Reviewerly/exHarmony huggingface dataset:
Optimization and Upgrade of Special Database Management System for University Archives Based on Cloud Computing

Abstract With the progress of society and the development of science and technology, the data management of archives in colleges and universities has also changed from traditional manual operation to information technology system operation, which has taken a big step forward in the speed of data archive management. At the same time, following in the footsteps of the party and the state, according to the education reform measures issued by them, further implementation and implementation. With the innovation of education model, the reform of management method, and the expansion of teaching scale, the types and quantity of files are also increasing, and the scope is also expanding, which leads to the continuous increase of management difficulty. This requires data management to achieve informatization to meet the needs of society. The purpose of this paper is to study the optimization and upgrading of the special database management system of the archives of colleges and universities based on cloud computing. Based on cloud computing, this paper proposes the establishment of a special database management system for college archives and the methods and key technologies used in the process of optimization and upgrading. First, analyze the characteristics of the database management system, and then conduct demand analysis and functional analysis of the system. Secondly, research the advantages and disadvantages between the traditional data management of colleges and universities and the current latest database management system. Finally, according to the needs of the special database and system function requirements, the overall design of the archives management system is carried out, database modeling tools are used to optimize the database, and then its operation is studied, and system testing is carried out to enable it to meet the needs of university archives management. The experimental results show that the focus of database function optimization in different universities is different. University A prefers timeliness, accounting for 46%. University B thinks security is the most important, accounting for as high as 51%. University C is relatively even, both at 30%. The conclusion is to select the most practical and demanding functions for further optimization, and choose an overall design suitable for the special data of colleges and universities.
P.0194 Virtual reality and post-traumatic stress disorder

Library 3.0 is gaining popularity among research and academic library users faster than had been anticipated. In fact, it is currently deemed one of the most exciting advances in research and academic library development. The increasing popularity of Library 3.0 is attributed partly to its potential to provide an environment conducive to exploration, innovation, inspiration and personalisation. It emphasises context and not just the content of information service provision. Thus, it is anchored in a number of human factors, such as community, relationships, connection, conversation, personalisation, comfort, simplicity, play, progress and passion, which define progressiveness. Significantly, Library 3.0 is not about quick fixes but is a model which enables libraries to grow beyond being book-based and extend the information experience to wherever, however and whenever the users want it.
Heuristic extraction of co-occurrence patterns for effective construction of graph-based interpretable decision sets

Graph-based Interpretable Decision Set, gIDS in short, is an unordered set of graph classification rules composed with subgraph patterns. gIDS is recognized as an accurate and human interpretable graph classifier, and thus it can expect to be utilized in a wide variety of application areas including safety-critical domains. However, building gIDS often requires unfeasible computation time, since we need to solve three data mining problems, namely enumeration of frequent subgraph patterns, derivation of subgraph-based class association rules, and finding the optimal combination among class association rules. In this paper, in order to make gIDS more practical by reducing the building time, we consider the replacement of the complete enumeration of association rules in the second stage by heuristic rule extraction, and develop a heuristic rule extractor by extending the COM (co-occurrence rule miner) that efficiently derives a small set of rules with high classification ability. Our developed heuristic extractor can suppress the number of rules obtained, which also expects to reduce the computation time for combinatorial optimization in the third stage. The results of experiments using real world dataset show that our proposal can reduce computation time while keeping the classification performance and interpretability.
Structuring information retrieval interface in LIS: Simplicity vs. efficiency

The author attempts to generalize on the problems of providing retrieval, in particular, subject search, in library information systems and to find solutions. The key requirements to e-catalog retrieval interface as considered in foreign and domestic publications are presented. The problems of providing simplicity of retrieval interface are analyzed. The diverse first search pages of OPACs and e-catalog of Russian and foreign libraries are described. The author suggests a search page version comprising unit search box with the possibility of advanced search and user friendly option of bibliographic, subject, full-text and complex search. Every proposed search types are defined based on SIBID standards, or in case the terms are not provided by existing standards, the author provides her own definitions for them. The requirement of mandatory subject search in library information system, desirably for various types and degrees of complexity, is substantiated. Key requirements to retrieval tools of library information systems are formulated and detailed.
Automated Testing Complex «Smart Garden»

