Reviewerly/exHarmony huggingface dataset:
A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.
Video hashing with secondary frames and invariant moments

Video hashing is a useful technique of many multimedia systems, such as video copy detection, video authentication, tampering localization, video retrieval, and anti-privacy search. In this paper, we propose a novel video hashing with secondary frames and invariant moments. An important contribution is the secondary frame construction with 3D discrete wavelet transform, which can reach initial data compression and robustness against noise and compression. In addition, since invariant moments are robust and discriminative features, hash generation based on invariant moments extracted from secondary frames can ensure good classification of the proposed video hashing. Extensive experiments on 8300 videos are conducted to validate efficiency of the proposed video hashing. The results show that the proposed video hashing can resist many digital operations and has good discrimination. Performance comparisons with some state-of-the-art algorithms illustrate that the proposed video hashing outperforms the compared algorithms in classification in terms of receiver operating characteristic results.
Investigation on quality enhancement of old and fragile artworks using non-linear filter and histogram equalization techniques

Traditional Cultural artwork requires utmost care and may tend to degrade if not preserved properly. These old artworks can regain their quality with the help of modern image processing tools. Image processing forms an integral part of the scientific and research community. Removal of the unwanted noise forms the first step in image processing. Various filters have been developed and modified for noise removal over the years. Enhancement of the image by improving its contrast or brightness is also a crucial and important step. This paper discusses about the improvement of image quality by implementing a combination of the non-linear median filter with three different histogram equalization techniques (CLAHE, BBHE, and DSIHE). MATLAB is used for the implementation of the above mentioned combinations. Reference quality metrics parameters like AMBE, PSNR, NMSE as well as Non-reference quality metrics parameters like CPP, BRISQUE, PIQE are determined for the quality assessment of the processed image.
Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments.
Multi-robot competitive tracking based on k-WTA neural network with one single neuron

A k-winners-take-all (k-WTA) neural network is designed and applied to a task assignment problem in a multi-robot competitive target tracking scenario in this paper. The proposed neural network features a single neuron and a non-hard-limiting activation function, which greatly simplifies the model structure and reduces the computation cost. This neural network has finite-time convergence property and can be applied to real-time situations. The stability and convergence property of the neural network is theoretically analyzed. Simulations of handling a situation that a target moves at a higher speed than tracking robots are conducted to demonstrate the effectiveness of the designed scheme.
A partially asynchronous global parallel genetic algorithm

Population-based meta-heuristics such as Genetic Algorithms (GA) are ideal for exploiting multiple processor cores. With parallel architectures now standard computationally intensive methods need to harness them to best effect. A synchronous globally parallel GA creates and evaluates population members in parallel at each generation resulting in considerable processor time spent waiting for threads. An asynchronous approach whereby parallel threads continue evolution without waiting addresses this issue but can result in memory conflicts. This paper introduces an asynchronous global GA model for shared memory CPUs without memory conflicts. Experiments demonstrate performance gains of 1.35 to 12 fold dependant on problem and population sizes. However, an asynchronous model leads to non-uniform evolution reducing accuracy. Consequently, this paper demonstrates that combining synchronous and asynchronous methods into a partially asynchronous model retains a speed advantage whilst improving solution accuracy.
Gaussian Perturbation Specular Reflection Learning and Golden-Sine-Mechanism-Based Elephant Herding Optimization for Global Optimization Problems

Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.
An investigation of machine translation output quality and the influencing factors of source texts

Abstract The use of machine translation (MT) in the academic context has increased in recent years. Hence, language teachers have found it difficult to ignore MT, which has led to some concerns. Among the concerns, its accuracy has become a major factor that shapes language teachers’ pedagogical decision to use MT in their language classrooms. Despite the urgency of the issue, studies on MT output quality in foreign language education remain scarce. Moreover, as MT is advancing every year, updated studies are imperative. Therefore, the present study investigated the quality of MT outputs (Google Translate) from Korean to English by comparing it with the English-translated texts of intermediate English as a foreign language students. The study also examined the factors within the source texts that affect the quality of MT outputs. Five trained evaluators examined multiple aspects of MT output samples ( N = 104) and students’ English texts ( N = 104), including mechanics, vocabulary, grammar, and context. The results showed that both texts were equally comprehensible, but MT outperformed the students in most aspects under investigation. The study further found that only two factors in the source texts – punctuation and sentence complexity – influenced MT output quality, whereas lexical and grammatical accuracy, lexical diversity, and contextual understanding did not affect it. Based on the results, the study presents classroom implications for using MT for educational purposes.
Incident Factors in the Sustainable Development of Digital Teaching Competence in Dual Vocational Education and Training Teachers

