Reviewerly/exHarmony huggingface dataset:
Classification of SCADA Alarms and False Alarm Identification Using Support Vector Machine for Wind Turbine Management

The efficiency of a wind turbine depends on operations and maintenance activities. Large amounts of complex data are efficiently categorized by machine learning-based classifiers. The implementation of several support vector machine algorithms for the prediction and detection of false alarms in wind turbines is the novelty proposed in this research. A reliable tool for assessing the effectiveness of classification algorithms is K-Fold cross validation. The proposed methodology is verified by using SCADA data from an actual wind turbine. The outcomes show a 98,6% accuracy rate for the quadratic support vector machine classifier. The analysis of the misclassifications obtained from the confusion matrix provides the necessary information, together with the alarm log and maintenance record, to determine whether it is a false alarm. The classifier can reduce the number of false alarms referred to as misclassifications by 25%. These results demonstrate that the suggested methodology is effective at identifying false alarms.
Exploring Cryptocurrency: How do individual behaviors in the financial world affect the potential of economic bubbles?

Cryptocurrency has made waves recently with its absurd increase in prices and volume. In this paper, I sought to understand, fundamentally, what factors could have helped its rise to the top. On the other hand, large ascents like this in the past have shown to be fatal, such as the housing market bubble of 2008. It struck me that cryptocurrency seemed to be heading down a similar path, especially with its unpredictable volatility and lack of practical usage in daily life. I explored some of the behavioral trends that are prevalent in finance, and used them to understand the 2008 bubble. I then further explored cryptocurrency behavior to draw comparisons, which were not lacking. By understanding what was behind cryptocurrency’s incredible rise and what behaviors and trends may factor into its volume, I was able to come to the conclusion that cryptocurrency is an asset class that treads dangerously along the lines of forming bubbles.
Optimized Acceleration of Single Shot Detection for Edge Computing Based-on FPGA

Single Shot Detection (SSD) based Deep Neural Network (DNN) has been widely used in edge computing due to the properties of one-stage detection and high accuracy. Especially in Field Programmable Gate Array (FPGA) based acceleration, SSD can complete read-time object detection with higher efficiency. However, the acceleration of SSD has been suffered from constraints of system integration, size and power in strict scenarios. It requires all network inference to be done within single chip, as well as the acceleration of post-processing algorithm in SSD. To realize SSD acceleration in single chip, this paper proposes an optimized acceleration method of SSD for autonomous body detection scenario. The original post-processing algorithm is optimized through judging the probability threshold, which reduces the redundant computing operations of location box without losing detection accuracy. Meanwhile, an acceleration architecture is constructed to satisfy hardware constrained platform. In the experimental results, the optimized execution time is changed from 46.024ms to 9.277ms in software, and the accelerated time is further reduced to 1.117ms in hardware, which achieves performance improvement in 7.305 times. The proposed method can also be applied to other applications for the acceleration of post-processing algorithm.
Label Assignment for Aligning Features in Anchor-free Models

Most anchor-free methods perform object detection in the way of dense recommendation, which assumes one point can conduct high-quality category prediction and regression estimation simultaneously. However, due to different task drivers, valid classification and regression features may not locate around the same sampling point in the training phrase. This problem is called feature misalignment. To solve it, this paper proposes a new label assignment strategy with feature filter to guide the network to focus on sampling points with aligned features. In addition, we establish mutually independent multi-layer quality distributions to model objects’ priori information on different feature pyramid scale levels. Equipped with our method, the generated foreground weight map gathers more to centers of the classification and regression heatmaps. Experiment results show that without bells and whistles, our method achieves 45.3% AP on COCO test-dev under the default 2x schedule, outperforming other related methods.
Deep Feature Filtering and Contextual Information Gathering Network for RGB-D Salient Object Detection

Along with the update of depth sensors, salient object detection (SOD) models are evolving in dividend and are being increasingly investigated. Due to the essential difference between depth and RGB images, existing SOD models mostly focus on finding a reasonable way to facilitate multimodal (i.e., RGB and depth) features fusion while ignoring the interference of low-resolution depth maps on the inference stage. To this end, we first propose a Deep Feature Filtering and Contextual Information Gathering Network for RGB-D Salient Object Detection (DCNet). First, a filter based on an attention mechanism is used to select important information in the depth features, which can adequately filter the low-quality depth maps. Moreover, before integrating multi-level feature complementarity, we incorporate a pre-processing step, the contextual information gathering module, which consists of separable (Sep) convolution, dilated (Dil) convolution, adaptive averaging pooling (AVP), and residual networks, to capture information diversity from a global perspective. We conclude by proposing a new mixed loss function based on binary cross-entropy loss (BCE) and intersection over union loss (IOU) for supervised network optimization. With four evaluation metrics on five public datasets, DCNet outperformed ten start-of-the-art methods in terms of simplicity and efficiency.
Continuous Policy Multi-Agent Deep Reinforcement Learning with Generalizable Episodic Memory