The article examines the composition and purpose of the elements of the automated complex &#x201C;Smart Garden&#x201D;, designed to develop software and hardware solutions for building an intelligent management system for intensive garden objects. The structure of an intelligent monitoring and control system for an automated complex is given. The choice of a wireless standard and a network for collecting information from geographically distributed sensors of the monitoring and control system is substantiated. The option of constructing an automated workstation for the operator-technologist of the automated complex &#x201C;Smart Garden&#x201D; is being considered.
Abnormal actions detection of robotic arm via 3D convolution neural network and support vector data description

Robotic arms are powerful assistants in many industrial production environments, and they run periodically in accordance with preset actions to complete specified operations. However, they may act abnormally when encountering unexpected situation and then lead to unnecessary loss. Recognizing the abnormal actions of robotic arms through surveillance video can automatically help us to understand their operating status and discover possible abnormalities in time. We designed a deep learning architecture based on 3D convolution for abnormal action recognition. The 3D convolutional layer can extract the spatial and temporal features of the robotic arm movements from the video frame difference sequence. The features are compressed and streamlined by the maximum pooling layer to obtain concise and effective robotic arm action features. Finally, the fully connected layer is used to classify the features to recognize the abnormal robotic arm tasks. Support vector data description (SVDD) model is employed to detect abnormal actions of the robotic arm, and the well-trained SVDD model can distinguish the normal actions from the three kinds of abnormal actions with the Area Under Curve (AUC) 99.17%.
Named-Entity Recognition Using CRF and BERT

Named-entity recognition (NER) is a natural language processing technique. It is also called entity identification or entity extraction. It identifies named entities in text and classifies them into predefined categories. For example, extracted entities can be the names of organizations, locations, times, quantities, people, monetary values, and more present in text.
Controlling a Small Mobile 3-Pi Robot Movement in a Maze Via the Neural Network Using Back-Propagation Learning Method

Abstract The contribution is focused on technical implementation of controlling a small mobile 3Pi robot in a maze along a predefined guide line where the control of the acquired direction of the robot’s movement was provided by a neural network. The weights (memory) of the neuron were calculated using a feedforward neural network learning via the Back-propagation method. This article fastens on the paper by the title “Movement control of a small mobile 3-pi robot in a maze using artificial neural network”, where Hebbian learning was used for a single-layer neural network. The reflectance infra-red sensors performed as input sensors. The result of this research is the evaluation based on the experiments that served to compare different training sets with the learning methods when moving a mobile robot in a maze.
ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications

Quantum Variational Methods are promising near-term applications of quantum machines, not only because of their potential advantages in solving certain computational tasks and understanding quantum physics but also because of their feasibility on near-term quantum machines. However, many challenges remain in order to unleash the full potential of quantum variational methods, especially in the design of efficient training methods for each domain-specific quantum variational ansatzes. This paper proposes a theory-guided principle in order to tackle the training issue of quantum variational methods and highlights some successful examples.
Combination of Optical Character Recognition Engines for Documents Containing Sparse Text and Alphanumeric Codes

Many companies that buy machines, parts, or tools retain documents such as notes, receipts, forms, or instruction manuals over the years, and they may find themselves in need of digitizing these accumulated documents. Thus, when using optical character recognition (OCR) systems in these documents, it is possible to note that these systems can present two main difficulties. The first is to locate the sparse text in a noncontinuous way, and the second is to match words that are closer to codes and less to words in human language. Although there are many works in the literature about sparse texts, such as forms and tables, there is usually not much concern about the issue with codes in which one can not rely on dictionaries or even both problems together. Therefore, to correct this issue without having to search for extensive databases or conduct training and development of new models, this work proposed to take advantage of pre-trained models of OCR such as from the Tesseract engine or the Google Cloud’s Vision API. In order to do so, we proposed the exploration of combination strategies, including a new one based on median string. The experimental results achieved up to 3.09% improvement in character accuracy and 1.16% in word accuracy in comparison to the best individual performances from the engines when our method based on string combination was adopted.
Introduction to and Hands-On Use Cases with HathiTrust Research Center's Extracted Features 2.0 Dataset

