Reviewerly/exHarmony huggingface dataset:
Formation of Students’ Social Competence in a Virtual Educational Environment

The formation of students’ social competence in the course of educational process in virtual educational environment represents an urgent pedagogical problem. The author proposes to present the development level of students’ social competence by introducing certain variables determined on a relative scale into a mathematical model of the educational process. The article carries out a mathematical apparatus that allows to calculate the parameters of the educational process and variables determined on quantitative and relative scales simultaneously. It is a sharp increase in the number of mathematical operations, which imposes certain limitations on hardware, that represents possible limitation to the proposed method. One can observe that the successful start of professional activity depends on the development of the social component of a student’s professional. Further research should improve the mathematical model to personalize the educational process depending on the level of social competence as part of a student’s professional competence.
Automatic Equipment Design of Intelligent Manufacturing Flexible Production Line Based on Industrial Motorized Spindle

Based on the previous research on the production line automation, this paper carries out further research and further design and development on the basis of the original production line automation equipment. In this paper, the overall design of the automatic production line is carried out, and the various systems in the automatic production line are optimized, and the backward instruments are eliminated, and then some more advanced and convenient instruments are applied. Then, the hardware and software of the automatic production line are studied respectively, and the human-computer interaction module and real-time main control circuit module are re developed, and the electric shaft is applied to the automatic production line. Finally, the fuzzy PID controller of the stepping motor is designed. The experiment shows that the fuzzy PID control scheme is better than the traditional PID control scheme. After the rationalization of the system, the quality robustness of proactive planning is improved obviously. Then, the temperature of motorized spindle was tested.
Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.
Innovation of Accounting Industry Based on Artificial Intelligence

In recent years, with the rapid development of artificial intelligence, various industries have gradually introduced artificial intelligence to replace part of the manpower. Since 2016, artificial intelligence technology has been introduced into the financial field. Big Four have successively launched financial robots. More and more basic accounting positions have been replaced by robots. The unemployment rate of accountants without certificate has risen sharply, and the accounting industry is facing new challenges. Traditional financial work is done by artificial intelligence. Compared with manpower, artificial intelligence has a lower cost and more efficient. More and more accountants need to realize the transition from financial accounting to management accounting based on their own advantages. With the rapid development of science and technology, now that the future is coming. Accountants need to improve their comprehensive capabilities to adapt to the new environment in the era of artificial intelligence.
Whose view is it anyway? Media coverage of litigation in for-profit firms’ role in the opioid crisis

The United States opioid epidemic continues to bring suffering to individuals and families as well as a crushing economic toll to communities. Stakeholders require resources to combat the epidemic. Firms that manufacture, distribute, and market opioids are the primary defendants of lawsuits, which play out in public view. This study investigates the public statements made by firms and media coverage in opioid lawsuits through the lens of their word choices. We use text analysis to uncover the similarities and differences in the language used to discuss the opioid crisis. The results provide guidance for marketing, public relations, and communications.
Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning(A-CZSL)

Most existing artificial neural networks(ANNs) fail to learn continually due to catastrophic forgetting, while humans can do the same by maintaining previous tasks' performances. Although storing all the previous data can alleviate the problem, it takes a large memory, infeasible in real-world utilization. We propose a continual zero-shot learning model(A-CZSL) that is more suitable in real-case scenarios to address the issue that can learn sequentially and distinguish classes the model has not seen during training. Further, to enhance the reliability, we develop A -CZSL for a single head continual learning setting where task identity is revealed during the training process but not during the testing. We present a hybrid network that consists of a shared VAE module to hold information of all tasks and task-specific private VAE modules for each task. The model's size grows with each task to prevent catastrophic forgetting of task-specific skills, and it includes a replay approach to preserve shared skills. We demonstrate our hybrid model outperforms the baselines and is effective on several datasets, i.e., CUB, AWA1, AWA2, and aPY. We show our method is superior in class sequentially learning with ZSL(Zero-Shot Learning) and GZSL(Generalized Zero-Shot Learning). The code url is available at the arxiv paper.
BiP-Net: Bidirectional Perspective Strategy Based Arbitrary-Shaped Text Detection Network

