Reviewerly/exHarmony huggingface dataset:
Transfer Learning and Attention Mechanism for Breast Cancer Classification

Since the existing deep learning frameworks still cannot meet the high requirements of accuracy and efficiency in practical clinical diagnosis, a breast cancer classification model based on deep transfer learning and visual attention mechanism is proposed. Firstly, in order to overcome the overfitting effect brought by a small number of samples of breast histopathological images, transfer learning is carried out on the basis of two network frameworks, namely VGG16 and ResNet50, and the ImageNet dataset is used as the transfer source domain and the shallow convolutions are frozen. By doing this, training efficiency can be effectively improved since there is no need to train shallow general features repeatedly. Secondly, the attention modules are combined to focus on the feature description of breast lesions. The classification accuracy and efficiency of the model can be effectively improved since they vastly reduce the redundant and interference information. Finally, the soft voting is applied to fuse the class probability outputs from the two individual classifiers to obtain the final classification result. The experimental results of the BreaKHis dataset show that the two-classification accuracy of the proposed network reaches 99.45%, and the eight-classification accuracy reaches 94.11%. Compared with some recent breast image classification algorithms, such as BiCNN, CSDCNN, BHCNet, the proposed method is better in terms of many indicators, such as Accuracy, Precision, Recall, and F1_ score.
Effect Of The Learning Rate, Hidden Layer, And Epoch In Lung Cancer Prediction

Lung cancer is one of the deadliest diseases in the world after breast cancer, with a prevalence rate of 11.4%. This research aims to predict the level of accuracy of whether a person is diagnosed with suspected cancer or not. The method used in this study uses a backpropagation neural network using a combination of the learning rate, hidden layer, and epoch values. The results showed that the learning rate value of 0.1, the value of hidden layer 100, and epoch 1000 resulted in predictions with the best accuracy value of 97%.
An Approach Of Dismissal Of Noise In Face Recognition Using Convolution Neural Network (CNN)

The motivation for this paper is the trend in deep learning and artificial intelligence. In last few years, there has been lot of advancement in face recognition technique. The problem faced in this technique is noise in the data-set. We have tried to overcome this problem by using a code which enables the webcam and collects the data. Deep learning is used in a order to detect face. Algorithm consists of following procedure: Calculating Integral Image, computation Haar like elements, AdaBoost Learning design, Cascade Filter. The following steps were used for coding. Further, training of algorithm and followed by testing. We need to train the algorithm. Further testing is done. Large part of raw data is used for training the algorithm and a small part of the raw data are used for trail the algorithm. It is useful for security purpose, biometric, it can also be used for collecting attendance. It can be further improved by adding Infrared (IR) temperature sensor. This will help us not only to recognize the face but also provide the body temperature.
Mining User Reviews for Software Requirements of A New Mobile Banking Application

Migration to the new system or application is very challenging, especially if the users have to adapt to a new application that is implemented with direct conversion technique. It triggers many user reactions, one of them is their opinions and rate about the application in play store (Google Play Store for example). Application reviews can be used to elicit user requirements or to verify requirements. This paper demonstrated the result of mining application reviews to support software requirements elicitation. It motivated by research area natural language processing (NLP) for requirement engineering (RE). Training and testing conducted to a dataset contains about 1200 application reviews of a new mobile banking application by classifying them into two classes (req and other) using Multinomial Na&#x00EF;ve Bayes algorithm. Req is for opinions that contain requirement such as feature addition or user interface (UI) request while other is label for opinions/reviews contain non-requirements. The classification performance measured are accuracy score 0,8220 and one of class that has higher classifier performance is &#x201C;other&#x201D; class with value precision 0.83, recall 0.94 and F1 0.99. Even though, the result is not optimal yet, especially for &#x201C;req&#x201D; class, this research already implemented all categories of NLP technologies such as NLP techniques, NLP tools, and NLP resources.
A Distributed Decision Tree on Hadoop

Decision trees have become a very popular form of supervised learning due to their ease of implementation and understandability, ability to work with non-normalize data, and low runtime when predicting class labels. Unfortunately, these benefits come at the cost of a large runtime when constructing the tree that greatly increases as the size of the data set increases. The objective of this study is to implement a decision tree using Hadoop, which can greatly reduce runtime over large data sets and offers scalability for future changes in the size of the data.
Digital Signatures

