Reviewerly/exHarmony huggingface dataset:
Virtual Machine Scheduling in Cloud Environment Based on Annealing Algorithm and Improved Particle Swarm Algorithm

In order to reduce the power consumption in the cloud environment and the placement of virtual machines in the cloud environment, this paper proposes an annealing algorithm to optimize the placement of the particle swarm by analyzing the particle swarm algorithm. This algorithm optimizes the particle swarm algorithm in all directions, and dynamically optimizes the inertia coefficient of the particle swarm algorithm based on the Gaussian function. With the help of simulated annealing algorithm, the local optimal position is disturbed to improve the ability of jumping out of the local optimal. Optimize the total energy consumption of the data center as the objective function. Based on the relationship between the local optimal solution, the global optimal solution, and the inertia coefficient, the particle swarm algorithm is improved. The simulation experiments of CloudSim, a cloud computing simulation platform, show that the improved algorithm has better convergence speed, higher optimization accuracy, and reduced power consumption.
Graph Embedding Relation Network for Few-Shot Learning

Deep neural networks have achieved great success in image classification tasks based on a large amount of training data, but still lack the capability of learning to recognize new categories with few training instances, which is the setting of few-shot learning. In this paper, we propose a new end-to-end few-shot metric learning approach named Graph Embedding Relation Network (GERN), with a shallow and a deep variant. By learning the embeddings of images, employing Graph Neural Network (GNN) and using embedding concatenations, GERNs can construct stronger inter-class and intra-class relations between images, which improves models' performances. Comprehensive experiments on miniImageNet and tieredImageNet demonstrate that compared with existing few-shot metric learning approaches, GERN has made significant improvements on one-shot and few-shot classification, and has achieved highly competitive performances compared with several state-of-the-art approaches.
Improving activity data collection with on-device personalization using fine-tuning

One of the biggest challenges of activity data collection is the unavoidability of relying on users and keep them engaged to provide labels consistently. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. In this study, we propose on-device personalization using fine-tuning convolutional neural networks as a mechanism in optimizing human effort in data labeling. First, we transfer the knowledge gained by on-cloud pre-training based on crowdsourced data to mobile devices. Second, we incrementally fine-tune a personalized model on every individual device using its locally accumulated input. Then, we utilize estimated activities customized according to the on-device model inference as feedback to motivate participants to improve data labeling. We conducted a verification study and gathered activity labels with smartphone sensors. Our preliminary evaluation results indicate that the proposed method outperformed the baseline method by approximately 8% regarding accuracy recognition.
Application of the Kalman Filter in Functional Magnetic Resonance Image Data

The Kalman-Bucy filter was applied on the preprocessing of the functional magnetic resonance image-fMRI. Numerical simulations of hemodynamic response added Gaussian noise were performed to evaluate the performance of the filter. After the proceeding was applied in auditory real data. The Kohonen’s self-organized map was employed as tools to compare the performance of the Kalman’s filter with another type of pre-processing. The results of the application of Kalman-Bucy filter for simulated data and real auditory data showed that it can be used as a tool in the temporal filtering step in fMRI data.
Cordic-based Softmax Acceleration Method of Convolution Neural Network on FPGA

With the vigorous development of computing power, Convolutional Neural Network (CNN) is developing rapidly, and new CNN structures with more layers and better performance continue to appear. Field Programmable Gate Array(FPGA) has gradually become the best choice for people to deploy and accelerate CNNs as a current research hotspot. This paper has studied the hardware acceleration method of FPGA to implement and simulate the Softmax layer of Alexnet on Vivado 2018.1. Combined with the features of FPGA, the Cordic algorithm is used to implement basic operations such as division and exponential functions, instead of consuming floating-point arithmetic resources. The paper proposes a method to shrink the convergence domain and analyzes the errors generated by the different digits of data after quantization and fixed-point inputs. The relative error of the Softmax layer exponential function is controlled below 0.0146% by reducing the bit width which satisfied the design requirements and saved resources. This method can complete the calculation and classification of the Softmax layer in 66.5 cycles without processing the layer data at fixed points, which greatly improves the calculation speed of the Softmax layer.
NLP Relational Queries and Its Application