For years the technological revolution has been transforming all facets of our society. Teaching could obviously not be an exception but is quite the opposite because of its role in training the individuals of that society. Teachers at all levels of education are subjected to an adaptation process to develop the digital skills necessary for this transformation. This process must be permanent as there are still major deficiencies in teachers' ICT knowledge and rejection of their application. This study aims to see whether the inadequacy of digital teaching skills also occurs in the Dual Vocational Training modality. To this end, a descriptive quantitative method has been carried out in a sample of teachers from the Professional Formation Dual system in the Autonomous Community of Andalusia. The results show an insufficient level of digital skills that is therefore improvable, finding some factors that influence, to some extent, the various components of digital competence such as prior teacher training, the locality in which their school resides or the category of teaching to which he belongs. Therefore, it is advised to continue to promote in-service training in digital competence for in-service teachers in order to achieve sustainable educational development.
LIRA-V: Lightweight Remote Attestation for Constrained RISC-V Devices

This paper presents LIRA-V, a lightweight system for performing remote attestation between constrained devices using the RISC-V architecture. We propose using read-only memory and the RISC-V Physical Memory Protection (PMP) primitive to build a trust anchor for remote attestation and secure channel creation. Moreover, we show how LIRA-V can be used for trusted communication between two devices using mutual attestation. We present the design, implementation and evaluation of LIRA-V using an off-the-shelf RISC-V microcontroller and present performance results to demonstrate its suitability. To our knowledge, we present the first remote attestation mechanism suitable for constrained RISC-V devices, with applications to cyber-physical systems and Internet of Things (IoT) devices.
Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere. By maintaining the minimum hyperspherical energy during training, OPT can greatly improve the empirical generalization. Specifically, OPT fixes the randomly initialized weights of the neurons and learns an orthogonal transformation that applies to these neurons. We consider multiple ways to learn such an orthogonal transformation, including unrolling orthogonalization algorithms, applying orthogonal parameterization, and designing orthogonality-preserving gradient descent. For better scalability, we propose the stochastic OPT which performs orthogonal transformation stochastically for partial dimensions of neurons. Interestingly, OPT reveals that learning a proper coordinate system for neurons is crucial to generalization. We provide some insights on why OPT yields better generalization. Extensive experiments validate the superiority of OPT over the standard training.
Weakly-supervised hand part segmentation from depth images

Existing learning-based methods require a large number of labeled data to produce accurate part segmentation labels. However, acquiring ground truth labels is costly, giving rise to a need for methods that either require fewer labels or can utilize other currently available labels as a form of weak supervision for training. In this paper, in order to mitigate the burden of labeled-data acquisition, we propose a data-driven method for hand part segmentation on depth maps without any need for extra effort to obtain segmentation labels. The proposed method uses the labels already provided by public datasets in terms of major 3D hand joint locations to learn to estimate the hand shape and pose given a depth map. Given the pose and shape of a hand, the corresponding 3D hand mesh is generated using a deformable hand model and then rendered to a color image using a texture based on Linear Blend Skinning (LBS) weights of the hand model. The segmentation labels are then computed from the rendered color image. Since segmentation labels are not provided with current public datasets, we manually annotate a subset of the NYU dataset to perform quantitative evaluation of our method and show that a mIoU of 42% can be achieved with a model trained without using segmentation-based labels. Both qualitative and quantitative results confirm the effectiveness of our method. The code is publicly available for research purposes at: https://git.io/JmCBS.
Cross-domain sentiment classification based on key pivot and non-pivot extraction

Cross-domain sentiment classification (CDSC) aims to predict the sentiment polarity of reviews in an unsupervised target domain by utilizing a classifier learned from a supervised source domain. Most existing methods mainly construct a knowledge transfer model to enhance the category consistency between the source and target domains. These methods not only have little interpretation of transferable information, but are also weak at capturing the sentiment characteristics of a specific domain. For cross-domain reviews, pivots, that is, the domain-shared sentiment words and non-pivots, that is, the domain-specific sentiment words, are critical sentiment clues in the CDSC task. In this study, we comprehensively use domain-shared and domain-specific sentiment information to construct a knowledge transfer model with interpretability for the CDSC task. Specifically, we construct a novel hierarchical attention network called KPE-net, which automatically extracts pivots between two domains. When pivots are used as the bridge, a joint attention learning network called NKPE-net is built to capture non-pivots of different domains. Finally, combining the sentiment factors generated by key pivots and non-pivots, a sentiment-sensitive network model (SSNM) is proposed to realize the transfer of attention to emotions across domains. Experiments on the Amazon review dataset demonstrated the superiority of the proposed model.
Distributed maximum correntropy unscented Kalman filtering with state equality constraints

Abstract This article investigates the distributed maximum correntropy unscented Kalman filtering problem for nonlinear systems via a sensor network. The system dynamics is subject to state equality constraints and non‐Gaussian noise. By utilizing the maximum correntropy criterion to handle non‐Gaussian noise, a centralized maximum correntropy constrained unscented Kalman filter is first proposed. Then, two novel distributed maximum correntropy constrained unscented Kalman filters with special features are designed. Specifically, the first one is developed by approximating the centralized filter with each sensor's own and its neighbors' measurements. The other one is designed by fusing state estimates. It is worth mentioning that these two distributed algorithms only need finite steps to fuse information over the sensor network rather than infinite steps to achieve the average consensus. Finally, the validity of the proposed algorithms is demonstrated by simulation experiments, with a detailed comparison.
Do Bots Modify the Workflow of GitHub Teams?

