Reviewerly/exHarmony huggingface dataset:
A Policy Optimization Algorithm Based on Sample Adaptive Reuse and Dual-Clipping for Robotic Action Control

When applying deep reinforcement learning in the real physical environment for decision-making, how to improve the sample efficiency while ensuring training stability is an urgent problem that needs to be solved. In order to solve this problem, some of on-policy algorithms are proposed and have achieved state-of-the-art performance. However, these on-policy algorithms, such as proximal policy optimization (PPO) algorithm, have the drawback of extremely low sample efficiency. In this study, we proposed a novel policy optimization method named improved proximal policy optimization algorithm based on sample adaptive reuse and dual-clipping (SARD-PPO) for robotic action control, which combines the advantage of the on-policy methods in training stability with the advantage of the off-policy methods in sample efficiency. First, we analyzed the clipping mechanism of the PPO algorithm, devised a more constrained clipping mechanism based on the analysis of the relationship between the clipping mechanism and the objective constraints, and developed a policy updating method that reuses the old samples of the prior policy in a more principle-based way. Second, we ensured the stability of the algorithm through element-level dual-clipping, as well as adaptive adjustment and reuse of the entire policy trajectory. The experimental results on six tasks in the MuJoCo benchmark indicate that SARD-PPO can significantly improve policy performance while balancing policy stability and sample efficiency, outperforming the PPO algorithm in terms of overall performance.
Foreground Discovery in Streaming Videos with Dynamic Construction of Content Graphs

We study the problem of unknown foreground discovery in image streaming scenarios, where no prior information about the dynamic scene is assumed. Contrary to existing co-segmentation principles where the entire dataset is given, in streams new information emerges as content appears and disappears continually. Any object classes to be observed in the scene are unknown, therefore no detection model can be trained for the specific class set. We also assume there is no available repository of trained features from convolutional neural nets, i.e., transfer learning is not applicable. We focus on the progressive discovery of foreground, which may or may not correspond to contextual objects of interest, depending on the camera trajectory, or, in general, the perceived motion. Without any form of supervision, we construct in a bottom up fashion dynamic graphs that capture region saliency and relative topology. Such graphs are continually updated over time, and along with occlusion information, as fundamental property of the foreground-background relationship, foreground is computed for each frame of the stream. We validate our method using indoor and outdoor scenes of varying complexity with respect to content, objects motion, camera trajectory, and occlusions.
Solar Power Ramp Event Grading Forecasting for Solar Power Plants Based on All-Sky Images

Along with the increasing penetration rate of solar power in the grid, the rapid power fluctuations caused by passing clouds has become a critical issue for the grid safe operations. This paper addresses the Solar Power Ramp Event Grading (SPREG) prediction and criterion problem aiming to meet the grid requirements. An alternative grading forecasting method is proposed, which utilizes Solar-blocking Feature Flag (SFF) extracted from all- sky images as the precursor feature to map the fluctuations caused by passing clouds. Then a bidirectional LSTM model with a Temporal Pattern Atten- tion (TPA) mechanism is employed to extract the spatio-temporal features for SPREG forecasting. By this means, the proposed SFF strategy could significantly improve the accuracy of SPREG prediction in terms of different evaluation metrics. Experimental comparison results based on the collected data demonstrate the efficacy and performance improvement of the proposed method in reference to several existing related approaches.
Image Colorization Algorithm Based on Deep Learning

As we know, image colorization is widely used in computer graphics, which has become a research hotspot in the field of image processing.Image colorization is widely used in computer graphics, which has become a research hotspot in the field of image processing.The current image colorization technology has the phenomenon of single coloring effect and unreal color, which is too complicated to be implemented and struggled to gain popularity. In this paper, a new method based on convolution neural network is proposed to study the reasonable coloring of human images, which ensures the realism of the coloring effect and the diversity of coloring at the same time.First, this paper selects about 5000 pictures of people and plants from the Imagenet dataset, and makes a small dataset containing only people and backgrounds. Secondly, in order to obtain the image segmentation results, this paper improves the U-net network and carries out three times of down sampling and three times of up sampling. Finally, we add the expanded convolution, and use the sigmiod activation function to replace the ReLU(The Rectified Linear Unit) activation function and put the BN(Batch Normalization) before the activation function. Experimental results show that our proposed image colorization algorithm based on deep learning scheme can reduce the training time of the network and achieve higher quality segmentation results.
Feature Distillation Siamese Networks for Object Tracking