Recent advances in natural language processing have shown the effectiveness of statistical and neural networkbased algorithms in a deep understanding of textual data. We demonstrate that the result of NLP analysis on text documents can enrich relational data in a way so that structured queries can be used to derive further value from text data. In this paper, we present how we can perform analytics on a scientific research dataset based on both the relational data and NLP topic modeling. The integrated NLP features together with the classical relational query constructs allow one to explore the topic structure of the DBLP dataset with flexibility and precision.
Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease

Health is so important for human beings. Thanks to the technological developments both in medicine and information technologies, the success percentages of both medical diagnosing and medical treatment systems are increasing day by day. Cancer is the most common causes of death in today’s world and is generally diagnosed at the last stages. Cancer has many types such as breast cancer, skin cancer, leukemia and etc. Diagnosis of cancer at early stages is very important for the success of medical treatments. The aim of this study was to evaluate the classification performances of some popular algorithms on the way to design an efficient computer aided breast and/or skin cancer diagnosing system to support the doctors and patients. For this purpose, same machine learning and deep learning algorithms were applied on immunotherapy dataset and breast cancer Coimbra dataset from UCI machine learning data repository. Feature selection by information gain and reliefF were applied on datasets before classification in order to increase the efficiency of classification processes. Support Vector Machines (SVM), Random Forest (RF), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms were used for classification experiments. Accuracy values are used for performance metric. According to these results, RNN has shown the best performance among the others with 92% on both datasets. This shows that deep learning algorithms especially RNN has great potential to diagnose the cancer from dataset with high success ratios.
Online Markov Blanket Discovery With Streaming Features

The Markov blanket (MB) in Bayesian networks has attracted much attention since the MB of a target attribute (T) is the minimal feature subset with maximum prediction ability for classification. Nevertheless, traditional MB discovery methods such as IAMB, HITON-MB, and MMMB are not suitable for streaming features. Meanwhile, online feature selection with streaming features (OSFSF) methods such as Alpha-investing and SAOLA, focus only on the relevance and ignores causality of the features, they cannot mine the MB, or only find the parents and children (PC) such as OSFS. Therefore, these methods have weaker interpretability and do not have sufficient prediction accuracy. We propose a novel algorithm for online markov blanket discovery with streaming features to tackle abovementioned issues, named OMBSF. OMBSF finds the MB of T containing parents, children, and spouses, discards false positives from the PC and spouse sets online, distinguishes between the PC and spouse in real-time. An empirical study demonstrates that OMBSF finds a more accurate MB when the volume of features is high and it significantly improves the prediction accuracy than other algorithms. Moreover, OMBSF obtains a bigger feature subset than that obtained by OSFS, demonstrating that OMBSF can identify the spouse in the MB that are not identified using OSFS.
A Novel Fault Detection Method Based on Adversarial Auto-Encoder

In this paper, we propose a novel unsupervised fault detection method based on adversarial auto-encoder (AAE). The proposed method not only has powerful feature representation ability as auto-encoder (AE), but also has enhanced discrimination ability by imposing prior distribution for the latent variables. The network architectures of AAE include three parts: the encoder, the decoder and the discriminator for latent variables. The original operational data can be considered as sampling from a low-dimensional latent variables subspace. The encoder tries to learn the latent variables subspace from high-dimensional operational data, the decoder tries to reconstruct the operational data from the subspace. The discriminator helps AAE impose the latent variables subspace to follow a prior distribution. Then, a novel fault detection method based on AAE is proposed. Finally, it is shown by experiments that the proposed method outperforms traditional AE on Tennessee Eastman Process.
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic

Self-assessment is a key to safety and robustness in automated driving. In order to design safer and more robust automated driving functions, the goal is to self-assess the performance of each module in a whole automated driving system. One crucial component in automated driving systems is the tracking of surrounding objects, where the Kalman filter is the most fundamental tracking algorithm. For Kalman filters, some classical online consistency measures exist for self-assessment, which are based on classical probability theory. However, these classical approaches lack the ability to measure the explicit statistical uncertainty within the self-assessment, which is an important quality measure, particularly, if only a small number of samples is available for the self-assessment. In this work, we propose a novel online self-assessment method using subjective logic, which is a modern extension of probabilistic logic that explicitly models the statistical uncertainty. Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.
A Detection Driven Adaptive Birth Density for the Labeled Multi-Bernoulli Filter

Modeling a suitable birth density is a challenge when using Bernoulli filters such as the Labeled Multi-Bernoulli (LMB) filter. The birth density of newborn targets is unknown in most applications, but must be given as a prior to the filter. Usually the birth density stays unchanged or is designed based on the measurements from previous time steps. In this paper, we assume that the true initial state of new objects is normally distributed. The expected value and covariance of the underlying density are unknown parameters. Using the estimated multi-object state of the LMB and the Rauch-Tung-Striebel (RTS) recursion, these parameters are recursively estimated and adapted after a target is detected. The main contribution of this paper is an algorithm to estimate the parameters of the birth density and its integration into the LMB framework. Monte Carlo simulations are used to evaluate the detection driven adaptive birth density in two scenarios. The approach can also be applied to filters that are able to estimate trajectories.
Network Security Analysis of Industrial Control System Based on Attack-Defense Tree

In order to cope with the network attack of industrial control system, this paper proposes a quantifiable attack-defense tree model. In order to reduce the influence of subjective factors on weight calculation and the probability of attack events, the Fuzzy Analytic Hierarchy Process and the Attack-Defense Tree model are combined. First, the model provides a variety of security attributes for attack and defense leaf nodes. Secondly, combining the characteristics of leaf nodes, a fuzzy consistency matrix is constructed to calculate the security attribute weight of leaf nodes, and the probability of attack and defense leaf nodes. Then, the influence of defense node on attack behavior is analyzed. Finally, the network risk assessment of typical airport oil supply automatic control system has been undertaken as a case study using this attack-defense tree model. The result shows that this model can truly reflect the impact of defense measures on the attack behavior, and provide a reference for the network security scheme.
Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system dynamic and measurement models through accounting for their impacts when updating the state estimate and covariance. However, the performance of the SKF may break down when the measurements are subject to non-Gaussian and heavy-tail noise. To address this, we impose the Wishart prior distribution on the precision matrix of measurement noise, such that the measurement likelihood now has heavier tails than the Gaussian distribution to deal with the potential occurrence of outliers. Variational inference is invoked to establish analytically tractable methods for computing the posterior of the system state, system biases, and the measurement noise precision matrix. The principle of the SKF considers the effect of system biases but does not actively estimate them when two variants of outlier-robust SKFs are incorporated. We evaluate their performance in terms of estimation accuracy and filtering consistency using simulations and real-world data. Promising results are obtained.
Robust $\delta$-Generalized Labeled Multi-Bernoulli Filter for Nonlinear Systems with Heavy-tailed Noises

To solve the problem of multi-target tracking with heavy-tailed process noise and measurement noise, a Student's t mixture δ-generalized labeled multi-Bernoulli ( δ-GLMB) filter is proposed for nonlinear systems. A third-degree Spherical-Radial rule is utilized to calculate the probability density functions of the prediction and update of target states for nonlinear multi-target models. The performance of the proposed Student's t mixture δ-GLMB filter for nonlinear systems is compared with the Sequential Monte Carlo δ-GLMB (SMC- δ-GLMB) filter through simulation experiments. Simulation results demonstrated that the proposed filter can achieve a good trade-off between efficiency and tracking accuracy.
State-Feedback Based Fuzzy Control Design for Discrete-Time Interval Type-2 Fuzzy Bilinear Delay Systems