Detecting irregular-shaped text instances is the main challenge for text detection. Existing approaches can be roughly divided into top-down and bottom-up perspective methods. The former encodes text contours into unified units, which always fails to fit highly curved text contours. The latter represents text instances by a number of local units, where the complicated network and post-processing lead to slow detection speed. In this paper, to detect arbitrary-shaped text instances with high detection accuracy and speed simultaneously, we propose a Bidirectional Perspective strategy based Network (BiP-Net). Specifically, a new text representation strategy is proposed to represent text contours from a topdown perspective, which can fit highly curved text contours effectively. Moreover, a contour connecting (CC) algorithm is proposed to avoid the information loss of text contours by rebuilding interval contours from a bottom-up perspective. The experimental results on MSRA-TD500, CTW1500, and ICDAR2015 datasets demonstrate the superiority of BiP-Net against several state-of-the-art methods.
Fundamental study on underwater trafficability for tracked vehicle

In recent years, water disasters have increased in Japan. In water disaster, remote controlled vehicles which work for disaster recovery must run in water environment. Since underwater ground is likely to be soft, the vehicle has a risk of stuck. If a vehicle gets stuck at disaster sites, rescue work is difficult because it is not easily to access to that area. We must make a method for judging whether to run or not. For this purpose, we must quantitatively clarify the relationship between the trafficability and the strength, bearing capacity, etc. of underwater ground. We measured the cone index of underwater ground. From results, we confirmed that fragile layer was formed on the surface layer in underwater ground. We measured drawbar pull of a tracked carrier in test field. As a result, maximum drawbar pull of underwater ground was lower than on the ground. After slip occurs, drawbar pull of underwater ground was smaller than ground significantly.
Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective

This paper is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this problem is proposed. In particular, a notion termed modality "\emph{usefulness}", which takes a value of 1 or 0, is used for indicating whether the observation of this modality is useful or not. For $n$ modalities involved, $2^n$ combinations of their "\emph{usefulness}" values exist. Each combination defines one hypothetical model of the true data generative process. Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging (DMA) based particle filter (PF) algorithm. This DMA algorithm employs $2^n$ models, while all models share the same state-transition function and a unique set of particle values. That makes its computational complexity only slightly larger than a single model based PF algorithm, especially for scenarios in which $n$ is small. Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods. Code and data are available at https://github.com/robinlau1981/fusion.
Weighted Modal Logic in Epistemic and Deontic Contexts

We introduce a type of weighted modal logic with explicit weights both in the language and in the models. The framework has its applications in epistemic logic for reasoning about agents’ knowledge based on their capability, and in deontic logic for agents’ choices based on their deontic capability or utilities. We make use of weighted Kripke models with the weights understood epistemically as a similarity measure between states and deontically as a measure of expected utilities. We present sound and complete axiomatizations for the logics, and discuss variants and possible extensions.
Analysis on the Current Situation of Intelligent Informatization Construction in University Library

With the development of informatization, information resources are playing an increasingly important role in today’s society, and the construction of information resources has become very urgent and necessary. University library information resources are an important part of information resources, and it is also very necessary to study the current situation of university library information construction. This article first studies the current domestic and foreign research on the information construction of university libraries, then analyzes the problems existing in the information resource construction of my country’s university libraries, and proposes improvement measures. Finally, the annual funding of four university libraries in China Contrast and analyze the evaluation of the library environment by college students. The results show that our country can make more reasonable allocations for the annual funding of university libraries, and there is still room for improvement in the environment of university libraries.
An Efficient and Balanced Graph Partition Algorithm for the Subgraph-Centric Programming Model on Large-scale Power-law Graphs

