Reviewerly/exHarmony huggingface dataset:
Estate libraries of Tambov Governorate in the second half of the 19th – early 20th century

We analyze the formation of library collections estates of Tambov Governorate in the second half of the 19th – early 20th century. The relevance of the chosen problem is determined by the importance of updating knowledge about the library and estate culture of the Russian province. The novelty of the work lies in the formation of new conclusions about the problem of development and fate of libraries of individual owners, the importance of manor collections for the information environment of the post-reform period, the introduction of new data on the book collections of Tambov owners. It is shown that due to the lack of reliable information about the pre-revolutionary collections of individual owners verification of the appearance history of individual publications in the estates occurs on fragmentary data. Therefore, the most promising form of book culture study is the work on the systematization of the region collections, the creation of new and additions to existing collections of Tambov books. We study book collection of Karaulskoye estate of Chicherins, the Pavlovskoye manor of Volkonsky, analyze the literature composition of the library of Glazkovo estate of Mansurov family, book collection of Sosnovskoye estate of Benckendorff, reveal the joint books library of Leonid Vasilievich Voeikov and Dmitriy Vasilievich Polenov in Naryshkinskaya library of the city of Tambov. It is proved that in the second half of the 19th – early 20th century in the governorate was created a number of interesting books collections reflecting the unique documents on the history of Russia and foreign countries, the unique literature of foreign publishers. On the basis of a number of noble book collections were created public libraries, which greatly contributed to the development of public education. For some owners, the gathered collections were a source of knowledge in the social and economic modernization of the estate, allowing them to actively develop their households.
Essence and structure of the social experience of preschoolers

The substantiation of the essence and structure of the social experience of preschoolers is presented. A brief review of the approaches available in scientific sources to highlight the components of children’s social experience is given. We analyze the structure of social experience, consisting of only three components that all scientists present as components: motivational, cognitive, behavioral. It is concluded that such a structure has significant shortcomings: it is not synergistic and does not take into account the emotional component of the preschoolers’ experience. The shortcoming of a three-component structure is eliminated through the introduction of an emotional component, since the activities of preschoolers are predominantly involuntary and are mainly determined by their emotional state at a particular point in time. We substantiate the validity of the proposed four-component structure of the social experience of preschoolers, analyze the relationships between the identified components, describe in detail the meaning and content of each of them. Based on the developed structure, the definition of the social experience of preschoolers is formulated.
Real-Time Logistic Monitoring (RTLM)

The average rate of fruit that perishes during transport varies between 10 and 25%. This volume of losses justifies the need for means of quality control. Under the SaaS paradigm, the RTLM (Real-Time Logistic Monitoring) aims to answer to this problem by implementing a real-time monitoring system of the conditions in which the fruit is transported. The RTLM project includes a responsive web application, a monitoring services system, an ETL engine, and a set of integrated services through sensor plugins developed with Arduíno technology.
Research and Application of Blockchain Technology in Transportation Administrative Law Enforcement

According to the overall goal of rule the society by law, combined with specific requirements such as the records of the entire process of law enforcement in the three systems, the information such as law enforcement evidence plays a key supporting role. When design a law enforcement case handling information system, it is necessary to fully consider the collection, generation, storage, and transmission of law enforcement information how to ensure that the information is safe and reliable and not tampered. “Let law enforcement data runs on the blockchain” which use blockchain, big data and other related technologies to store and transmit information such as law enforcement evidence securely, and comprehensively improve the effectiveness and security of law enforcement data.
Enterprise Architecture Model oriented to Architecture, Engineering and Construction Industry : Integration of Geospatial Concepts in a Specific Framework

A model is presented to integrate the geographical dimension - specific ontology - in the architecture descriptions of organizations dedicated to the Architecture, Engineering and Construction (AEC) industry, housing construction subsector. For this, the fundamental concepts and properties of a representative company are studied and the affected geographical elements that are considered in accordance with the strategic direction of the organization in the business layers, information systems and technology are determined. The discipline of integration of the geographical component required the evaluation of the needs of the organization’s value chain using an adaptation of the Architecture Description Method (ADM), proposed by The Open Group, to generate a Specific Model of Geospatial Enterprise Architecture.
Research on the Autonomous Learning Mode in the Environment of Computer Network Technology

Abstract Autonomous learning is an important part of basic education for all. At the same time, with the continuous development of computer network technology, it provides a new model for autonomous learning to some extent and improves the efficiency of learning. This paper mainly studies the needs of residents’ autonomous learning under the computer network technology environment and how to combine computer technology for efficient autonomous learning. Through the analysis of this paper, for the students to create a good learning atmosphere, provide reliable learning guidance, and how to implement the “happy learning”, “learn well”, “learned” as the guiding goal of the educational training model to provide reference.
Black-Box Watermarking for Generative Adversarial Networks.

