Reviewerly/exHarmony huggingface dataset:
Upscaling Operators of Essential Services Incident Response Teams

The eHealth sector in Portugal faces significant cybersecurity challenges, including increasing cyber threats and vulnerabilities, inadequate cybersecurity measures, and a short-age of skilled cybersecurity professionals. The importance of addressing these challenges has been emphasised by the European Union's Network and Information Systems (NIS) Directive, which aims to ensure a high common level of cybersecurity across the EU by requiring Member States to adopt national cybersecurity measures and cooperate on incident response. In response to these challenges, we propose in this work, a project capable of improving the cybersecurity posture of eHealth systems in Portugal. The project proposes a novel approach to establishing connections with various organisations, including the national CERT.PT and PANORAMA, for incident response and information sharing. The tasks involved in the project include risk assessment, penetration testing, skills gap analysis, training, incident notification and communication, and controls implementation. By implementing these tasks, the project has the potential to improve the incident response capabilities and overall cybersecurity posture of eHealth systems.
Design of Electronic Logbook Based on Finite State Machine

Logbook is an important legal document for recording and analyzing navigation conditions, accumulating data, and summarizing experience. The completeness and accuracy of manual logbooks are largely determined by the navigational officer's proficiency and status. Voyage Date Recorder records objective navigation information, but its content doesn't contain any navigation logic. In this paper, an electronic logbook based on finite state machine is proposed. By abstracting the voyage into ship's initial state and a subsequent chain of events, a finite state machine can represent all states, events, and their logical relationship in logbook, which processes log events during the voyage to obtain log information, and then saves these data for reappearing at any time.
A 3D Object Tracking Method Based on Graph Neural Network and Spatio-Temporal Dependency Knowledge

In road traffic scenes, the personalized motion of the target object, the interweaving motion of the target object and its neighbors, and the interaction and collaboration between the target object and the traffic environment are very complicated. It is difficult to accurately track a target that is heavily occluded or continuously occluded through affinity calculation or associated tracking. In response to this problem, the applicant first proposed a graph neural network based method with multi-feature learning to explore the coordinated change law of the target object's position, direction, and speed, and mine the spatio-temporal dependence knowledge in the coordinated change of the target object's motion, and improve the graph node aggregation feature discrimination. And then the applicant proposes a graph neural network based method with multi-source feature interweaving, exploring the interweaving motion law of the target object and neighboring moving objects, and iteratively updating the graph node features through multi-source feature aggregation to mine the relationship between neighboring data samples. Finally, a graph neural network based method with environmental interaction and collaboration is proposed to explore the interaction and collaboration rules between the target object and the traffic environment. With the design of the target-environment multi-level graph node feature update strategy, the fine-grained multi-source traffic environment facilities are aggregated to mine the knowledge of the temporal and spatial dependence of the interaction between the target object and the traffic environment. Therefore, the proposed method can accurately calibrate and stably track the target object under severe occlusion or continuous occlusion.
A Firefly Algorithm for Two-dimensional Grayscale Image Segmentation

To solve the 2-dimensional (2D) grayscale image segmentation problem, a 2D image segmentation method based on the firefly algorithm is proposed in this paper, which uses a Tsallis entropy as an image segmentation evaluation index and solves the optimal threshold by the firefly algorithm to obtain the optimal image segmentation results. The experimental simulation results show that the proposed 2D image segmentation method based on Firefly algorithm image decomposition results are significantly better than other commonly used methods. The proposed method will have certain application value in image segmentation.
Simulation of Music Art Teaching Quality Evaluation System Based on Trusted Neural Network

With the continuous development of digital technology and the arrival of the multimedia era, more music information has been discovered by people. With the influx of music resources, the quality of music education is facing a severe test. In order to solve some problems in current university music and art teaching, this article proposes a new evaluation index system to enhance course resources by effectively exploring and utilizing relevant subject resources in university music and art teaching in order to improve the quality of university music and art teaching. In order to solve this problem, this paper proposes an automatic recommendation system of classroom music resources based on a Convolutional neural network. Firstly, based on collecting and analyzing user information, a matrix calculation method based on implicit semantics is proposed to construct personalized user preferences. Then, by preprocessing the collected voice data, Mel scores that can reflect musical features are extracted. On this basis, the Convolutional neural network is used to build a regression model of users and songs in the hidden public space to predict the hidden attributes of music. Finally, by analyzing the similarity between user preferences and implicit music features, TopN recommendations suitable for different users are obtained. Through experiments, the effectiveness of this method was verified, and the utilization efficiency of music resources was improved.
DB-LSH 2.0: Locality-Sensitive Hashing With Query-Based Dynamic Bucketing

