Reviewerly/exHarmony huggingface dataset:
The Development and Model Comparison For Entity Relation Extraction Task

Relation extraction is a popular topic in natural language processing. This survey will introduce several major relation extraction methods, from feature and kernel-based to neural networks. We will discuss the basic idea and the performance of these methods and hope that this article will give readers a general concept of relation extraction.
Smoothness preserving layout for dynamic labels by hybrid optimization

Stable label movement and smooth label trajectory are critical for effective information understanding. Sudden label changes cannot be avoided by whatever forced directed methods due to the unreliability of resultant force or global optimization methods due to the complex trade-off on the different aspects. To solve this problem, we proposed a hybrid optimization method by taking advantages of the merits of both approaches. We first detect the spatial-temporal intersection regions from whole trajectories of the features, and initialize the layout by optimization in decreasing order by the number of the involved features. The label movements between the spatial-temporal intersection regions are determined by force directed methods. To cope with some features with high speed relative to neighbors, we introduced a force from future, called temporal force, so that the labels of related features can elude ahead of time and retain smooth movements. We also proposed a strategy by optimizing the label layout to predict the trajectories of features so that such global optimization method can be applied to streaming data.Supplementary material is available in the online version of this article at 10.1007/s41095-021-0231-y.
Performance analysis of CPU &amp; GPU for Real Time Image/Video

Computer vision algorithms are used in applications which require the given system to process and display images or video inputs. However, this will be computationally intensive on the machine that is performing the automated task based on the visual inputs and results in a large overhead or a lag between input and output processed video. To address this issues, parallel processing is a very useful strategy and can be achieved by dividing the computational tasks among the given hardware of the system which would make use of it efficiently and would work around the limitations of the hardware in the system. In this paper, the parallelism is achieved by multi-threading the algorithm that will divide the computation among the number of cores in the CPU and further optimize by dividing the load with both CPU and GPU for an efficient use of the system hardware and to obtain an optimized result. When the algorithms are executed without any optimization, the obtained output video fps is high and is undesirable. In this proposed method, a speedup factor of 91 times is recorded in AIELI algorithm.
Large-area Superconducting Micro-wire Single-photon Detectors Prepared by Laser Direct Writing Lithography

We have fabricated MoSi thin films by magnetron sputtering. The zero resistance superconducting transition temperature of 5 nm thin films reaches 4.1 K. Then, by optimizing lithography conditions, micron lines were prepared by laser direct writing lithography. The line width is from1.0 μm to 2.0 μm of the detection region, which the micro-wires have high uniformity in a large area, and the largest photosensitive area is up to 1000×1000 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . we preliminarily characterize the performance of the detectors, including I-V characteristics, dark counts, and photon counts rate.
Design of Discrete Mathematics Network Teaching Platform Based on Wechat Public Platform

By investigating the knowledge architecture of discrete mathematics, combined with the actual situation of the course, the network teaching platform of discrete mathematics based on wechat public platform is developed and designed. The platform has passed the test of function test and realized the function of teaching assistance, which has a certain reference value for the current exploration of intelligent education.
Optimizing Fair Approximate Nearest Neighbor Searches Using Threaded B+-Trees

Similarity search in high-dimensional spaces is an important primitive operation in many diverse application domains. Locality Sensitive Hashing (LSH) is a popular technique for solving the Approximate Nearest Neighbor (ANN) problem in high-dimensional spaces. Along with creating fair machine learning models, there is also a need for creating data structures that target different types of fairness. In this paper, we propose a fair variant of the ANN problem that targets Equal opportunity in group fairness in the ANN domain. We formally introduce the notion of fair ANN for Equal opportunity in group fairness. Additionally, we present an efficient disk-based index structure for finding Fair approximate nearest neighbors using Locality Sensitive Hashing (FairLSH). Moreover, we present an advanced version of FairLSH that uses cost models to further balance the trade-off between I/O cost and processing time. Finally, we experimentally show that FairLSH returns fair results with a very low I/O cost and processing time when compared with the state-of-the-art LSH techniques.
SISTEM INFORMASI SIDANG PENGADILAN AGAMA NEGERI MERANGIN BERBASIS WEB

