Reviewerly/exHarmony huggingface dataset:
Shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges

Internet of Things (IoT) systems are often complex and have to deal with many challenges at the same time, both from a human and technical perspective. In this vision paper, we (i) describe IoT-Together , the Mixed-initiative Paradigm that we devise for IoT user-system collaboration and (ii) critically analyze related architectural challenges.
Research on the Path of Improving Digital Literacy of Teachers in Vocational Colleges

Digital Literacy of Teachers is an important condition to realize the high-quality development of vocational education, and also the key to improve the quality of talent training, which determines the professional qualification and core competitiveness of students in the future digital workplace. Higher vocational colleges should build a big data platform, strengthen the intelligent management of education and teaching work, improve the behavior supervision of digital literacy, form a collaborative governance system and sustainable development ecology, and help improve Digital Literacy of Teachers.
Fromm's " The Art of Love for Ethnic College Students the Revelation of Emotional Education

This article expounds the theoretical core of Fromm's book The Art of Love, as well as the analysis of the characteristics of ethnic college students in the thoughts and behavior of love. This paper discusses the enlightenment of Frome's theory to the emotional education of college students in ethnic colleges, guides and helps them to understand love, correctly understand the essence of love, and cultivate their practical ability of love. Expect to provide some reference and reference for readers.
Openings of Interactions in Immersive Virtual Reality: Identifying and Recognising Prospective Participants

This chapter studies openings of interactions in immersive virtual reality (VR). Immersive VR refers to computer-generated and visually rich environments that create for the user a powerful sense of immersion. The data are video recordings of interactions collected in a multiplayer online game called Rec Room. The method is conversation analysis. The analysis focuses on openings of interactions between participants who either know each other or who do not know each other. We show how the shape of the openings reflects the complexity and fragmented nature of immersive VR as an interactional setting and the resources in it. We conclude that the openings are designed to minimise problems in the establishing of mutual orientation and intersubjectivity.
AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community.
Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new fitness function is designed in addition to new smart crossover and mutation operators. The effectiveness of this algorithm is demonstrated on various datasets. Furthermore, the performance of GAk-MEANS has been compared with other genetic algorithms, such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function. Additionally, the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques. Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others. In particular, GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35% (72%–96.14%), which leads to an increase of the accuracy by an average of 3.78% (1.05%–6.32%) compared to using all features. When compared with the genetic algorithm using the Sammon Error Function, GAk-MEANS is able to reduce the size of the subset of selected features by 41.29% on average, improve the accuracy by 5.37%, and reduce the time by 70.71%. When compared with the genetic algorithm using the Sum of Squared Error Function, GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%, and improve the accuracy by 9.81%, but the time is increased by a factor of 3. When compared with the machine-learning based methods, we observed that GAk-MEANS is able to increase the accuracy by 13.67% on average with an 88.76% average increase in time.
Scalable Multi-Robot Motion Planning for Congested Environments With Topological Guidance

Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.
Load Reconstruction With Arbitrary Super Resolutions

Load super-resolution reconstruction (LSR) has become an active direction in recent years. This letter proposes a new LSR technique. By training the LSR model only one time, the system is capable of reconstructing a low-resolution load profile to be load profiles with different high resolutions that could be arbitrarily specified by the user of the system. Experiments based on a real-world dataset are conducted to validate the ability and effectiveness of the LSR system.
The Use of Digital Image Correlation (DIC) to Investigate the Effect of Layering and Bedding Plane Orientations on Young's Modulus and Poisson's Ratio Measurements of Buckinghorse Shale Samples