In recent years, Siamese-based trackers have shown remarkable improvement in visual tracking. The general trends are interested in making deeper and more complicated networks to pursue higher accuracy. However, these advances result in cumbersome trackers with respect to size and speed, which hinders the deployment of deep trackers in edge devices. Due to the tracking scenario complexity and temporal coherence, the backbone network of deep trackers emphasizes the target appearance information more. However, the appearance feature is sensitive to parameter variation, making the traditional deep model compression methods hard to compress a deep tracker. To bridge the gap between deep Siamese trackers and practical use, we propose a new feature distillation algorithm suitable for deep trackers in this paper. Firstly, motivated by the concept of divide-and-conquer, we formulate the feature distillation into a stepwise distillation problem and perform distillation on each minimum unit to relieve the hard-to-distill problem of appearance feature. Secondly, we reconstruct the student model into a combination of convolution kernels and a point-wise convolutional layer, which enables the student model to inherit all the parameters of the teacher model during initialization. Finally, we propose a 3step warm-up training strategy to address the student model's degradation and structural adaptation problem during training. Extensive experiments on seven benchmarks demonstrate that our proposed method compresses the fully convolutional Siamese Networks SiamFC and its variant Siamd and achieves leading tracking performance with only 2.1 MB model size while running at 225 fps.
Kernel Based Multiscale Partial Renyi Transfer Entropy and its Applications

Transfer entropy is an effective method for quantifying information flow and causal orientation in nonlinear systems. Based on this, we combine NA-MVMD and transfer entropy together, while considering the case of multivariate systems, and propose a kernel-based multiscale partial renyi transfer entropy. We validate the method with a henon mapping and a VAR model, demonstrating the robustness of the model to noise and the effect of the parameter Î± taken on the experiment. For the real dataset, we used multi-channel EEG signals performing a mental arithmetic task and computed the information flow of each EEG in different states at different wavebands and compared them separately. In conclusion, the method is able to measure information transfer at different scales, is robust to noise and endpoint effects, and overcomes the interference of other signals in a multivariate system on the target signal, providing a new perspective for exploring the multilevel spatio-temporal properties of EEG.
Illumination Detection in IIIF Medieval Manuscripts Using Deep Learning

Illuminated manuscripts are essential iconographic sources for medieval studies. With the massive adoption of IIIF, old and new digital collections of manuscripts are accessible online and provide interoperable image data. However, finding illuminations within the manuscripts’ pages is increasingly time consuming. This article proposes an approach based on machine learning and transfer learning that browses IIIF manuscript pages and detects the illuminated ones. To evaluate our approach, a group of domain experts created a new dataset of manually annotated IIIF manuscripts. The preliminary results show that our algorithm detects the main illuminated pages in a manuscript, thus reducing experts’ search time.
SAR Complex Images Subpixel Co-registration

Generation of correct SAR interferometric images and differential interferometric images, a precise co-registration of SAR complex images is required. In the present study, an algorithm for modeling pseudo SAR images is suggested to illustrate subpixel co-registration. Based on the spectral definition of the spatial SAR images a spectral co-registration algorithm with subpixel precision is developed and numerically experimented.
Photovoltaic Energy Usage for Public Educational Building: A Case Study

This work presents a case study of a photovoltaic energy system for public educational building. The aim of the case under consideration is to provide technical measures to improve the energy efficiency of the building, according to the technical specification and current regulations. Public educational buildings are mainly used during daylight hours, where solar power will be used for own consumption. Analysis of the energy usage for public building is made and an estimation on a daily basis shows optimal requirements for such systems. Achieved results could be used in the design process of public buildings autonomous electric power supply systems, zero energy building and near zero energy building concepts.
Development of algorithms for an information-measuring system for assessing changes in the physical variables of the environment based on physical and mathematical models of ecological systems

Abstract The article deals with complex heterogeneous systems with distributed parameters and their mathematical part. Four algorithms for estimating parabolic and hyperbolic systems are considered. The developed algorithms will make it possible to carry out a fairly complete analysis in the temporal and spatial domains of measurement, and also serve as the basis for new models of information, measurement and control systems that make it possible to explore vast territories. These developments are necessary to assess the change in the physical variables of the environment, in particular, the most accurate prediction of the spread of pollution in temporal and spatial coordinates
FILM ART AND CONVERSATION CIRCLE AS AN LEARNING STRATEGIES IN BIOETHICS FOR COLLECTIVE SUBJECTS