Designing a trial schedule application at the Bangko Religious Court institution, as a form to facilitate providing information to a number of related parties who need information about the trial schedule that has been determined by the Bangko Religious court. This information system provides many conveniences and can streamline the activities carried out manually with this built system that can be transferred using computerization to facilitate providing information, entering data and storing data safely and easily. The main elements of making data processing applications consist of input, process and output, while the input data used are employee data, party data, case data, trial agenda data and agenda type data. Processing data that is processed is employee data, party data, case data, agenda type data and will produce an output of trial data report. The report of the Bangko Religious Court session will be published to several parties who need it, of course the information of this trial must be known in advance by the Head of the Bangko Religious Court to be followed up as specified procedures. Software development using php programming language using MySQL database. From this research produced a data processing application design of the Bangko Religious Court trial which can facilitate the administration in processing data at the Bangko Religious Court institution to provide information about the trial schedule to the parties concerned.
Collaborative Postal Collages: Staying Connected Through the Distance

NEAR THE END OF FEBRUARY 2020, our university Art Education Club officers introduced members to a collaborative collage artmaking project, inspired by Marty McCutcheon’s Check It Out project, descr...
Technical and Social sensor aggregation for smart environment enhancement

The uptake of urban city monitoring systems has been synonymous with the rise of Internet of Things (IoT) technologies since their conception. These systems are increasingly incorporated in a wide range of domains, such as disaster awareness, management, and prevention by contextualizing events. In this study, we have developed a platform capable of capturing and associating data from both social media and sensing devices, towards early discovery of disaster-related events by authorities and citizens alike. Establishing these points of association, which we dub as the ’Feedback Loop’, forms the basis of our research endeavors. In this paper, we present the architectural components put in place to realize this, at the hardware and software level, as well as the challenges we had to overcome in deploying these at scale within the Engomi Business Region of Nicosia, Cyprus. Pivotal to its final form involved the utilization of Big Data processing technologies to allow for flexibility of sensor deployment and high availability throughout its lifetime. Coupled with state-of-the-art Natural Language Processing (NLP) and anomaly detection algorithms, geared towards spatiotemporal sensor data and natural language processing, our eco-system features a holistic solution towards enhanced decision support.
Adaptive Software System Based on Ontological Approach for People With Cognitive Impairments

The paper presents a method of creating an adaptive software system to help people with cognitive impairments, based on the use of an ontological model of the subject area. The specifics of creating software tools to help people with cognitive impairments are analysed. The features of using the ontological approach for the formation of adaptive functionality and graphical interface are revealed and their advantages over traditional methods are analysed. It was found that when using this method, there is no need to recompile and fully deploy the software system in the event of a change in business logic. An ontological model of the subject area has been designed, which will make it possible to customize the system for the needs of a particular user. The architecture of a software system based on an ontological model of the subject area is proposed, which takes into account the possibility of personalizing the components of the system and the user interface without the need to re-deploy the system. The process of adaptation of a mobile application based on data about health disorders of the user using an ontological model of the subject area is disclosed. The result of the research is the development of a software system that implements the proposed adaptation process and allows to modify a mobile application for the needs of a specific user, using an ontological model of the subject area.
An Improved SPIHT Algorithm for Lossy Image Coding

The raw image data occupies a large storage space and communication bandwidth. Compression of the image data saves storage space and transmission bandwidth by removing the redundancy. In many image compression algorithms, wavelet transform can decompose the image with multi-level resolution, and can perform embedded coding. The author studies the set partitioning in hierarchical trees (SPIHT) algorithm based on wavelet transform and proposed an improved SPIHT algorithm (NE-SPIHT). First, the initialization of LIS in the SPIHT coding scheme is improved, The LIP and LIS are initialized to the nodes with no descendants in the lowest frequency subband. Secondly, when the nodes in the LIS chain table are judged as significant, its four child nodes are regarded as one whole for judgment, reducing the output bits. Experimental results demonstrate that the NE-SPIHT algorithm outperform SPIHT in terms of PSNR and SSIM, so applying NE-SPIHT method to the practical image compression can achieve a high compression efficiency.
ReFlowNet: Revisiting Coarse-to-fine Learning of Optical Flow

Recent work on optical flow estimation has been recognized as a computer vision task based on multi-resolution learning. However, existing methods only extract features by using plain convolution, while feature representation in the network affects the accuracy of optical flow estimation. To solve this problem, we propose a symmetrical factorization convolution block to extracting features for rich semantic information, increasing the depth and nonlinearity of the network. In addition, warping operation is often employed in optical flow estimation networks, but it brings low confidence information in computation. A confidence map module is proposed in order to compensate for the warping operation. In this paper, we propose a new optical flow estimation network called ReFlowNet. Experiments demonstrate that our network achieves competitive performance with other methods on the MPI-Sintel dataset.
Nuclei Detection in Images of Hematoxylin and Eosin-Stained Tissues Using Normalization of Value Channel in HSV Color Space