This tutorial will introduce attendees to the HathiTrust Research Center's Extracted Features Dataset, and demo new data fields and functionality introduced in the latest version, 2.0. Generated from the over 17 million volumes in the HathiTrust Digital Library, the EF 2.0 Dataset supports text and data mining methods while still adhering to a public domain, restriction-free data model. This tutorial will introduce the EF 2.0 Dataset, the key concepts behind its creation, and hands-on research use cases for the dataset using IPython notebooks.
Optimal FPGA-oriented lightweight network architecture search under multi-objective constraints

The neural architecture search (NAS) technology has developed rapidly, but most algorithms mainly target the GPUs, causing the searched models to lack consideration of the target hardware platform. This paper proposes a novel NAS algorithm specific for the unique calculation and storage character of FPGA to accelerate the deployment efficiency of neural networks and automated build the FPGA-oriented neural structure. Firstly, we design the search space based on the lightweight candidate blocks. Then, we measure the real simulation latency of each candidate block on the FPGA platform and for the first time using it to direct the NAS process. Eventually, this paper proposed a differentiable representation function of hardware latency and built a multi-objective NAS framework based on real simulation latency and FLOPS. In addition, by analyzing the relationship between the network convergence process and the model size, we achieve an optimal trade-off balance between precision and latency and build a more suitable network structure for deployment on FPGA. Compared with state-of-the-art manual and automated lightweight models, our method has relatively better accuracy and latency performance. Extensive experiments on the ImageNet dataset show that the proposed method can achieve 2x speedup compared with the similar accuracy manual lightweight network (MobileNet V2) and even get 3x speedup compared with the equivalent auto lightweight network (DARTS).
Face Mask Detection and e-Challan Generation: A ollaborative Approach

During the Covid-19 pandemic we consider that it is essential for companies and offices to open, but what if employees are not wearing a mask or not following the protocols while at workƒ This work provides a solution to the problem through a face recognition system with an e-challan number generator. The proposed system is easy to deploy in places like schools, offices, companies where we have the facial database of the employees available. The application which is termed No Mask Challan enables the organization to strictly follow the protocols laid down for conduct during a pandemic.
Properties of prime numbers and their relation to sound vibrations

Abstract The article introduces new properties of prime numbers. An alternative method for finding prime numbers using linear algebra tools is discussed. The probabilistic approach is used to analyze the use of a new property as a means of finding prime numbers. Mathematical calculations of determinants composed of prime numbers were converted to sound according to a given pattern. To optimize the process of calculating and converting numbers to, a software implementation was performed. A MIDI format saved in a folder has been created. In the process of processing the received data, the program recorded a melody, which, when changing scales, gives a beautiful sound.
A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder

This work presents a novel quantification of the cancer extension using a latent space embedded metrics of a variational autoencoder which captures the invariant patterns of the disease and projects them into a smaller latent space where data relations are linear, making it possible to apply simple metrics to quantify complicated relations. Selected patches of non-small cell lung cancer are projected to such latent space and a logistic regression model assigns an Euclidean distance between the patches projected in space. A simple grouping strategy quantitatively stratifies the characteristic patterns of the most representative patches for both adenocarcinoma and squamous cell lung cancer classes but it also estimates the composition of a mixture of patterns. This approach is fully interpretable, integrable with a pathology work flow and an objective characterization of diseases with complex patterns.
The application of image processing for Human-Computer Interface (HCI) using the Eye

Abstract The field of Human-Computer Interaction (HCI) has been developing tremendously since the past decade. The existence of smartphones or modern computers is already a norm in society these days which utilizes touch, voice and typing as a means for input. To further increase the variety of interaction, human eyes are set to be a good candidate for another form of HCI. The amount of information which the human eyes contain are extremely useful, hence, various methods and algorithm for eye gaze tracking are implemented in multiple sectors. However, some eye-tracking method requires infrared rays to be projected into the eye of the user which could potentially cause enzyme denaturation when the eye is subjected to those rays under extreme exposure. Therefore, to avoid potential harm from the eye-tracking method that utilizes infrared rays, this paper proposes an image-based eye tracking system using the Viola-Jones algorithm and Circular Hough Transform (CHT) algorithm. The proposed method uses visible light instead of infrared rays to control the mouse pointer using the eye gaze of the user. This research aims to implement the proposed algorithm for people with hand disability to interact with computers using their eye gaze.
GAN-Based Two-Step Pipeline for Real-World Image Super-Resolution