ABSTRACT Anisotropy due to heterogeneities in rocks, such as shale, affects the mechanical properties of the rock depending on their orientation and arrangement. For instance, layering and pre-existing cracks can create potential weak planes. Hence, a measurement technique that can capture spatially detailed deformation, such as digital image correlation (DIC), is important for the investigation of the effect of rock heterogeneities. This technique enables a larger field of view than conventional strain gauges measurements. In this study, the effect of bedding planes on the macroscopic failure patterns of shale samples from the Buckinghorse Formation was investigated using unconfined compressive strength (UCS) tests. The DIC analysis showed areas of displacement discontinuity and localized strain caused by bedding plane orientation, and that layering has a significant effect on the Young's modulus computation based on the location of sampling and the length of measurement. With load perpendicular to the bedding, the Young's modulus showed significant variance even when measuring across large portions of the sample. As the virtual extensometer length increased, localized effects were smoothed, and the values approached the strain and Young's moduli from the LVDT instrumentation. While the variance of the calculated Poisson's ratio measured along different loading orientations appeared consistent, the stress-strain plots using horizontal virtual extensometers (lateral strain) presented more details on lateral differences with respect to location. Hence, it was shown that local variability in stiffness could potentially affect the mechanical characterization of rocks with strong heterogeneities. DIC analysis, which captures this variability, should be used to complement conventional laboratory instrumentation. INTRODUCTION Rocks have various heterogeneities that may affect their mechanical properties. Shale exhibits a transverse isotropic elastic response due to layering from sedimentation. The Buckinghorse shale formation, which is a dark gray shale formation found as part of the Lower Cretaceous shales in northeastern British Columbia (BC) (Chalmers and Bustin, 2008) was selected to be used in this study.
Bunching Effects at Emitted Radiation from GeV Electrons in a Strong Laser at Future TW Experiments

Future experiments that consider the collision of GeV electrons and TW laser have as central objective to probe non-perturbative QED at the strong field where processes derived from Schwinger critic field can be observed. Nevertheless, the huge photon density will allow to electron to absorb and emit various photons as creating a bunching of them, fact that might be an obstacle to observe a clean signal. In this paper, with basic principles of classical electrodynamics, the radiated intensity is numerically estimated. Unclear nonlinear final states emerge as potential background to reduce signal efficiency drastically.
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing

Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, finding diverse, high-performing controllers requires expensive network training and extensive tuning of a large number of hyperparameters. On the other hand, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), an evolution strategies (ES)-based quality diversity algorithm, does not have these limitations and has achieved state-of-the-art performance on standard QD benchmarks. However, CMA-MAE cannot scale to modern neural network controllers due to its quadratic complexity. We leverage efficient approximation methods in ES to propose three new CMA-MAE variants that scale to high dimensions. Our experiments show that the variants outperform ES-based baselines in benchmark robotic locomotion tasks, while being comparable with or exceeding state-of-the-art deep reinforcement learning-based quality diversity algorithms.
Certificateless broadcast signcryption scheme supporting equality test in smart grid

With the development of cloud computing and the application of Internet of Things (IoT) in the smart grid, a massive amount of sensitive data is produced by the terminal equipment. This vast amount of data is subject to various attacks during transmission, from which users must be protected. However, most of the existing schemes require a large amount of network bandwidth resources and cannot ensure the receiver’s anonymity. To solve these shortcomings, we construct a broadcast signcryption scheme supporting equality test based on certificateless cryptosystem. The scheme employs a symmetric encryption algorithm to improve encryption and transmission efficiency; The Lagrange interpolation theorem is used to encrypt the user’s identity to ensure the privacy preservation of terminal devices; And a trusted third party is used to eliminate duplicated ciphertext for identical messages using an equality test, resulting in efficient network bandwidth utilization. Experimental analysis shows that our work has greater advantages in the field of practical broadcast services.
Research on deep learning-based instruction action recognition method for assistant duty officers

With the vigorous development of railway industry, railway passenger transport and freight has become the most important link in today's transport industry. The operation of receiving and sending trains at the station is related to the transportation safety of the whole railway, so the action standard of the railway assistant on duty is more strict. In this paper, an improved action recognition algorithm for assistant duty officers based on dual flow convolutional neural network embedded attention mechanism is proposed. First, the TV-L1 method was used for optical flow processing of the video, and the mediapipe was used to obtain the coordinate data of human bone points. The two kinds of data were used as network input for training. Then, the trained network model is fused, and the two flow convolutional neural networks embed attention mechanisms. VGG16 network is selected for both spatial network and time network. Finally, the two models were weighted and fused by softmax function as the output result to complete the command action recognition of the assistant on duty. Experiments were carried out on the dataset UCF101, and the accuracy of the model on this dataset was 86.7%. Experimental results show that the improved network model has better recognition effect.
Multimodal discourse analysis of automotive websites

