Reviewerly/exHarmony huggingface dataset:
PREDIKSI PRODUKSI ROTI COKLAT BERDASARKAN DATA TRENDS GOOGLE MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING

To win the competition in the market, it requires the ability of a professional company manager to be able to meet market demand in an appropriate amount by paying attention to the inventory of goods so that they can get maximum profit. But the production process that occurs at Rotte Bakery is erratic every day which only relies on demand estimates, causing shortages and advantages in each production. Therefore, predictions are needed to make the right decisions in predicting the amount of production that will be produced in the following year. The data used are historical data on brown bread sales at Rotte Bakery for the period 2018-2020 and search data for brown bread in Riau for the period 2018-2020 (Google Trend). The results obtained from this study are the results of the analysis of the exponential method to obtain predictive information and the level of accuracy with MAPE to find the smallest error
GENERAL CHARACTERISTICS OF CRIMES AGAINST JUSTICE

This article attempts to analyze the field of combating crime in the Republic of Uzbekistan recent years, in particular, one of the crimes against justice - interference in the investigation or resolution of court cases. One of the main objectives of the article is to investigate the social necessity of crime, the continuous improvement of the legal framework of the industry, the elimination of legal problems that may arise in the fight against these crimes. At the same time, constructive signs of a crime are defined as a legal direction of study. Objective and subjective aspects of criminal relations arising as a result of interference in the investigation or resolution of court cases, which is one of the criminal acts arising from the relations of justice, are highlighted. The main content of the study is the examination of the development of legislation in the settlement of situations arising in the relationship in this area. However, along with the forms of intervention and the activities of officials, scientific views were also analyzed. In addition, relevant conclusions were drawn for the further development of the rulemaking process in this area.
Learning to Infer Product Attribute Values From Descriptive Texts and Images

Online marketplaces are able to offer a staggering array of products that no physical store can match. While this makes it more likely for customers to find what they want, in order for online providers to ensure a smooth and efficient user experience, they must maintain well-organized catalogs, which depends greatly on the availability of per-product attribute values such as color, material, brand, to name a few. Unfortunately, such information is often incomplete or even missing in practice, and therefore we have to resort to predictive models as well as other sources of information to impute missing attribute values.
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. However, the incremental learning issue of CL has rarely been studied, which brings the limitation in applying it to real-world applications. Contrastive learning identifies the samples with the negative ones from the noise distribution that changes in the incremental scenarios. Therefore, only fitting the change of data without noise distribution causes bias, and directly retraining results in low efficiency. To bridge this research gap, we propose a self-supervised Incremental Contrastive Learning (ICL) framework consisting of (i) a novel Incremental InfoNCE (NCE-II) loss function by estimating the change of noise distribution for old data to guarantee no bias with respect to the retraining, (ii) a meta-optimization with deep reinforced Learning Rate Learning (LRL) mechanism which can adaptively learn the learning rate according to the status of the training processes and achieve fast convergence which is critical for incremental learning. Theoretically, the proposed ICL is equivalent to retraining, which is based on solid mathematical derivation. In practice, extensive experiments in different domains demonstrate that, without retraining a new model, ICL achieves up to 16.7x training speedup and 16.8x faster convergence with competitive results.
Bass Diminution

Abstract This chapter explains how bass lines may be elaborated through diminution, the difference between structural tones and elaborations (or diminutions), and how to recognize structural tones beneath an elaborated texture. The reader is then introduced to the idea that, unlike in Furno, most partimenti use diminution to “hide” the structural tones to a certain degree. The concept of bass diminution is important not only for further study of partimento, but also for improvisation.
A Synthetic Search Session Generator for Task-Aware Information Seeking and Retrieval

