Reviewerly/exHarmony huggingface dataset:
Enjoy Life from Within: A Proprioception Way

Brain is getting wide attention these days. Until very recently. it is deeply associated with Digital Computing. But Recently the importance of Analog is getting wide attention and such word as Brain-morphic Computing (BMC) is emerging. Although whether it is Digital or Analog, the attention is focused only on Brain. But as William Wordsworth indicated "Our heart leaps up when we behold a rainbow in the sky", our heart plays a very important role in our Emotion. The world has been the Industrial World and Euclidean Approach (EA) played a leading role. EA is interval scale-based with units and requires orthonormality among their datasets. But as the curse of dimensionality teaches us, we cannot solve the problem, if its number of dimensions becomes too large. The environments and situations of our daily life, however, change from moment to moment. But we spend our daily life safely and happily. Change is related with movement. And human movement is divided into two: Motion, which can be observed from outside and Motor, which is the movement inside of us, such as those of muscles, etc. Our discussion on human movements has been focused on Motion. Our muscles harden when we get close to the target object and they move together with our skeleton, forming the musculoskeletal system. Thus, we can control our motion trajectories. But at the first stage, our motion trajectories vary widely from time to time. This is because the environment and the situation vary from time to time and we make our efforts to adapt to these external changes. We coordinate all body parts to balance our body to adapt to the changing environment and the situation. In other words, our internal Motor or such parts as Muscles. etc. come to play an important role. Coordination becomes more and more important. In short, sensorimotor or proprioception has come to play an important role.
Effect of Weather Dynamics to Energy Yield of Dual Axis Sun Tracker-Based Solar PV generation

Generally, solar PV Panel is constructed at fix tilted angle at different locations, such as: street light poles, rooftops, residential buildings, and canopy. The energy production from solar PV generation system is vary and influenced by site (location) and weather parameters. Solar PV generation analyzed in this research is Dual Axis Sun Tracker (DAST) Solar PV Generation. Dual axis sun tracker is a system controller to ensure solar PV panel following the sun path. Furthermore, energy production from DAST-based solar PV generation were measured and collected using energy meter as well as by using automatic weather station for the weather parameters. From these measrument data, it can be correlated the influence of weather parameters to the energy production from DAST PV generation system. The correlation between weather parameters and energy production are analyzed by statistical analysis using SPSS software and multiple linear regression models. From the results and analysis, it can be shown the amount of energy production from DAST-based solar PV Generation are influenced by weather parameters, namely: temperature, humidity, ultraviolet radiation, and solar radiation with significant level up to 98.2%.
Spatio-Visual Fusion-Based Person Re-Identification for Overhead Fisheye Images

Person re-identification (PRID) has been thoroughly researched in typical surveillance scenarios where various scenes are monitored by side-mounted, rectilinear-lens cameras. To date, few methods have been proposed for fisheye cameras mounted overhead and their performance is lacking. In order to close this performance gap, we propose a multi-feature framework for fisheye PRID where we combine deep-learning, color-based and location-based features by means of novel feature fusion. We evaluate the performance of our framework for various feature combinations on FRIDA, a public fisheye PRID dataset. The results demonstrate that our multi-feature approach outperforms recent appearance-based deep-learning methods by almost 18% points and location-based methods by almost 3% points in matching accuracy. We also demonstrate the potential application of the proposed PRID framework to people counting in large, crowded indoor spaces.
Summarizing Web Archive Corpora via Social Media Storytelling by Automatically Selecting and Visualizing Exemplars

