Reviewerly/exHarmony huggingface dataset:
The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications

Breast cancer is ranked first as the most common cancer case affecting women in the world. Early detection of breast cancer can increase the chances of survival in patients. The role of the radiologist is necessary for the detection of breast cancer, and the radiologists often have limitations in conducting disease consultations with so many patients. The detection gives a subjective result because the process is based on the decision-making of the radiologists. In this work, we proposed a system to detect and classify breast cancer accurately to anticipate delays in patient handling and subjective result. We proposed a digital image processing method using mammograms to classify breast cancer into four categories based on tissue density, namely BI-RADS I, II, III, and IV. The main stages carried out in this research are images processing, feature extraction, data normalization, feature selection, classification, and parameter optimization. This method uses GLCM to extract texture features and two feature selection methods namely, RFE-RF and Chi-Square. The method was tested with various classifiers such as SVM, KNN, Random Forests, and Decision Trees. The hyper-parameters of the classifier were optimized using GridSearch. The final result is measure using accuracy. In this work, Random Forest with the RFE-RF gives the highest accuracy of 99.7%. Feature selection offers a significant impact on improving accuracy. The results of this work prove that our system can classify breast cancer with high accuracy. So that our system can solve problems to assist radiologists in screening mammograms and help make decisions to diagnose patients with breast cancer based on density.
Parallel Transport Convolution: Deformable Convolutional Networks on Manifold-Structured Data

Convolution has played a prominent role in various applications in science and engineering for many years and has become a key operation in many neural networks. There has been a recent growth of interest in generalizing convolutions on three-dimensional surfaces, often represented as compact manifolds. However, existing approaches cannot preserve all the desirable properties of Euclidean convolutions, namely, compactly supported filters, directionality, and transferability across different manifolds. This paper develops a new generalization of the convolution operation, referred to as parallel transport convolution (PTC), on Riemannian manifolds and their discrete counterparts. PTC is designed based on parallel transportation that can translate information along a manifold and intrinsically preserve directionality. Furthermore, PTC allows for the construction of compactly supported filters and is also robust to manifold deformations. This enables us to perform waveletlike operations and to define convolutional neural networks on curved domains.
Enabling Near Real-Time NLU-Driven Natural Language Programming through Dynamic Grammar Graph-Based Translation

Recently, natural language (NL)-based program synthesis has drawn increasing interest. Conventional methods that depend on some predefined domain-specific rules suffer from the lack of robustness and generality. Recent efforts on adopting deep learning to map queries to code requires a large number of labeled examples, making them not applicable on domains with scarce labeled examples. Although a third alternative, natural language understanding (NLU)-driven approach addresses the problems, the long response time hinders its adoption in practice, especially in an interactive scenario. This paper presents a solution to enable near real-time NLU-driven NL programming. The solution features a new algorithm, dynamic grammar graphbased translation (DGGT), for identifying the best grammar tree for a query via dynamic programming. It also introduces two new optimizations, grammar-based pruning and orphan node relocation, to further reduce the search space and address the special complexities from queries. Evaluations on two domains, text editing and program source code analysis, show that the DGGT algorithm and the optimizations shortens the response time of a state-of-the-art NLU-driven synthesizer by up to 1887× (25- 133× on average) while improving the accuracy by 2-12%.
A Larger Capacity Data Hiding Scheme Based on DNN

With the in-depth research in data hiding technology, the latest data hiding schemes using deep neural network (DNN) could hide the full-size color image into the same size color image. This paper proposed a larger capacity data hiding scheme based on DNN, which could hide large-size color image into small-size color image. This scheme used DNN in both the data hiding stage and data extraction stage. And the two networks were obtained through adversarial training and used in pairs. The information ratio between the carrier image and the secret image had reached 1 : 4. The image obtained after hiding the data did not change much compared with the carrier image, and the extracted image had the same size and no visual difference compared with the secret image. It could be seen from the simulation experiment that, compared with the existing DNN-based data hiding scheme, even if the capacity of the proposed scheme was 4 times larger than that of the existing scheme, the visual effect and PSNR were also better than some existing schemes.
A Transformer-based Attention Flow Model for Intelligent Question and Answering Chatbot