Nowadays, the parallel processing of power-law graphs is one of the biggest challenges in the field of graph computation. The subgraph-centric programming model is a promising approach and has been applied in many state-of-the-art distributed graph computing frameworks. The graph partition algorithm plays an important role in the overall performance of subgraph-centric frameworks. However, traditional graph partition algorithms have significant difficulties in processing large-scale power-law graphs. The major problem is the communication bottleneck found in many subgraph-centric frameworks. Detailed analysis indicates that the communication bottleneck is caused by the huge communication volume or the extreme message imbalance among partitioned subgraphs. The traditional partition algorithms do not consider both factors at the same time, especially on power-law graphs. In this paper, we propose a novel efficient and balanced vertex-cut graph partition algorithm (EBV) which grants appropriate weights to the overall communication cost and communication balance. We observe that the number of replicated vertices and the balance of edge and vertex assignment have a great influence on communication patterns of distributed subgraph-centric frameworks, which further affect the overall performance. Based on this insight, We design an evaluation function that quantifies the proportion of replicated vertices and the balance of edges and vertices assignments as important parameters. Besides, we sort the order of edge processing by the sum of end-vertices' degrees from small to large. Experiments show that EBV reduces replication factor and communication by at least 21.8% and 23.7% respectively than other self-based partition algorithms. When deployed in the subgraph-centric framework, it reduces the running time on power-law graphs by an average of 16.8% compared with the state-of-the-art partition algorithm. Our results indicate that EBV has a great potential in improving the performance of subgraph-centric frameworks for the parallel large-scale power-law graph processing.
Automated optimum visualization system for construction drawing reading

The task of reading drawings on construction sites has significant efficiency and cost problems. Recent products utilising laser projectors attempt to address the issue of drawing comprehension by projecting full scale versions of the drawings onto 3D surfaces, giving an in-place representation of the steps required to complete a task. However, they only allow projection in red or green at a single brightness level due to the inherent constraints of using a laser-based system, which could cause problems depending on the surface to be projected on and the ambient conditions. Thus, there is a need for a solution that is able to adjust the visualisation parameters of the displayed information based on the surface being projected onto. This study presents a system that automatically changes the visualisation parameters based on the colour and texture of the current surface to make drawings visible under any planar-like surfaces. The proposed system consists of software and hardware, and the software algorithm contains of two parts 1) the optimisation run that computes and updates the visualisation parameters and 2) the detection loop which runs continually and checks if the optimisation run needs to be triggered or not. In order to verify the proposed system, tests on 8 subjects with 4 background surfaces commonly found on site were performed. The test subjects were timed to find 10 bolt holes projected onto the surface using the optimisation system, which was then compared to a control case of black lines projected onto a white background. The system allowed users to complete the task on the real-world backgrounds in the same time as the control case, with the system resulting in up to a 600% decrease in recognition time on some backgrounds.
Bibliographic products as a part of the content of official library websites: achievements and challenges

The problem of creating bibliographic products in electronic format is considered. The purpose of the article is to analyze local history bibliographic products as part of the content of the central libraries sites of the Russian Federation subjects.It is established that the creation of bibliographic products in electronic format is empirical in nature. Most often, libraries create bibliographic databases and electronic guidebooks. Identification barriers that make it difficult for users to identify and use bibliographic products on library websites have been revealed.
A new Query Reformulation Approach using Web Result Clustering and User Profile

Query reformulation techniques are essential for information retrieval systems. These techniques eliminate the bad queries (short and ambiguous queries) and minimize users search time. There are two kinds of techniques in literature; the first uses the query logs with the click-through data to identify relevant queries and propose them to the user. The second group uses relevant terms extracted from different sources such as Wikipedia, Wordnet, and pseudo-relevant documents to expand the initial query and increase the likelihood with relevant documents. Both groups generate false queries because they do not consider the user interests and due to the assumption that the user clicks only on relevant results (first group) and that the top-k retrieved documents (top 5 to 15) are relevant (second group).In this paper, we propose a novel approach to reformulate the user queries using his profile (which contains the user interests). The approach retrieves documents related to the query from four different data sources (≈ 1000 doc). Then, it extracts the topics of these documents using the Lingo clustering algorithm (cluster the document with the same topic) and Text razor API (extract the potential topics of each cluster). Finally, it generates queries based on these topics and the user profile. The results show that the proposed approach outperforms the existing solution in [email protected], [email protected], and MAP.
Surfacing Challenges in Scrum for Women in Tech