People often create themed collections to make sense of an ever-increasing number of archived web pages. Some of these collections contain hundreds of thousands of documents. Thousands of collections exist, many covering the same topic. Few collections include standardized metadata. This scale makes understanding a collection an expensive proposition. Our Dark and Stormy Archives (DSA) five-process model implements a novel summarization method to help users understand a collection by combining web archives and social media storytelling. The five processes of the DSA model are: select exemplars, generate story metadata, generate document metadata, visualize the story, and distribute the story. Selecting exemplars produces a set of k documents from the N documents in the collection, where k &lt; &lt; N , thus reducing the number of documents visitors need to review to understand a collection. Generating story and document metadata selects images, titles, descriptions, and other content from these exemplars. Visualizing the story ties this metadata together in a format the visitor can consume. Without distributing the story, it is not shared for others to consume. We present a research study demonstrating that our algorithmic primitives can be combined to select relevant exemplars that are otherwise undiscoverable using a conventional search engine and query generation methods. Having demonstrated improved methods for selecting exemplars, we visualize the story. Previous work established that the social card is the best format for visitors to consume surrogates. The social card combines metadata fields, including the document’s title, a brief description, and a striking image. Social cards are commonly found on social media platforms. We discovered that these platforms perform poorly for mementos and rely on web page authors to supply the necessary values for these metadata fields. With web archives, we often encounter archived web pages that predate the existence of this metadata. To generate this missing metadata and ensure that storytelling is available for these documents, we apply machine learning to generate the images needed for social cards with a Precision@1 of 0.8314. We also provide the length values needed for executing automatic summarization algorithms to generate document descriptions. Applying these concepts helps us create the visualizations needed to fulfill the final processes of story generation. We close this work with examples and applications of this technology.
Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

steel research internationalVolume 94, Issue 7 2370071 Cover PictureFree Access Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm-Online Sequential-Deep Multilayer Extreme Learning Machine Algorithm Yijie Zhang, Yijie Zhang State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorJingguo Ding, Corresponding Author Jingguo Ding [email protected] orcid.org/0000-0003-4870-8000 State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorJie Sun, Jie Sun State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorDianhua Zhang, Dianhua Zhang State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this author Yijie Zhang, Yijie Zhang State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorJingguo Ding, Corresponding Author Jingguo Ding [email protected] orcid.org/0000-0003-4870-8000 State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorJie Sun, Jie Sun State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this authorDianhua Zhang, Dianhua Zhang State Key Lab of Rolling and Automation, Northeastern University, Shenyang, 110819 P. R. ChinaSearch for more papers by this author First published: 03 July 2023 https://doi.org/10.1002/srin.202370071AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Graphical Abstract In article number 2200832, Jingguo Ding and co-workers show that rolling data in information space comes from rolling production lines in physical space. The strip shape prediction model is proposed by combining a sparrow search algorithm (SSA) and a deep multilayer extreme learning machine (DELM). The prediction experiments show that the SSA-DELM model has the highest accuracy. Volume94, Issue7July 20232370071 RelatedInformation
Digital.VET : An innovative approach for teaching and training

VET (Vocational Education and Training) Reforms and Labour Market reforms have started a process aimed at filling the gap between demand and supply of competences. The demand of competences, in fact, is affected by factors requiring the constant adjustment of production and training processes as well as the greater connection between education/training system and enterprises. Digital training, included in National Programmes of ERASMUS+, is essential in order to ensure effective training practices for the current VET system which is undergoing organizational and methodological change. In this context, Digital Teaching in VET System is an innovative approach to support the objectives set out in national and European strategies for applying ICT to VET systems through teachers/trainers training. The use of Augmented Reality, Virtual Reality and Mixed Reality technologies is an important strategy to innovate teaching and improve attention of students during the training session. iDid, application for immersive teaching, represent the deliverable of the project that allows VET teachers and trainers to produce contents of lessons using the potentialities of AR/VR/MR streams, and to share contents in a community oriented approach to transfer know-how and also obtain important user feedbacks.
Did You Train on My Dataset? Towards Public Dataset Protection with CleanLabel Backdoor Watermarking