A digital signature is a cryptographic protocol that ensures the authenticity of a message. In this book chapter, we will discuss some algorithms for digital signature. We will also discuss some algorithms for the blind signature scheme, undeniable signature schemes, short signature schemes, and Hierarchical identity-based signature schemes. We will also discuss Signcryption.
Active Update of Mutation Matrix Adaptation for Variable Metric Evolution Strategy

Covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful evolutionary algorithms. To improve the efficiency of CMA-ES and make sufficient use of the sampled points, we propose an active update scheme for the mutation matrix which approximates the factorization of the covariance matrix. The active update effectively reduces the variance along undesired search directions with negative weights. We design a set of symmetric weights for all the sampled points as well as a larger learning rate. We conduct extensive experiments and show that the proposed active update scheme effectively accelerates the convergence to the optimum.
A Blockchain-based Access and Management System for IoT Devices

Recent developments in distributed ledger technologies have created a whole new set of possibilities in the way of managing trust, security, privacy and traceability in computer-based transactions, principles which are increasingly gaining importance in the world of IoT. Currently, IoT devices are generally based on centralized, client-server systems, where digital service providers have complete control over user data and information generated by their devices. In this paper we present the development of a decentralized access and management system for IoT devices, where operations on these devices, such as the installation and management of apps are handled by a blockchain-based identification and record system. The system prototype consists of a smartphone application acting as a management hub for the whole system, an IoT device API implementation for allowing secure access to management and data functionalities, and a set of smart contracts on the Ethereum blockchain, where all necessary information for the functioning of the system is stored. The system allows app developers to provide their apps as Docker containers for IoT devices without the need to publish them on a centralized app store, and a regular user can subscribe to and access the available applications by paying in cryptocurrency without revealing any private information to the system. A cost and performance evaluation have been performed to assess the feasibility of the proposed solution.
Learning Context-Aware Representation for Event Detection

Current event detection(ED) task has confronted with low-resource scenarios which lead to the limited information extraction for event triggers. Regular methods shows weak performance of the appropriate trigger representation learning ability. In this paper, we integrate event trigger context similarity from the aspect of weakly-supervised learning for original event detection model. In model's training part, we use KL-divergence objective to optimize event trigger context representation. With the help of auxiliary objective of text similarity learning, the original trigger context could show more categoric semantic representation in feature space. Above all, with the architecture consisting of BERT and sequence labeling, we could detect event triggers with types simultaneously and as our experiment result shows on ACE2005 dataset, our approach shows advantages in event detection task.
Introduction

In recent times, digital communication plays a critical role in the day to day life. With the massive growth of the digital world, there is always a concern about information security. In order to understand the security aspects, one needs to recognize the importance of signal processing. This is an area which deals with various operations of signals such as storing, transmitting, and filtering, etc. A signal is a well-known term used in electronics and telecommunications to carry information for an event. It can be represented as a kind of variation or any observed change in the occurrence, for example: a sound produced while speaking is referred to as speech signals, the heart functioning can be recognized as waves represented by electrocardiograms, television and mobile generates a signal in communication field, the two dimensional signals dealing with image referred to an image signal, etc.
Cloud Computing in the Palestinian IT Sector

Cloud Computing is a paradigm that refers to resources, services, infrastructure, and various applications available on demand through the internet, based on pay-per-use business model, without the need to have local resources. Thus, transfer from locally installed applications to internet-based services. Palestinian IT sector is a young and improving sector and cloud computing plays a major contribution in it. Multiple studies can be found in the literature discussing adoption factors and challenges of cloud computing in general. However, little work is available to investigate cloud computing adoption factors and challenges in the Palestinian software development companies. In this research we attempt to evaluate the cloud computing status in the Palestinian software development companies; identify the factors that affect software development companies&#x0027; intention to adopt cloud computing; and to determine the challenges of applying cloud computing in IT sector. We conducted a qualitative study; developers from eight Palestinian software companies were interviewed, followed by a questionnaire based survey, with 35 respondents from IT sector. The results suggest that cloud computing is widely adopted in the Palestinian development firms, it also shows that the main motivations and cloud adoption factors in the Palestinian development companies are: hardware and software cost savings, time saving, effort and management, availability, achieve greater scalability, flexibility and productivity. Furthermore, the study identifies challenges that face the Palestinian software development companies such as security issues, privacy, technical issues and lack of cloud experts.
Imagination with Generative Adversarial Networks for Object Detection