As companies start using deep learning to provide value to their customers, the demand for solutions to protect the ownership of trained models becomes evident. Several watermarking approaches have been proposed for protecting discriminative models. However, rapid progress in the task of photorealistic image synthesis, boosted by Generative Adversarial Networks (GANs), raises an urgent need for extending protection to generative models.We propose the first watermarking solution for GAN models. We leverage steganography techniques to watermark GAN training dataset, transfer the watermark from the dataset to GAN models, and then verify the watermark from generated images. In the experiments, we show that the hidden encoding characteristic of steganography allows preserving generation quality and supports the watermark secrecy against steganalysis attacks. We validate that our watermark verification is robust in wide ranges against several image perturbations. Critically, our solution treats GAN models as an independent component: watermark embedding is agnostic to GAN details and watermark verification relies only on accessing the APIs of black-box GANs.We further extend our watermarking applications to generated image detection and attribution, which delivers a practical potential to facilitate forensics against deep fakes and responsibility tracking of GAN misuse.
Teachers calling for organizational support to digitalize teaching

Purpose The digitalization of society places new demands on education. It is apparent since most countries have introduced curricula requirements to digitalize teaching. This study examines the organizational support teachers need to digitalize teaching. The study is being conducted in Sweden because they have experienced challenges with the introduction of new national digitalization requirements. Thus, this study explores the following research question: What organizational support do Swedish teachers describe they need to meet the curriculum requirements for digitalization? Design/methodology/approach Cultural–historical activity theory and qualitative methods have been used to explore the research aim and answer the question. Findings The results show that teachers need organizational support to gain equal and easy access to digital tools. Moreover, digital tools in an organization must be relevantly related to the requirements. Teachers also need support to increase their knowledge as well as the knowledge of the students. Also, organizations must support teachers by distributing the work of digitalization clearly and reasonably. These results, thus, show that teachers cannot be solely responsible for meeting these curriculum requirements. They need organizational support in the process. Originality/value The study reveals teachers' recurring problems concerning digitalized education and their need for organizational support. Thereby, the knowledge can be used to avoid similar problems, in organizations on different society levels. This contribution is useful for organizations, politicians, school leaders, principals and teachers who are introducing 1:1 and new curriculum requirements for digitalization of education.
Research on the Focused Crawler of Mineral Intelligence Service Based on Semantic Similarity

Abstract Large-scale general search engines have been unable to meet the needs of “specialized, sophisticated and deep” information in the field of mineral intelligence services. Vertical search engines have emerged at the historic moment, and the focused crawler is the key technology to achieve vertical search engines. This paper proposes a hybrid topic strategy based on text content and web link structure for the characteristics of mineral information field. In order to improve the topic relevance determination ability of the focused crawler, this article introduces HowNet and the word embedding technology in the field of natural language processing, and combines them to carry out the text-based topic relevance determination; at the same time, it also introduces the HITS algorithm based on the link structure of web pages. The topic strategy based on text content is organically combined with the topic strategy based on the webpage link structure to realize the recognition and prediction of the webpage topic. Simulation experiment results show that the method proposed in this paper can achieve a high recall rate and precision rate for the acquisition of mineral intelligence information on the Internet.
OEBR-GAN: Object Extraction and Background Recovery Generative Adversarial Networks

Generative adversarial networks (GAN) have been widely used in the field of image-to-image translation. In this paper, we have proposed a novel object extraction and background recovery (OEBR-GAN) model, which can extract objects from an image and then complete the image by inpainting the background of the image. This model has been developed for a solar panel installation project, where the user would like to input an original colored image of the roof, and as output, the user requires an edge detected roof image. However, the condition in user requirement is that any object that is hiding the roof edges should be removed first and the background of that part of the roof image should be recovered so that the user can obtain a complete connected edge detected image of the roof. Therefore, the model also completes the image by connecting the hidden edges of the roof. We could achieve the user objective by building a GAN model with a dual generator and dual discriminator network. The generators have been built using an encoder-decoder network with and without skip connections and the discriminators have been built using deep convolutional neural networks and encoder architecture. Quantitative comparisons in the result section shows that OEBR-GAN performs much better than other adversarial models on our collected dataset.
Towards Ground Truth Explainability on Tabular Data.