Locality-sensitive hashing (LSH) is a promising family of methods for the high-dimensional approximate nearest neighbor (ANN) search problem due to its sub-linear query time and strong theoretical guarantee. Existing LSH methods either suffer from large index sizes and hash boundary problems, or incur a linear cost for high-quality candidate identification. This dilemma is addressed in a novel method called DB-LSH proposed in this paper. It organizes the projected spaces with multi-dimensional indexes instead of fixed-width hash buckets, which significantly reduces space costs. High-quality candidates can be generated efficiently by dynamically constructing query-based hypercubic buckets with the required widths through index-based window queries. A novel incremental search strategy called DBI-LSH is also developed to further boost the query performance, which incrementally accesses the next best point for higher accuracy and efficiency. Considering the intermediate query information of each query, DBA-LSH is designed to adaptively tune termination conditions without scarifying the success probability. Our theoretical analysis proves that DB-LSH has a smaller query cost than the existing work while DBA-LSH and DBI-LSH have lower expected query costs than DB-LSH. An extensive range of experiments on real-world data show the superiority of our approaches over the state-of-the-art methods in both efficiency and accuracy.
Binary Search-Based Fast Scheduling Algorithms for Reliability-Aware Energy-Efficient Task Graph Scheduling with Fault Tolerance

Among the available processor-level energy savings schemes, dynamic voltage and frequency scaling (DVFS) is very popular and effective due to its widespread cross-platform use in designing energy-efficient scheduling algorithms. However, rapid frequency switching by DVFS based algorithms while minimizing the energy consumptions may result transient failures in the system. To avoid such failures and their catastrophic consequences, energy-efficient scheduling algorithms with the capabilities to provide more reliable task schedules are always in demand. Therefore, this paper introduces two novel low complexity energy-efficient task scheduling algorithms for heterogeneous computing environments. We term the first algorithm as ‘binary search-based energy-efficient scheduling with reliability goal (BSESRG)’ for running parallel task graphs in heterogeneous computing systems. We show that the proposed BSESRG has the capability to reduce energy consumption, and shorten the total schedule length by meeting the reliability goals upto a certain threshold. Then, we present our second algorithm, the ‘binary search-based energy-efficient fault-tolerant scheduling with reliability goal (BSESRG-FT), which ensures meeting the reliability goals with simultaneous consideration of fault tolerance. The proposed BSESRG-FT is able to reach higher reliability goals, reduce energy consumption, and shorten the total schedule length of a parallel task graph on heterogeneous platforms. We demonstrate the working of both BSESRG and BSESRG-FT through simulation experiments considering real-world task graphs, and show the supremacy of the two proposed algorithms over their respective peers (viz., ESRG and EFSRG) in terms of energy savings, schedule lengths, run times and reliability goals. The superiority of the proposed BSESRG and BSESRG-FT over their respective competitors are also validated on the real benchmark MiBench. Moreover, from the complexity analysis, we respectively find the time complexities of BSESRG and BSESRG-FT as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O\mathbf {(|\mathcal {X}|\times |P| \times log_{2}|F|)}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O\mathbf {(|\mathcal {X}|\times |P|^{2}\times log_{2}|F|)}$</tex-math></inline-formula> confirming their better computational efficiency than the respective peers.
Unpaired Artistic Portrait Style Transfer via Asymmetric Double-Stream GAN