Assistive and home service robots have been an important as well as lucrative applications in the burgeoning robotics technology. In this paper we introduce an approach for home service mobile robots to assist and fetch objects based on speech commands given by the user, from its surroundings. We have tried to make the robot learn to fetch using imaginative learning (TI) by making use of text to image GANs in the object detection pipeline. The robot will fetch the object from different rooms in the environment by imagining the object based on the user&#x0027;s speech command and compare it with real ones. We use deep learning techniques and models such as speech to text, text to image deep learning, deep image similarity, classical computer vision, SLAM and path planning of robot to achieve the overall fetching object pipeline and finally compare similarity score of generated images and detected images using Deep Image Similarity by the robot.
Literature Review

Time expressions and named entities play important roles in data mining, natural language processing, and cognitive computation. Researchers from these areas have devoted considerable effort in the last two decades to define the problem of time expression analysis, design the standards for time expression annotation, build annotated corpora for time expressions, and develop methods to identify time expressions from free text. In this literature review, we go through the previous works, aiming to draw an overview of the development of time expression and named entity analysis in different aspects, such as language factor, domain and textual type, types of time expressions, and the evaluation and resources for the time expression analysis as well as methods that researchers develop for time expression recognition and normalization, named entity recognition and classification. In addition, we also review the development of jointly modeling multiple syntactic and semantic tasks.
Lossless Compression of Hyperspectral Imagery by Assimilating Decorrelation and Pre-processing with Efficient Displaying Using Multiscale HDR Approach

The instinctive essence of the Hyperspectral imagery cube is its immense information having both spatial and spectral correlation. In the interest of reducing the storage and bandwidth requisites, an effective lossless Hyperspectral compression system is proposed. The imagery is enforced to preprocessing stage preceding decorrelation. Preprocessing stage comprises band normalization and band ordering techniques. A technique named Greedy heap sorting is addressed to sort the bands. The proposed plan accords with a Compression ratio (CR) of 8.12 and bits per pixel (bpp) of 1.67. The performance of the system is comparable to earlier algorithms for lossless Hyperspectral image compression concerning compression ratio and bpp. An experiment conducted on AVIRIS images substantiates that the methodology presented surpasses the IP3-OBPS-BPS method by a percentage increase of 116.44 in CR and a percentage decrease of 58.49 in bpp. The multiscale High Dynamic Range (HDR) approach is used to project Hyperspectral images on devices with Low Dynamic Range (LDR) devices. The Bilateral filter is used for the decomposition of the image into multiple base layers and detail layer. The PSNR obtained is 37.4911 for the compressed HDR image, which signifies the better quality of the reconstructed image with a 4.8% compression ratio.
Conceptual Framework for Next-Generation Software Ecosystems

This paper proposes a conceptual framework for developing new technologies that will solve today&#x2019;s technical issues in software development and operations (DevOps) and support the future software ecosystems. The proposed framework perceives resources essential for software DevOps from three perspectives: products, people, and technical information, and actively utilizes and link the latest digital technologies such as AI, natural language processing, microservices, and blockchain. The goal is not to aim fully automate software DevOps, but also to achieve high economic efficiency and sustainability by eliminating waste in software DevOps, assuming a human-centered society. The principal approaches of new technology development in the framework are "product up-cycling", "placement of the right people and AI in the right places", and "quality control linked to external technical information." New technologies to be developed with these approaches will expand conventional concepts in software DevOps with three dimensions of "reuse," "human resources," and "quality control."
Usability Engineering

In order to achieve usabilityUsability and positive user experienceUser experience also in an intercultural context, cultural influences on usability and UX as well as cultural influences on usability engineeringUsability engineering have to be investigated. In this chapter, the theoretical background regarding interculturally adapted human–machine interactionInteraction (HMI) is examined and cultural aspects are linked with models, roles, and principles of the human-centered design process (planning and design activities) and then transferred to intercultural usability engineeringUsability engineering . Subsequently, the steps of the process of the intercultural usability engine are presented.
Big Data Analytics in Cloud Computing