In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes from recent work in explainability. Today's datasets are typically larger and more complex - requiring less interpretable models. In the setting of \textit{post hoc} explainability, there is no ground truth for explanations. Inspired by recent work in explaining image classifiers that does provide ground truth, we propose a similar solution for tabular data. Using copulas, a concise specification of the desired statistical properties of a dataset, users can build intuition around explainability using controlled data sets and experimentation. The current capabilities are demonstrated on three use cases: one dimensional logistic regression, impact of correlation from informative features, impact of correlation from redundant variables.
Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification

While supervised techniques in re-identification are extremely effective, the need for large amounts of annotations makes them impractical for large camera networks. One-shot re-identification, which uses a singular labeled tracklet for each identity along with a pool of unlabeled tracklets, is a potential candidate towards reducing this labeling effort. Current one-shot re-identification methods function by modeling the inter-relationships amongst the labeled and the unlabeled data, but fail to fully exploit such relationships that exist within the pool of unlabeled data itself. In this paper, we propose a new framework named Temporal Consistency Progressive Learning, which uses temporal coherence as a novel self-supervised auxiliary task in the one-shot learning paradigm to capture such relationships amongst the unlabeled tracklets. Optimizing two new losses, which enforce consistency on a local and global scale, our framework can learn learn richer and more discriminative representations. Extensive experiments on two challenging video re-identification datasets - MARS and DukeMTMC-VideoReID - demonstrate that our proposed method is able to estimate the true labels of the unlabeled data more accurately by up to $8\%$, and obtain significantly better re-identification performance compared to the existing state-of-the-art techniques.
SliceOut: Training Transformers and CNNs faster while using less memory.

We demonstrate 10-40% speedups and memory reduction with Wide ResNets, EfficientNets, and Transformer models, with minimal to no loss in accuracy, using SliceOut---a new dropout scheme designed to take advantage of GPU memory layout. By dropping contiguous sets of units at random, our method preserves the regularization properties of dropout while allowing for more efficient low-level implementation, resulting in training speedups through (1) fast memory access and matrix multiplication of smaller tensors, and (2) memory savings by avoiding allocating memory to zero units in weight gradients and activations. Despite its simplicity, our method is highly effective. We demonstrate its efficacy at scale with Wide ResNets & EfficientNets on CIFAR10/100 and ImageNet, as well as Transformers on the LM1B dataset. These speedups and memory savings in training can lead to $CO_2$ emissions reduction of up to 40% for training large models.
AI4D -- African Language Dataset Challenge

As language and speech technologies become more advanced, the lack of fundamental digital resources for African languages, such as data, spell checkers and Part of Speech taggers, means that the digital divide between these languages and others keeps growing. This work details the organisation of the AI4D - African Language Dataset Challenge, an effort to incentivize the creation, organization and discovery of African language datasets through a competitive challenge. We particularly encouraged the submission of annotated datasets which can be used for training task-specific supervised machine learning models.
Indian Sign Language Recognition Using Framework of Skin Color Detection, Viola- Jones Algorithm, Correlation-Coefficient Technique and Distance Based Neuro-Fuzzy Classification Approach

Sign language recognition is exceptionally a vast in the research field where one can utilize a few procedures like glove based method, image based method, sensors and so on, to acquire the signs. In this paper we have undertaken an image based technique to recognize sign language. In this paper we have proposed a framework which is comprised of 4 phases: in first phase we have applied various image processing techniques to smooth the image. Further we have used skin color detection and Viola-Jones algorithm, which are used to segment a face part and hand part from the image. Second phase consist of a distance count method and Correlation-Coefficient method to extract distance and similarity index features respectively. These features continue to 3rd phase to classify and identify the sign using Neuro-Fuzzy classification algorithm. Finally in the 4th phase we have used Natural Language Processing (NLP) to display the final word. Presented framework has been implemented on MATLAB. We have tested 20 videos (total 100 images) of different family relation signs and our experimental result is 95% accuracy.
Research on Existing Problems and Countermeasures in College English Translation Teaching

