Reviewerly/exHarmony huggingface dataset:
Temporal Logic Motion Planning With Convex Optimization via Graphs of Convex Sets

Temporal logic is a concise way of specifying complex tasks. However, motion planning to achieve temporal logic specifications is difficult, and existing methods struggle to scale to complex specifications and high-dimensional system dynamics. In this article, we cast linear temporal logic motion planning as a shortest path problem in a graph of convex sets and solve it with convex optimization. This approach brings together the best of modern optimization-based temporal logic planners and older automata-theoretic methods, addressing the limitations of each: we avoid clipping and pass-through by representing paths with continuous Bezier curves; computational complexity is polynomial (not exponential) in the number of sample points; global optimality can be certified (though it is not guaranteed); soundness and probabilistic completeness are guaranteed under mild assumptions; and, most importantly, the method scales to complex specifications and high-dimensional systems, including a 30-degree-of-freedom humanoid.
Fuzzy comprehensive evaluation and quantitative weight analysis in structure management of human resources

This research delves into the application effects of Fuzzy Comprehensive Evaluation (FCE) and quantitative weight analysis in the structure management of human resources (SMHR) to optimize the structure management. The research begins by analyzing the existing problems in SMHR, such as incomplete performance feedback and error-prone outsourcing decisions. By leveraging human resource management (HRM) characteristics, the researchers construct the SMHR evaluation index system. The Analytical Hierarchy Process (AHP) is employed to establish a hierarchical human resource structure model to determine the relative weight of each HRM indicator. Subsequently, the FCE method is utilized to build an SMHR optimization model, which is then scrutinized and assessed by means of an example. The findings indicate that the consistency ratio (C.R.) values of the first and second-level evaluation factors of the constructed model are less than 0.1, thus passing the consistency test and demonstrating credibility. Ultimately, the research effectively grades SMHR in the enterprise through the analysis of HRM optimization. Accordingly, this research presents a set of optimization suggestions and measures, including the establishment of a professional HRM operation team, acceleration of the construction of a professional talent team, enhancement of the intelligent level of the HRM center, and transition towards digital sharing. These proposed measures can serve as valuable experimental references for the optimization and improvement of HRM structures in future enterprises.
TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation

Real-time density estimation is ubiquitous in many applications, including computer vision and signal processing. Kernel density estimation is arguably one of the most commonly used density estimation techniques, and the use of "sliding window" mechanism adapts kernel density estimators to dynamic processes. In this article, we derive the asymptotic mean integrated squared error (AMISE) upper bound for the "sliding window" kernel density estimator. This upper bound provides a principled guide to devise a novel estimator, which we name the temporal adaptive kernel density estimator (TAKDE). Compared to heuristic approaches for "sliding window" kernel density estimator, TAKDE is theoretically optimal in terms of the worst-case AMISE. We provide numerical experiments using synthetic and real-world datasets, showing that TAKDE outperforms other state-of-the-art dynamic density estimators (including those outside of kernel family). In particular, TAKDE achieves a superior test log-likelihood with a smaller run-time.
Policy recommendations to ensure that research software is openly accessible and reusable

Research data is optimized when it can be freely accessed and reused. To maximize research equity, transparency, and reproducibility, policymakers should take concrete steps to ensure that research software is openly accessible and reusable.
Automatic highlight detection in videos of martial arts tricking

Abstract We propose a novel strategy for the automatic detection of highlight events in user-generated tricking videos, to the best of our knowledge, the first one specifically tailored for this complex sport. Most current methods for related sports leverage high-level semantics such as predefined camera angles or common editing practices, or rely on depth cameras to achieve automatic detection. However, our approach only relies on the contents (themselves) in the frames of a given video, and consists in a four stage pipeline. The first stage identifies foreground key points of interest along with an estimation of their motion in the video frames. In the second stage, these points are grouped into regions of interest based on their proximity and motion. Their behavior over time is evaluated in the third stage to generate an attention map indicating the regions participating in the most relevant events. The fourth and final stage provides the extracted video sequences where highlights have been identified. Experimental results attest to the effectiveness of our approach, which shows high recall and precision values at frame level, with detections that fit well the ground truth events.
Two Medoid-Based Algorithms for Clustering Sets

This paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous medical history. The first proposed algorithm is based on K-medoids and the second algorithm extends the random swap algorithm, which has proven to be capable of efficient and careful clustering; both algorithms depend on a distance function among data objects (sets), which can use application-sensitive weights or priorities. The proposed distance function makes it possible to exploit several seeding methods that can improve clustering accuracy. A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the O(N2) computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for the handling of non-trivial datasets. This paper applies the algorithms to several benchmark case studies of sets and demonstrates how accurate and time-efficient clustering solutions can be achieved.
Review on positional significance of LSTM and CNN in the multilayer deep neural architecture for efficient sentiment classification

The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks.
Research on patrol platform of fixed wing UAV for ultra long distance transmission and distribution lines

Under the influence of social development, weather and geology, the difficulty of long-distance route operation and maintenance gradually increases. In this paper, a ground station system suitable for taking off and landing in different places is developed to make the fixed wing UAV no longer fly back, improve the length of single flight operation, optimize flight efficiency, and solve the problem that inspection personnel still need to travel by bus.
The Impact of Mobile Learning on Students’ Attitudes towards Learning in an Educational Technology Course

As technology has explosively and globally revolutionized the teaching and learning processes at educational institutions, enormous and innovative technological developments, along with their tools and applications, have recently invaded the education system. Using mobile learning (m-learning) employs wireless technologies for thinking, communicating, learning, and sharing to disseminate and exchange knowledge. Consequently, assessing the learning attitudes of students toward mobile learning is crucial, as learning attitudes impact their motivation, performance, and beliefs about mobile learning. However, mobile learning seems under-researched and may require additional efforts from researchers, especially in the context of the Middle East. Hence, this study’s contribution is enhancing our knowledge about students’ attitudes towards mobile-based learning. Therefore, the study goal was to investigate m-learning’s effect on the learning attitudes among technology education students. An explanatory sequential mixed approach was utilized to examine the attitudes of 50 students who took an educational technology class. A quasi-experiment was conducted and a phenomenological approach was adopted. Data from the experimental group and the control group were gathered. Focus group discussions with three groups and 25 semi-structured interviews were performed with students who experienced m-learning in their course. ANCOVA was conducted and revealed the impact of m-learning on the attitudes and their components. An inductive and deductive content analysis was conducted. Eleven subthemes stemmed out of three main themes. These subthemes included: personalized learning, visualization of learning motivation, less learning frustration, enhancing participation, learning on familiar devices, and social interaction, which emerged from the data. The researchers recommended that higher education institutions adhere to a set of guiding principles when creating m-learning policies. Additionally, they should customize the m-learning environment with higher levels of interactivity to meet students’ needs and learning styles to improve their attitudes towards m-learning.
Badminton action recognition based on improved I3D convolutional neural network

In order to solve the issue of inaccurate recognition of time series of badminton action by 2D networks and the high cost of using skeleton data for badminton action recognition, this article aims to explore the use of RVM (Robust Video Matting) to extract the silhouette of individuals in videos, combined with an improved I3D network (referred to as the SI3D network) for action classification in badminton matches. We collected a dataset of badminton match videos featuring multiple players and labeled them according to different action categories. By using the SI3D network for training, this article achieved good performance on the test set, with an accuracy of 91.5%. This study demonstrates the effectiveness of the I3D network in badminton action classification and provides a new research direction for action recognition in sports.
The development of an immersive mixed-reality application to improve the ecological validity of eating and sensory behavior research

