Reviewerly/exHarmony huggingface dataset:
Prediction method of motor magnetic field based on improved Linknet model

Purpose Under the condition of small data set, a prediction model of motor magnetic field is established based on deep learning method. This paper aims to complete the magnetic field prediction quickly and accurately. Design/methodology/approach An improved Linknet model is proposed to predict the motor magnetic field. This is a digital twin technology, which can predict the function values of other points according to the function values of typical sampling points. The results of magnetic field distribution are represented by color images. By predicting the pixels of the image, the corresponding magnetic field distribution is obtained. The model not only considers the correlation between pixels but also retains the spatial information in the original input image and can well learn the mapping relationship between motor structure and magnetic field. Findings The model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation. Originality/value Under the condition of small data set, the model can well learn the mapping relationship between motor structure and magnetic field, so as to complete the magnetic field prediction quickly and accurately. In the future, according to the characteristics of magnetic field distribution, it will lay a foundation for solving the problems of rapid optimization, real-time simulation and physical field control of electrical equipment.
Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions

In communication, a human would recognize the emotion of an interlocutor and respond with an appropriate emotion, such as empathy and comfort.Toward developing a dialogue system with such a human-like ability, we propose a method to build a dialogue corpus annotated with two kinds of emotions.We collect dialogues from Twitter and annotate each utterance with the emotion that a speaker put into the utterance (expressed emotion) and the emotion that a listener felt after listening to the utterance (experienced emotion).We built a dialogue corpus in Japanese using this method, and its statistical analysis revealed the differences between expressed and experienced emotions.We conducted experiments on recognition of the two kinds of emotions.The experimental results indicated the difficulty in recognizing experienced emotions and the effectiveness of multi-task learning of the two kinds of emotions.We hope that the constructed corpus will facilitate the study on emotion recognition in a dialogue and emotion-aware dialogue response generation.
Secure Attendance System using Raspberry Pi Face Recognition

This study aims to develop a machine-learning-based attendance management system using face recognition and Raspberry Pi. The proposed system is composed of two main subsystems. The first is a Raspberry Pi, to be installed in each class, and the second is a web application fed by data from the Raspberry Pi. To take attendance, an instructor commands a Raspberry Pi camera through a web-based subsystem. Then, the camera takes a picture of the whole class and detects faces using trained Haar Cascades. It sends back a file with the class picture and Cartesian coordinates of the detected faces. The web application parses the file, looking for the coordinates of faces. For each Region of Interest, it uses the Support Vector Machine algorithm to recognize faces based on their HOG (Histogram of Oriented Gradients) features. The recognizer uses a pre-built dataset of that particular class containing the students’ personal photos, names and ID numbers. Features of each face were extracted using HOG and trained to construct the model over a given class of students. Once every detected face is recognized, the application generates a report for the instructor showing the list of students’ names and attendance status.
Voice Reenactment with F0 and timing constraints and adversarial learning of conversions

This paper introduces voice reenactement as the task of voice conversion (VC) in which the expressivity of the source speaker is preserved during conversion while the identity of a target speaker is transferred. To do so, an original neural-VC architecture is proposed based on sequence-to-sequence voice conversion (S2S-VC) in which the speech prosody of the source speaker is preserved during conversion. First, the S2S-VC architecture is modified to synchronize the converted speech with the source speech by phonetic duration encoding; second, the decoder is conditioned on the desired sequence of F0- values and an explicit F0-loss is formulated between the F0 of the source speaker and the F0 of the converted speech. Finally, adversarial learning of conversion is integrated within the S2S-VC architecture to exploit advantages of both reconstruction of original speech with ground truth and converted speech with manipulated attributes. An experimental evaluation on the VCTK speech database shows that the speech prosody can be efficiently preserved during conversion, and that the proposed adversarial learning consistently improves the conversion and the naturalness of the reenacted speech.
Application of Fuzzy Clustering Model in the Classification of Sports Training Movements