Multi-agent reinforcement learning (MARL) has been plagued by low sample efficiency. It needs far more samples than human learning to achieve convergence and learn successful strategies. And this situation is more serious in continuous state and policy space. Episodic memory (EM), as an effective method to improve the sample efficiency of reinforcement learning (RL) by imitating the ability of human rapid learning, has currently made little effort in continuous policy space and MARL. Therefore, we propose a continuous policy multi-agent reinforcement learning method with generalizable episodic memory (ECM). It establishes a centralized memory parameter network and memory buffer for each agent, and updates memory through implicit planning, so that the episodic memory model can use neural networks to learn successful strategies from the past successful experience. Thus, the model can adapt to the continuous policy space. Moreover, ECM combines MARL's idea of decentralized execution and centralized training (CTDE) with episodic memory model to make the model adapt to multi-agent task environment. Simulation results show that ECM method can effectively improve the sample efficiency of MARL algorithm, and the learned strategy has higher accuracy.
An Optimization Method for Dual-arm Robot Task Scheduling Considering Collision Risk

An optimization method is presented for dual-arm robot task scheduling in this paper. The purpose of task scheduling is to assign tasks and determine optimal task paths for dual-arm robot. The robot tasks are considered as a set of task points. The task scheduling is modeled as a Multi-Objective Multiple Traveling Salesman Problem (MOMTSP) to optimize three goals (total time consumption, takt time and collision risk). An improved algorithm based on Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed for the MOMTSP solution. The simulation demonstrates that the improved algorithm performs faster iterations and better results. Experimental results are shown to prove the method improvement compared with the classical method based on genetic algorithm. The proposed method significantly reduces the collision risk and time consumption.
Novel Enhanced Unsupervised Quantum Clustering Algorithm

Every day, humans generate approximately 2.5 billion gigabytes of new data. While this data is crucial in applications such as drug development and cybersecurity, it is mostly unstructured and unlabeled which makes it difficult to process effectively. Businesses and governments alike solve this problem by using machine learning algorithms to characterize the data and make decisions with it. However, conventional machine learning processes consume excessive computational resources and time: one of the most prominent, K-means clustering, has an approximate time complexity of O(n^2), where n is the input data size. This quadratic time complexity causes scalability problems with large datasets. In this project, I have created an improved, more scalable clustering algorithm by leveraging quantum computation. The approach (new Q-means) involves an Angle Embedding Feature Map, an d-dimensional SWAP Test, and a dynamic, threshold distance-based termination criteria. After implementing this algorithm using the IBM Qiskit SDK, the new Q-means algorithm was run several times on different dimensional datasets. On an average, the new Q-means performed 18% better than the K-means algorithm based on the Adjusted Rand Index. In terms of the simulation execution time, the new Q-means was about six times faster than the existing Q-means due to a multi-threaded implementation and faster convergence. The time complexity of the new Q-means without a Quantum Random Access Memory (QRAM) is O(n^2). While the existing Q-means algorithm, which utilizes QRAM, has a time complexity of O(n(log n)^p), the time complexity of the new Q-means with QRAM is significantly better—O(n).
Ensembled Autoencoder Regularization for Multi-structure Segmentation for Kidney Cancer Treatment

The kidney cancer is one of the most common cancer types, which treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution, especially in an automated way that saves time and does not require skilled clinicians. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation. With mentioned approach we achieved 10th place in the KiPA2022 challenge.
Claim Success, but Blame the Bot? User Reactions to Service Failure and Recovery in Interactions with Humanoid Service Robots

Service robots are changing the nature of service delivery in the digital economy. However, frequently occurring service failures represent a great challenge to achieve service robot acceptance. To understand how different service outcomes in interactions with service robots affect usage intentions, this research investigates (1) how users attribute failures committed by humanoid service robots and (2) whether responsibility attribution varies depending on service robot design. In a 3 (success vs. failure vs. failure with recovery) ✕ 2 (warm vs. competent service robot design) between-subject online experiment, this research finds evidence for the self-serving bias in a service robot context, that is, attributing successes to oneself, but blaming others for failures. This effect emerges independently from service robot design. Furthermore, recovery through human intervention can mitigate consequences of failure only for robots with warm design. The authors discuss consequences for applications of humanoid service robots and implications for further research.
METAPHYSICS OF RELATIONAL PICTURE OF MICROWORLD