The modern eating environment has been implicated as a driving force of the obesity epidemic. Mixed reality applications may improve traditional methodological assessments of eating behavior by improving the ecological validity of the laboratory setting.Research experts evaluated the utility and ecological validity of a mixed reality application that allowed immersion within virtual environments through utilizing the passthrough cameras of the head mounted display to view and interact with real foods. An initial evaluation was conducted that involved three virtual environments: a traditional laboratory booth, a non-textured restaurant, and a full-textured restaurant. The feedback from the initial evaluation was used to create a new virtual restaurant environment and a subsequent evaluation was conducted.Nearly all research experts suggested adding social cues such as people and background noise to create a more authentic and ecologically valid experience. The experts scored the new virtual restaurant environment to be more acceptable than eating or conducting research in a sensory booth but scored lower when compared to conducting research in a real-world restaurant setting.The results of this evaluation suggest that mixed reality applications may be a new methodology to assess environmental influences of eating behavior and may be a promising direction for eating behavior and sensory science research.
PINOT

short-paper Open Access Share on PINOT: Programmable Infrastructure for Networking Authors: Roman Beltiukov UC Santa Barbara, USA UC Santa Barbara, USA 0000-0001-8270-0219View Profile , Sanjay Chandrasekaran UC Santa Barbara, USA UC Santa Barbara, USA 0009-0007-1004-7573View Profile , Arpit Gupta UC Santa Barbara, USA UC Santa Barbara, USA 0000-0002-6378-7440View Profile , Walter Willinger NIKSUN Inc., USA NIKSUN Inc., USA 0000-0002-1384-8188View Profile Authors Info & Claims ANRW '23: Proceedings of the Applied Networking Research WorkshopJuly 2023Pages 51–53https://doi.org/10.1145/3606464.3606485Published:22 July 2023Publication History 0citation64DownloadsMetricsTotal Citations0Total Downloads64Last 12 Months64Last 6 weeks34 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling

Abstract In answer set programming (ASP), the user can define declaratively a problem and solve it with efficient solvers; practical applications of ASP are countless and several constraint problems have been successfully solved with ASP. On the other hand, solution time usually grows in a superlinear way (often, exponential) with respect to the size of the instance, which is impractical for large instances. A widely used approach is to split the optimization problem into subproblems (SPs) that are solved in sequence, some committing to the values assigned by others, and reconstructing a valid assignment for the whole problem by juxtaposing the solutions of the single SPs. On the one hand, this approach is much faster due to the superlinear behavior; on the other hand, it does not provide any guarantee of optimality: committing to the assignment of one SP can rule out the optimal solution from the search space. In other research areas, logic-Based Benders decomposition (LBBD) proved effective; in LBBD, the problem is decomposed into a master problem (MP) and one or several SPs. The solution of the MP is passed to the SPs that can possibly fail. In case of failure, a no-good is returned to the MP that is solved again with the addition of the new constraint. The solution process is iterated until a valid solution is obtained for all the SPs or the MP is proven infeasible. The obtained solution is provably optimal under very mild conditions. In this paper, we apply for the first time LBBD to ASP, exploiting an application in health care as case study. Experimental results show the effectiveness of the approach. We believe that the availability of LBBD can further increase the practical applicability of ASP technologies.
Human tracking robotic camera based on image processing for live streaming of conferences and seminars

There are numerous scenarios where the photographer is in difficulty and unable to capture or shoot video as required. This could be due to several factors such as limited space, decreased visibility, and an obstacle in the way. Therefore, this project implements the idea to capture and shoot video of the desired subject through an automatically controlled robotic camera with no need for a photographic bloke. The system comprises functions such as detection, tracking, live streaming, and video/audio recording along with the features of Radio-Frequency-Identification (RFID). Therefore, this robotic camera will detect the desired subject, track and focus it with the help of its position driven through movable motors sensing the RFID tag in case the object is non-stationary. The video/audio will be recorded on a computer along with the live streaming available on an Android-based device. The Viola-Jones algorithm of the image processing technique is used to detect the particular subject features and C for accessing the movable camera protocols. The RFID transmitter and receiver are used to sense the RFID card and serve the purpose to track the subject using the algorithms of image processing, with the advantage of ignoring other obstacles between the camera and the detected subject. Thus, adding a novel functionality to the existing systems, that lacks the feature of focusing the camera on the subject, when an obstacle is detected in between. The live streaming is achieved wirelessly through an open-source platform X-operating system, Apache, MySQL, Php, Perl (XAMPP). The idea is verified through concluded arrangements in self-made scenarios in response to the speed, distance, light, and background noise of the detected subject, which delivered encouraging results. Therefore, the designed system can be used for live conferences, seminars, and other multimedia-required arrangements.
Density-based one-shot active learning for image segmentation