Abstract In this paper, we study the multimodal discourse of automotive websites, aiming to contribute to the improvement and upgrading of automotive websites. To this end, this paper mines the data of advertising discourse, website visual effect, and feedback of auto website usage based on the KNN algorithm optimized by K-value selection strategy and analyzes the language effect of advertising discourse, rhetorical techniques, website page design, feedback of usage, and website advertising visual effect of the auto website with the help of multimodal discourse analysis framework. In terms of language, most of the page language and advertising language are concerned with syllabic symmetry, harmonization, and rhyming and focus on rhetorical techniques, such as 21% of the ads use similes and 14% use puns. In terms of content, more than 80% of people were satisfied with the design of the website and felt that the design of the website pages was reasonable, simple, and generous, and the ratio of positive to negative markers such as “satisfaction points” and “car quality evaluation” was close to 7:3, indicating a good recognition. The ratio of positive to negative marks for “satisfaction points” and “car quality evaluation” is close to 7:3, indicating good recognition. In terms of visual design, more than 80% of people think that video ads on auto websites are very effective and can arouse audiences’ interest and make them participate in them actively. Although the overall satisfaction of Chinese auto websites is good, there are many areas for improvement, and these aspects need to be optimized to present better results.
Software Cost and Effort Estimation: Current Approaches and Future Trends

Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations.
Scalable Agile Frameworks in Large Enterprise Project Portfolio Management

This article explores the implementation of scalable agile frameworks in project portfolio management (PPM) of large companies when companies should approach an agile transformation process that works successfully in their PPM. This study adds to the limited knowledge on how companies find and manage the challenges they may be susceptible to during planning or anticipation of agile transformation. The qualitative case study method allows for the analysis of project portfolios with the implementation of scalable agile frameworks in large companies. Fifty-nine project portfolios from 22 companies were studied, and 43 semi-structured in-depth interviews were conducted. The results found portfolios of projects with high variability in service, product, and innovation and hybrid implementations of the Scaled Agile Framework (SAFe), Spotify Model, and Scrum, as well as different challenges related to the implementation of scalable agile frameworks in PPM, organizational culture, resistance to change, and strategic leadership. These findings show that agile frameworks are a viable option for achieving fast time-to-market, increasing team productivity, and improving overall communication. Given that the study addressed fifty-nine portfolios of projects in large companies, the analytical generalizations allowed us to find and verify theoretically significant patterns that can only be applied to this type of company and not to SMEs. Finally, the findings suggest the need for managerial development that promotes a broader orientation of scalable agile frameworks in PPM; specifically, better knowledge and skills about implementing these frameworks in companies to lead and organize an agile transformation successfully.
Periodograms and the Method of Averaged Periodograms [Lecture Notes]

In this “Lecture Notes” column, we show that it is possible to use deterministic arguments to gain some intuition into why using periodograms without averaging does not work well and why they “fail” in the way they do. We then explain how the probabilistic case can be seen as an extension of the deterministic case. Next, we give a brief description of the method of averaged periodograms and explain how the deterministic perspective points to additional cases where the method of averaged periodograms should prove effective. Finally, we provide several numerical examples to demonstrate the theoretical material, and “A Probabilistic Argument” provides a fairly detailed probabilistic justification that is an extension of the deterministic one.
A Novel Style Takagi-Sugeno-Kang Fuzzy Classifier with Its Fast Training on Style Data

The classification of style data generally depends on both physical features of data and distinct styles originating from their own homogeneities. As a first attempt, a novel style Takagi-Sugeno-Kang (TSK) fuzzy classifier called STSK is developed in this study. STSK as an interpretable classifier consists of interpretable fuzzy rules with powerful representation ability on explicit physical features, implicit style features and the proposed style matrix of style data. By separating all features including the implicit style features in the antecedent parts of all fuzzy rules into fixed fuzzy partitions, followed by randomly assigned Gaussian membership functions with interpretable linguistic terms, the interpretable antecedents and hence the interpretable fuzzy rules can be assured for STSK. The training of STSK can be accomplished by solving the designed objective function through iteratively augmenting style features and updating the consequent parameters (including the style matrices) of all fuzzy rules in the form of their analytical solutions. Furthermore, by means of the Sherman-Morrison and Schur complement formulas, a fast learning algorithm F-STSK is derived to speed up the training of STSK to ensure at least comparable classification performance and simultaneously avoid successive yet time-consuming matrix inversion calculations caused by iterative style feature augmentation. Extensive experimental results on benchmark datasets validate the effectiveness of the proposed fuzzy classifier STSK and its fast learning algorithm. Moreover, six case studies about style data demonstrate the superiority of STSK over the comparison methods. The code of this study can be downloaded from https://github.com/gusuhang10/STSK.