Reviewerly/exHarmony huggingface dataset:
Crime Prediction with Quantitative Information

Aiming at the poor prediction effect in the crime prediction task, a crime prediction model incorporating quantitative information is proposed. According to the numerical basis of judicial sentencing, quantify the values in the text, use the self-attention mechanism and maximum pooling to highlight the weight of quantitative information and keywords, then use the bidirectional long and short memory cycle network to extract the joint features between quantitative information and keywords, and use the capsule network to extract the depth features; Due to the imbalance of data, a priori weight is used in the loss function to balance the data. Experiments show that this method has better prediction effect than other models.
A Deep Learning-Based Entity-Relationship Extraction Method in the Field of Electric Power Public Opinion

Entity-relationship extraction can obtain key information elements from texts. Electricity opinion texts have the characteristics of complex entity relationships and less annotated data, so it is difficult to find entity information with mutual relationships from text data in the field of electricity opinion. To solve the above problems, we propose the ABAC (ALBERT-BiLSTM-ATT-CRF) model to extract entity relationships in electric power opinion texts. By using the pre-training model of ALBERT and combining the five-stroke sequence, radicals and pinyin of Chinese characters to extract features, and the features of these parts are fused to enhance the ability of extracting text feature vectors. The experimental results show that the accuracy of entity-relationship extraction has been significantly improved, which verifies the effectiveness of the model designed in this paper for entity-relationship extraction in the field of electric power public opinion.
Observations of the oil-polluted soil of Absheron Peninsula using Landsat 8 OLI and Sentinel 2A imagery

The Absheron Peninsula is the biggest urbanized area in Azerbaijan. Along with the growth of the massive oil production, the role of the Peninsula has increased and big ecological problems have arisen. In this research, the investigation of the possibility of detecting hydrocarbons in sandy soil through Landsat 8 OLI and Sentinel 2 A satellite and drone images and chemical analysis was conducted. The main study was based on the satellite imagery of Landsat 8 OLI and Sentinel 2A, employing NDVI calculations and analyses. In order to calculate NDVI, ESRI ArcGIS 10.3 software has been used. The multispectral images with 30m spatial resolution of Landsat 8 and 10 m resolution multispectral images of Sentinel 2 were used. Additionally, drone observations lead to obtaining high-resolution data about soil pollution in the study area. Also, field samples were taken to the laboratory and necessary chemical analysis was performed for validation purposes. This study showed that multispectral remote sensing can be used to detect hydrocarbons in the soil in oil production areas. Hydrocarbon-bearing substances’ absorption into the soil results in a low value of NDVI in the study area. The observations in the winter and summer seasons show that the seasonal changes in weather conditions affect both the amount of oil contamination in the soil and the detection process of soil pollution by oil using remote sensing.
The Artist’s Website. Discourse Features of Online Identity and Brand

"The Artist’s Website. Discourse Features of Online Identity and Brand. The present study aims to reveal those discourse particularities displayed by the artist’s home page/website (used interchangeably for our current purposes ) as a device to create an artist’s online identity. Specific methods (and steps) throughout the process of creating an online identity overlap with the advertising strategies of branding. Our interest concerns the discourse strategies involved in the two activities in the context of online communication. The particularities of the artist’s home page have been studied from a genre analysis perspective. The aim is to demonstrate that the genre features of the home page (as a cyber genre) determine or at least influence the site’s content. The present research also aims to determine how the content is negotiated within the website’s structure to serve the communicative goals of establishing an online identity and an artist’s brand. The findings reveal several discourse strategies at play to reach the final purpose and that they belong to advertising and e-commerce, on the one hand, and the specialized field of the visual arts, on the other. Keywords. Internet communication, WWW, digital genres, discourse features, artist identity and branding "
Unscented Kalman filter of graph signals

We consider the nonlinear filtering problem of graph signals, where the measurements are generated based on graph topology. We propose a graph-based unscented Kalman filter (UKF) by using the decomposition of graph Laplacian matrix in the design of Kalman gain matrix. We demonstrate that the graph-based UKF reduces to the UKF for the graph Fourier transform of signals with a diagonal Kalman gain matrix, so that each vertex signal can be updated independently and more accurate results can be derived to reduce accumulation errors. Simulation results are provided to verify the effectiveness of the proposed filter.
Learning-based fringe analysis with uncertainty quantification

