Reviewerly/exHarmony huggingface dataset:
Real Time Object Detection Using SSD For Bank Security

Abstract Object detection in real time applications are more challenging and thus faces more difficulties in designing object detection applications. Object detection is a method of identifying predefined objects from images, videos or from other input sources. It consist of different steps for identifying the object from its surroundings. Earlier detection system uses different techniques for objection detection with computationally demanding networks that requires high GPU and needs more power. Considering the current circumstances in the society, it is better to design real time object detection system for security applications. The newly evolved technique such as Tensorflow(TF) along with OpenCV can be used used for object detection. The CCTV footages can be used as input and the monitoring of it can be done inorder to find any security violations. The proposed real time object detection system for bank security mainly aims in providing security to bank vaults. This system can also be used for other security applications where human entry is restricted. The proposed model can be used for different security related applications effectively.
Rehabilitation Brace Based on the Internet of Things 3D Printing Technology in the Treatment and Repair of Joint Trauma

More and more people pay attention to the printing speed and quality of 3D printing tools. In order to understand whether the 3D printing rehabilitation brace can play a role in the treatment and repair of joint trauma, we used 3D printing technology to print the rehabilitation brace and compared with the traditional rehabilitation brace. The printed parts were analyzed in detail. The experimental results prove that the rehabilitation braces made by the two methods can play a role in the repair of joint trauma. However, 3D printed rehabilitation braces can better meet the needs of patients with detailed patient data in application. The braces are more suitable, and their production speed is about 35% faster than traditional methods. Through the survey of patients and doctors, it is found that the satisfaction of patients and doctors with printed braces is above 89%, while the satisfaction with traditionally made braces is only about 60%. This shows that the rehabilitation brace based on the Internet of Things 3D printing technology has a more significant role in the treatment and repair of joint trauma, and the effect is better.
Application of Computer Technology in Optimal Design of Overall Structure of Special Machinery

With the transformation and upgrading of my country’s industrial structure, the level of manufacturing automation has gradually improved. According to research, the design of mechanical products is mostly completed by improvement or innovation on the basis of existing design knowledge. Knowledge reuse is a technique to ensure the maximization of design resource utilization by reusing design knowledge. This article applies knowledge reuse technology to the development and design of mechanical products. By integrating the technical logic of the functional analysis system with the development of quality functions, the transformation of customer demand information and product function design is realized, and the task of the product design plan analysis phase is completed. This paper uses the finite element analysis software ANSYS to explore a new nonlinear finite element modeling method and conducts simulation experiments. At the same time, this paper improves the genetic algorithm, which effectively improves the optimization efficiency and completes the parameter optimization under multiobjective and multistructure conditions. From the experimental results, it takes 328.64 seconds for the basic genetic algorithm to search for the optimal solution of the complex problem. The improved time is shortened to 86.31 seconds, and the efficiency is increased by 73.74%. This shows that the improved genetic algorithm has better robustness and can find the optimal solution in a shorter calculation time. For complex problems such as the optimization of the overall structure of special machinery, the improved genetic algorithm obviously helps to improve the optimization efficiency and improves the effectiveness and pertinence of product design schemes.
Efficient FPGA Modular Multiplication Implementation

Barrett's algorithm is the most commonly known method of performing a modular multiplication, which is the core of many modern encryption algorithms such as RSA. Barrett's algorithm requires an accurate quotient estimation which in turn requires accurate multiplications. These multiplications operating on word sizes of thousands of bits are particularly expensive to implement in FPGAs, requiring many hundreds or even thousands of embedded DSP components along with large amounts of logic and routing. In this work we show that approximate quotient estimates as results of aggressive multiplier truncations can significantly reduce implementation cost. The looser modified Barrett's output [0; YM) is reduced to [0; M) using a shallow reduction technique based on table lookups and wide additions, taking advantage of new techniques which have recently been introduced for FPGA. We first use these techniques to develop an improved standard Barrett's implementation for 1024b modular multiplication, followed by our approximate method which reduces logic cost in the LSB truncated multiplier by approximately 10%. The effect is more pronounced for very large word sizes, where our relaxed error bounds in the LSB truncated multiplication can reduce the number of operations by 20%.
COVID-19 and digital library services: an overview on legal information