The question and answer system is a key step and core component in building an intelligent voice assistant, and traditional question and answer systems use various deep learning networks to model this task, in our task, we propose a novel neural network-based model structure that uses the Transformer layer to enhance the model semantic feature extraction capability, and to compensate for the fact that the Transformer is not good at capturing location information, we use a bidirectional LSTM layer to enhance the model&#x0027;s temporal feature extraction capability. In the experiments with SQUAD data, our model achieves an EM index of 68.5 and an Fl score of 77.7, which proves the effectiveness of our model.
The Journey Towards Successful Application of Maintenance 4.0 and Service Management 4.0

The paper discusses the role of maintenance in the digital era and propose directions for supporting the technology and business transformation towards Maintenance 4.0 and Service Management 4.0. For achieving this, the paper summaries previous research in the area and conceptualizes the transformation of maintenance towards Maintenance 4.0 and Service Management 4.0. The concept applies a systems approach on digitalization and recognizes the need for combining several working areas (production and maintenance management, information systems management, improvement processes and business management) for successful digital transformation within maintenance. Service Management 4.0 describes how maintenance becomes a business opportunity and modular maintenance offerings a way to approach the new opportunities. The concepts proposed in the paper provide support for industries in the digital transformation process towards Maintenance 4.0 and Service management 4.0.
Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation.
Accelerated SVM Algorithm for Sensors Fusion-based Activity Classification in Lightweighted Edge Devices

Smart homes assist users by providing convenient services from human activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require high computing power and memory usage. Therefore, they are inapplicable for resource-limited embedded systems such as smart homes. In this study, we propose a memory-efficient, high-speed ML algorithm for smart home activity data classification. We propose a method for comprehending activity data as image data, thereby using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding each cluster of preprocessed data. Finally, the classification process is done by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on &#x2018;Raspberry Pi 3&#x2019; by loading trained hyperplanes and performing classification on 1,000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved performance to 45&#x0025;, memory usage to 15.41 &#x0025;, and execution time per accuracy to 41.3&#x0025;.
Wireless Sensor Network Security Based on Improved Identity Encryption

In order to protect network information security and improve the security of wireless sensor networks, based on chaotic systems, we propose a wireless sensor algorithm based on improved identity encryption. First, the basic principle of chaotic system mapping is specifically analyzed; the two chaotic mapping systems are rectified to obtain the hybrid chaotic mapping system according to the demand of wireless sensing network. After that, an encryption framework and key are designed and the hybrid mapping system is applied to the encryption framework to encrypt the data. In this way, the length of the encryption algorithm is lengthened and the defensibility of the encrypted content is improved. Finally, the performance of the proposed encryption algorithm is tested in terms of information entropy, statistical methods, and ciphertext randomness. The test results show that compared with other encryption algorithms, the running speed of the encryption algorithm proposed in this paper is only 18.51 ms, which is faster than that of other encryption algorithms, and the memory consumption is only 27%, which is much lower than that of other algorithms. It can be seen that the proposed encryption algorithm has strong encryption effect and superior algorithm performance for data encryption in wireless sensor networks.
A Secure Secret Key-Sharing System for Resource-Constrained IoT Devices using MQTT

The MQTT (Message Queue Telemetry Transport) protocol has garnered significant attention as a communication protocol for a variety of IoT applications. Although it is a lightweight and energy-efficient communication protocol, it is not equipped with sufficient security mechanisms by default. Usually, secure socket layer/transport layer security (SSL/TLS) is used as a security mechanism in the MQTT protocol. However, it is not suitable for resource-constrained IoT devices because of the huge computational load involved in public key cryptography. In this paper, we propose a lightweight secure secret key-sharing system based on a secret-sharing scheme for resource-constrained IoT devices. The proposed system uses a (k, n)-threshold secret-sharing scheme to securely share a secret key for data encryption between the publisher and its subscriber hosts without compromising the lightweight nature of the MQTT protocol. A prototype of the proposed system is implemented using real IoT devices and its effectiveness and performance are evaluated. The experimental results demonstrate that the proposed system outperforms existing public key-based systems in terms of key-sharing delay.
Secure Big Data Collection and Processing: Framework, Means and Opportunities