Hematoxylin and Eosin staining is one of the most popular techniques used in histopathology, but it has not been yet fully automated. Nuclei segmentation still stays unsolved among the problems that present themselves. Many approaches have been developed, but they often focus on the tissue of one organ. Standardizing the method of segmentation and detection of HE stained tissue samples could provide benefits for histopathologists, because even currently they have to perform this kind of time-consuming tasks. This article presents an approach to nuclei segmentation and detection using the transfer from RGB color space to HSV and normalization of Value channel. It also presents the results of different methods of binarization after normalizing Value channel.
Eliminating False Positives of Hough Transform with Constructive Testing in Line Detection

Routines for Hough transform for line detection use multiple parameters that can be difficult to determine sometimes. Even when a good deal of effort is spent on determining these pa-rameters, the true positives found are often accompanied by some false positives. In this paper, we propose a method called constructive testing for post-processing the lines returned by Hough transform routines with the objective to eliminate false positives. Given a detected line, constructive testing builds a small set of parallel lines based on it. Then, the detected line's distinctiveness as a line in contrast to the other constructed par-allel lines is computed with sample statistics. The detected lines are accepted or rejected based on their distinctiveness. Experimental results show determining a threshold of distinc-tiveness is intuitive and easy and that it effectively eliminates a large number of false positives.
A Multi-Task Online Monitoring System for UAV

Due to the limitations of volume, weight and power consumption, the accident rate of unmanned aerial vehicle (UAV) is much higher than that of manned aircraft. Therefore, UAV condition monitoring has become a research hotspot. However, most of the current UAVs do not support the deployment of multiple complex models due to limited airborne resources. In this paper, a multi-task online monitoring system is developed, which can realize real-time analysis of multiple airborne parameters and meet the needs of dynamic monitoring. First, we build different algorithmic models for different monitoring task's requirements, and complete the training of the models. After that, we use High Level Synthesis (HLS) tools to optimize and accelerate each algorithmic model and realize the condition monitoring function. Then, dynamic reconfiguration is adopted to complete the dynamic update of models according to different task requirements. Finally, the dynamically reconfigurable models are deployed in an on-board embedded hardware platform. The experimental results show that the system can realize real-time condition monitoring of multiple tasks.
Network Compression for end-to-end trainable neural networks using Approximate Computing

Approximate computing is increasing in demand to reduce the resources and time needed to run applications for several error-resilient applications. Neural networks through approximate computation is a popular framework able to achieve significant energy consumption. The proposed research aims to improve the quality of the approximation and energy efficiency gain. Two neural networks that are an approximator and a predictor are implemented in this research work to obtain a good approximation quality. The approximator generates the approximation results, although the predictor determines input data is reliable to approximation with the provided prerequisites for quality or not. Neural networks do incredible things, but they suffer from computing and memory obstacles that limit their use. This research also aims to create a method that can compress neural networks to minimize memory and reduce computing capacity with negligible loss of accuracy. Experimental results show remarkable improvements in approximation quality and energy-efficiency compared to the “OnePass” training and “Iterative” training. The experimental results show a 50% more invocation and reduction in training time when compared with the previous approximated neural network frameworks.
A Pseudonym-based Anonymous Routing Mechanism under Multi-controller SDN Architecture

Anonymous communication technology has been proposed to conceal the identity and communication relationship of both parties in communication, mitigating the threats of communication surveillance. However, the traditional anonymous communication scheme is designed on the traditional network architecture, network devices have limited control over the network, which leads to the inefficiency of anonymous communication. Software Defined Networks (SDN) is a novel network architecture that manages network through controller. It has the characteristics of high efficiency and flexibility. However, in recent years, researches about anonymous communication of SDN exist many challenges, such as limitation of scenarios and lack of anonymity. To address these challenges, this paper, inspired by pseudonym changing, proposes a pseudonym-based anonymous routing mechanism under multi-controller SDN architecture, in which pseudonym-changing is used for hiding the identity of the communicating parties. Besides, to avoid routing error caused by address conflicts, an effective hash checking method is proposed to avoid anonymous address conflicts. Based on that, we also present an anonymity enhanced scheme that employs phantom routing to hide the sender in the routing path and a multicast mechanism to prevent traffic analysis attacks. Anonymity analysis and experiment show that this scheme can provide strong anonymity protection for distributed SDN without much time cost, and has good practicability.
A Predictive Model of Handwritten Digits Recognition Using Expert Systems