Purpose The purpose of this paper is to investigate the initiatives providing legal information during the COVID-19 emergency, focusing on the fundamental role of digital libraries in creating, managing and sharing services to support and ensure access to legal information in times of emergency. Design/methodology/approach To have an overall view of the legal information on COVID-19 available on digital libraries during the outbreak, a desk-research on the Web was conducted looking for both public and private sources of information. The selection of the sources was based on the type of information and services offered, the quality of information structuring, together with the data updating and the target users. Findings The survey provided a huge and heterogeneous amount of legal information resources on COVID-19. The analysis on the source of information selected showed different kinds of approaches adopted by digital libraries in relation to types of information provided, information categorization, target audiences, purposes and services offered. Research limitations/implications Two limitations have been identified: lack of previous studies in the domain and size of the samples cited. The conducted research should be seen as the “building block” upon which further research should be broadened and deepened. Limited samples are cited because of a rational choice; nevertheless, future research should be conducted also addressing a quantitative choice on identifying sources. Practical implications The research proposes to give users practical guidance, namely, a first set of authoritative sources which gives legal information on COVID-19. Originality/value This review paper could be seen as a first study on the issues related to accessing and sharing legal data at the time of the COVID-19; the research could be a starting point for devising a new form of communication of legal information in times of crisis.
Feedback Neural Network Based Super-Resolution of DEM for Generating High Fidelity Features

High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super-resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images(RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different terrains having diverse geographical structures.
Visual servoing of robot manipulator with weak field-of-view constraints

Aiming at the problem of servoing task failure caused by the manipulated object deviating from the camera field-of-view (FOV) during the robot manipulator visual servoing (VS) process, a new VS method based on an improved tracking learning detection (TLD) algorithm is proposed in this article, which allows the manipulated object to deviate from the camera FOV in several continuous frames and maintains the smoothness of the robot manipulator motion during VS. Firstly, to implement the robot manipulator visual object tracking task with strong robustness under the weak FOV constraints, an improved TLD algorithm is proposed. Then, the algorithm is used to extract the image features (object in the camera FOV) or predict image features (object out of the camera FOV) of the manipulated object in the current frame. And then, the position of the manipulated object in the current image is further estimated. Finally, the visual sliding mode control law is designed according to the image feature errors to control the motion of the robot manipulator so as to complete the visual tracking task of the robot manipulator to the manipulated object in complex natural scenes with high robustness. Several robot manipulator VS experiments were conducted on a six-degrees-of-freedom MOTOMANSV3 industrial manipulator under different natural scenes. The experimental results show that the proposed robot manipulator VS method can relax the FOV constraint requirements on real-time visibility of manipulated object and effectively solve the problem of servoing task failure caused by the object deviating from the camera FOV during the VS.
Qualitative Simulation Algorithm for Resource Scheduling in Enterprise Management Cloud Mode

Aiming at the problem of resource scheduling optimization in enterprise management cloud mode, a customizable fuzzy clustering cloud resource scheduling algorithm based on trust sensitivity is proposed. Firstly, on the one hand, a fuzzy clustering method is used to divide cloud resource scheduling into two aspects: cloud user resource scheduling and cloud task resource scheduling. On the other hand, a trust-sensitive mechanism is introduced into cloud task scheduling to prevent malicious node attacks or dishonest recommendation from node providers. At the same time, in the cloud task scheduling, cloud resources are divided according to the comprehensive performance of resources, and the trust sensitivity coefficient of each type of task resources is calculated. Then, according to the trust sensitivity coefficient, the matching cloud tasks are selected for users. Through the comparison of simulation experiments, the customized fuzzy clustering cloud resource scheduling algorithm proposed in this paper reduces the user’s cost of selecting cloud service provider in the cloud resource scheduling. It not only embodies the principle of cloud resource allocation on demand but also can give full play to the advantages of cloud resources and improve the throughput of the whole cloud system and the satisfaction of cloud users.
An Empirical Study on Type Annotations