In this paper we will study the two unlike but related technologies &#x2013; Big Data and cloud computing &#x2013; and also examines the benefits and outcomes of using cloud computing for Big Data analytics. As information is being produced at a phenomenal scale and it is originating from every direction, such a monstrous measure of information makes enormous or complex informational indexes. These informational collections are known as Big Data. The blend of cloud and big data can be attributed to fresh IT (Information Technology) wave that is causing remarkable progress across IT departments of various industries. In this new environment of big data and cloud there are challenges associated with data storage as data are accumulating rapidly.With rapidly expansion of Big data requires government agencies to extract relevant data and make sense of it in order to make evidence-based policy decisions, with a focus on translating data into information and subsequently information into knowledge. The way to do this is to employ so-called big data analytics, which involves analyzing numerous data sets in order to reveal information such as specific patterns, correlations, and trends, among other things.Big data analytics places rigorous needs on networks, storage, and servers. This is why few businesses use the cloud for this. Big data and cloud are combining to create new business prospects that support big data research while also overcoming numerous architectural challenges.For these mutually exclusive principles to coexist a solution architecture is required to truly exploit. Future breakthroughs and research difficulties in cloud computing that support Big Data analytics are presented in this review.
Knowledge distillation

The need to develop faster, more lightweight and flexible Deep Learning (DL) models has led to the development of a wide variety of methods. Among the most well-known methods for improving the accuracy of lightweight DL models is knowledge distillation, also known as knowledge transfer. Knowledge distillation is capable of improving the effectiveness of the training process by transferring the knowledge encoded in a large and complex neural network into a smaller and faster one. This chapter aims to provide an introduction to knowledge distillation approaches by presenting some of the most representative methods that will equip the reader with the necessary knowledge and tools to apply these methods in practice, as well as to follow this rapidly advancing field. First, we present the seminal neural network distillation approach, which kick–started the field. Then we present a generalization of this approach, which provides a general probabilistic view on knowledge distillation, allowing for going beyond classification tasks and overcoming some significant limitations of earlier methods. Multilayer knowledge distillation approaches, which employ multiple layers for the process of knowledge transfer and further improve the effectiveness of distillation, are also presented and discussed. Finally, we present and discuss some more advanced ways to train the teacher model, allowing for deriving more effective distillation methods, such as online distillation methods, which allow for simultaneously training both the teacher and student models, as well as, self-distillation methods, which do not even use a different teacher model and are capable of reusing the knowledge extracted from the student model.
SmartCrypt: Secure Storing and Sharing of Time Series Data Streams in IIoT

To provide ubiquitous access, scalability and sharing possibilities, the Industrial Internet of Things (IIoT) applications utilize the cloud to store collected data streams. However, secure storing and sharing of the massive and continuously generated data poses significant privacy risks, including data breaches. This paper proposes SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data using a novel symmetric homomorphic encryption scheme. Simulation results show that SmartCrypt reduces query time by 17% and improves through-put by 9% over the benchmark scheme.
Application of Digital Map Technology for Selection of the Fastest Path in the Smart City of Jakarta

The background of this research is how to find out the best application in choosing the fastest route based on existing digital map applications. This article will discuss the advantages and disadvantages of an application, especially digital maps, especially in the city of DKI Jakarta. the research method is a literature review method and uses a survey tool to the people of the city of Jakarta which contains the use of digital map applications so that they can get to their destination quickly by using a literature review so they can find the latest research problems so that they can determine clear and focused research problems. The problem in this research is how to find out the best application to determine the fastest route to the destination. With this transportation application, the use of digital maps will be maximized so that it can help people reach their destination quickly and can reduce congestion on the highway. The purpose and objective of this study is to provide the best advice for the people of DKI Jakarta so that they can choose the appropriate application and can help quickly achieve their goals, especially being able to avoid congestion on the road and can help the police reduce the density of vehicles on the highway, the digital map with the highest level of accuracy is Google Maps with 92%, and the digital map with the lowest level of accuracy is Scout GPS Navigation with 84%. Then the percentage of application convenience level that has the highest value is Google Maps with a percentage of 90%, and the lowest percentage of application ease with a percentage of 83% is Back Country Navigator.
Propositional union closed team logics

In this paper, we study several propositional team logics that are closed under unions, including propositional inclusion logic. We show that all these logics are expressively complete, and we introduce sound and complete systems of natural deduction for these logics. We also discuss the locality property and its connection with interpolation in these logics.