Reviewerly/exHarmony huggingface dataset:
Path‐flow matching: Two‐sided matching and multiobjective evolutionary algorithm for traffic scheduling in cloud data<sup>*</sup> center network

Abstract Improving the operational efficiency of data center has always been an important direction for the development of ICT. In this paper, we apply two‐sided matching decision‐making process in game theory to traffic scheduling problem in data center network. From the perspective of matching between flow and path, the traffic scheduling is properly arranged. We first propose and model the path‐flow matching problem, considering the preference ordering, then formulate the problem as a multiobjective optimization problem with the target to ensure the stability and satisfaction from the matching scheme, and design a preference‐based path‐flow ordering method Extended PIAS, and finally propose a lightweight scheduling algorithm LinkGame based on multiobjective evolutionary algorithm. Compared with the previous scheduling methods (ECMP, Hedera, and Fincher), experiment results demonstrate that LinkGame can simultaneously consider the stability and satisfaction of the matching results, with improved bandwidth utilization and flow completion time.
Neural Network Traffic Signs Detection System Development

The convolutional neural networks as a modern approach represent great results in various tasks connected to image processing. Such field as computer vision is an appropriate place for neural networks utilization as an image information extractor. Simultaneously, autonomous cars require image processing system for traffic signs detection. The work represents convolutional neural network system for traffic signs detection. The “You Only Look Once V3” (YOLOv3) architecture is used with Keras software library. The system provides multiobject detection with confidence percentage and bounding box coverage. During training the system is accelerated on GPU with multithreaded data augmentation. The database used is German Traffic Signs Detection Benchmark that contains 900 images.
Selected approaches to supervised learning

Many books can and have been written about learning from labeled data. Therefore, this chapter is not a summary of these methods, nor an overarching mathematical framework, since that would be duplicative of other works and beyond the scope of a single chapter. Instead, this chapter provides a structure, beginning with fundamentals, then illustrating a few popular methods, and ending with a few that deserve more attention. The authors hope that these will pique the reader's interest in the citations for further exploration. The chapter begins with an overview of the most widely-used approach, backpropagation, including backpropagation through time and stochastic gradient descent. Section 4.2 covers recurrent neural networks and approaches for their training (such as Extended Kalman Filter and Recurrent Neural Network), and 4.3 covers Long Short-Term Memory. Section 4.4 briefly discusses convolutional neural networks, deep learning and the related topic of variational autoencoders. Section 4.5 discusses Random Forest, Classification and Regression Trees, and related approaches.
A device for paired zeroing of numbers in a residue system

Abstract The paper studies the problem of developing a high-speed number correction device in computing systems operating in the basis of non-positional arithmetic in residual classes. The topicality of the problem is necessitated by the search of solutions allowing reducing the time for digital processing of signals in non-positional neuroprocessors. The device allows correcting errors with single and multiple control bases of the residue system. The work presents the structure of the unit that realizes the paired zeroing on numbers in the system of residual classes. It was demonstrated that the suggested solutions allow appreciably reducing the time consumption by digital processing of signals in neuroprocessors destined for operations of summation and multiplication.
Detection and segmentation of cancer regions in cervical images using fuzzy logic and adaptive neuro fuzzy inference system classification method

Abstract Cervical cancer is one of the most severe death causing cancers in developing countries. The mortality rates of the cervical cancer are high in developing countries due to their unawareness about such cancer. This article proposes an efficient Fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) classification method based cancer region detection and segmentation in cervical images. The thick and thin edges are detected using fuzzy logic and these detected edges are fused pixel level image fusion technique. In this article, fuzzy rules are constructed for detecting edges for cancer region detection. Then, Gabor transform is applied on the fused cervical image for the transformation of pixels. The texture features are extracted from the Gabor transformed image and these features are classified using ANFIS classification approach. Further, morphological operations are used to segment the cancer regions in classified abnormal cervical image. The average classification rate of the proposed cervical cancer detection system is about 98.8%. The proposed cervical cancer segmentation methodology stated in this article achieves 98.1% of sensitivity, 99.4% of specificity, and 99.3% of accuracy.
Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Set of Functions

