Reviewerly/exHarmony huggingface dataset:
An Improved Faster R-CNN for Colorectal Cancer Cell Detection

Image recognition and object detection of colorectal cancer cell are significant in the medical community. It can help the doctors to diagnose the disease, assess the patient's cure status, and choose a suitable treatment plan. At present, object detection methods based on neural networks have been widely used. However, when applying to the colorectal cancer cell' image object detection, it has a shortage due to the complex background and small object cells. Because of this problem, this paper proposes an improved Faster R-CNN algorithm with multi-scale detection and a multi loss function. In our algorithm, during the feature extraction period, multi-scale detection is not only retaining the semantic information but also the details of the edges and textures of the image. During the recognition period, the discriminative features of cells can be learned from the multi loss in the complex background. The experiment result shows that the accuracy of the improved Faster R-CNN is 2.4% higher than that of the Faster R-CNN.
Aether - A Novel Method to Eliminate False Positives in Private Automated Contact Tracing

Contact tracing has shown promise to mitigate the negative effects of serious infectious diseases, such as COVID-19. Contact tracing can identify people who have potentially been exposed to COVID-19, as well as to enforce quarantine rules. However, many of these methods store location data, and therefore present privacy concerns. Bluetooth technology provides an effective solution to avoid violating fundamental privacy rights. However, Bluetooth technology does not have a good way of knowing whether devices are on the same level or different levels. In the case of multistory buildings, false positives can occur between two people who are within close proximity but on different floors. This paper proposes a novel solution in which the built-in barometer in mobile devices is used in combination with Bluetooth to avoid false positives. This paper establishes the viability and effectiveness of the solution through various experiments to make the current Bluetooth contact tracing technology even more accurate, which will ultimately save countless lives.
WKLM-FA: A Web Application for Automated Human-Computer Interaction Modeling of Web Form Filling Tasks

This paper presents the web-based KLM-Form Analyzer (WKLM-FA), a web application that allows the automated estimation of time that a user needs to fill out a web form. Based on feedback from previous studies, this implementation comes to fulfill the need for a cross-platform application of human-computer interaction models, namely the Keystroke Level Model (KLM) and Fitts's Law. WKLM-FA is a new web-based implementation of the KLM-Form Analyzer (KLM-FA), a desktop application that we have developed in our previous work. WKLM-FA also provides additional functionality compared to KLM-FA, such as a user account mechanism, an evaluation tracking feature, and an admin panel to support multiuser functionality. WKLM-FA is the first online KLM-based time estimation tool for web forms and it can be easily used not only by HCI evaluators but also for educational purposes teaching the KLM and the Fitts's law.
Iterative Phase Functions on the Circle and Their Projections: Connecting Circle Maps, Waveshaping, and Phase Modulation

In memoriam of Jean-Claude Risset’s recent passing, we revisit two of his contributions to sound synthesis, namely waveshaping and feedback modulation synthesis as starting points to develop the connection of a plenthora of oscillatory synthesis methods through iterative phase functions, motivated by the theory of circle maps, which describes any iterated function from the circle to itself. Circle maps have played an important role in developing the theory of dynamical systems with respect to such phenomena as mode-locking, parametric study of stability, and transitions to chaotic regimes. This formulation allows us to bring a wide range of oscillatory methods under one functional description and clarifies their relationship, such as showing that sine circle maps and feedback FM are near-identical synthesis methods.
LDA-based Speaker Verification in Multi-Enrollment Scenario using Expected Vector Approach