For users working on a complex search task, it is common to address different goals at various stages of the task through query iterations. While addressing these goals, users go through different task states as well. Understanding these task states latent under users' interactions is crucial in identifying users' changing intents and search behaviors to simulate and achieve real-time adaptive search recommendations and retrievals. However, the availability of sizeable real-world web search logs is scarce due to various ethical and privacy concerns, thus often challenging to develop generalizable task-aware computation models. Furthermore, session logs with task state labels are rarer. For many researchers who lack the resources to directly and at scale collect data from users and conduct a time-consuming data annotation process, this becomes a considerable bottleneck to furthering their research. Synthetic search sessions have the potential to address this gap. This paper shares a parsimonious model to simulate synthetic web search sessions with task state information, which interactive information retrieval (IIR) and search personalization studies could utilize to develop and evaluate task-based search and retrieval systems.
FEMA-CL: Fair Efficient Multi-Agent Course learning

Abstract Sociology shows that blindly pursuing the fairness of resource distribution will significantly reduce people’s enthusiasm for work, which is not conducive to the increase of total social material resources. Promoting the fairness of the social system in stages, that is, achieving fairness on the premise of a certain material basis, can not only ensure the efficiency of the total social material accumulation, but also promote the fairness of the social system. Therefore, inspired by the above, we introduced multi-stage curriculum learning into learning fair policy of multi-agent systems, and proposed a novel Fair and Effective Multi-Agent Curriculum Learning (FEMA-CL). The multi-stage course learning progressively promotes the learning fairness and efficiency of large-scale multi-agent systems through three stages: selfish stage, soft fair stage and global fair stage. Our method is easy to learn fairness and efficiency, and has carried out extensive experiments in three typical multi-agent scenarios. Compared with the current popular work, our method has superior performance.
Research on online test question recommendation technology based on multi-dimensional user dynamic portrait

Abstract This paper proposes a research method of personalized recommendation of test questions for users by fully collecting and studying the user’s characteristic data in multiple dimensions, establishing a dynamic user portrait, and mining and analyzing the characteristic information of test questions in the test question bank. Firstly, collect the user’s data, calculate the user’s similarity by analyzing the user’s behavior sequence, and use the cosine similarity algorithm to calculate the text similarity of the test questions. Then, the tag system is established to analyze the user activity, and the TF-IDF algorithm is used to calculate the user’s proficiency in different keyword classification questions. Based on the above information, a hybrid recommendation mechanism is adopted to provide users with personalized test question recommendation services.
Multi-task Joint Learning to Enhance Named Entity Recognition

Abstract Named Entity Recognition (NER) models have achieved good performance in recent years but also have some shortcomings. Existing models regard NER as a sequence labeling task for label prediction, without considering the impact of different stages in the entity recognition process on the final result. NER can be viewed as two separate subtasks: boundary detection task and type prediction task. The two subtasks can transmit information and cooperate in the process of entity recognition, so the synergy of the two subtasks is beneficial to NER. In this paper, we propose a method to split the NER task into multiple subtasks and use the information of each subtask to enhance the NER task. According to the characteristics of different subtasks, we use different feature extraction method models to extract structural information useful for this task effectively. Using the information extracted from each subtask or the final results of the subtasks, the performance of NER was enhanced through the gating network. We conducted extensive experiments on the CONLL2003 dataset, and the experimental results show that our proposed multi-task joint learning enhances the effectiveness of the named entity recognition model.
Neural Network Analysis Based on Data Parallelism

Abstract With the development of Internet technology, many image data emerge on the Internet, and deep learning algorithms can extract abstract features from these data for image classification by using multi-layer nonlinear transformation. Therefore, deep learning algorithm has become a research hotspot in image processing. Aiming at the complex semantic features of images, an efficient deep learning algorithm has essential research significance for extracting and analyzing the semantic information about images quickly and effectively. This paper mainly explores the application of deep learning in image classification. Three neural models, LeNet, VGG, and ResNet are selected to test CIFAR10, and their training duration and accuracy are compared. Secondly, this paper further explores the impact of trying three neural network models on different graphics cards on their performance, and further researches and learns the application of these models. Finally, this paper tests the data parallelism solution of PyTorch, and the results and problems of the three models in this process are analyzed.
Target Recognition Network Based on Improved Convolutional Regression Network