Abstract Statistical disclosure control is important for the dissemination of statistical outputs. There is an increasing need for greater confidentiality protection during data collection and processing by National Statistical Offices. In particular, various transactions and remote sensing signals are examples of useful but very detailed big data that can be highly sensitive. Moreover, possible conflicts of interest may arise for data suppliers who operate commercially. In this paper, we formulate statistical disclosure control for data collection and processing as an optimisation problem. Even when it is difficult to specify and solve the problem unequivocally, the formulation can still provide the basis for comparing different disclosure control methods. We develop a general compartmented system that adapts and implements non-perturbative methods in the related fields of linking sensitive data and secure computation. We illustrate how the system can be configured to yield variously required tables and microdata sets with sufficiently low disclosure risks.
The Creation of One Truth: Single-Ledger Entries for Multiple Stakeholders Using Blockchain Technology to Address the Reconciliation Problem

ABSTRACT Reconciliation of transactions between multiple parties is a time-consuming process. This paper presents a new conceptual framework that may lower costs and shorten the reconciliation time. The proposed framework recommends recording transactions on the blockchain before populating the transacting parties’ respective enterprise resource planning (ERP) systems, which will be the source of a single truth. This information flow will result in all involved parties having access to the same transaction details. Further, the paper presents a technical feasibility demonstration with two examples, where all involved parties are executing transaction details on the blockchain after meeting the governing smart contract constraints using their digital wallets. The new conceptual framework has been specifically designed to create one truth for all parties involved in a transaction that in turn may eliminate what is perceived to be unnecessary redundancy in current accounting systems.
A co-calibration framework for the accuracy assessment of vision-based tracking systems

Advancements in Head-Mounted-Display (HMD) technology have led to an increasing focus in the development of Augmented Reality (AR) applications in the image-guided surgery field. These applications are often enabled by third-party vision-based tracking techniques, allowing virtual models to be registered to their corresponding real-world objects. The accuracy of the underlying vision-based tracking technique is critical towards the efficacy of these systems, and must be thoroughly evaluated before integration into clinical practice. In this paper, we propose a framework for the purpose of evaluating the technical accuracy of the HMD’s intrinsic vision-based tracking techniques using an extrinsic tracking system as ground truth. Specifically, we assess the tracking accuracy of the Vuforia Augmented Reality Software Development Kit, a vision-based tracking technique commonly used in conjunction with the Microsoft Hololens 2, against a commercial optical tracking system using a co calibration apparatus. The framework follows a two-stage pipeline of first calibrating the cameras with respect to the optical tracker (hand-eye calibration), and then calibrating a Vuforia target to the optical tracker using a custom calibration apparatus. We then evaluate the absolute tracking accuracy of three Vuforia target types (image, cylinder, and cube) using a stand-alone Logitech webcam and the front-facing camera on the Hololens 2. The hand-eye calibration projection errors were 1.4 ± 0.6 pixels for the Logitech camera and 2.3 ± 1.2 pixels for the Hololens 2 camera. The cylinder target provided the most stable and accurate tracking, with mean errors of 12.5 ± 0.6 mm and 10.7 ± 0.0 mm for the Logitech and Hololens 2 cameras, respectively. These results show that Vuforia has promising potential for integration into surgical navigation systems, but the type and size of target must be optimized for the particular surgical scenario to minimize tracking error. Future work will use our framework to perform a more robust analysis of optimal target shapes and sizes for vision-based navigation systems, both independently and when fused with the Simultaneous Localization and Mapping (SLAM) based tracking embedded with the Microsoft Hololens 2.
The systems framework dispels causal illusions and organizes multi-causal models

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo &amp; Tensorflow in OpenCV

