Reviewerly/exHarmony huggingface dataset:
Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms

One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer aided diagnosis by intelligent image analysis can help in the diagnosis tasks associated with this disease. Here we propose an automated system for histopathological image analysis that is based on deep learning neural networks with convolutional layers. Rather than a single network, an ensemble of them is built so as to attain higher recognition rates, which are obtained by computing a consensus decision from the individual networks of the ensemble. A final step involves the optimization of the set of networks that are included in the ensemble by a genetic algorithm. Experimental results are provided with a set of benchmark images, with favorable outcomes.
A Novel Optimized Convolutional Neural Network Based On Attention Pooling for Text Classification

Abstract Convolutional neural network (CNN) is widely used in Natural Language Processing (NLP) and has achieved relatively good results. Since the convolutional layer of CNN can be initialized by encoding important semantic features, CNN can extract important semantic features in the convolutional layer. The pooling layer of CNN uses the feature of max-pooling to filter the features, but it does not consider the key information in the sentence and the context semantic information in the text. Therefore, a novel neural network algorithm combining with the attention mechanism and the pooling layer is proposed in this paper to make the model pay attention to the keywords in the sentence and automatically maintain the most meaningful text message, thereby improving the performance of text classification. Our paper uses important information features to initialize the convolution filter so that the model can notice the important semantic features. The resulting feature is enhanced by combining the attention mechanism in the pooling layer, so that the model can maintain the important information features of the text. Experiments show that the proposed model achieves excellent results for multiple text classification tasks, including sentiment classification and topic classification.
Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model

Light field technology has increasingly attracted the attention of the research community with its many possible applications. The lenslet array in commercial plenoptic cameras helps capture both the spatial and angular information of light rays in a single exposure. While the resulting high dimensionality of light field data enables its superior capabilities, it also impedes its extensive adoption. Hence, there is a compelling need for efficient compression of light field images. Existing solutions are commonly composed of several separate modules, some of which may not have been designed for the specific structure and quality of light field data. This increases the complexity of the codec and results in impractical decoding runtimes. We propose a new learning-based, disparity-aided model for compression of 4D light field images capable of parallel decoding. The model is endto-end trainable, eliminating the need for hand-tuning separate modules and allowing joint learning of rate and distortion. The disparity-aided approach ensures the structural integrity of the reconstructed light fields. Comparisons with the state of the art show encouraging performance in terms of PSNR and MS-SSIM metrics. Also, there is a notable gain in the encoding and decoding runtimes. Source code is available at https://moha23.github.io/LFDAAE.
Fundamentals of Machine Learning

This introductory chapter describes the techniques of machine learning. Primarily the concept of machine learning in the context of artificial intelligence and data analytics is explained. The application process of the above to big data is introduced. Classification of machine learning approaches is described. Then some of the variedly used statistical and artificial intelligence-based machine learning techniques are described in brief. The techniques discussed include decision tree, linear regression, least square method, artificial neural network, clustering techniques. The concepts of deep learning are also introduced.
Brief Announcement: Brokering with Hashed Timelock Contracts is NP-Hard

In recent years, many different cryptocurrencies have risen in popularity. Since coins vary in fiat value and functionality, it has become important to securely exchange between them. A common exchange method is hashed timelock contracts (HTLC). However, this method did not support brokerage transactions that allow parties to leverage assets they gain during the transaction. We consider HTLC with brokering. The transaction fees for HTLC is a direct function of the size of the leader set. Thus, brokers are interested in finding the minimum leader set of a given transaction graph. We show that finding the minimum leader set on general transaction graphs with brokering is NP-hard. We then introduce flower transaction graphs, a common type of transaction graphs with brokering, and show that finding the minimum leader set of a flower graph is also NP-hard through a reduction from the knapsack problem.
Formal Methods in Human-Computer Interaction and Adaptive Instructional Systems