With the development of image style transfer technologies, portrait style transfer has attracted growing attention in this research community. In this article, we present an asymmetric double-stream generative adversarial network (ADS-GAN) to solve the problems that caused by cartoonization and other style transfer techniques when they are applied to portrait photos, such as facial deformation, contours missing, and stiff lines. By observing the characteristics between source and target images, we propose an edge contour retention (ECR) regularized loss to constrain the local and global contours of generated portrait images to avoid the portrait deformation. In addition, a content-style feature fusion module is introduced for further learning of the target image style, which uses a style attention mechanism to integrate features and embeds style features into content features of portrait photos according to the attention weights. Finally, a guided filter is introduced in content encoder to smooth the textures and specific details of source image, thereby eliminating its negative impact on style transfer. We conducted overall unified optimization training on all components and got an ADS-GAN for unpaired artistic portrait style transfer. Qualitative comparisons and quantitative analyses demonstrate that the proposed method generates superior results than benchmark work in preserving the overall structure and contours of portrait; ablation and parameter study demonstrate the effectiveness of each component in our framework.
Research on the Real-time Vehicle Detection Technology Based on Bic-YOLOv5s

In order to improve the precision and real time performance of real time vehicle detection, a method of detecting BiC-YOLOv5s based on YOLOv5s is presented in this paper. In the YOLOv5s network, the attention mechanism CBAM is inserted in front of the detecting layer to improve detection precision, Then the PANet pyramid network in YOLOv5s is reduced to a Bifpn network to reduce the extra computational complexity resulting from adding a CBAM module. In this paper, we extend the experiment data, and compare the improved YOLOv5s model with SSD, YOLOv4 and Faster-RCNN. Experiments on the vehicle data set indicate that YOLOv5s have an average precision (mAP) of 85.8%, a detection speed of 32.64FPS, and an average precision (mAP) of the modified BiC-YOLOv5s is 88.1%, and a detection speed of 30.57FPS. Compared with YOLOv5s, the overall performance of BiC-YOLOv5s is superior to that of SSD, YOLOv4 and Faster-RCNN. It also meets the request of detecting precision and real time.
Multi-Time Scale Optimal Dispatch for the Wind Power Integrated System With Demand Response of Data Centers Based on Neural Network-Based Model Predictive Control

Data centers (DCs) are energy consumers with high electricity demand. Due to their Spatio-temporal demand response (DR) capabilities, DCs are crucial DR participants. In view of the difference in real-time requirements and continuity requirements of computing jobs, this work builds a detailed DR model of DCs. To make full use of the latest wind power predictive information, a multi-time scale optimal dispatch based on model predictive control (MPC) is proposed for wind power accommodation improvement and system operating security enhancement. As the real-time optimal dispatch is a quadratic programming problem and the total number of dispatching periods is large, the BP neural network is applied in this work to improve the computation speed. Finally, the proposed model is tested on an IEEE 30-bus power system with wind farms and DCs. Simulation results verify that DCs’ DR participation plays an important role in promoting wind power accommodation and system load adjustment. Besides, it is proven that our proposed BP neural network-based MPC method can obtain optimal dispatching results with low computation costs.
Text to Image Synthesis Using DALL-E

With the help of a stable diffusion model v1-4, this project attempts to create a text-to-image generating system.The suggested method builds on the fundamentals of Noise Contrastive Estimation (NCE) and Score Matching to iteratively produce high-quality images from textual descriptions.To prepare the data for diffusion modeling, a sizable dataset of text-image pairs must be gathered and preprocessed throughout the training phase.Because of its carefully thought-out architecture and loss function, the model can gradually introduce noise to the input data, improving its expressiveness and producing realistic visuals.Hyperparameter adjustment and evaluation on various datasets are done iteratively during the system's refinement phase in order to improve performance.The effective implementation of this text-to-image creation system creates opportunities for applications in many fields, including art, production of design and content.
AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.
BERT-PAGG: a Chinese relationship extraction model fusing PAGG and entity location information