The huge supporting training data on the Internet has been a key factor in the success of deep learning models. However, this abundance of public-available data also raises concerns about the unauthorized exploitation of datasets for commercial purposes, which is forbidden by dataset licenses. In this paper, we propose a backdoor-based watermarking approach that serves as a general framework for safeguarding publicavailable data. By inserting a small number of watermarking samples into the dataset, our approach enables the learning model to implicitly learn a secret function set by defenders. This hidden function can then be used as a watermark to track down third-party models that use the dataset illegally. Unfortunately, existing backdoor insertion methods often entail adding arbitrary and mislabeled data to the training set, leading to a significant drop in performance and easy detection by anomaly detection algorithms. To overcome this challenge, we introduce a clean-label backdoor watermarking framework that uses imperceptible perturbations to replace mislabeled samples. As a result, the watermarking samples remain consistent with the original labels, making them difficult to detect. Our experiments on text, image, and audio datasets demonstrate that the proposed framework effectively safeguards datasets with minimal impact on original task performance. We also show that adding just 1% of watermarking samples can inject a traceable watermarking function and that our watermarking samples are stealthy and look benign upon visual inspection.
BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability

Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code is released via https://zmiclab.github.io/projects.html.
The Nature of Electronic Contracts Using Blockchain Technology – Currency Bitcoin as an Example

Purpose: This study aimed to assess the extent of the use and the safety of dealing withBitcoin through blockchain technology. Method/approach: Bitcoin model applied for electronic contracts. The method of functional-cost analysis for technical and economic analysis of system development for NFT creation is used in work. Theoretical framework: Bitcoin is a currency that depends on modern technological techniques with many advantages because they are used to send and receive money over the Internet and can be used in legitimate and illegal businesses. Therefore, quick international measures must be taken to regulate them legally through unified international conventions that regulate the ways of legal dealing with them. Results and Conclusions: International laws must govern and regulate the use of Bitcoin, as it is a currency traded over the Internet and can be used in legitimate and illegal businesses. And that the smart contract, which is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party, can be used, especially since the International legislation lacks a legal regulation of virtual currencies. Research implications: The findings indicated the importance of stipulating special laws that regulate the use of Bitcoin. Originality/value: This work can provide possible solutions to reduce the risks of using Bitcoin, such as stipulating laws and regulations to regulate their use internationally.
Challenges in Designing Blended Learning: the Experience of the “Schools of Instructional Design”

The concept of “blended learning” has been widely discussed in Russian higher education. However, there are different interpretations of the concept and its status is not completely clear in the Russian regulatory environment. Although the effectiveness of this mode of education has been repeatedly recognised, systematic measures for the introduction of blended learning by universities are not being implemented. One of the factors for the success of such an implementation is the high-quality instructional design for blended learning. Difficulties in instructional design use can lead to the abandonment of blended learning or the adoption of less efficient practices. This paper explores the challenges faced by representatives of Russian universities involved in designing and teaching courses in a blended mode during the COVID-19 pandemic. The method used in the study is an online survey which was conducted in 2020-2021. The respondents were participants in the professional development program “School of Instructional Design for Blended Learning”. The results of the study showed that the respondents’ professional deficits in the design of blended courses were associated with the development of new ways of designing and teaching, growth of the course quality and student-centeredness, and more efficient allocation of time resources and use of online tools. The development stage, which involves the creation or updating of digital learning resources, turned out to be the most challenging for designing. Most respondents used LMS but were not satisfied with the quality of its application; also, the majority faced the problem of retaining student engagement in blended learning. The respondents would like to improve their formative and summative assessment practices and strengthen the course quality assessment practices. Commitment to overcome the identified difficulties can contribute to the creation of more effective practice-oriented professional development programs on teaching the blended learning design for educators, instructional designers and academic advisors in Russian higher education, leading to an increase in its quality.
Anomaly detection based on improved isolated forest