In this paper a fuzzy search algorithm harmony (FHS) is presented. The main difference between previous work is that this method uses a fuzzy system for dynamic parameter adaptation of the two main parameters throughout the iterations of the algorithm, which are: harmony memory accepting (HMR) and pitch adjustment (PArate), with the rules of the fuzzy system control the intensification and diversification of the search space is achieved. This method was applied to the mathematical functions provided by the CEC 2017, which are unimodal, multimodal, hybrid and composite functions to verify the efficiency of the proposed method. A comparison is presented to verify the results obtained with the original harmony search algorithm and the fuzzy harmony search algorithm.
Matrix Modification of RSA Digital Signature Scheme

Representation of message in the form of a matrix in place of integers can reduce the key sizes along with the storage space without compromising the security. In this paper we propose modification of RSA digital signature scheme using square matrices of order h × h. Instead of relying on the Euler’s totient function it relies on the exponentiation modulus, which is obtained by the concept of the general linear group of order h.
Using Bio-Inspired Features Selection Algorithms in Software Effort Estimation: A Systematic Literature Review

Feature selection algorithms select the best and relevant set of features of the datasets which leads to an increase in the accuracy of predictions when employed with the machine learning techniques. Different feature selection algorithms are used in the domain of Software Development Effort Estimations (SDEE) and recently the use of bio-inspired feature selection algorithms got the attention of the researchers, which provided the best results in terms of the accuracy measures. In this paper, we manage to systematically evaluate and assess different bio-inspired feature selection algorithms which have been employed and investigated in the studies related to SDEE with the aim of increasing the accuracy of estimations. To the best of our knowledge, there is no Systematic Literature Review (SLR) which investigated the use of bio-inspired algorithms in SDEE. Since, the use of bio-inspired algorithms in the area of SDEE started in the late 2000, we have considered the studies published between 2007-2018. We have selected about 30 different studies from five digital libraries, i.e., IEEE explore, Springer, ScienceDirect, ACM digital library, and Google Scholar, after the filtering of inclusion/exclusion and quality assessment criteria. The main findings of our SLR are that Genetic Algorithms (GA) and Particle Swarm Optimizations (PSO) are widely used bio-inspired algorithms. Moreover, GA and PSO are the algorithms which outperform baseline estimation techniques (estimation techniques employed without any feature selection algorithms) in more number of experiments, in terms of prediction accuracy.
A Differentiable Generative Adversarial Network for Open Domain Dialogue

This work presents a novel methodology to train open domain neural dialogue systems within the framework of Generative Adversarial Networks with gradient based optimization methods. We avoid the non-differentiability related to text-generating networks approximating the word vector corresponding to each generated token via a top-k softmax. We show that a weighted average of the word vectors of the most probable tokens computed from the probabilities resulting of the top-k softmax leads to a good approximation of the word vector of the generated token. Finally we demonstrate through a human evaluation process that training a neural dialogue system via adversarial learning with this method successfully discourages it from producing generic responses. Instead it tends to produce more informative and variate ones.
Moral Hazards in Cyber Vulnerability Markets

Reports on the emergence and growth of the cyber vulnerability market which arose from bug bounty programs initiated in the 1980s. Originally created to provide programmers, end users, and security professionals with an opportunity to report code vulnerabilities, it has since become a cottage industry that serves many masters with very different motives.
Similarity measurement in times of topic modelling

The article describes the evaluation criterion for similarity search in the patent search systems suggested by the authors. The use of artificial intelligence methods, artificial neural networks and machine learning for improving similarity search is described. The results of the comparison based on the proposed criteria for similarity search in Google Patent system, Yandex Patents system and the Russian patent office retrieval system PatSearch are given.
TF-BIV

With the emergence of virtualization technologies, various services have been migrated to the cloud. Beyond the tenants' own security controls implemented in the virtual machine (VM), the binary integrity verification mechanism in the virtual machine manager (VMM) provides stronger protections against malware. Unfortunately, none of existing integrity verification mechanisms in the cloud provides complete transparency and fine-grained efficiency. Some schemes selectively check the integrity of sensitive binaries, but they require modifications to the VMs (e.g., integrating monitoring libraries) to trigger verification. Others, although need no modification to the VMs, have to enforce checking on all the binaries, because they cannot distinguish binary images for the sensitive processes from the binaries for insensitive ones, leading to significant performance overheads. In this paper, we present TF-BIV, a transparent and fine-grained binary integrity verification scheme, which does not require any modification or software/driver installation in the VM. TF-BIV identifies the sensitive processes at the creation, and checks the integrity of the binaries (including the guest OS kernel and the dependant binaries) related to these processes. The provided transparency and efficiency are achieved by leveraging existing hardware virtualization supports (i.e., Intel extended page table) and debugging features (i.e., monitor trap flag). We have implemented the TF-BIV prototype based on QEMU-KVM. To demonstrate the usability of TF-BIV, we adopted it for cloud-based cryptographic services, to achieve the strict invoking controls. In addition to the password-based authentication, TF-BIV further achieves process-level authorization to the invokers. Intensive evaluation shows that TF-BIV implements the designed binary integrity verification with only about 3.6% performance overhead.
Comparing and Evaluating Application-specific Boot Times of Virtualized Instances