Recently, the deep learning technology has been successfully applied to many applications of optical metrology, e.g., fringe-pattern analysis, fringe denoising, digital holography, and three-dimensional shape measurements. However, deep neural networks (DNNs) cannot always produce a provably correct solution, and the prediction error cannot be easily detected and evaluated unless the ground truth is available. This issue is critical for optical metrology, as the reliability and repeatability of the measurement are of major importance for high-stakes scenarios. As most deep neural networks are driven by data completely and work without considering any physical principles, how to believe the prediction of the DNN in optical metrology is a big challenge. Inspired by recent successful Bayesian deep learning approaches, we demonstrate that a Bayesian convolutional neural network (BNN) can be trained to not only retrieve the phase from a single fringe pattern but also produce uncertainty maps depicting the pixel-wise confidence measure of the estimated phase. Experimental results show that the proposed BNN can quantify the reliability of phase predictions under conditions of various training dataset sizes and never-before-experienced inputs. We believe that a DNN that can provide confidence measure of the estimated phase is crucial to fringe-pattern analysis and it has great potentials for inspiring novel and reliable learning-based optical metrology approaches.
Optimizing Execution Time and Costs of Cross-Silo Federated Learning Applications with Datasets on different Cloud Providers

Under the coordination of a central server, Federate Learning (FL) enables a set of clients to collaboratively train a global machine learning model without exchanging their local data. When such clients have powerful machines, it is called cross-silo FL, and they store their data in private repositories denoted silos. We are interested in this paper in cross-silo FL where silos are geographically located in different regions of multi-cloud providers. Thus, aiming at minimizing financial costs and execution times of a cross-silo FL application, we propose a model based on a scheduling problem mathematical formulation, which receives as input both the application parameters and the cloud providers' resource features where clients' data are stored and renders the best assignment of clients and server to virtual machines. This formulation is part of a framework proposal to execute FL applications in different cloud providers. Taking as a use case a Tumor-Infiltrating Lymphocytes Classification problem, an FL application whose clients' datasets spread over different cloud providers' data repositories, evaluation results show that our model is scalable and improves the execution time and financial costs of the FL application by up to 53.70% and 48.34% in a scenario with 50 clients, executing in around 200 seconds, when compared to results where VMs are randomly selected. Experimental results with client silos in different Google (GCP) and Amazon (AWS) cloud regions also confirmed the effectiveness of our proposed model in a real multi-cloud environment.
Research on the Reform and Construction of Talents Training Mode Based on Modern Apprenticeship

Modern apprenticeship is a talent training mode in which schools cooperate with enterprises to strengthen practical teaching with teachers. Promoting modern apprenticeship is the inevitable requirement of social and economic development, and the conclusion of the reform and development of vocational education talent training mode. The practice of our school in exploring modern apprenticeship system shows that the implementation of modern apprenticeship in higher vocational education should solve the problems of education mode, student identity, curriculum system, tutor mechanism and evaluation method from five aspects: building a "dual subject" talent training mode, solving the problem of "dual identity" apprenticeship, forming a "dual system" school enterprise curriculum structure, building a "dual tutor" full-time and part-time teacher team, and establishing a "dual standard" evaluation system.
Scoping Review for Establishing an Effective Lifestyle Intervention Education Model

Abstract Background In order to face a socially healthy aging society, research on successful aging and lifestyles is being actively conducted. However, there are many studies reporting the positive effects of lifestyle interventions, but studies on educational models that ultimately train experts who perform lifestyle interventions are insufficient. Therefore, this study was conducted to prepare basic data for establishing an effective lifestyle intervention expert training model, suitable for the future domestic situation by analyzing lifestyle intervention expert training programs in progress at home and abroad. Method This study was conducted according to the five research stage of the scoping review suggested by Arskey and O'Malley. Through literature search, only programs that can clearly identify the educational purpose, target, method, and process were selected from among the lifestyle intervention specialist education programs conducted by 28 organizations that have established ties with the Lifestyle Medicine Global Alliance (LMGA). Result As a result of the study, six programs were selected. By country, the United States had the highest number of four, which had two cases of qualifications for education limited to medical, health, and health workers and other two cases of anyone interested could participate without any special restrictions. It was confirmed that 5 programs had an online‐based untacted education delivery method, and most of the educational contents were focused on physical activity, diet, mental health (sleep and stress management), and drug management. Conclusion This study confirmed the need to develop a new lifestyle intervention specialist education program. Therefore, based on the results of this study, it is considered that it is necessary to develop a new professional curriculum for multilateral lifestyle interventions with contents that can develop activity participation and digital literacy competency in consideration of the future domestic situation.
Covers and Embeddings

To specify a discrete quantum walk on a graph, we need more than just the graph. In general we need some kind of ordering on the edges on each vertex, and this extra structure is closely related to machinery used to describe embeddings of graph in surfaces. in this chapter we explain this connection.
vieCap4H Challenge 2021: Vietnamese Image Captioning for Healthcare Domain using Swin Transformer and Attention-based LSTM