Relationship extraction is one of the important tasks of constructing knowledge graph. In recent years, many scholars have introduced external information other than entities into relationship extraction models, which perform better than traditional relationship extraction methods. However, they ignore the importance of the relative position between entities. Considering the relative position between entity pairs and the influence of sentence level information on the performance of relationship extraction model, this article proposes a BERT-PAGG relationship extraction model. The model introduces the location information of entities, and combines the local features extracted by PAGG module with the entity vector representation output by BERT. Specifically, BERT-PAGG integrates entity location information into local features through segmented convolution neural network, uses attention mechanism to capture more effective semantic features, and finally regulates the transmission of information flow through gating mechanism. Experimental results on two open Chinese relation extraction datasets show that the proposed method achieves the best results compared with other models. At the same time, ablation experiments show that PAGG module can effectively use external information, and the introduction of this module makes the Macro-F1 value of the model increase by at least 2.82%.
Structural design of architectural 3D printing equipment

Abstract Referring to the development status of building 3D printing equipment at home and abroad, this paper compares various schemes, and designs and analyzes the performance parameters of the mechanical system and various mechanisms of building 3D printing equipment. The detailed design scheme of each component is present and provides a reference for the development of 3D printing equipment for large buildings in the future.
Importance of open science and science communication practices from the perspective of stakeholders

Introduction:Composed by several movements, Open Science has been gaining considerable notoriety in the search for increasing confidence in the research results, seeking transparency in all the elements that make up a scientific investigation process. Objective:In this scenario, this article aims to analyze the importance attributed by actors involved with Open Science and its movements and initiatives. Method:A questionnaire was sent to personalities involved in the scientific process: two groups of messaging and communication applications in audio and video over the internet and a discussion list of actors interested in the topic, composed of librarians, researchers, professors and editors. Structured by 1 open question and 20 variables related to Open Science, the respondents were instructed to indicate on a 5-point Likert scale the importance of each one, observing the context of scientific communication developed at the public university. Results:With the assistance of SPSS, descriptive statistical analysis of frequency and mean were performed. In addition, a factor analysis was applied and the possibility of condensing the information into a total of four components was identified. Conclusion:Through this study, it was concluded that, in spite of alternating thefrequency, the most important mode was registered for all variables, indicating that all the initiatives and movements listed in the study were considered important by the actors involved with Open Science.
Uncensored Library Project: astrategy to circumvent censorship in countries predatory of press freedom

Introduction: The UncensoredLibrary is a virtual anti-censorship library, created in 2020 within the game Minecraft by Reporters WithoutBorders to circumvent censorship in countries where access to information is restricted.Objective: Its purposeis to discuss The Uncensored Library as a means of access to censored information in countries identified as limiting freedom of the press; examine their adequacy and the characteristics of virtual libraries and reflect on the application of content curation in their collection composition. Methodology: As a method, an exploratory study of a theoretical nature was undertaken, configured as a casestudy. Systematic observation, bibliographic and documentary research were usedfor data collection.Result:verifies it can be characterized as a virtual library with limitations. In the construction of the collection,it is noticed that a still rustic form of content curation has been applied, but that the process has the potentialto be applied in other initiativesof this kind.Conclusion: The Uncensored Library has limitations on access to hosted items, however, it achieves the purpose of sharing censored articles in countries predatory of press freedom
Development of android-based learning media on tool sterilization materials

According to the analysis of student needs for sterilization material for tissue culture courses, it shows that learning media still has a limited role in the learning process. In terms of material and visualization, the learning media used cannot be explained in detail. Students still don't understand the material and really need Android-based learning media that is more flexible and well visualized. The purpose of this study was to find out the analysis of student needs, the design of instructional media, the level of feasibility of learning media from experts and the responses of lecturers and students as well as evaluations of students. The research method used is R&amp;amp;D, ADDIE Model (Analysis, Design, Development, Implementation, and Evaluation). The results of the study were 96.70% of students needed Android-based learning media, media content designs namely: Instructions for Use, CP-MK, References, Materials, Videos, Exercises, Developer Profiles and EXIT. The feasibility level of the media is categorized as very feasible, the results of material experts are 100%, media experts are 96.5% and learning design experts are 94.5%. then the lecturer response was 83% and the student response was 92.52% included in the very decent category. then the evaluation stage obtained an n-gain score of 0.60 categorized as quite effective. The limitation of the research problem is that the material developed is limited to the topic of tool sterilization for tissue culture courses and the development used is limited to the evaluation stage, namely the media effectiveness testing stage.