With the increasing demand for digital services, requirements for their underlying technologies continue to steadily increase. When originally introduced, virtualization significantly changed the landscape of server provisioning and continues to be the technology of choice for cloud infrastructure. Flexibility has become essential, especially with the explosion of the number of connected devices. One key performance indicator of cloud platforms in regards to flexibility is the time it takes a virtualized instance to be up and running on the hardware. While there are studies measuring that time, none of them include the availability of the service inside the instance. Therefore, we extend the definition of a reachable instance to include the startup time of the actual service, which is closer to real-world examples. In this work we compare for the first time traditional virtual machines (VMs), container virtualization and unikernels in this new key performance indicator to further understand each technology's applicability in actual scenarios. We show that the type of virtualization has an impact on the overhead of service startup time compared to the boot time of the instance. Unikernels achieve the lowest values in both boot time and startup time. The best result we observed for a webserver response to a HTTP request is 40 ms, which is achieved by using MirageOS, and is about a tenth of the time a Docker container needs for the same task.
Visual Attention Analysis During Program Debugging Using Virtual Reality Eye Tracker

The immersion of virtual reality (VR) has transcended the existing experience of multimedia teaching. This paper aims to design a virtual reality eye tracker device to analyze the cognitive process of program debugging by adopting virtual reality technology to build a 3D code rendering system and, at the same time, using eye tracking technology to study visual attention as well as to analyze and compare the differences in internal behavioral cognition in terms of program debugging. This paper has 32 students as participants who have studied C++ programming language courses for more than one year in the department of computer science. With Unity 3D development tool, the experiment creates a virtual classroom scene and C++ programming language code. The participants’ eye movements are recorded by an eye tracker device integrated in a Head-Mounted Display (HMD). The eye movement defines the regions of interest (ROIs) according to the division of the program’s function, and the difference in visual attention between various ROIs in the code is discussed when the participant performs the program debugging task. The finding results are expected to improve the dilemma of the existing programming teaching, so that the instructors can provide appropriate teaching aids for students to achieve the purpose of programming teaching and improving the students’ programming competence.
Modeling Approach and Evaluation Criteria for Adaptable Architectural Runtime Model Instances

An architectural runtime model is a causally connected abstract representation of a system that allows monitoring the system and adapting its configuration. Since systems are often constructed to operate continuously, the corresponding runtime model instances need to be long-living and available without interruptions. An interruption occurs if a model needs to be re-instantiated with a new version of the modeling language implementation to support other kinds of information. Adaptable runtime models instances can render such interruptions unnecessary and enable changing information demands at runtime. They support multiple abstraction levels for different model parts and allow adjusting over time which details of the system and its environment are represented. This helps to focus the attention for effective and efficient decision making. In this vision paper we present the fundamental idea of a generic modeling language for adaptable architectural runtime model instances and propose requirements and quality characteristics as criteria for its evaluation.
Fourier-CPPNs for Image Synthesis

Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, b) colour values. Recently, CPPNs have been used for creating interesting imagery for creative purposes, e.g., neural art. However their architecture biases generated images to be overly smooth, lacking high-frequency detail. In this work, we extend CPPNs to explicitly model the frequency information for each pixel output, capturing frequencies beyond the DC component. We show that our Fourier-CPPNs (F-CPPNs) provide improved visual detail for image synthesis.
Reconstruction of multi-frame semi-sparse scanning probe microscopy images using dependent Gaussian process