This study presents our approach on the automatic Vietnamese image captioning for healthcare domain in text processing tasks of Vietnamese Language and Speech Processing (VLSP) Challenge 2021, as shown in Figure~\ref{fig:example}. In recent years, image captioning often employs a convolutional neural network-based architecture as an encoder and a long short-term memory (LSTM) as a decoder to generate sentences. These models perform remarkably well in different datasets. Our proposed model also has an encoder and a decoder, but we instead use a Swin Transformer in the encoder, and a LSTM combined with an attention module in the decoder. The study presents our training experiments and techniques used during the competition. Our model achieves a BLEU4 score of 0.293 on the vietCap4H dataset, and the score is ranked the 3$^{rd}$ place on the private leaderboard. Our code can be found at \url{https://git.io/JDdJm}.
Squeezing Accumulators in Binary Neural Networks for Extremely Resource-Constrained Applications

The cost and power consumption of BNN (Binarized Neural Network) hardware is dominated by additions. In particular, accumulators account for a large fraction of hardware overhead, which could be effectively reduced by using reduced-width accumulators. However, it is not straightforward to find the optimal accumulator width due to the complex interplay between width, scale, and the effect of training. In this paper we present algorithmic and hardware-level methods to find the optimal accumulator size for BNN hardware with minimal impact on the quality of result. First, we present partial sum scaling, a top-down approach to minimize the BNN accumulator size based on advanced quantization techniques. We also present an efficient, zero-overhead hardware design for partial sum scaling. Second, we evaluate a bottom-up approach that is to use saturating accumulator, which is more robust against overflows. Our experimental results using CIFAR-10 dataset demonstrate that our partial sum scaling along with our optimized accumulator architecture can reduce the area and power consumption of datapath by 15.50% and 27.03%, respectively, with little impact on inference performance (less than 2%), compared to using 16-bit accumulator.
On Minimizing the Read Latency of Flash Memory to Preserve Inter-Tree Locality in Random Forest

Many prior research works have been widely discussed how to bring machine learning algorithms to embedded systems. Because of resource constraints, embedded platforms for machine learning applications play the role of a predictor. That is, an inference model will be constructed on a personal computer or a server platform, and then integrated into embedded systems for just-in-time inference. With the consideration of the limited main memory space in embedded systems, an important problem for embedded machine learning systems is how to efficiently move inference model between the main memory and a secondary storage (e.g., flash memory). For tackling this problem, we need to consider how to preserve the locality inside the inference model during model construction. Therefore, we have proposed a solution, namely locality-aware random forest (LaRF), to preserve the inter-locality of all decision trees within a random forest model during the model construction process. Owing to the locality preservation, LaRF can improve the read latency by 81.5% at least, compared to the original random forest library.
Recent RL Algorithms

Reinforcement learning has taken some of its biggest strides in the past few years, with innovative algorithms paving the way for reinforcement learning to beat human opponents at many games and creating new performance benchmarks. This chapter describes the recent advances in reinforcement learning algorithm development, explaining the underlying theory and elucidating it with additional examples. In doing so, it provides valuable insights into the theory and implementation of these algorithms and a window into using these algorithms in practical problems.
Sistem Prediksi Keuntungan Influencer Pengguna E-Commerce Shopee Affiliates menggunakan Metode Naïve Bayes

Shopee Affiliate is one of Shopee's e-commerce programs to make it easier to market products. However, with the popularity of this program, there are still many people who do not know the advantages of this program. As a result, in this e-commerce, not all sellers benefit (loss) from the products sold. In order to avoid the problem of losses on marketed products, this study aims to produce a profit prediction system for shoppe affiliate e-commerce users. To build the system, this research uses the waterfall method which is used to complete the prediction system. The first stage is to collect data from social media and references related to the prediction system, then design a prediction system, after carrying out the process of system creation and implementation and testing. The test uses blackbox to test the system and accuracy test to determine the level of accuracy of this system. The result of this prediction system is to gain knowledge in the form of profit rate patterns of influencers of shopee affiliate e-commerce users. Testing the accuracy of the system built has a very good performance with a percentage of 100%. So that the profit prediction of shopee affiliate e-commerce users is feasible to be implemented. With this system, it is hoped that the community will be able to increase sales at e-commerce shopee.
DEVELOPING INTEREST IN MATHEMATICS THROUGH INCLUDING A REGIONAL COMPONENT OUT OF LESSON WORK