In order to accurately analyze the movements of sports training using artificial intelligence techniques, an improved fuzzy clustering model is proposed in this study. The fuzzy C-means is used to granulate the multilabel space, and the correlation degree between different variable labels is obtained through information gain. Aiming at the problem of multilabel information classification, an appropriate membership function is selected, which is used to map all information samples and obtain the membership degree of its category. Considering the slow training efficiency of fuzzy support vector machine, the clustering method is used to optimize the fuzzy support vector machine, establish the optimal hyperplane, and complete the classification according to their respective attributes in high-dimensional space. Finally, the proposed algorithm and other algorithms are experimentally compared on the published KTH and Weizmann human behavior data sets. Experimental results show that the proposed method is effective and robust.
Research on Performance Optimization Algorithm of Resource and Environment Audit Based on Computer Technology

With the rapid development of the economy and society, the sustainable development of resources and environment has been paid more and more attention. As an important part of the national environmental supervision system, resources and environmental audits have attracted great attention from the society. In order to explore the application effect of intelligent networking technology in resource and environment audit, this study carries out monitoring sampling, information collection, and later data processing optimization in resource and environment audit projects through the reference of artificial intelligence technology and method and combined with Internet of things big data analysis, which brings new Turks to the development of natural resource and environment audit and helps to improve the efficiency of audit work. Through the application of new technology in the sampling process of environmental information, the staff use new technology to collect, analyze, and mine data and explore how the new technology can promote the efficiency of resource and environmental audit and better promote the construction of ecological civilization.
Technical perspective: DFI: The Data Flow Interface for High-Speed Networks

Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.
The Classification of Scientific Abstracts Using Text Statistical Features

Automated abstracts classification could significantly facilitate scientific literature screening. The classification of short texts could be based on their statistical properties. This research aimed to evaluate the quality of short medical abstracts classification primarily based on text statistical features. Twelve experiments with machine learning models over the sets of text features were performed on a dataset of 671 article abstracts. Each experiment was repeated 300 times to estimate the classification quality, ending up with 3600 tests total. We achieved the best result (F1 = 0.775) using a random forest machine learning model with keywords and three-dimensional Word2Vec embeddings. The classification of scientific abstracts might be implemented using straightforward and computationally inexpensive methods presented in this paper. The approach we described is expected to facilitate literature selection by researchers.
Research on combined radio frequency fingerprint identification model with limited samples

Abstract Aiming to solve the real problem of civilian aircraft identification, a novel combined radio frequency fingerprint (RFF) identification model is proposed, consisting of data analyzing processing, standard characteristic parameter database establishment, classification and optimization. In data analyzing processing step, discrimination was realized for wavelet coefficients, instantaneous phase, Hilbert huang transform energy spectrum, coefficients, time field envelope, probability density function, on basis of which, characteristic parameters were confirmed. In standard characteristic parameter database establishment step, a standard database was found through direct measurement method to avoid losing the RFF feature. In classification step, single character assortment rule and combined classifying rule were defined, with correlative concept and threshold concept. Finally, optimization for the model was realized by modifying parameters manually. Results show that, though hardware was limited and amount of samples were fewer, average identification rate is near to 69.75 percent, providing a theoretical reference for the real problem of identifying different aircrafts.
Work in progress: Self-diagnosis of digital competences and contributions to the evaluation of the performance of the Engineering professors

This article presents the important advances in quality system supervised and regulated by various instances in Peru, as well as the need-in particular-to strengthen the professor role; focusing on the need to develop the digital skills of professors. But, for this, it is necessary to establish the baselines to develop strategies in working with the professors. The objective of this study is to present the results of the application of 343 engineering professors from the Continental University to the DigCompEdu Check-In, which allow us to identify the contribution of the dimensions and criteria of this framework. At the end, a discussion of results is carried out to contrast with various investigations related to the development of Engineering professors’ digital skills.
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators

In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. Firstly, we demonstrate our model's ability to control structural discourse and semantic features of generated text in open generation evaluation. Then we experiment on the two challenging long-form text tasks of argument generation and story generation. Evaluation using automated metrics and a metric with high correlation to human evaluation, shows that our model performs competitively against existing models, while offering significantly more controls over generated text than alternative methods.
Applied educational practice and research development of ‘goals of best practice’ (GsBP): philosophical inquiries and conceptual analysis for consideration