Converting real written data and information into digital form for storage in a digital format necessitates the use of expensive manual labor. The paper aims to debate a response to some of the issues since the spectrum has been limited to just handwritten digits (0–9). The ‘MNIST DATASET,’ which includes training and test datasets for handwritten digits (0–9) with a resolution of (28 × 28), or 784 pixels, was used. The MNIST data set contains 60,000 training data and 10,000 test data.
Research and design of face and expression recognition system based on convolutional neural network

With the development of human-computer interaction technology and machine learning technology, facial expression recognition technology has gradually become a research hotspot. In recent years, with the rapid development of convolutional neural network and facial expression recognition, automatic facial expression recognition system has been widely used. In this paper, the method of facial expression recognition based on convolution neural network is studied. Because the differences between facial expressions are very subtle, in order to improve the ability of convolution neural network to extract facial expression features and its recognition accuracy, continuous convolution is introduced into convolution neural network model. The improved model uses small-scale convolution kernel to make the extracted facial expression features more precise, Two continuous convolution layers enhance the nonlinear expression ability of the model. With the development of human-computer interaction technology and machine learning technology, facial expression recognition technology has gradually become a research hotspot.
Research on Blockchain-Based Interactive Operation and Transaction Service Technology of Customer-side Flexible Resources

The traditional generation right transaction ledger has disadvantages such as high operation and maintenance costs, non-public transactions, and insecure information storage, as well as long subsidy accounting cycle and low subsidy issuing efficiency, which is not conducive to users' simultaneous distributed energy transactions and demand response operations. Blockchain technology has the characteristics of decentralization, strong traceability and high transaction transparency. Using Blockchain technology can reduce the pressure of audit, supervision, data maintenance and other aspects of energy transactions. Traditional single consensus chain is not enough to support complex transactions of multi-business energy block chain. Therefore, this paper proposes the application of combined block chain to improve and innovate user transaction mode, so as to fit the transaction scenario of client-side distributed energy.
American and Indian Sign Language Translation using Convolutional Neural Networks

Sign language is used by the hearing impaired people for the purpose of communication on daily basis. It is an optical language where hand gestures are used to communicate instead of voice. Although sign language has been used by the hearing impaired for a long time now, its translation presents a major challenge in communication. In this paper, the use of an image processing approach is presented for recognizing the static alphabets of sign language in two popular and widely used sign languages; American Sign Language and Indian Sign Language. Initially two datasets of preprocessed image gestures are created followed by a three layered Convolutional Neural Network being trained on the datasets. In the later stage, images are captured and pre-processed in real-time, preparing them to be recognized by the CNN model. This paper also includes an approach of translating the two sign languages to English speech and vice versa. Further, the born-deaf people have difficulty in reading text and learning written languages. Optical character recognition is used to identify text characters from images and are converted to ASL and ISL.
Estimation of Emotional Scenes from Lifelog Videos with Moving Average of Facial Expression Intensity

Lifelog video which saves the situation of daily life as video data has been proposed. In recent years, we are able to save much video data of daily life. But it has been difficult to select necessary data from a large amount of accumulated data. This paper proposes retrieval system with facial expression with the aim of retrieving important scenes from lifelog video data efficiently. In this paper, the emotional scene is tried to be detected with moving average of facial expression intensity. After the detection of the emotional scenes for lifelog videos by the proposed method is conducted, the proposed method is evaluated by comparing the proposed method with the method of preceding research through an evaluation experiment. As a result of the experiment, it is showed that the retrieval performance was improved.
QMCDS: Quantum Memory for Cloud Data Storage

Cloud computing is emerging as a very prominent and promising technology with its unique characteristics like low cost, scalability, elasticity, and on demand services. On the other hand, quantum computing is also gaining increasing research attention due to its enormous computational power. The future will soon be defined by the combined capabilities of both technologies. The challenges of cloud computing like threats to confidentiality, integrity, authentication, availability, and authorization are deteriorating day by day. The cloud Storage as a Service also suffers from such threats and challenges. Due to the rapid advancements in cryptanalytical methodologies and techniques, existing solutions to secure cloud storage based on traditional cryptography techniques will no longer be viable. Henceforth, this research work proposes a Quantum Memory for Cloud Data Storage (QMCDS) as a solution after performing a detailed analysis on the difficulties and limitations in cloud storage. The proposed solution is based on Qubits and the same is also discussed here. The proposed research work is an effort to pave way towards developing solutions by keeping quantum computing at center in the cloud quantum computing technology.