The article is devoted to the activation of interest in mathematics among primary school students. The article shows that the development of interest in mathematics among younger schoolchildren can be carried out by composing text tasks of a regional nature in extracurricular classes. The authors give examples of the organization of such work.
PSYCHO-ACOUSTIC ASSESSMENT OF VIOLINS WITH DIFFERENT ANATOMICAL FEATURES OF WOOD

In this paper, the manufacturing technologies of violins, followed by a study on the psycho-acoustic evaluation of violins with different anatomic structure of wood are presented. The evaluation was performed by music experts (performers, teachers, students) based on music auditions of excerpts recorded on the violins studied. The musical performance was performed by the same violinist, under the same conditions. Respondents rated five acoustic criteria. The statistic results showed that age, experience, gender influence the acoustic perception and also that the geometric characteristics of the violins produce different acoustic impressions.
Hasil Keputusan Nilai Preferensi Metode Simple Additive Weighting Terhadap Rekomendasi Wisata Sejarah di Kepulauan Seribu

&#x0D; &#x0D; &#x0D; One of the great potentials that can improve the economy of local communities is development in the tourism sector. Kepulauan Seribu is an archipelago area in the north of Jakarta, this area has tourism potential in the form of a cluster of islands. This group of islands has different characteristics to be used as a tourist attraction, including marine tourism, historical tourism and nature reserve tourism (conservation). Historical tourism in the Kepulauan Seribu has its own charm judging from the historical relics of the Netherlands before Indonesian independence. Islands that have historical tourism potential are the reason for conducting research related to the selection of tourist destinations in the Kepulauan Seribu, such as Kelor, Onrust and Cipir island. The obstacles of the community in finding the right tourism potential are still lacking information from the aspects of attractions, accessibility and facilities. To assist tourists in choosing a place, a decision support system is needed that can be completed using the Simple Additive Weighting method. There are five assessment criteria including scenery, photo spots, transportation, toilets and places to eat/resto. Determination of weights on criteria using a statistical approach from the questionnaire results shared with a likert scale. The result of the preferred tourist recommendation preference value is the island of Kelor because there are seven highest scores obtained by the island compared to Onrust island and Cipir island.&#x0D; &#x0D; &#x0D;
Attemps of scientific reflection on the role of e-learning of the future in the area of digital transformation: nеw opportunities and experiences with DevSecOps

The introduction of such an important component as e-learning into the education system is an important tool for the informatization of education problem implementation and requires certain knowledge. It is also necessary to form the conditions for mastering the latest pedagogical approaches and methods that have acquired new forms or appeared under the influence of the introduction of information technologies into educational activities. The use of the latest technologies in educational activities is one of the key, as it increases the opportunities for the development of creative potential and helps in mastering professional knowledge, which is based on the awareness of one's own educational and developmental goals. The purpose of the research is to analyze the specific features of e-learning development in the period of digital transformation and to figure out the new opportunities of DecSecOps. The main methods used in the work were: comparative method, method of abstraction, deductive method, concretization. The results states that the formation of electronic education in Belarus is connected with the opportunities provided by information and communication technologies, and falls on the first decade of the XXI century, when the understanding and implementation of this educational practice at the institutional and regulatory levels begins. Belarus is gradually joining the system of distance education, is involved in cooperation with international open universities on the introduction of e-learning, and is also forming its own educational electronic environment. The conclusion is oriented on the realization that in the time of digitalization and technological transformation, in the countries worldwide can be noted the tendency in the development of DevSecOps.
Information sharing through digitalisation in decentralised supply chains

This study investigates the impact of private information on decision making process and how emerging technologies can facilitate information sharing and reduce misinformation in decentralised settings. Focusing on business environments, we examine if information sharing between distinct partners can be a mutually beneficial option. In principle, information affects the preferences and the actions of decision makers and usually contributes to inefficiencies for the entire system. A supply chain with two rational firms is considered; the firms have conflicting objectives and possess information that cannot be verified. Real-time communication through a cloud platform is allowed, before the firms finalise their strategies. During the communication phase, both firms are free to report whatever information optimises their individual objectives, even fake. Misinformation seems a plausible option, especially in competitive environments, since the firms may take advantages from such behaviour. We demonstrate that sharing the actual information can be beneficial for both, under the implementation of an appropriate mechanism that considers the welfare of the entire chain. Despite the individualistic behaviour of independent decision makers, it is doable to eliminate entirely information asymmetry and misinformation. This happens by including sufficient incentives on a mechanism that induce firms to reveal their information, because it is in their self-interest to do so. The value of information and the expected benefits of the voluntary information sharing are calculated, indicating the potential improvement.