Abstract For refine the regression precision and speed of convolutional neural recognition network, propose improved detection and identification algorithm on the basis of deep learning algorithms, mainly aiming at improving the accuracy and speed of regression prediction of target recognition. Firstly, the acquired images are preprocessed in batches to obtain image datasets with different sizes of detection objects, that is, the enhancement of small sample images. Secondly, the one-stage target detection network SSD algorithm is improved. ResNet50 network is used to extract features, while FPN is fused with the main algorithm to ameliorate the detection network, which purpose is to improve the target detection accuracy. The test results on remote sensing datasets show that the actual accuracy was elevated to about 0.9% compared to the conventional algorithm. The speed in the actual measurement is also increased by 0.6 frame/s.
The Design of Calibration System Based on Cable Accessories

Abstract In today’s society, people can’t live without electricity. During the operation of power equipment, there may be various potential safety risks. In particular, partial discharge is easy to occur at the terminal. If people can not deal with the problem of partial discharge in time, equipment continue to live.Setting can cause security, so the electric power industry staff attaches great importance to the local discharge monitoring problem.Ultrasonic technology is better so it is gradually into the application of partial discharge detection.But the ultrasonic detection technology is to detect the intensity of ultrasound, and our traditional method is to measure the discharge of partial discharge method, Therefore, in order to obtain the relationship between ultrasonic intensity and partial discharge, the following experiments are made. After we pass the power frequency voltage withstand device, the transformer is continuously pressurized.Gain DB and discharge PC were obtained by digital partial discharge instrument and ultrasonic partial discharge instrument. After fitting, the linear relation is obtained and the quality inspection of PD detector is completed.It can make the detection effect of partial discharge detector more complete, and provide the basis for the quality inspection of the detector, This system also provides a strong support for the wide application of ultrasonic partial discharge detector.
Historically Informed HCI: Reflecting on Contemporary Technology through Anachronistic Fiction

As computing technology comes to dominate every aspect of social and political life, HCI must take greater account of History. The article considers four different historical periods impacted by division and denunciation: the European Witch Hunts, the Soviet Purges, the McCarthy Era, and the Chinese Cultural Revolution. Historians have identified patterns common to such periods including: the unity of accusation and action; condemnation as a show of virtue, and defense of the accused as collusion with enemies. These patterns are mapped to findings from social media research such as: impulsive shares are easy to make but difficult to retract; angry posts travel fastest and furthest; likes and retweets express group identity and solidarity. Anachronistic memes, tweets and selfies explore what previous eras might have looked like if contemporary technology had existed in the past. It is argued that such anachronistic fiction may be a useful method for exploring the potential impact of particular design choices.
PCCA-Model: an attention module for medical image segmentation

Convolutional neural networks have been increasingly employed in the field of medical image segmentation. Based on the idea that the human visual cortex differs in terms of the size of the receptive field and can sense the stimulus location, we propose the pyramid channel coordinate attention (PCCA) module to fuse multiscale features in the channel direction, aggregate local and global channel information, combine them with the location information in the spatial direction, and then integrate them into the existing semantic segmentation network. We conducted numerous experiments on the datasets, namely LiTS, ISIC-2018, and CX, and obtained state-of-the-art results.
Refining large knowledge bases using co-occurring information in associated KBs

To clean and correct abnormal information in domain-oriented knowledge bases (KBs) such as DBpedia automatically is one of the focuses of large KB correction. It is of paramount importance to improve the accuracy of different application systems, such as Q&amp;amp;A systems, which are based on these KBs. In this paper, a triples correction assessment (TCA) framework is proposed to repair erroneous triples in original KBs by finding co-occurring similar triples in other target KBs. TCA uses two new strategies to search for negative candidates to clean KBs. One triple matching algorithm in TCA is proposed to correct erroneous information, and similar metrics are applied to validate the revised triples. The experimental results demonstrate the effectiveness of TCA for knowledge correction with DBpedia and Wikidata datasets.
Adaptive Backdoor Attack against Deep Neural Networks