Building reliable interactive systems has been identified as an important and difficult task from the late '60s. One approach to augment the reliability of interactive systems is to use formal models during system development. Formal methods have received attention for the design and analysis of human-computer interaction (HCI) for thirty years. The field of adaptive instructional systems (AIS) in general, and intelligent tutoring systems in particulars, have been mostly relying on empirical methods for training systems validation (the system supports learning), rather than formal methods for verification (the system meets its specifications). Empirical methods focus on the validity of pedagogical interventions at the individual task and problem sequence levels, using learning analytic methods such as Bayesian knowledge tracing, additive factors models, or machine learning models of human performance. The purpose of the paper is to explore some parallel and the applicability of HCI formal models to AIS. The paper: a) presents key concepts related to HCI formal models using semi-formal representations (workflow graphs), b) gives examples of formal properties to be verified, c) discuss briefly formal notations, and d) defines adaptive human-computer interaction. The last section of the paper discuss the similarity between HCI formal models and AIS standard modules, and identifies some area of applicability of HCI formal models to AIS design, recognizing the central value of AIS empirical methods at the foundation of AIS iterative design.
SP‐search‐based path planning algorithm for mobile robots using quintic trigonometric Bézier curves

Abstract Path planning is an essential step for mobile robots. This paper presents a novel shape parameter search‐ (SP‐Search) based path planning algorithm for mobile robots using quintic trigonometric Bézier curve and its two shape parameters. The proposed search algorithm recursively divides the shape parameter space into a grid and uses a multi‐objective function to determine the shape parameter values. The multi‐objective function is expressed by the sum of four terms multiplied by coefficients: the integral of the squared jerk, the integral of the squared curvature, the mean of minimum distances between the predefined skeleton and quintic trigonometric Bézier path, and the arclength. Optimal shape parameters can be determined according to the required cases by the user using four coefficient values. Experimental results illustrate the feasibility of the proposed SP‐Search algorithm. Also, we have developed another intuitive algorithm, the Brute Force Grid Search algorithm, which is the brute force approach for shape parameter search to compare the performance of the proposed SP‐search algorithm for path planning problems as a baseline. The experimental results show that the SP‐Search algorithm finds better shape parameter values with a considerably small number of searched points.
Medical Data Encryption based on a Modified Sinusoidal 1D Chaotic Map and Its Microcontroller Implementation

This work considers a modification of the map proposed in Wang et al. (2020), by replacing its fixed term by a squared hyperbolic tangent term. The modified map is studied and is shown to have a plethora of chaos related phenomena, like period doubling route to chaos, crisis, antimonotonicity, and an infinite number of equilibrium points inside a bounded domain. Then, a pseudo-random bit generator is designed from the given map and it is applied to the encryption of medical data. Specifically, an electrophysiological signal is considered. The resulting design is finally implemented on a 32 bit microcontroller.
Interactive Anomaly Detection in Dynamic Communication Networks

Network flows are the basic components of the Internet. Considering the serious consequences of abnormal flows, it is crucial to provide timely anomaly detection in dynamic communication networks. To obtain accurate anomaly detection results in dynamic networks, supervision from experts is highly demanded. However, to obtain high-quality ground truth of abnormal flows, we suffer from two major problems: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">limited labor resources</i> : experts with the latest domain knowledge are much fewer than the large number of flows; and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic environment</i> : considering the new abnormal patterns (i.e., new attacks) and continuously changing network structures, it requires timely supervision to adaptively update the parameters. To tackle these problems, we propose HADDN, a novel bandit framework for periodic-updated anomaly detection in dynamic communication networks. We formulate the task as a bandit problem, where by interactions, supervision is offered by human experts to provide the ground truth to a fraction of flows. We construct semi-parametric expected rewards to optimize the estimation of flows’ abnormality in limited interactions. Also, we utilize feature-based clusters and structural correlations to make connections between historical flows and new flows to improve both efficiency and accuracy of abnormality estimation. What’s more, we provide two implementations for the semi-parametric expected reward of the proposed HADDN with theoretical proof. Experimental evaluations on public datasets demonstrate the substantial improvement of our proposed approaches compared to state-of-art anomaly detection methods.
Generalized Optical Flow based Motion Vector Refinement in AV1