The paper indicates principles and fundamentals of binary pre-geometry in the framework of the relational paradigm based on the formalism of binary systems of complex relations of rank (4,4). Description of elementary particles (hadrons) in terms of the values of roots of the characteristic equation of complex 3×3-matrices has been proposed. The baryons are shown to be described by the subtype I solutions of algebraic classification, and the mesons by D and O ones (with identical roots), which justifies the conventional types of hadrons, the values of their electric charges. Algebraic formulae for their mass have been presented. The meson solutions form the basis of three nuclear structures of Mendeleev’s table.
Web- Based Chat Application Using REACT

A chat application is a useful tool in the present time because it enables people to communicate with one another from anywhere and in real-time without inconvenience. This web-based chat tool also eliminates the need for users to call each other every time they have a question about their work. Nowadays it is being used a lot in every field whether it is online banking, online e-commerce, or business. The best feature of the app is that together we can send a similar text to several people at the same time because we may create a group of a specific number of people or send messages to a large number of users, including Sender Message wishes to convey. The biggest example of this is when you're on a website, such as a bank, and you encounter bank account-related issues, you can use the website's chat feature to send an instant message to an agent so that the question can be answered quickly and efficiently. In today's time, apps like WhatsApp, Skype, and Messenger are the biggest examples of chatting apps. WhatsApp itself has been downloaded over 1 billion times on the Play store alone. The popularity of the chat application can be gauged from this fact. Today’s chatting apps have become so advanced that with their help we can easily make video calls, and send audio files, documents and locations too. By lowering average contact costs, live chat lowers overall contact center expenses. This increases efficiency by allowing live chat agents to manage many chats at once, minimizing the need for additional representatives (Goel, A. K., 2022). In this research paper, we have mentioned a react-based web chat application and also we have compared react based chat application with another technology " PHP "that will help us to understand which technology is suitable for developing chat applications. There's no need for third-party client software with this web-based real-time chatting tool. The app is built with React. js, and Node.js with the Express package, as well as a Mongo DB database. Text messages are sent to and received from the server, and via a point-to-point connection between the servers. Transfer of data from is enhanced. The concept of virtual environments has been adopted due to the use of the React framework, which increases performance by a factor of about 6 over traditional PHP-based apps.
Deciding FO-rewritability of Regular Languages and Ontology-Mediated Queries in Linear Temporal Logic

Our concern is the problem of determining the data complexity of answering an ontology-mediated query (OMQ) formulated in linear temporal logic LTL over (Z,&lt;) and deciding whether it is rewritable to an FO(&lt;)-query, possibly with some extra predicates. First, we observe that, in line with the circuit complexity and FO-definability of regular languages, OMQ answering in AC0, ACC0 and NC1 coincides with FO(&lt;,≡)-rewritability using unary predicates x ≡ 0 (mod n), FO(&lt;,MOD)-rewritability, and FO(RPR)-rewritability using relational primitive recursion, respectively. We prove that, similarly to known PSᴘᴀᴄᴇ-completeness of recognising FO(&lt;)-definability of regular languages, deciding FO(&lt;,≡)- and FO(&lt;,MOD)-definability is also PSᴘᴀᴄᴇ-complete (unless ACC0 = NC1). We then use this result to show that deciding FO(&lt;)-, FO(&lt;,≡)- and FO(&lt;,MOD)-rewritability of LTL OMQs is ExᴘSᴘᴀᴄᴇ-complete, and that these problems become PSᴘᴀᴄᴇ-complete for OMQs with a linear Horn ontology and an atomic query, and also a positive query in the cases of FO(&lt;)- and FO(&lt;,≡)-rewritability. Further, we consider FO(&lt;)-rewritability of OMQs with a binary-clause ontology and identify OMQ classes, for which deciding it is PSᴘᴀᴄᴇ-, Π2p- and coNP-complete.
Time management and translation method in translating a scientific article: A case study on a professional translator

As an activity of transferring a message from a source text to a target text, translation certainly involves several processes. Different processes require certain amount of time allocation depending on the translator’s cognitive load. Studying translation process is essential to reveal what actually happened while a translator was doing his/her tasks, contributing to a better translation product. This study aimed at finding out the time management and the translation method in translating a scientific article by a professional translator. The scientific article is composed of more than five thousand words; thus, the researcher reduced the limit of the words to one thousand words to avoid the technical problems. This qualitative study used a case study as the research method. The participant of this research was a professional translator from the Association of Indonesian Translator (HPI). The data were collected using a keyboard logging program (Translog-II) and a screen recording tool (OBS Studio). Based on the data analysis it was found that the professional translator (i) distributed more time in the drafting stage; and (ii) applied the linear and inline methods during the translation process. It was found that the professional translator focused translating on word by word, phrase by phrase, and paragraph by paragraph. It is concluded that the activities in each stage of translation process contributes to the result of translation.
Usage of Water Marking-Based Techniques for Performing Necessary Operations on a Plaintext Image in the Cloud