Multi-Enrollment scoring scenario, where multiple utterances are available for an enrollment speaker, is one of the less explored problems in the Probabilistic Linear Discriminant Analysis (PLDA) scoring literature. Since the closed-form PLDA scoring formula for multi-enrollment scenario is impractical, alternate heuristic approaches are widely used for such scenarios in both i-vector and x-vector based speaker verification systems. In this paper, we describe an Expected Vector approach to obtain a vector from multiple enrollment utterances. Expected Vector approach uses a trained PLDA model to compute the expected class center given a set of vectors for that particular PLDA model. By using such an approach, a more meaningful class center representation can be obtained. This vector can be used to score a trial using two-vector scoring formula for a given PLDA model. We compare the performance of the proposed approach with various heuristic approaches and show that it provides significant improvements in terms of Equal Error Rate (EER) and minimum Detection Cost Function (minDCF). We show our results on x-vector system trained on Voxceleb dataset with various implementations of PLDA and trials designed on Voxceleb and Librispeech dataset.
Prediction of Software Bugs Using Machine Learning Algorithm

Programming Bug Prediction is a significant issue in programming advancement and upkeep forms, which worries with the by and large of programming victories. This is on the grounds that foreseeing the product blames in prior stage improves the product quality, unwavering quality, proficiency and diminishes the product cost. Nonetheless, creating hearty bug expectation model is a difficult errand and numerous procedures have been projected in the writing. This exhibits a product bug forecast form dependent on AI calculations. The managed ML calculations have been utilized to foresee future programming flaws dependent on authentic information. The classifier is bolster vector relapse. The assessment procedure indicated that Machine Learning calculations be able to be utilized adequately with elevated precision rate. Moreover, a correlation measure is functional to contrast the projected expectation form and different methodologies. The gathered outcomes indicated that the AI approach has a superior exhibition.
Automatic Camera Switching in Soccer Game using Decision Tree

Cameras management is challenging due in to the need of professional director for controlling and switching between cameras to select the best scéne among them. The aim of this paper is substituting the director of soccer game broadcasting through building automatic camera switching system that works as a TV director that discovers the best scene from a set of cameras and broadcasts it over the network in real time, taking into account the cinematic instructions represented by using the 180-degree rule when filming and avoiding jumping between scenes. Three cameras broadcasting the soccer game in real-time. Every scene in each camera is been analyzed and evaluated by using five parameters are frame direction, previous frame direction, finding ball, number of players, and their position. Many algorithms were used for analyzing the frame such as optical flow for determining the direction of frame, Euclidean distance and Extended Kalman filter for finding the ball, and contour and moment for detecting the players and determining their position. The results of the research were the proposed work was able to create one video stream from three video sources.
Flow visualization by Matlab® based image analysis of high-speed polymer melt extrusion film casting process for determining necking defect and quantifying surface velocity profiles

The primary objective of this research paper is to detect and quantify the necking defect and surface velocity profiles in high-speed polymer melt extrusion film casting (EFC) process using Matlab® based image processing techniques. Extrusion film casting is an industrially important manufacturing process and is used on an industrial scale to produce thousands of kilograms of polymer films/sheets and coated products. In this research, the necking defect in an EFC process has been studied experimentally and the effects of macromolecular architecture such as long chain branching (LCB) on the extent of necking have been determined using image processing methodology. The methodology is based on the analysis of a sequence of image frames taken with the help of a commercial CCD camera over a specific target area of the EFC process. The image sequence is then analyzed using Matlab® based image processing toolbox wherein a customized algorithm is written and executed to determine the edges of the extruded molten polymeric film to quantify the necking defect. Alongwith the necking defect, particle tracking velocimetry (PTV) technique is also used in conjunction with the Matlab® software to determine the centerline and transverse velocity profiles in the extruded molten film. It is concluded from this study that image processing techniques provide valuable insights into quantifying both the necking defect and the associated velocity profiles in the molten extruded film.
Toward a robust Maghrebian Font Segmentation and Classification

With the development of deep learning methods and the technologies behind, developing an Optical Character Recognition system able to treat complex historical fonts becomes possible. In this paper, we intend to evaluate and compare different segmentation and classification methods. The reported results suggested the best segmentation algorithm and the best classification architecture based on deep learning theory to deal with the specificity of the Maghrebian Font Database.
A Dynamic Feature Weighting Method for Mangrove Pests Image Classification with Heavy-tailed Distributions