Image segmentation is a key step in image processing tasks, which has significant applications in computer vision field such as medical image analysis, scene understanding and video monitoring, etc. However, image segmentation tasks usually require a large number of labeled samples to obtain great performance of convolutional neural networks (CNNs). Active learning (AL) can select valuable samples for annotation, so as to reduce the annotation cost as much as possible while maintaining the performance of CNNs. Further, one-shot AL can select valuable samples by once, which eliminates the need for iterative sample selection and annotation. However, existing one-shot AL approaches extremely rely on complex clustering algorithm, which brings a limitation in practice, i.e., we often do not know how to set the hyperparameters. In this paper, we propose a clustering-free one-shot AL framework, which is based on self-supervised feature learning and density-based query strategy. Our framework can select samples with high local density robustly against hyperparameters. The experimental results are impressive that state-of-the-art one-shot active learning performance can be achieved with simple density-based sampling.
Tephra Community Tools for Archiving Sample Information, Analytical Methods, Samples Geochemistry, and Standards Geochemistry at SESAR and EarthChem

Journal Article Tephra Community Tools for Archiving Sample Information, Analytical Methods, Samples Geochemistry, and Standards Geochemistry at SESAR and EarthChem Get access Stephen Kuehn, Stephen Kuehn Physical and Environmental Sciences, Concord University, Athens, WV, USA Corresponding author: sckuehn@concord.edu Search for other works by this author on: Oxford Academic Google Scholar Marcus Bursik, Marcus Bursik Department of Geology, SUNY Buffalo, Buffalo, NY, USA Search for other works by this author on: Oxford Academic Google Scholar Andrei Kurbatov, Andrei Kurbatov CCI, University of Maine, Orono, ME, USA Search for other works by this author on: Oxford Academic Google Scholar Kerstin Lehnert, Kerstin Lehnert Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA Search for other works by this author on: Oxford Academic Google Scholar Matthew Loewen, Matthew Loewen Alaska Volcano Observatory, US Geological Survey, Anchorage, AK, USA Search for other works by this author on: Oxford Academic Google Scholar Lucia Profeta, Lucia Profeta Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA Search for other works by this author on: Oxford Academic Google Scholar Sarah Ramdeen, Sarah Ramdeen Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA Search for other works by this author on: Oxford Academic Google Scholar Kristi Wallace Kristi Wallace Alaska Volcano Observatory, US Geological Survey, Anchorage, AK, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Page 242, https://doi.org/10.1093/micmic/ozad067.108 Published: 22 July 2023
The impact of fundamental factors and sentiments on the valuation of cryptocurrencies

The valuation of cryptocurrencies is important given the increasing significance of this potential asset class. However, most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources. In this study, a robust cryptocurrency valuation method is developed using the up-to-date datasets. Using various panel regression models and moving-window regression tests, the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1, 2009 to April 30, 2023. The research shows the importance of sentiments and suggests that fear and greed index can indicate when to make cryptocurrency investment, while Google search interest of cryptocurrency are crucial when choosing the appropriate type of cryptocurrency. Moreover, consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins, while their impacts on cryptocurrencies with stablecoins are insignificant. Other fundamental factors, such as the type of supply and the presence of smart contracts, do not have a significant influence on cryptocurrency. Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors, portfolio managers and policymakers in assessing the utility level of each cryptocurrency.
From semantics to types: The case of the imperative λ-calculus

We study the logical semantics of an untyped λ-calculus equipped with operators representing read and write operations from and to a global store. Such a logic consists of an intersection type assignment system, which we derive from the denotational semantics of the calculus, based on the monadic approach to model computational λ-calculi. The system is obtained by constructing a filter model in the category of ω-algebraic lattices, such that the typing rules can be recovered out of the term interpretation. By construction, the so-obtained type system satisfies the “type-semantics” property and completeness.