In this critical situations where people are fighting with dangerous pandemic disease; it is required to maintain the situation by indulging with social distancing or it can also be pronounced as physical distancing. Social or physical distancing may reflects to reduce the virus from spreading. There are several places where it should be followed properly to stop spreading COVID-19 like railway stations, malls, marts, airports and many more. It is advised to maintain at least 6 feet of social distancing as per the WHO guidelines. Various researches have been done to automatically detect the physical distancing violations but an ideal system should be available to detect it effectively with high level of accuracy. Here the system is based on PP-Yolo (PaddlePaddle - You only look once) and Tensorflow library. Tensorflow is an object detection or pattern recognition tool through which pedestrian can be detected automatically and then PP-Yolo classifies the distance between the pedestrians or classifying whether persons are following the physical distancing rule or not. Violation detection is bit challenging for any system because a crowd may have uncertain structures that can hardly classified distance among them. This challenge can be accepted through various researchers but not met the desired precision. Proposed system is intended to detect the physical distancing rule violations effectively and acquiring high level of accuracy with minimal false alarm rate.
Recovering Container Class Types in C++ Binaries

We present TIARA, a novel approach to recovering container classes in c++ binaries. Given a variable address in a c++ binary, TIARA first applies a new type-relevant slicing algorithm incorporated with a decay function, TSLICE, to obtain an inter-procedural forward slice of instructions expressed as a CFG to summarize how the variable is used in the binary (as our primary contribution). TIARA then makes use of a GCN (Graph Convolutional Network) to learn and predict the container type for the variable (as our secondary contribution). According to our evaluation, TIARA can advance the state of the art in inferring commonly used container types in a set of eight large real-world COTS c++ binaries efficiently (in terms of the overall analysis time) and effectively (in terms of precision, recall and F1 score).
Coral monitoring system based on polarization vision

Coral ecosystem not only breeds abundant organisms, but also deem to be a very important fishery and tourism resources. The reduction of coral will also have a bad impact on the marine. Scattering and absorption leads to complicate the capturing process of a clear image. Aiming to the needs of marine ecological diversity research, in addition to the privilege of underwater polarization imaging, an underwater binocular polarization vision system is proposed in this paper. This system consists of underwater vehicle, vision acquisition system and image processing software. The internal image acquisition hardware module made up out of microcomputer, two polarization camera modules and information transmission module. While the dynamic systems of underwater vehicle consist of six propellers. The proposed system realizes a real-time control and transmit polarization image through cable connected to information transmission module. Due to its autonomous function, it is capable to achieve missions without diver support. After the system is designed and implemented, a large number of underwater coral images are captured and tested at the offshore of Shenzhen to build our polarization image database. This database classified into 15 different kinds of offshore coral, which provides data support for subsequent algorithms such as clear imaging algorithm based on polarization vision. Finally, clear image processing algorithm is carried out with the captured data.
Restaurant based recommender system based on sentimental analysis

Sentiment analysis is now widely used in the creation of recommender systems in a variety of areas, as well as the restaurant and food service industries. Most restaurant recommender systems based on sentiment analysis, on the other hand, depend only on static data such food quality, cost, and service quality. Personalised suggestions are generated by analysing user views and extracting their dietary preferences, which is a study need in the literature. A context-aware recommender system is suggested in this study, which extracts people&#x0027;s food preferences from their comments and proposes restaurants based on those choices. Finally, adjacent open eateries are suggested depending on how well they match the user&#x0027;s choices. The Kaggle platform was utilised for review, and feedback from various users was gathered. The system&#x0027;s In three scenarios, accuracy, recall, and f-measure are evaluated for the Wu-palmer module: top1, top3, and top5. The same dataset was used to measure the cosine similarity and the Jaccard similarity, and their accuracy and precision were calculated, and these three modules were compared. The findings show that the suggested systems can deliver suggestions with 92.8 percent (Wu-Palmer) accuracy, 87 percent for cosine similarity, and 82 percent for Jaccard similarity. Giving users a high level of precision, Wu-Palmer has the greatest accuracy compared to the other two Cosine and Jaccard Similarity algorithms. Furthermore, in these areas the system exceeds past studies.