Type annotations connect variables to domain-specific types. They enable the power of type checking and can detect faults early. In practice, type annotations have a reputation of being burdensome to developers. We lack, however, an empirical understanding of how and why they are burdensome. Hence, we seek to measure the baseline accuracy and speed for developers making type annotations to previously unseen code. We also study the impact of one or more type suggestions. We conduct an empirical study of 97 developers using 20 randomly selected code artifacts from the robotics domain containing physical unit types. We find that subjects select the correct physical type with just 51% accuracy, and a single correct annotation takes about 2 minutes on average. Showing subjects a single suggestion has a strong and significant impact on accuracy both when correct and incorrect, while showing three suggestions retains the significant benefits without the negative effects. We also find that suggestions do not come with a time penalty. We require subjects to explain their annotation choices, and we qualitatively analyze their explanations. We find that identifier names and reasoning about code operations are the primary clues for selecting a type. We also examine two state-of-the-art automated type annotation systems and find opportunities for their improvement.
Levee-Crack Detection from Satellite or Drone Imagery Using Machine Learning Approaches

This paper describes and compares different approaches for detecting cracks in the concrete toe, other general areas of levees and dams using satellite or drone images. The dataset was sourced from real drone flight data and manually collected and annotated as needed. We compare old and modern algorithms alike to determine which ones perform best in this case. We also explain the reasonings for a particularly interesting case of the viola jones algorithm, where our calculated accuracy is 100%. We study stacking (85% accuracy), and the latest deep learning techniques (90.90% accuracy) as well. This research hopes to help the U.S. Army Engineers Corps integrate the model into drones to better monitor the levee areas prone to disaster.
Fairness by Explicability and Adversarial SHAP Learning

The ability to understand and trust the fairness of model predictions, particularly when considering the outcomes of unprivileged groups, is critical to the deployment and adoption of machine learning systems. SHAP values provide a unified framework for interpreting model predictions and feature attribution but do not address the problem of fairness directly. In this work, we propose a new definition of fairness that emphasises the role of an external auditor and model explicability. To satisfy this definition, we develop a framework for mitigating model bias using regularizations constructed from the SHAP values of an adversarial surrogate model. We focus on the binary classification task with a single unprivileged group and link our fairness explicability constraints to classical statistical fairness metrics. We demonstrate our approaches using gradient and adaptive boosting on: a synthetic dataset, the UCI Adult (Census) dataset and a real-world credit scoring dataset. The models produced were fairer and performant.
White Blood Cell Segmentation Based on Visual Attention Mechanism and Model Fitting

White blood cell segmentation is a crucial step in developing a computer-aided automatic cell analysis system. To improve the precision of the leukocyte segmentation, this paper presents a white blood cell segmentation algorithm based on visual attention mechanism and model-fitting. The proposed method first employs a color space volume based on visual attention mechanism and an adaptive threshold method to segment the nucleus. Then, the edge region of the image is removed and the initial white blood cell region at the center is obtained. After that, the edge detection is performed to extract the whole leukocyte. The cytoplasm of the leukocyte is obtained by subtracting the nucleus from the entire leukocyte. Finally, the model-fitting method is used to solve the problem of leukocyte adhesion. Experimental results on an image dataset containing 300 leukocyte images show that the proposed method performs well over the state-of-the-art methods.
Designing Drone Systems with Papyrus for Robotics

Drones/UAVs are able to perform air operations that are difficult to carry out by manned aircrafts. Their use brings significant environmental benefits and economic savings while reducing risks to human life. Recently, a number of approaches introduced a support for the development of drone software systems. However, the development of such systems is still largely done following ad-hoc processes without capturing systematically all requirements and constraints, and without a clear architectural vision. Therefore, in this paper, we introduce the Papyrus for Robotics tool. This tool is compliant with the model-driven development approach proposed by the RobMoSys project. The tool distinguishes different stakeholders and artefacts, and has a support for high-level behavior modeling. In addition, due to the nature of the drone domain, safety concerns play an important role. For instance, a drone needs to fly in safe areas only (i.e. geo-caging) or keep a sufficient safety-distance from other drones/flying objects in the space. Thus, the tool has a specific support for safety concerns. To ensure the applicability of our tool, we have used it to perform a case study in the context of the COMP4DRONES project that targets reducing the development cost of drone systems.
Prioritization-Based Taxonomy of Global Software Development Challenges: A FAHP Based Analysis