Using two analytical dimensions and including team and individual experiences to study processes, we developed a framework to surface gender issues in Scrum. We share what works and doesn’t work for women in Scrum and where improvements can be made.
A robust way of steganography by using blocks of an image in spatial domain

Steganography is a way to convey secret communication, with rapid electronic communication and high demand of using the internet, steganography has become a wide field of research and discussion. In this paper a new approach for hiding information in cover image proposed in spatial domain, the proposed approach divides the host image into blocks of size (8x8) pixels and message bits are embeds into the pixels of a cover image. The 64-pixel values of each block converted to be represented in binary system and compared with corresponding secret data bits for finding the matching and hold 6-pixels. The search process performed by comparing each secret data bit (8-bits) with created binary plane at the cover image, if matching is found the last row of the created binary plane which is (LSB) is modified to indicate the location of the matched bits sequence “which is the secret data” and number of the row, if matching is not found in all 7th rows the secret sequence is copied in to the corresponding 8th row location.The payload of this technique is 6 pixels’ message (48-bits) in each block. In the experiments secret messages are randomly embedded into different images. The quality of the stego-image from which the original text message is extracted is not affected at all. For validation of the presented mechanism, the capacity, the circuit complexity, and the measurement of distortion against steganalysis is evaluated using the peak-signal-to-noise ratio (PSNR) are analyzed.
Image De-Speckling Based on the Coefficient of Variation, Improved Guided Filter, and Fast Bilateral Filter

Images are widely used in engineering. Unfortunately, medical ultrasound images and synthetic aperture radar (SAR) images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, first, an optimized adaptive Wiener filter (OAWF) is proposed. OAWF can be applied to the input image without the need for logarithmic transform. In addition its performance is improved. Next, the coefficient of variation (CV) is computed from the input image. With the help of CV, the guided filter converts to an improved guided filter (IGF). Next, the improved guided filter is applied on the image. Subsequently, the fast bilateral filter is applied on the image. The proposed filter has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.
A Meta Learning-Based Approach for Zero-Shot Co-Training

The lack of labeled data is one of the main obstacles to the application of machine learning algorithms in a variety of domains.Semi-supervised learning, where additional samples are automatically labeled, is a common and cost-effective approach to address this challenge.A popular semi-supervised labeling approach is co-training, where two views of the data -achieved by the training of two learning models on different feature subsets -iteratively provide each other with additional newly-labeled samples.Despite being effective in many cases, existing co-training algorithms often suffer from low labeling accuracy and a heuristic sample-selection strategy that hurt their performance.We propose Co-training using Meta-learning (CoMet), a novel approach that addresses many of the shortcomings of existing cotraining methods.Instead of employing a greedy labeling approach of individual samples, CoMet evaluates batches of samples and is thus able to select samples that complement each other.Additionally, our approach employs a meta-learning approach that enables it to leverage insights from previously-evaluated datasets and apply these insights to other datasets.Extensive evaluation on 35 datasets shows CoMet significantly outperforms other leading co-training approaches, particularly when the amount of available labeled data is very small.Moreover, our analysis shows that CoMet's labeling accuracy and consistency of performance are also superior to those of existing approaches.
Serendipity in Library Retrieval: Reinforcing Discovery through Visualization of Bibliographic Relationships

This work starts with a background study of serendipity, its meaning in the context of information discovery, its interrelation with bibliographic relationships, and the technical possibilities for practical implementation of a relationships based visual navigation in a library discovery interface to achieve serendipitous resource discovery. The methodology for developing the prototype consists of two components – theoretical base and practical steps. The theoretical framework includes an indepth study of the concept of bibliographic relationships as proposed by experts, as reflected in bibliographic data models and as included in bibliographic formats and metadata schemas. The practical sides deal with the application of the theoretical framework in designing a prototype that, in addition to other typical retrieval features, supports visual navigational facility driven by bibliographic relationships. The components, tools and standards of the entire software architecture are all open source and open standards.