this paper, we provide an overview of modern Social VR, critically review current scholarship in the area, raise ethical considerations for conducting research on these sites, and highlight unexplored areas.
Adversarial Defense for Automatic Speaker Verification by Cascaded Self-Supervised Learning Models

Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and more malicious attackers attempt to launch adversarial attacks at ASV systems. In the midst of the arms race between attack and defense in ASV, how to effectively improve the robustness of ASV against adversarial attacks remains an open question. We note that the self-supervised learning models possess the ability to mitigate superficial perturbations in the input after pretraining. Hence, with the goal of effective defense in ASV against adversarial attacks, we propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks in scenarios where attackers may either be aware or unaware of the self-supervised learning models.
Improved political optimizer for complex landscapes and engineering optimization problems

Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon’s rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.
Wanting To Live Here: Design After Anthropocentric Functionalism

Design research has recently turned to theoretical perspectives, including care ethics and posthumanism, to counter the industrial processes that have led to climate crisis. As design theorists and ethnographers of interaction, we researched experimental eco-farming in a community that shared many of these theoretical and ideological commitments. Our goal was not to offer an account of use and provide design implications in support of it. Instead, we chose to identify concrete practices and artifacts that embody the sorts of industrial transformations that we are seeking—even if they are manifest in an imperfect or partial form. We encountered practices focused on community building, local resilience to climate disruptions, experiments in eco-farming, economic survival, and attracting the next generation. One interlocutor translated these concerns into a simple binary, asking, “do we want to live here?” This paper contributes to a design research agenda that might (eventually) provide an affirmative answer.
It Takes More Than One Hand to Clap: On the Role of ‘Care’ in Maintaining Design Results.

Within Participatory- and Co-Design projects, the issue of sustainability and maintenance of the co-designed artefacts is a crucial yet largely unresolved issue. In this paper, we look back on four years of work on co-designing tools that assist refugees and migrants in their efforts to settle in Germany, the last of which the project has been independently maintained by our community collaborators. We reflect on the role of pre-existing care practices amongst our community collaborators, and a continued openness throughout the project, that allowed a complex constellation of actors to be involved in its ongoing maintenance and our own, often mundane activities which have contributed to the sustainability of the results. Situating our account within an HCI for Social Justice agenda, we thereby contribute to an ongoing discussion about the sustainability of such activities.
A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Ant colony optimization (ACO) algorithms have originally been designed for static optimization problems, where the input data is known in advance and is not subject to changes over time. Later, the long term memory of ACO proved effective for reoptimization over environment changes when extended to deal with dynamic combinatorial optimization problems (DCOPs). Among the major proposals of this kind, several adaptations of ACO procedures to improve information reuse can be identified, as well as a population-based ACO algorithm (P-ACO) specifically designed for DCOPs. Indeed, P-ACO drew the attention of the research community due to its ability to faster process pheromone information, but the few works assessing the effectiveness of the adapted ACO procedures and also P-ACO are not enough to reach more general conclusions on the current state-of-the-art in ACO for dynamic optimization. In this work, we conduct an extensive experimental campaign to evaluate the most common ACO procedure adaptations identified in the literature, using as underlying algorithms the state-of-the-art in ACO for static optimization (MAX-MIN Ant System, MMAS) and the most relevant ACO algorithm proposed for dynamic optimization (P-ACO). A variant of the traveling salesman problem with dynamic demands (DTSP) is used as test benchmark, similarly to most investigations on ACO for combinatorial optimization. Besides the carefully designed experimental setup we adopt, our work represents a significant contribution for, at least, three reasons. First, our work is the first to acknowledge that DCOPs require custom-configured parameter settings, and also the first to use automatic configuration tools for this task. Concretely, we show how the hypervolume indicator can be used to evaluate and configure the anytime behavior of algorithms for DCOPs. Second, we directly compare MMAS and P-ACO, isolating local search as an experimental factor. While P-ACO proves indeed effective in the absence of local search, MMAS is able to consistently outperform it when local search is adopted. Finally, we conduct an experimental investigation on the DCOP-specific components proposed for ACO, once again isolating local search. Results show that those components contribute very little to performance when algorithms are allowed to use local search, but are remarkably effective in its absence. In fact, coupled with DCOP components, MMAS outperforms P-ACO for a large part of our experimental setup.
Pose-on-the-Go: Approximating User Pose with Smartphone Sensor Fusion and Inverse Kinematics