A major drawback of scanning probe microscopes (SPMs) is the time-consuming image acquisition, typically on the order of tens of seconds to minutes. Sparse raster scanning and non-raster scanning have been demonstrated to be effective approaches to improve the efficiency of SPMs. However, these strategies entail the challenge of reconstructing accurate images from semi-sparse sampling points, which means the distribution of the scattered sampling points is considerably sparser in the direction perpendicular to the scanning path. In this paper, we focus on the multi-frame semi-sparse video image reconstruction. In this setting, one of the frames is regarded as the reference frame while the others are regarded as the target frames. Instead of reconstructing the target frames independently, we propose a dependent Gaussian process regression model to make full use of the information provided by the reference frame and the currently processed target frame. The proposed method takes advantage of the intrinsic correlation between images. Thus, the accuracy of the reconstruction can be improved greatly. Comparisons with the renowned Delaunay triangulation-based interpolation method and the Gaussian process regression method prove that the proposed method has dramatically improved the reconstruction accuracy of semi-sparse video images.
An Improved Extreme Learning Machine Based on Auto-Encoder for Production Predictive Modeling of Industrial Processes

Industrial process data has the characteristics of complexity, variability and noisy, which brings challenges to data-driven production predictive modeling for industrial processes basing on the traditional extreme learning machine (ELM). Therefore, this paper proposes an improved ELM based on auto-encoder (AE) (AE-ELM). The AE can extract the main features with lower-dimension by eliminating the linear correlation among the original complex data. Then, the main features are used as the inputs of the ELM. For the purpose of verifying the effectiveness of the proposed method, the AE-ELM model has been experimented on the production prediction of the pure terephthalic acid (PTA). The experimental results prove that the AE-ELM is less sensitive to the structure of the traditional ELM and principal components extraction based robust ELM (PCE-RELM). Moreover, the modeling accuracy can be improved by 2.4%, which has certain guiding significance for process modeling and production prediction.
Insights for Improving Diagram Editing Gained from an Empirical Study

The modeling process plays an important role in software and systems engineering. Models are often created on paper or whiteboards even though software for modeling exists. We conducted an empirical study to identify how diagram editing can be improved by investigating how people model on whiteboards. The study was designed as a qualitative study and had 34 participants from four different education fields. The participants were recorded in video and audio while performing two tasks in pairs on a whiteboard. In the first task they had to model a subset of a network plan of the local public transport system. In the second task they had to model the daily schedule of a canteen kitchen. After finishing the tasks, we conducted a semi-structured interview. Using this study, we identified minor different preferences between different education fields and gained several insights for improving diagram editing, e., g., support for drawing sketches concurrently and complementary to models, supporting flexible mechanisms for highlighting different parts of a model, better support for making shapes in the model look consistent.
A New Approach to Calculate Resource Limits with Fairness in Kubernetes

Containerization has become a new approach that facilitates application deployment and delivers scalability, productivity, security, and portability. As a first promising platform, Docker was proposed in 2013 to automate the deployment of applications. There are many advantages of Docker for delivering cloud native services. However, its widespread use has revealed problems such as performance overhead. In order to deal with those problems, Kubernetes was introduced in 2015 as a container orchestration platform to simplify the management of containers. Kubernetes simplifies managing a large scale number of docker containers, however, the fairness is a missing point in the Kubernetes that has been applied in other platforms such as Apache Hadoop, YARN and Mesos. Assigning resource limits fairly among the pods in kubernetes becomes a challenging issue as some applications may require intensive resources such as CPU and memory that should be maximized to satisfy them. In order to do that, in this paper, we practice a novel way to assign resource limits fairly among the pods in the Kubernetes environment.
Crowd-Counting through a Cascaded, Multi-Task Convolutional Neural Network

Deep learning is one of the most popular technologies and research areas in machine learning. Convolutional Neural Networks (CNNs) are a typical artificial neural network underpinning deep learning. They have been used in many fields including image recognition, natural language process, and through games such as AlphaGo. A CNN has many advantages such as efficient feature extraction, the simplicity of data format required and the small number of (hyper-)parameters that are required. This paper focuses on a particular application of deep learning: crowd counting. To address this, we apply a Multi-task, Cascaded Convolutional Neural Network (MTCNN). Compared to other models, this model has a good performance and requires a shorter inference time, with shallower network structure and smaller size. In order to demonstrate the value and feasibility of the technology and provide a friendly operating environment for users, the application was realised on both the iOS and Android platforms. A web platform was also developed to visualize the real-time data using a Firebase server.
A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling. The dashboard was developed in R Shiny. We present results of our approach applied to an observational study of patients with moderate to severe rheumatoid arthritis (RA) on first-line biologic treatment.