In educational settings, the notion of ‘achieving optimal best’ in a subject matter (e.g., an elementary school student's achievement in her Year 6 Science project) is relevant for educators to consider. Optimal best, or optimal functioning, reflects the proactivity, positivity, and motivation of a person. How does a student achieve optimal best in Calculus? Our extensive research of optimal best has recognized the potency of the process of human optimization, which may operate to explain a person's successful experience and achievement of optimal best. Recently, researchers have considered another theoretical concept, which is termed as ‘goals of best practice’ (i.e., abbreviated as ‘GsBP’). Goals of best practice, in brief, are personal goals that a person may construct and set for a particular context. Specifically, however, a ‘personal goal’ may indicate and espouse a person's plan of intent to either remain on course without any desire or aspiration (i.e., ‘goal of actual best’, denoted as ‘GAB’) or, alternatively, to strive for maximization (i.e., ‘goal of optimal best’, denoted as ‘GOB’). This article is theoretical and conceptual, reflecting the use of the paradigm of philosophical psychology to advance the study of the concept of GsBP. Specifically, we contend that our conceptual analysis of GsBP, entailing both GAB and GOB may provide a logical basis and rationale for the proposition of educational implications for consideration and inquiries for continuing research development.
Quantifying relevance in learning and inference

Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Big Data promise to open new windows on complex systems such as cells, the brain or our societies. Yet, the puzzling success of Artificial Intelligence and Machine Learning shows that we still have a poor conceptual understanding of learning. These applications push statistical inference into uncharted territories where data is high-dimensional and scarce, and prior information on “true” models is scant if not totally absent. Here we review recent progress on understanding learning, based on the notion of "relevance". The relevance, as we define it here, quantifies the amount of information that a dataset or the internal representation of a learning machine contains on the generative model of the data. This allows us to define maximally informative samples, on one hand, and optimal learning machines on the other. These are ideal limits of samples and of machines, that contain the maximal amount of information about the unknown generative process, at a given resolution (or level of compression). Both ideal limits exhibit critical features in the statistical sense: Maximally informative samples are characterised by a power-law frequency distribution (statistical criticality) and optimal learning machines by an anomalously large susceptibility. The trade-off between resolution (i.e. compression) and relevance distinguishes the regime of noisy representations from that of lossy compression. These are separated by a special point characterised by Zipf's law statistics. This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance. Criticality in optimal learning machines manifests in an exponential degeneracy of energy levels, that leads to unusual thermodynamic properties. This distinctive features is consistent with the invariance of the classification under coarse graining of the output, which is a desirable property of learning machines. This theoretical framework is corroborated by empirical analysis showing i) how the concept of relevance can be useful to identify relevant variables in high-dimensional inference and ii) that widely used machine learning architectures approach reasonably well the ideal limit of optimal learning machines, within the limits of the data with which they are trained.
An Evaluation of Viewport Estimation Methods in 360-Degree Video Streaming

360-degree video is an integral part of Virtual Reality systems. However, transmission of 360 video over the network is challenging due to its large size. To reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) has been proposed. A key issue in VAS is how to estimate future user viewing directions. In this paper, we carry out an evaluation of typical viewport estimation methods for VAS. It is found that Long-Short Term Memory (LSTM)based method achieves the best trade-off between the accuracy and redundancy. Using cross-user behaviors achieve the highest accuracy at the expense of high redundancy. Meanwhile, the widely used linear regression-based method has performance comparable to that of the simple method using the last viewport position. In addition, we also found that all considered methods suffer significant degradation in performance when the prediction horizon increases beyond 1 second.
Application of a Gamified Approach to Learning in the Treatment of Problems in Software Process Improvement: Analysis and Discussion of Results