We present Pose-on-the-Go, a full-body pose estimation system that uses sensors already found in today’s smartphones. This stands in contrast to prior systems, which require worn or external sensors. We achieve this result via extensive sensor fusion, leveraging a phone’s front and rear cameras, the user-facing depth camera, touchscreen, and IMU. Even still, we are missing data about a user’s body (e.g., angle of the elbow joint), and so we use inverse kinematics to estimate and animate probable body poses. We provide a detailed evaluation of our system, benchmarking it against a professional-grade Vicon tracking system. We conclude with a series of demonstration applications that underscore the unique potential of our approach, which could be enabled on many modern smartphones with a simple software update.
An AR-Enabled Interactive Car Door to Extend In-Car Infotainment Systems for Rear Seat Passengers

Modern cars create a high-tech interactive space by providing entertainment and information functionalities to the driver and partly to the passengers. By introducing rear-seat infotainment systems especially in luxury cars, manufacturers started to also focus on the passenger's experience. However, such systems offer mainly standard entertainment and internet-based services. To enhance the user experience of rear seat passengers, we present the concept of an interactive car door that enables passengers to engage with and explore their surroundings. Our system consists of an interactive door panel that shows points of interest along the progressing route, more detailed information is shown on the AR-enabled side window in addition to the rear seat display. Results from a pilot study (n = 11) show that our concept leads towards a positive user experience. The qualitative feedback reveals that such an interactive car door helps to make riding as a passenger more attractive and pleasant.
Automatic Extraction of Opinion-Based Q&amp;A from Online Developer Chats

Virtual conversational assistants designed specifically for software engineers could have a huge impact on the time it takes for software engineers to get help. Research efforts are focusing on virtual assistants that support specific software development tasks such as bug repair and pair programming. In this paper, we study the use of online chat platforms as a resource towards collecting developer opinions that could potentially help in building opinion Q&A systems, as a specialized instance of virtual assistants and chatbots for software engineers. Opinion Q&A has a stronger presence in chats than in other developer communications, thus mining them can provide a valuable resource for developers in quickly getting insight about a specific development topic (e.g., What is the best Java library for parsing JSON?). We address the problem of opinion Q&A extraction by developing automatic identification of opinion-asking questions and extraction of participants' answers from public online developer chats. We evaluate our automatic approaches on chats spanning six programming communities and two platforms. Our results show that a heuristic approach to opinion-asking questions works well (.87 precision), and a deep learning approach customized to the software domain outperforms heuristics-based, machine-learning-based and deep learning for answer extraction in community question answering.
Resisting the Medicalisation of Menopause: Reclaiming the Body through Design

The menopause transition involves bodily-rooted, socially-shaped changes, often in a context of medicalisation that marginalises people based on their age and gender. With the goal of addressing this social justice matter with a participatory design approach, we started to cultivate partnerships with people going through menopause. This paper reports on interviews with 12 women and a design workshop with three. Our data analysis highlights their experiences from a holistic perspective that reclaims the primacy of the body and acknowledges the entanglement of the physical and the psychosocial. Participants' design concepts show how design can come close the body to make space for menopause experiences, recognising and transforming them. We discuss how HCI can actively engage with the body to promote appreciation for it during menopause, and call for design that accompanies people in resisting the medicalisation of menopause as an enactment of social justice in everyday life.
Runtime Analysis of a Heavy-Tailed $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm on Jump Functions

It was recently observed that the $$(1+(\lambda ,\lambda ))$$ genetic algorithm can comparably easily escape the local optimum of the jump functions benchmark. Consequently, this algorithm can optimize the jump function with jump size k in an expected runtime of only $$n^{(k + 1)/2}k^{-k/2}e^{O(k)}$$ fitness evaluations (Antipov, Doerr, Karavaev (GECCO 2020)). This performance, however, was obtained with non-standard parameter setting depending on the jump size k. To overcome this difficulty, we propose to choose two parameters of the $$(1+(\lambda ,\lambda ))$$ genetic algorithm randomly from a power-law distribution. Via a mathematical runtime analysis, we show that this algorithm with natural instance-independent choices of the power-law parameters on all jump functions with jump size at most n/4 has a performance close to what the best instance-specific parameters in the previous work obtained. This price for instance-independence can be made as small as an $$O(n\log (n))$$ factor. Given the difficulty of the jump problem and the runtime losses from using mildly suboptimal fixed parameters (also discussed in this work), this appears to be a fair price.