Most of the data in agriculture and forest obey heavy-tailed distribution, in which the data distribution is extremely imbalanced and the amount of most classes is poor. In this paper, we propose a feature centroid based dynamic feature weighting method between head and tail classes for mangrove pest images classification. In the proposed method, firstly, we design a convolutional neural network to extract the clustering centroid as a representation of the subclass. Secondly, we use the centroid feature memory to achieve the dynamic feature weighting through extraction, injection and fusion, which enrich the feature space of the tail classes while retaining identity. Thirdly, to localize the pests in natural background, we optimize the weakly-supervised localization algorithm with a multiscale local maximum index strategy to extract saliency maps. Furthermore, to carry out our research we establish a heavy-tailed distributed image dataset named MIPDGC based on mangrove forest pest. The experimental results prove that the dynamic feature weighting method can obtain higher classification accuracy and better performance than other general methods on the classification accuracy of the MIPDGC.
Quantum Algorithms and Methods

This chapter is devoted to basic quantum algorithms and methods. The chapter starts by revisiting the quantum parallelism concept and describing its power in calculating a global property of a certain function by performing only one evaluation of that function, namely Deutsch’s and the Deutsch–Jozsa algorithms. We also describe the Bernstein–Vazirani algorithm, which is able to determine a string encoded in a function in only one computational step. Furthermore, the Grover search algorithm to perform a search for an entry in an unstructured database is described. Next, the quantum Fourier transform is described, which is a basic algorithm used in many other quantum algorithms. In addition, an algorithm to evaluate the period of a function is provided. How to can crack the Rivest–Shamir–Adleman encryption protocol is also described. Then, Shor's factorization algorithm is provided. Furthermore, Simon's algorithm is described. Next, the quantum phase estimation algorithm is described. Quantum computational complexity and Turing machine representation are discussed as well. The quantum circuits are imperfect, which prevents us from running well-known quantum algorithms using the gates-based quantum computing approach. To solve this problem, adiabatic quantum computing and variational (quantum) circuits are introduced. After summarizing the chapter, a set of problems is offered to provide a deeper understanding of the material presented in this chapter.
Stereo Extrapolation: View synthesis with multiplane images

We present a solution to the view synthesis problem. This problem has gained a lot of attention recently because of some new applications like augmented and virtual reality. View synthesis means creating novel views for a scene given some known images. In this research, we try to enhance the current methods for stereo baseline magnification. We built our proposed model upon an existing technique that uses the layered representation called multiplane images (MPIs). Our model uses the same representation but we argue that our model can reconstruct better output images using a different mixing technique. Our model can produce more compelling results with less blur and noise.
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search

Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This work extends top-k MIPS with a budgeted setting, that asks for the best approximate top-k MIPS given a limited budget of computational operations. We investigate recent advanced sampling algorithms, including wedge and diamond sampling, to solve budgeted top-k MIPS. First, we show that diamond sampling is essentially a combination of wedge sampling and basic sampling for top-k MIPS. Our theoretical analysis and empirical evaluation show that wedge is competitive (often superior) to diamond on approximating top-k MIPS regarding both efficiency and accuracy. Second, we propose dWedge, a very simple deterministic variant of wedge sampling for budgeted top-k MIPS. Empirically, dWedge provides significantly higher accuracy than other budgeted top-k MIPS solvers while maintaining a similar speedup.
Using optimal choice of parameters for meta-extreme learning machine method in wind energy application

Purpose This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process. Design/methodology/approach The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes ( N h ) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated. Findings It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and N h = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time. Originality/value The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.
S-GAT: Accelerating Graph Attention Networks Inference on FPGA Platform with Shift Operation