The specialized literature defines Software Process Improvement (SPI) as the fundamental approach to improving software products in software development organizations. In this context, studies report several problems and difficulties that organizations face during the implementation of improvements. Although there are studies that address the problems highlighted in improvement programs, few studies still seek to identify practices in order to mitigate the negative effects of critical factors. Thus, the objective of this work is to discuss and analyze the results obtained in an Experience Report that consisted of a dynamic application with gamification elements in an SPI context related to learning in the treatment of recurring problems or difficulties in the implementation of improvements. It is noteworthy that the dynamics can be developed in organizations (commercial and academic) regardless of the improvement model or standard desired, since it was elaborated in relation to common problems experienced and identified in the SPI implementations scenario. The analysis of the obtained data was performed using an Evaluation Framework for Gamification in Software Engineering, which provided a standard structure for the design of evaluation studies for gamification cases.
UNSUPERVISED HARMONIOUS IMAGE COMPOSITION FOR DISASTER VICTIM DETECTION

Abstract. Deep detection networks trained with a large amount of annotated data achieve high accuracy in detecting various objects, such as pedestrians, cars, lanes, etc. These models have been deployed and used in many scenarios. A disaster victim detector is very useful when searching for victims who are partially buried by debris caused by earthquake or building collapse. However, considering that larger quantities of real images with buried victims are difficult to obtain for training, a deep detector model cannot give full play to its advantages. In this paper we generate realistic images for training a victim detector. We first randomly cut out human body parts from an open source human data set and paste them into the ruins background images. Then, we propose an unsupervised generative adversarial network (GAN) to harmonize the body parts to fit the style (illumination, texture and color characteristics) of the background. These generated images are finally used to fine-tune a detection network YOLOv5. We evaluate both the AP (average precision) for IoU (Intersection over Union) 0.5 and for IoU ∈ [0.5:0.05:0.95], which are denoted as AP@0:5 and AP@[.5 : .95], respectively. The best experimental results show that the YOLOv5l pre-trained on the COCO data set performs poorly on detecting victims, and the AP@[.5 : .95] is only 19.5%. The model that uses our composite images as fine-tuning data can effectively detect victims, and increases the AP@[.5 : .95] to 33.6%. The AP@0:5 increases from 32.4% to 53.4%. Our unsupervised harmonization method further improves the results by 2.1% and 6.1%, respectively.
Validation Study on Migration Test for Plastic Food Utensils, Containers, and Packaging

Migration test of food utensils, containers, and packaging is an important test method for confirming the safety and their compliance to the standards. However, there is little report on inter-laboratory study which was performed to evaluate the entire migration test, including migration operations and quantification. An interlaboratory study was performed participating 22 laboratories using 8 types of model synthetic resin samples containing 10 substances with a wide range of Log Pow values to evaluate the accuracy of the entire migration test. As a result, most of HorRat (r) values met the target criteria (0.3<HorRat (r) ≦1.3), but many of HorRat (R) values exceed the target criteria (0.5<HorRat (R) ≦2.0). The results showed good accuracy within the laboratory. However, we found that there was a problem in accuracy between laboratories. The main reason was considered to be the difference in the preparation of migration solution between the laboratories, such as temperature and time control during the migration operation.
Stunning Doodle: A Tool for Joint Visualization and Analysis of Knowledge Graphs and Graph Embeddings

In recent years, the growing application of Knowledge Graphs to new and diverse domains has created the need to make these resources accessible and understandable by users with increasingly diverse backgrounds. Visualization techniques have been widely employed as means to facilitate the exploration and comprehension of such data sources. Moreover, the emerging use of Knowledge Graph Embeddings as input features of Machine Learning methods has given even more visibility to this kind of representation, but raising the new issue of understandability and interpretability of such embeddings. In this paper, we show how visualization techniques can be used to jointly explore and interpret both Knowledge Graphs and Graph Embeddings. We present Stunning Doodle, a tool that enriches the classical visualization of Knowledge Graphs with additional information meant to enable the visual analysis and comprehension of Graph Embeddings. The idea is to help the user figure out the logical connection between (1) the information captured by the Graph Embeddings and (2) the structure and semantics of the Knowledge Graph from which they are generated. We detail the use of Stunning Doodle in a real-world scenario and we show how it has been helpful to interpret different Graph Embeddings and to choose the most suitable with respect to a specific final goal.