Bi-directional optical flow (BDOF) is a coding tool that has been recently adopted into Versatile Video Coding (VVC) standard. In BDOF, gradients of prediction samples are exploited to refine motion vector (MV) per subblock within a prediction block, and thus enhance inter prediction quality in a two-pass framework. In this work, we extend the concept of BDOF to a more general compound prediction framework and integrated this framework into the AV1 codec. In particular, we support MV refinement not only in bi-directional compound prediction, but also in uni-directional prediction, where the two reference blocks are both from the past or both from the future. Furthermore, two reference blocks are allowed to have arbitrary temporal distances to the current block. For implementation on the AV1 codec, five additional inter compound modes have been added, and the proposed method is performed on top of those modes. Experimental results show more than 2.8% BD rate saving on the Google test set.
Hausdorff IoU and Context Maximum Selection NMS: Improving Object Detection in Remote Sensing Images With a Novel Metric and Postprocessing Module

The object detectors based on deep convolution neural network have achieved significant success in the field of remote sensing images. Intersection over Union (IoU) and No-maximum suppression (NMS) are the essential components of state-of-the-art anchor-based object detectors. However, as a localization evaluation metric, IoU does not precisely match the boundary box regression, leading to inaccurate regression of the object detector. Therefore, we introduce Hausdorff distance and combine it with IoU as a new evaluation metric (HIoU). NMS is an integral part of the object detection pipeline. However, it may lose relatively small object information in the case of high overlap. Because of the denseness of objects, this defect is more prominent in remote sensing image object detection. Therefore, we consider the context information of location confidence and propose the context maximum selection NMS (Cms-NMS) algorithm. Finally, we integrate HIoU and Cms-NMS into state-of-the-art object detectors, respectively. The performance of these object detectors is improved on the benchmark datasets NWPUVHR-10 and RSOD without any additional hyperparameters. The experiments show that HIoU and Cms-NMS are compatible, and using them together can further improve the detectors’ accuracy.
Deep Reinforcement Learning for Intelligent Cloud Resource Management

For cloud computing, elaborately managing resources and workloads to optimize various metrics such as performance, cost and energy is of strategic importance, but by no means trivial. Traditional model- or heuristic-based solutions are highly knowledge- and labor-intensive, as well as problem-specific. Recently, with the booming of AI, researchers in cloud computing community are motivated to revisit cloud resource/workload management problem by applying the emerging deep reinforcement learning (DRL) method. In this paper, we first identify the motivations of applying DRL to the long-standing and challenging cloud management problems. Then we provide a selective survey of the recent advances with analysis of their design principles and benefits. Based on those pilot attempts, we summarize the general workflow and conduct a case study to illustrate how to apply DRL for intelligent cloud resource/workload management. The goal of this article is to provide a broad guideline on DRL-based intelligent cloud management to help stimulate researchers to develop innovative algorithms, frameworks and standards.
Optimizing UI layouts for deformable face-rig manipulation

Complex deformable face-rigs have many independent parameters that control the shape of the object. A human face has upwards of 50 parameters (FACS Action Units), making conventional UI controls hard to find and operate. Animators address this problem by tediously hand-crafting in-situ layouts of UI controls that serve as visual deformation proxies, and facilitate rapid shape exploration. We propose the automatic creation of such in-situ UI control layouts. We distill the design choices made by animators into mathematical objectives that we optimize as the solution to an integer quadratic programming problem. Our evaluation is three-fold: we show the impact of our design principles on the resulting layouts; we show automated UI layouts for complex and diverse face rigs, comparable to animator handcrafted layouts; and we conduct a user study showing our UI layout to be an effective approach to face-rig manipulation, preferable to a baseline slider interface.
A Historical Review of Immersive Storytelling Technologies

This chapter is focused on describing the history and the current relevance of user experience (UX) techniques that combine data science and AI in the research field of interactive and immersive storytelling, including virtual and augmented realities. It initially presents a brief history of interactive storytelling, video games, VR and AR, AI and data science, and the user experience (UX) techniques used in those areas. Later, the chapter describes the UX techniques in depth, using AI and data science that work best and are more useful for testing interactive media products, describing examples of its applications briefly. Finally, the chapter presents conclusions in relationship with utopias and dystopias regarding the future use of UX, AI, and data science in several areas such as edutainment, social media, media arts, and business, among others.
Real-Time Sentiment Analysis for Polish Dialog Systems Using MT as Pivot