Mostly, the prior works on single image super-resolution are dependent on high-resolution image and their bicubically downsampled low-resolution image pairs. Such methods have achieved outstanding results in single image super-resolution. Yet, these methods struggle to generalize real-world low-resolution images. Real-world low-resolution images have large varieties of degradation, and modeling these degradation accurately is a challenging task. Although some works have been proposed to address this problem, their results still lack fine perceptual details. Here, we use a GAN-based two-step pipeline to address this challenging problem of real-world image super-resolution. At first, we train a GAN network that transforms real-world low-resolution images to a space of bicubic images of the same size. This network is trained on real-world low-resolution images as input and bicubically downsampled version of their corresponding high-resolution images as ground truth. Then, we employ the nESRGAN+ network trained on bicubically downsampled low-resolution and high-resolution image pairs to super-resolve the transformed bicubic alike images. Hence, the first network transforms the wide varieties of degraded images into the bicubic space, and the second network upscales the first output by the factor of four. We show the effectiveness of this work by evaluating its output on various benchmark test datasets and comparing our results with other works. We also show that our work outperforms prior works in both qualitative and quantitative comparison. We have published our source code and trained models here for further research and improvement.Gaire, Rebati Raman Subedi, Ronast Sharma, Ashim Subedi, Shishir Ghimire, Sharad Kumar Shakya, Subarna
A prohibited object detection and recognition system for X-ray images

The object detection and recognition system for X-ray images containing prohibited object divided into two parts: The construction of synthetic data containing prohibited objects, as well as the detection and recognition of prohibited objects. Among them, the construction of prohibited object data includes object classification, attribute analysis, object image acquisition, fusion of object image and X-ray image. After the above steps, the X-ray images with prohibited objects such as knives and guns are similar to the real images are obtained, which effectively solves the problem of insufficient X-ray image. In the process of detection and recognition of prohibited objects, by changing the color and shape of training data, the data source is expanded, which makes the system have higher robustness and improves the detection efficiency of prohibited objects. The detection and recognition system can effectively complete the real-time detection and recognition of knives and guns in luggage, save a lot of manpower and material resources, and ensure the safety of road, railway and air transportation.
Review: Digital Forensics

With the advancement and growing science of technology and the internet, the threats to data and digital devices have been increasing due to hackers and data invigilators. So the branch of DIGITAL FORENSIC has been set up for the investigation of the cybercrimes committed through the means of the internet, network, digital devices, etc. There are millions of internet users worldwide who are targeted by these hackers, and they lose their data to these data thieves unknowingly. This data can be misused by cybercriminals for various purposes. This branch of forensic science tracks and investigates these cyber criminals and finds the appropriate evidence against them. This paper surveys the work of this branch gives you a brief explanation about the various sub-branches, job opportunities available, and several tools used in this investigation process.
Korean automatic spacing using pretrained transformer encoder and analysis

Automatic spacing in Korean is used to correct spacing units in a given input sentence. The demand for automatic spacing has been increasing owing to frequent incorrect spacing in recent media, such as the Internet and mobile networks. Therefore, herein, we propose a transformer encoder that reads a sentence bidirectionally and can be pretrained using an out-of-task corpus. Notably, our model exhibited the highest character accuracy (98.42%) among the existing automatic spacing models for Korean. We experimentally validated the effectiveness of bidirectional encoding and pretraining for automatic spacing in Korean. Moreover, we conclude that pretraining is more important than fine-tuning and data size.
Design and Implementation of an Emergency Pregnancy Referral System Using Rule-Based Expert System Forward Chaining Method

There are many diseases and disorders in pregnancy that can lead to an emergency pregnancy. Delays in recognizing and making a diagnosis, delays in making decisions and delays in making referrals are three causes of higher maternal and infant mortality' rates. Several problems with delays in the diagnosis and referral process have become very important discussions and solutions must be sought. Because good referral management is the key to reducing maternal and infant mortality. Likewise, establishing the right pregnancy diagnosis when it is not too late is one of the efforts to prevent pregnancy emergencies. This study aims to design and implement a rule-based expert system forward chaining method for emergency pregnancy referrals and to measure the level of system accuracy based on the results of validation tests. The input of this research is symptom data which is then processed using a rule-based expert system forward chaining and produces output information on the type of disease and the place of reference. The results of the validation test, the probability- value of the system accuracy is 78.4% and the system inaccuracy is 21.6% so that this reference application can be declared to be running well.