In recent years, the number of parameters of deep neural networks (DNNs) has been increasing rapidly. The training of DNNs is typically computation-intensive. As a result, many users leverage cloud computing and outsource their training procedures. Outsourcing computation results in a potential risk called backdoor attack, in which a welltrained DNN would perform abnormally on inputs with a certain trigger. Backdoor attacks can also be classified as attacks that exploit fake images. However, most backdoor attacks design a uniform trigger for all images, which can be easily detected and removed. In this paper, we propose a novel adaptive backdoor attack. We overcome this defect and design a generator to assign a unique trigger for each image depending on its texture. To achieve this goal, we use a texture complexity metric to create a special mask for each image, which forces the trigger to be embedded into the rich texture regions. The trigger is distributed in texture regions, which makes it invisible to humans. Besides the stealthiness of triggers, we limit the range of modification of backdoor models to evade detection. Experiments show that our method is efficient in multiple datasets, and traditional detectors cannot reveal the existence of a backdoor.
For the functional purpose of the third mission of the university and the implementation of new knowledge in educational programs

The importance of the "third mission" of higher educational institutions has recently become a subject of wide discussion, both in political and academic discourse. Despite the above, very little is known about what the "Third Mission of the University" actually entails and what results in it brings. The article is intended to ensure the implementation of new knowledge bounded by the "third mission" in university educational programs. The work is intended for new students in educational programs of all three levels of higher educational institutions to the urgent measures of methodological provision of knowledge implementation and is a material with the function of a "road map" for manager-strategists, economists, policy-makers, entrepreneurs, business professionals, scientists-researchers and students of all three levels, different profiles.
Comparison of Statistical Production Models for a Solar and a Wind Power Plant

Mathematical models to characterize and forecast the power production of photovoltaic and eolian plants are justified by the benefits of these sustainable energies, the increased usage in recent years, and the necessity to be integrated into the general energy system. In this paper, starting from two collections of data representing the power production hourly measured at a solar plant and a wind farm, adequate time series methods have been used to draw appropriate statistical models for their productions. The data are smoothed in both cases using moving average and continuous time series have been obtained leading to some models in good agreement with experimental data. For the solar power plant, the developed models can predict the specific power of the next day, next week, and next month, with the most accurate being the monthly model, while for wind power only a monthly model could be validated. Using the CUSUM (cumulative sum control chart) method, the analyzed data formed stationary time series with seasonality. The similar methods used for both sets of data (from the solar plant and wind farm) were analyzed and compared. When compare with other studies which propose production models starting from different measurements involving meteorological data and/or machinery characteristics, an innovative element of this paper consists in the data set on which it is based, this being the production itself. The novelty and the importance of this research reside in the simplicity and the possibility to be reproduced for other related conditions even though every new set of data (provided from other power plants) requires further investigation.
Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach

Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.
COVID-19 Tracking Applications Acceptance among General Populace: An Overview in Malaysia

The COVID-19 pandemic forced governments to implement strategies for contact tracing due to the disease’s ease of spread. The Malaysian government has sought to develop and implement a digital contact-tracking application to make it easier and faster to detect the spread; the system has become an integral part of the exit strategy from mandated lockdowns. These applications keep track of the user’s proximity with others who are in the system to inform them early on if they are at a risk of infection. The effectiveness of these applications depends on the willingness of users to install and allow the application to track their location at all times. Therefore, this research aims to identify the factors that would stimulate or slow down the adoption of contact-tracing apps.
FINITE GROUPS WITH INDEPENDENT GENERATING SETS OF ONLY TWO SIZES

Abstract A generating set S for a group G is independent if the subgroup generated by $S\setminus \{s\}$ is properly contained in G for all $s \in S.$ We describe the structure of finite groups G such that there are precisely two numbers appearing as the cardinalities of independent generating sets for G .