To provide the image with security multiple encryption techniques are used. This way the image is converted to a form in which it is not read by a third party unless they have the watermark which is known to only the user. Some cryptographic protocols have been constructed, the emergence of quantum computers and quantum algorithms presents a problematic scenario for those protocols. Data-driven and Feedback-Enhanced Trust (DFET) is developed due to the multi cloud environment created by the multiple data centers. A method for quantum picture watermarking is provided to blend a grayscale quantum watermark into a quantum carrier image.Multiple image search protocols are used in which the image is searched based on their specialities. for example, Content-Based Image Retrieval (CBIR), Convolutional Neural Network (CNN). scale-invariant feature transform (SIFT) approach for encrypted images that protects privacy. PRISMA checklist in addition to a few components of the Cochrane Quality Assessment. social media and gaming applications have been widely adopted by the general public and the number of social media and gaming applications has increased, with the help of white hacking it has established a security technique, stealth robot conceals itself from the intended target/victim by using stealth tactics. Making it easier for protection.
Primary Preservice Science Teachers’ Perceptions of Practical Work in Remote Learning Environments

Science practical work is renowned for providing authentic environments for science learning in ways that reduce the abstractness of concepts. Significant resources are used to provide facilities such as laboratories to ensure that practical work is implemented in science learning. Practical work is important for primary preservice science teachers, who in turn will implement the instructional strategy in their future classrooms. The rise in remote learning has prompted researchers and instructors to reimagine ways of facilitating practical work in ways that involve human-machine interactions in significant ways. This study used an interpretive paradigm and an explorative single-case-study design to explore primary preservice science teachers’ perceptions of conducting practical work in remote learning environments. A framework based on the Internet of Things- (IoT) enabled tools was used to mediate the understanding of the study findings. Data were collected from 25 preservice teachers by means of experiment reports and observation of practical work activities. The findings of the study showed that in the absence of proper systems for conducting practical work remotely and limited internet connectivity, the participating preservice teachers used internet searches to inform them of how to conduct experiments using household materials. The experiment reports comprised experiment demonstrations developed through the use of filmmaking applications, cloud computing tools, and social media collaborations. The paper makes recommendations to expand preservice teachers’ technological competencies to include the use of virtual laboratories to conduct practical work in remote learning environments.
A Multiplatform Application for Automatic Recognition of Personality Traits in Learning Environments

The present work shows the development of a data collection platform that allows the researcher to collect new video and voice data sets in Spanish.It also allows the application of a standardized personality test and stores this information to analyze the effectiveness of the automatic personality recognizers concerning the results of a standardized personality test of the same participant.Thus, it has elements to improve the evaluated models.These optimized models can then be integrated into intelligent learning environments to personalize and adapt the content presented to students based on their dominant personality traits.To evaluate the developed platform, an intervention was conducted to apply the standardized personality test and record videos of the participants.The data collected were also used to evaluate three machine learning models for automatic personality recognition.
PENGARUH JUMLAH HELAI BENANG TERHADAP NILAI ESTETIKA DAN MUTU SULAMAN TERAWANG HARDANGER PADA SARUNG BANTAL SOFA

Hardanger embroidery is embroidery done on plain weave fabrics. The results of the embroidery are in the form of geometric holes. The background of this research is that hardanger embroidery appears with aesthetics an poor quality. The aim of the research was to improvw the aesthetics and quality of hardanger embroidery including a.) to determine the effect of using 1 thread on the aesthetic value and quality of hardanger embroidery b.) to determinne th effek of use 2 thread on the aesthetic value and quality of hardanger embroidery c.) to determine the effek of use 3 thread on th aesthetic value and quality of hardanger embroidery. This research was experimental research. The statistical test result with SPSS obtained significant aesthetic value of hardanger embroidery was 0.97, 0.60 and 0.61 with p&lt;0.05 so that H0 is accepted. There is no significant effect on the aesthetics of embroidery using a different number of threads. The significant value of embroidery quality is 0.000 with p&gt;0.05 so H0 is rejected. There is a significant effect on the quality of embroidery using a different number of thread strans. Keywords: Terawang; Aesthetic, Quality