Deep learning has been successful in many fields such as acoustics, image, and natural language processing. However, due to the unique characteristics of graphs, deep learning using universal graph data is not easy. The Graph Attention Networks (GATs) show the best performance in multiple authoritative node classification benchmark tests (including transductive and inductive). The purpose of this research is to design and implement an FPGA-based accelerator called S-GAT for graph attention networks that achieves excellent performance on acceleration and energy efficiency without losing accuracy, and does not rely on DSPs and large amounts of on-chip memory. We design S-GAT with software and hardware co-optimization. Specifically, we use model compression and feature quantization to reduce the model size, and use shift addition units (SAUs) to convert multiplication into shift operation to further reduce the computation requirements. We integrate the above optimizations into a universal hardware pipeline for various structures of GATs. At last, we evaluate our design on an Inspur F10A board with an Intel Arria 10 GX1150 and 16 GB DDR3 memory. Experimental results show that S-GAT can achieve 7.34 times speedup over Nvidia Tesla V100 and 593 times over Xeon CPU Gold 5115 while maintaining accuracy, and 48 times and 2400 times on energy efficiency respectively.
Blockchain-based Searchable Proxy Re-encryption Scheme for EHR Security Storage and Sharing

Abstract The sharing of electronic health records (EHR) has shown significant advantages in the accurate diagnosis of patients and the development of medical institutions. However, due to the privacy and sensitivity of medical data, it is easy to cause security issues such as difficulty in data sharing among different medical institutions and easy leakage of data privacy. Because the blockchain has the characteristics of non-tampering, anonymity, and decentralization. We propose a blockchain-based searchable proxy re-encryption scheme for EHR security storage and sharing. First, we use blockchain and cloud server to store encrypted EHR together to prevent EHR from being tampered with and leaked. Secondly, we use a certificateless encryption and proxy re-encryption based on identity and type scheme as a data sharing protocol. Meanwhile, searchable encryption technology is used to generate a keyword index. Moreover, the proxy node is selected by the delegated proof-of-stake (DPOS) consensus algorithm, which ensures the privacy, immutability and security. It realizes the safe access of third-party data users to medical health data. Finally, security analysis and evaluation show that our scheme can resist identity disguise and replay attacks. In addition, it has stronger security and higher efficiency.
Message Passing Over Cloud Using Cascade Ciphering With Randomized Algorithm

To solve the problem of poor security and performance caused by traditional encryption algorithm in the cloud data storage, we propose a cascade ciphering with randomized algorithm. The multiple encryption is a process to encrypt the data multiple times using the same algorithm or different algorithms, to provide multilayer and multilevel security over unreliable wireless network during communication. The encryption algorithms are AES, MD5, SHA 129, SHA 256, Whirlpool and Blowfish.
Evaluating single web service trust employing a three-level neuro-fuzzy system considering k-means clustering

Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services.
Using Fractals and Turtle Geometry to Visually Explain the Spread of a Virus to Kids: A STEM Multitarget Activity

A lockdown was ordered in Spain on March 2020 due to the COVID-19 pandemic. The first author, advised by the second author, developed a tale and video (in Spanish) with a simplified explanation of virus propagation for their teenager son, justifying the need to stay at home. The tale and video relate the spread of the virus to fractal trees and aim to raise awareness about the transmission of the disease. The video is available from the web page of the Instituto de Matemática Interdisciplinar of the first author’s university. The code was implemented in Scratch 3 and takes advantage of the “Turtle Geometry” (there is an ulterior version using Maple, available from Mapleprimes). This article includes the English version of the original tale, describes the Scratch 3 code, and details possible derived STEM activities. We plan to experiment them in the classroom during the 2020–2021 academic year.
Efficient and Privacy Preserving Approximation of Distributed Statistical Queries

In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release was intensively investigated. However, despite a considerable progress, computational complexity and consequently, the performance of the computations, due to an increasing size of data, remains a limiting factor in real-world deployments. Especially in the case of privacy-preserving computations. In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research in the past that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on an approximation of intersection set both with and without a privacy-preserving mechanism. An analysis of the bound on the error as a function of the sample size is discussed and a heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.