We live in a time when dialogue systems are becoming a very popular tool. It is estimated that in 2021 more than 80% of communication with customers on the first line of service will be based on chatbots. They enter not only the retail market but also various other industries, e.g., they are used for medical interviews, information gathering or preliminary assessment and classification of problems. Unfortunately, when these work incorrectly it leads to dissatisfaction. Such systems have the possibility of contacting a human consultant with a special command, but this is not the point. The dialog system should provide a good, uninterrupted and fluid experience and not show that it is an artificial creation. Analysing the sentiment of the entire dialogue in real time can provide a solution to this problem. In our study, we focus on studying the methods of analysing the sentiment of dialogues based on machine learning for the English language and the morphologically complex Polish language, which also represents a language with a small amount of training resources. We analyse the methods directly and use the machine translator as an intermediary, thus checking the quality changes between models based on limited resources and those based on much larger English but machine translated texts. We manage to obtain over 89% accuracy using BERT-based models. We make recommendations in this regard, also taking into account the cost aspect of implementing and maintaining such a system.
Stereoscopic Video Quality Assessment in the Context of Internet of Things

Under the background of Internet of Things, users pursue a higher quality of immersive visual experience, including virtual reality, augmented reality, and stereoscopic information. In order to ensure the quality of experience, an effective quality assessment method is proposed to accurately determine the quality of information. Specifically, for the large amount of stereo video data, the representative sequence of stereo video is extract to reduce the data volume. Inspired by binocular channel theory, operators of the difference map and the novel fusion map are executed on these sequences. Then a dynamic texture descriptor, known as volume local binary pattern, is employed to represent the spatio-temporal domain to predict the stereo video quality. Experiments have been executed on two public databases and results show that our method has excellent quality prediction capability.
Transcending Doors in Library Service Provision

Libraries are storehouses of information that support decision making in society. The COVID-19 pandemic shook the foundations of the operations of libraries. Libraries had to shut their doors on their cherished patrons. This created information hunger and anxiety. Librarians were also locked outside their libraries and could not provide access to the information resources within their libraries. Using technology, services such as remote/off-campus and online services were provided to users. Looking through the lens of the pandemic, the attention of librarians is drawn to the need to invest in electronic resources, the need to invest in emerging technologies for the provision of library services, and the need for disaster management policies and plans.
Research on College English Teaching Model Based on Decision Trees

English teaching has always attracted much attention.However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives.Teachers attach particular importance to the choice of the curriculum structure and teaching material.Students are busy comprehending the assignments their teachers deem important.Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored.The random forest algorithm in machine learning applications could play important role improving on the current English teaching system.A random forest model is constructed using a decision trees selection method, which focuses on 19 attributes of the English teaching model.Results show, to begin with, that the indigenous teaching plan and environment fail to adapt to the pace of knowledge iteration in the era of big data.Moreover, interactions between teachers and students appear to be shallow with little constructive interaction, causing a decline in the relationship between teachers and students.Last, there is still no signs of any legitimate construction in terms of in-person English teaching, relativity attribute, corpus and platform.Therefore, this paper has proposed a new English teaching model to adapt to the current college English teaching environment.The experimental results show that the method is effective and feasible for the current college English teaching.
Empirical Study of Most Popular PHP Framework

There have been several PHP frameworks for developers to pick from in the latest years. Developers face problems choosing the most relevant frameworks and appropriate support functions to include in their project. For this reason, a clear understanding of the various frameworks is now becoming an important necessity for web developers. In this paper, we collected several research papers talking about different PHP frameworks, and we compared them with each other in several ways which are: Request per second, Memory usage, Response time and, Number of files using several PHP frameworks: Laravel, CakePHP, Codeigniter, symfony2, Phalcon, Symfony, and Yii. the Laravel outperforms other MVC framework in request per second, memory usage. On the other hand, phalcon outperforms other MVC frameworks in response time and number of files.
Formalizing Axiomatic Systems for Propositional Logic in Isabelle/HOL

We formalize soundness and completeness proofs for a number of axiomatic systems for propositional logic in the proof assistant Isabelle/HOL.