Anomaly detection is one of the hot research fields of machine learning and data mining, which is mainly used in fault diagnosis, intrusion detection, fraud detection and other fields. At present, there have been a lot of effective research work, especially the anomaly detection method based on isolated forest, but there are still many difficulties in processing high-dimensional data. A new anomaly detection algorithm k-nearest neighbor based isolation forest (KNIF) is proposed. In this method, the hypersphere is used as the isolation tool, the k-nearest neighbor method is used to construct the isolation forest, and the outlier calculation method based on distance is constructed. Sufficient experiments show that KNIF method can effectively detect anomalies in complex distribution environment and adapt to different distribution scenarios.
Three-Dimensional Path Planning of UAVs in a Complex Dynamic Environment Based on Environment Exploration Twin Delayed Deep Deterministic Policy Gradient

Unmanned Aerial Vehicle (UAV) path planning research refers to the UAV automatically planning an optimal path to the destination under the corresponding environment, while avoiding collision with obstacles in this process. In order to solve the problem of 3D path planning of UAV in a dynamic environment, a heuristic dynamic reward function is designed to guide the UAV. We propose the Environment Exploration Twin Delayed Deep Deterministic Policy Gradient (EE-TD3) algorithm, which combines the symmetrical 3D environment exploration coding mechanism on the basis of TD3 algorithm. The EE-TD3 algorithm model can effectively avoid collisions, improve the training efficiency, and achieve faster convergence speed. Finally, the performance of the EE-TD3 algorithm and other deep reinforcement learning algorithms was tested in the simulation environment. The results show that the EE-TD3 algorithm is better than other algorithms in solving the 3D path planning problem of UAV.
Information locking and its resource-efficient extraction

Locally indistinguishable states are useful to distribute information among spatially separated parties such that the information is locked. This implies that the parties are not able to extract the information completely via local operations and classical communication (LOCC), while it might be possible via LOCC when the parties share entanglement. In this work, we consider an information distribution protocol using orthogonal states for $m\ensuremath{\ge}3$ spatially separated parties such that even if any $k\ensuremath{\le}(m\ensuremath{-}1)$ parties collaborate still the information cannot be revealed completely. Such a protocol is useful to understand up to what extent the encoded information remains locked. However, if required, the parties can share entanglement and extract the information completely by LOCC. To make the process resource efficient, it should consume less number of entangled states. We show that though the set of states, which are locally indistinguishable across every bipartition, are sufficient for the above protocol, they may consume a higher number of entangled states when aiming for complete information extraction. We establish this by constructing a class of locally indistinguishable sets of orthogonal states which can be employed to accomplish the above protocol and these sets consume less number of entangled states, compared with the former sets, for complete information extraction. In fact, this difference in the number of required entangled states for complete information extraction grows linearly with the number of parties. This study sheds light on suitable use of local indistinguishability property of quantum states as a resource, and thus we demonstrate an efficient way of information distribution.
Deep reinforcement learning for key distribution based on quantum repeaters

This work examines secret key rates of key distribution based on quantum repeaters in a broad parameter space of the communication distance and coherence time of the quantum memories. As the first step in this task, a Markov decision process modeling the distribution of entangled quantum states via quantum repeaters is developed. Based on this model, a simulation is implemented, which is employed to determine secret key rates under naively controlled, limited memory storage times for a wide range of parameters. The complexity of the quantum state evolution in a multiple-segment quantum repeater chain motivates the use of deep reinforcement learning to search for optimal solutions for the memory storage time limits, typically referred to as memory cutoffs. The main contribution in this work is to explore very general cutoff strategies which dynamically adapt to the state of the quantum repeater. An implementation of this approach is presented, with our focus on four-segment quantum repeaters, achieving a proof of concept of its validity by finding exemplary solutions that outperform the naive strategies.
Research on College English Spoken Pronunciation Recognition and Error Correction Algorithm Based on Sphinx

The current computer-assisted English learning system is generally used in speech error recognition. Based on Sphinx’s automatic assessment technology of spoken English errors, a research scheme is proposed to take the standard of normal pronunciation based on pronunciation error judgment. Feedback suggestions are also proposed for the judgment in this scheme. The test results show that the scheme can significantly reduce oral errors and improve the accuracy of error detection.