The “College English Curriculum Requirements” promulgated by the Ministry of Education of China has detailed regulations on the five aspects of English listening, speaking, reading, writing, and translating for ordinary undergraduates. However, the foreign language translation ability of most college students in China is still the weak link in the English ability structure. With the further opening up of all walks of life in China, the role of English in daily life and work is becoming more and more important. This paper analyzes and summarizes the current situation and problems of college English translation teaching in China, and proposes corresponding improvement measures.
Stroke Extraction of Handwritten Gujarati Consonant Using Structural Features

Handwriting synthesis aims to generate the artificial font that exactly mimics the writing style of the individual user. It has several applications such as improvement of text recognition, font personalization, writer identification and it spreading as the technology becomes popular. For font synthesis of handwritten Gujarati script using structural features of character, the detailed analysis of the shape and characteristic of Gujarati characters is required. Gujarati characters classified according to different features such as vertical lines, horizontal lines, close region (loop), endpoints, cross points, concave curve, and convex curve. Moreover, Gujarati characters are structured symbols, each of which is composed of more than one basic structured components called a stroke. Stroke is the most popular way to analyze and describe the character structure. Therefore, this document proposed a methodology for extracting stroke from the thinned binary image and separate the stroke based on the endpoint and junction point. This methodology uses 3 × 3 pattern mask to detect the junction point and endpoint by considering all its orientation. Removal of these points from thin character splits skeleton into different strokes. The process continue until each stroke have only two endpoints. The stroke from Character having a minimum number of junction point extracted easily. Still, it expected to improve results for a character having multiple junction points.
Innovations in Human Resource Management: Impact of Social Media Use on Employees’ Performance

Social media has changed the modes of all aspects of business operations, particularly human resource management practices. Firms are increasingly using social media tools to facilitate information sharing among their employees in an attempt to improve the innovation process and firm performance. It is expected that using new information technologies such as social media will enable the firm to act proficiently on business opportunities and reconfigure human resources by utilizing networks to routinize the business's knowledge and innovation competencies. This study aims to examine how different purposes of social media use to influence employees' level of job performance directly or indirectly through job satisfaction as a mediator. Two purposes are identified: work-related purposes and personal purposes. A closed questions survey tool was used to gather the data from the employees of three leading organizations in the Tulkarm district. Two hundred eighty-two valid questionnaires were analyzed using SPSS and IBM SPSS Amos 24. The findings revealed that the impact of using social media for work purposes on job performance is fully mediated by job satisfaction, while social media use for personal purposes does not influence job performance directly nor indirectly. The current study enriches the available literature by examining social media use from two perspectives: work-related and personal purposes, thus added value to the available literature, particularly in the Palestinian context. Practically, managers could benefit from the work by adopting relevant strategies to guide this use in a way that motivate the employees towards achieving the goals of the organizations. Such as offering reliable internet services which may help in encouraging the employees or even attracting the ones who are not engaged yet in social media to start using it for work purposes, as for personal purposes, specific policies can be adopted to monitor this use within controls. The article recommends organizations to utilize and direct social media use for work purposes towards achieving the goals of the organizations. Keywords: communication, job satisfaction, performance, personal purposes, social media.
Software Cost Estimation Using Function Point Analysis

Software Cost Estimation is an important activity in software development projects. Various measurement methods are widely applied to estimate software cost, and one of them is Function Point Method. Function Point (FPA) method measures the software by measuring software functionality that provided to users based on design logic. The measurement consist of several steps: calculate Unadjusted Function Point (UFP), Value Adjustment Factor (VAF), Adjusted Function Point (AFP) and estimate the software cost. Software project that became the object of this research were four licensing projects from local government with total cost 229.680.000. The estimated total cost of four projects using Function Point method is 216.956.881 rupiahs. The percentage gap between the actual with the function point that is equal to 5.54%. It can be concluded that FPA is an accurate method and this cost estimation can be used by developers in determining the cost of software to be built