The ever-increasing complexity of modern software engineering projects makes the usage of automated assistants imperative. Bots can be used to complete repetitive tasks during development and testing, as well as promoting communication between team members through issue reporting and documentation. Although the ultimate aim of these automated assistants is to speed taskwork completion, their inclusion into GitHub repositories may affect teamwork as well. This paper studies the question of how bots modify the team workflow. We examined the event sequences of repositories with bots and without bots using a contrast motif discovery method to detect subsequences that are more prevalent in one set of event sequences vs. the other. Our study reveals that teams with bots are more likely to intersperse comments throughout their coding activities, while not actually being more prolific commenters.
In Cloud We Trust? Co-opting Occupational Gatekeepers to Produce Normalized Trust in Platform-Mediated Interorganizational Relationships

Interorganizational trust plays an important role in facilitating business relationships, especially for the organizational adoption of new services. Prior research suggests that interorganizational trust develops when the trustor has adequate confidence in the reliability of the trustee’s services. Nevertheless, reliability breakdowns are also an inevitable part of service provisioning. Such breakdowns are especially prominent and visible in the context of platform-based services. Yet platform-based services continue to be adopted and used by organizational customers. This increased adoption and use of such services despite their inconsistent reliability pose the following question. How is trust produced in platform-mediated interorganizational relationships? To examine this question, I conducted a 20-month field study of a cloud computing platform provider and its customers, focusing on the practices of trust production in the wake of reliability breakdowns. Here, I describe customer concerns about the platform’s inconsistent reliability that hampered the development of interorganizational trust. I then identify four practices of trust work enacted by the platform provider to address some of these concerns and to co-opt the occupational gatekeepers in customer organizations who are responsible for technology adoption decisions. Following this, I describe how and why these occupational gatekeepers performed justification work to rationalize the continued use of the platform despite its inconsistent reliability. Together, trust work and justification work facilitate the coproduction of interorganizational trust through normalizing reliability breakdowns as “business-as-usual” events. Synthesizing these findings, I developed a normalization model of trust production, and discuss the implications of normalized trust for platform-mediated interorganizational relationships in the digital economy.
Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro‐Imaging Infrared Spectroscopy

Abstract Processes for formation, cooling, and altering Earth's ocean crust are not yet completely understood due to challenges in access and sampling. Here, we use contiguous micro‐imaging infrared spectroscopy to develop complete‐core maps of mineral occurrence and investigate spatial patterns in the hydrothermal alteration of 1.2 km of oceanic crust recovered from Oman Drilling Project Holes GT1A, GT2A, and GT3A drilled in the Samail Ophiolite, Oman. The imaging spectrometer shortwave infrared sensor measured reflectance of light at wavelengths 1.0–2.6 μm at 250–260 μm/pixel, resulting in &gt;1 billion independent measurements. We map distributions of nine key primary and secondary minerals/mineral groups—clinopyroxene, amphibole, calcite, chlorite, epidote, gypsum, kaolinite/montmorillonite, prehnite, and zeolite—and find differences in their spatial occurrences and pervasiveness. Accuracy of spectral mapping of occurrence is 68%–100%, established using X‐ray diffraction measurements from the core description. The sheeted dikes and gabbros of upper oceanic crust Hole GT3A show more pervasive alteration and alteration dominated by chlorite, amphibole, and epidote. The foliated/layered gabbros of GT2A from intermediate crustal depths have similarly widespread chlorite but more zeolite and little amphibole and epidote. The layered gabbros of the lower oceanic crust (GT1A) have remnant pyroxene and 2X less chlorite, but alteration is extensive within and surrounding major fault zones with widespread occurrences of amphibole. The results indicate greater distribution of higher temperature alteration minerals in the upper oceanic crust relative to deeper gabbros and highlight the importance of fault zones in hydrothermal convection in the lower ocean crust.
Flexibly regularized mixture models and application to image segmentation

Probabilistic finite mixture models are widely used for unsupervised clustering. These models can often be improved by adapting them to the topology of the data. For instance, in order to classify spatially adjacent data points similarly, it is common to introduce a Laplacian constraint on the posterior probability that each data point belongs to a class. Alternatively, the mixing probabilities can be treated as free parameters, while assuming Gauss-Markov or more complex priors to regularize those mixing probabilities. However, these approaches are constrained by the shape of the prior and often lead to complicated or intractable inference. Here, we propose a new parametrization of the Dirichlet distribution to flexibly regularize the mixing probabilities of over-parametrized mixture distributions. Using the Expectation-Maximization algorithm, we show that our approach allows us to define any linear update rule for the mixing probabilities, including spatial smoothing regularization as a special case. We then show that this flexible design can be extended to share class information between multiple mixture models. We apply our algorithm to artificial and natural image segmentation tasks, and we provide quantitative and qualitative comparison of the performance of Gaussian and Student-t mixtures on the Berkeley Segmentation Dataset. We also demonstrate how to propagate class information across the layers of deep convolutional neural networks in a probabilistically optimal way, suggesting a new interpretation for feedback signals in biological visual systems. Our flexible approach can be easily generalized to adapt probabilistic mixture models to arbitrary data topologies.