Global Software Development (GSD) continues to receive interest from software industry due to potential economic benefits. Management of GSD projects is not straightforward due to involvement of different geographically distributed teams who collaborate to produce a software. The objective of this study is to prioritize the challenges faced by practitioners during management of a GSD project. A questionnaire survey was developed to collect feedback from GSD practitioners about relative importance of 20 challenges reported in literature. Next, the Fuzzy Analytical Hierarchy Process (FAHP) was used to rank the challenges associated with management of GSD projects. The study provides a prioritization-based taxonomy of challenges associated with management of GSD projects. We believe software organizations can use the taxonomy to better plan and manage GSD projects.
The Application of Big Data Analytics in Online Foreign Language Learning among College Students : Empirical Research on Monitoring the Learning Outcomes and Predicting Final Grades

This paper conducts empirical research on the application of big data to online learning of foreign language with Zhuhai College of Jilin University as the subject. The software EVIEWS is utilized to analyze essential factors affecting online learning process and help learners predict final grades, which affords the benefits of learning supervision and feedback as well as a higher learning efficiency. Recommendations are also provided pertaining to current difficulties in combining big data and online learning.
Ensemble convolutional neural network for robust batik classification

Abstract Some researchers propose using the Convolutional Neural Network (CNN) method to classified batik images. It can extract features automatically without the need to define feature manually from the image. However, CNN’s weakness is that its accuracy is quite low, especially for small-sized datasets, compared to machine learning methods that use handcrafted feature extraction. In this research, an ensemble CNN method is proposed to improve the accuracy of the CNN method in classifying batik images. This method will train several CNN models at once, and then by voting and averaging techniques, the output label will be determined. Test results for two different datasets show this method can improve the accuracy of the CNN method and get an accuracy value of 100%. This method is also proven to extract features faster than the state-of-the-art method like MTCD+SVM, which is included in the hand-crafted feature extraction category.
Enhancing Student Learning Through Mobile Learning Groups

With the advent of mobile phones, particularly smartphones, there has been consideration interest in mobile e-learning. In particular, mobile phones provide an effective channel to complement existing channels to enhance the student learning experience. Coupled with artificial intelligence (e.g., chatbot), more innovative learning functions can be provided. In this paper, we make two contributions to the aforementioned development. First, we present an innovative AI5 model with five important learning elements: Inception, Interest, Instruction, Information and Inspiration, to facilitate mobile collaborative learning. Second, we present a mobile app prototype for students to form mobile learning groups. In each learning group, students can interact with one another and with a chatbot. Furthermore, the mobile app can facilitate the sharing of open educational resources.
Advances of Transformer-Based Models for News Headline Generation

Pretrained language models based on Transformer architecture are the reason for recent breakthroughs in many areas of NLP, including sentiment analysis, question answering, named entity recognition. Headline generation is a special kind of text summarization task. Models need to have strong natural language understanding that goes beyond the meaning of individual words and sentences and an ability to distinguish essential information to succeed in it. In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by 2.9 and 2.0 points respectively over previous best score achieved by Phrase-Based Attentional Transformer and CopyNet.
Parallel Algorithm For Constructing a Cubic Spline on Multi-Core Processors in a Cluster

The article explores the possibility of computing parallel data compression using cubic spline. For example, ways to parallel the process of digital processing of seismic signals have been considered. The main performance indicators of parallel algorithms have been compared with consecutive algorithms. Spline methods are a versatile signal processing tool. It is more accurate than other mathematical methods, information equality is faster, and maintenance costs are much lower. On the other hand, the equipment used in such systems must also meet high performance requirements. To achieve high speeds, parallel algorithms were developed using OpenMP and MPI technologies and implemented in the architecture of multi-core processors. A mathematical method for the parallel calculation of the coefficients of a cubic spline has been developed and a parallel signal processing algorithm has been developed on its basis. As an example, parallelization is a computation during seismic signal processing. The main indicators of efficiency and acceleration of the parallel algorithm were compared with the sequential algorithm. Explained the relevance of the use of parallel numerical systems, described the main approaches to the distribution of processes and methods of data processing, described the principles of parallel programming technology, studied the basic parameters of parallel algorithms for the initial calculation of the numerical value of cubic spline. The parallel algorithm considered for constructing the cubic spline of defect 1 as p - > n leads to the construction of a local cubic spline on each grid interval ω.