Films are important tools of communication, expression, and perception of the world and important tools in the teaching and learning process. The present article aimed to analyze the discourses that emerged from the cinematographic work, having the conversation circle as a strategy for problematizing bioethics in the work process of teachers, students, and health workers. This is a descriptive study with a qualitative approach, with data collected from an interview guided by a semi-structured script. Faculty, students, and health workers participated in the study. Data analysis based on the content analysis technique, supported by the Atlas.ti version 9.0 software, enables the identification of two thematic categories: film art as a strategy for bioethical reflection and bioethical implications in teachers’ daily practices, students, and health professionals. The results revealed that the use of cinematography engenders a new formulation of the teaching-learning process, allowing participants to reflect on everyday ethical dilemmas. It’s concluded that it’s imperative to broaden debates with bioethical approaches, anchored to cinematographic art, as an effective method to stimulate reflective thinking.
Hybrid RSS-TDOA Measurements Based Directional Target Localization in NLOS Environments

Localization constitutes the basic requirement of many applications in wireless sensor networks. Directional transmission has become very common with the development of electronic and antenna technologies. This paper aims to estimate both the position and transmission orientation of the directional target on the basis of combined received-signal-strength (RSS) and time-difference-of-arrival (TDOA) measurements in the non-line-of-sight (NLOS) environments. The localization problem is first formulized by the maximum likelihood estimation. Considering the problem is nonconvex, this paper then converts it into three relatively simple and intuitive sub-problems. Through converting them into generalized trust region subproblem (GTRS) and using bisection as well as multi-resolution search methods, the estimates is finally obtained. Simulation results show that the proposed method can estimate the orientation and position of the target effectively, its localization accuracy also outperforms the comparison methods significantly.
Design of Begian–Melek–Mendel structure-based interval type-2 fuzzy logic systems optimized with backpropagation algorithms

As an emerging technology, the efficient and energy conserving process of permanent magnetic drive (PMD) presents high uncertainties. This paper designs a type of Begian–Melek–Mendel (BMM) structure interval type-2 fuzzy logic systems (IT2 FLSs) for PMD process uncertain parameters forecasting. The antecedent, consequent, and input measurements of systems are all selected as the Gaussian type-2 primary membership functions with uncertain standard deviations. Then the backpropagation algorithms are used to tune the parameters of IT2 FLSs. According to the Monte Carlo simulation studies and convergence analysis, the proposed IT2 FLSs are proved to be superior to two corresponding T1 FLSs in generalization ability.
Research Data Management Practices at the University of Namibia: Moving Towards Adoption

The management of research data in academic institutions is increasing across most disciplines. In Namibia, the requirement to manage research data, making it available for the purposes of sharing, preservation and to support research findings, has not yet been mandated. At the University of Namibia (UNAM) there is no institutional research data management (RDM) culture, yet RDM may nevertheless be practiced among its researchers. The extent to which these practices have been adopted is, however, not known. This study investigated the extent of RDM adoption by researchers at UNAM. It identifies current or potential challenges in managing research data, and proposes solutions to some of these challenges that could aid the university as it attempts to encourage the adoption of RDM practices. The investigation used Rogersâ€™ Diffusion of Innovations (DOI) theory, with a focus on the innovation-decision process, as a means to establish where UNAM researchers are in the process of adopting RDM practices. The population under study were the UNAM faculty members who conduct research as part of their academic duties. Questionnaires were used to gather quantitative data. The study found that some researchers practice RDM to some extent out of their own free will, but there are many challenges that hinder these practices. Overall, though, there is a lack of interest in RDM as the knowledge of the concept among researchers is relatively low. The study found that most researchers were at the knowledge stage of the innovation-decision process and recommended, among other things, that the university puts effort into creating RDM awareness and encouraging data sharing, and that it moves forward with infrastructure and policy development so that RDM can be fully adopted by the researchers of the institution.
RBSmix: A Regulatable Privacy-Preserving Method for Cryptocurrency

Public chains represented by Bitcoin and Ethereum do not require users to use their real names, and transaction data are open to the whole network. Analysed based on this, researchers have achieved the deanonymization of blockchain transactions to a certain extent. Based on the existing blockchain transaction privacy protection scheme, the true link relationship between the transaction sender and receiver is hidden, which brings difficulties to regulation. In this paper, we propose a cryptocurrency mixing service RBSmix, which allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. RBSmix, through blind signature to prevent attackers from linking input and output addresses, by the threshold secret sharing algorithm, encryption technology, and a regulation team, combined with the idea of voting, tracks the source of funds for illegal addresses. Experiments show that the scheme scales to large numbers of users and can provide users with better privacy protection.
Algorithm for Swimmers’ Starting Posture Correction Based on Kinect

Effective identification and correction of swimmers’ improper postures can significantly improve athletes’ weekday swimming training quality. The human body’s affine deformation is prone to occur during swimming movements when performing posture recognition and correction, resulting in the creation of low-brightness action feature locations. The inability of coaches to identify and correct athletes’ improper posture in real time is a result of a lack of detection and correction. Additionally, the human skeleton motion data from the depth camera Kinect contains a high amount of noise and fewer skeleton nodes, and the data level of detail is low. To overcome this issue, this research proposes a network for enhancing Kinect skeleton motion data. The network is composed of six bidirectional cyclic autoencoder stacks. The stacking structure improves the smoothness and naturalness of the data, and the training phase includes hidden variable limitations to ensure that the bone motion data preserve a genuine bone shape when the degree of detail is raised. The trials demonstrate that the optimized data from the network have a better degree of smoothness and can keep a more realistic bone structure, enabling the goal of obtaining high-precision motion capture data with low-precision Kinect equipment to be met.
The effects of salience and ordinal position of exhibit objects on visitor attention in digital exhibitions

The aims of the present experiments were to investigate the impact of (a) ordinal position, (b) object salience (size and three-dimensionality), and (c) proximity to salient objects on visitor attention in digital exhibitions, and to make comparisons with trends observed in physical exhibitions. The results of two experiments involving 210 participants, conducted using a tablet computer, indicated that (a) the ordinal position and salience effects observed in physical exhibitions were also present in the digital medium; (b) however, the overshadowing effect by the salient objects on the adjacent ones observed in physical exhibitions did not seem to emerge when three-dimensionality was the salience parameter; and (c) the negative impact of the salient object’s presence on the overall attention to the exhibition observed in physical exhibitions seemed to be eliminated in digital exhibition environments regardless of the salience parameter.
Spectral aesthetics: cultivating a viral ecology

Situated at the intersection of visual art and the context of the Anthropocene, this article offers an eco-philosophical reading of the unfolding COVID-19 pandemic regarding the ecological entanglements between humans, viruses, and the environment. Deploying the trope of the spectrality as a ‘heuristic device’, it argues that confronting the spectral aspects of human-virus encounter provides a helpful way to properly understand the ecological implications of the present pandemic. Bringing contemporary Chinese artist Xu Bing's Air Memorial (2003) and Jia Zhangke's short film Visit (2020) into conversation with the theory of spectrality, I analyze what I call the ‘spectral aesthetics’ shared by both works. Specifically, it looks at how Xu's artwork elicits the spectral (im)materiality of viruses that is associated with the memory of the SARS epidemic, the materialisation of air, and the way that the spectre of air can keep haunting us across time and space. Then, it delves into the cinematic representation of a spectral atmosphere permeating the minuteness of human contact, arguing that the coronavirus opens up possibilities of perceiving, thinking, and living as spectres, evoking the sense of co-existence that entails the symbiotic relations among humans, viruses, and the environment.
Future home stories: participatory predicaments and methodological scaffolding in narrative speculation on alternative domestic lives

Not infrequently, smart home imaginaries and installations are envisaged for nuclear families dwelling in detached houses fitted with the latest Internet of Things (IoT) solutions. In our article, we follow one approach to escape this powerful but inadequate projection that entails inviting people to imagine alternative forms of domestic IoT use. Surveying the setup of these nascent endeavours, in particular attempts that pivot on narrative accounts and forward-oriented fictions on the design of new habitats, we show how these seek to evoke visions of technologically supported cohabitation and everyday life. Due to their inclusive ambitions, such approaches face participatory predicaments that arise from the sought-after spontaneity and creativity within a purposive process. In response, all of them resort to methodological scaffolding that helps their designers to reconcile the tension between the idiosyncrasies embraced by the procedures and the overarching requirements of a particular exercise.
Autonomous global path planning method for substation remote patrol robot

Current robot path planning methods only consider local obstacle avoidance, and there are conflicts between global planning path and local planning path, resulting in the planned path is not global optimal and the planning efficiency of the method is low. In order to solve the above problems, the autonomous global path planning method of substation remote patrol robot is studied. After establishing the kinematic plane model of the remote patrol robot, the kinematic relation of the robot in space is determined according to the coordinate relation. Artificial potential field is introduced to calculate the initial heuristic information of ant colony algorithm, and the left and right planning path is obtained by bidirectional search. Experimental results show that the average path planning time of the proposed method is 22.92ms, and the number of robot collisions is significantly reduced, which effectively ensures the safety of patrol work.