In this paper, we consider the fuzzy bilinear state-feedback control problem of discrete-time interval type-2 fuzzy bilinear systems with constant delay. First, considering the uncertainties in the membership functions, the nonlinear system are modeled into the interval type-2 fuzzy bilinear systems. Second, based on the Lyapunov stability theory, an interval type-2 fuzzy state-feedback control criterion is proposed to guarantee the interval type-2 fuzzy closed-loop bilinear system to be stable. The fuzzy controller can be obtained by using sequential linear programming matrix method. Finally, one example is introduced to demonstrate the effectiveness of the results.
Practical Query-based Order Revealing Encryption from Symmetric Searchable Encryption

In the 2010s, there has been significant interest in developing methods, such as searchable encryption for exact matching and order-preserving/-revealing encryption for range search, to perform search on encrypted data. However, the symmetric searchable encryption method has been steadily used not only in databases but also in full-text search engine because of its quick performance and high security against intruders and system administrators. Contrarily, order-preserving/-revealing encryption is rarely employed in practice: almost all related schemes suffer from inference attacks, and some schemes are secure but impractical because they require exponential storage size or communication complexity. In this study, we define the new security models based on order-revealing encryption (ORE) for performing range search, and explain that previous techniques are not satisfied with our weak security model. We present two generic constructions of ORE using the searchable encryption method. Our constructions offer practical performance such as the storage size of O(nb) and computation complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where the plaintext space is a set of n-bit binaries and b denotes the block size of the ciphertext generated via searchable encryption. The first construction gives the comparison result to the server, and the security considers a weak security model. The second construction hides the comparison result from the server, and only the secret-key owner can recover it.
Optimization of Metallic Structures by Applying Genetic Algorithm

Computer-aided design is becoming a reality today through the significant development of computing tools. These calculation codes are often intended for an advanced project design phase. On the other hand, there are very few tools for early design assistance or pre-project design. Therefore, in this research, a methodology is proposed for solving the problem of the global design of a simple metal structure based on the genetic algorithms approach using Matlab. A comparative study is made on a 2D metal gantry using the profiles available in the Algerian market, namely IPE—IPN and HEA—HEB so as to have an optimal dimensioning in the inelastic field.
A New Kind Linear Analysis of Invariant Bias of Midori-64 Related Keys

Midori-64 is a 64-bit packet length, 128-bit key SPN (Substitution Permutation Network) structure lightweight block cipher. Using the method of related-keys technology and linear analysis, the key invariant deviation linear analysis of Midori-64 was carried out. According to the nature of the key expansion algorithm of Midori-64, a suitable input-output linear mask and key difference are selected to construct a 7-round related-keys invariant linear discriminator. Then, 11 rounds of Midori-64 algorithm are densely defined. Key recovery attack. The data complexity and time complexity of the attack are respectively $$ 2^{62.99} $$ and $$ 2^{76.6} $$ . This paper is the linear analysis result of the first related key of the Midori algorithm.
Detection of Anomalous Behavioural Patterns In University Environment Using CNN-LSTM

The increase in the crime rate has led to the deployment of surveillance systems to different places such as airports, malls and schools in order to prevent crime. However, inaccurate detection of anomalous behavioural patterns due to false detection (false alarm) errors is the main challenge that affects the performance of surveillance systems. Although several interesting techniques have been reported in literature for the detection of anomalous behaviour using semi-supervised and supervised techniques, these still require improvement in terms of accuracy and minimizing of false detection errors in crowded environments. This paper presents a performance analysis of the convolutional neural network with long short-term memory (CNN-LSTM) in surveillance systems. The image features are extracted from the image frame sequences using the CNN, while the LSTM uses the gate mechanism to keep vital information for remembrance. The results are compared with existing detection models, including the mixture of probabilistic principal analysis, motion deep net, social force and dictionary-based models. Experiments are done on the University of California San Diego dataset using the proposed anomalous behavioural pattern detection system. The results obtained show that the proposed system outperforms the others mentioned with 86% accuracy.
Staged Tree Matching for Detecting Code Move across Files

In software development, developers often need to understand source code differences in their activities. GumTree is a tool that detects tree-based source code differences. GumTree constructs abstract syntax trees from the source code before and after a given change, and then, it identifies inserted/deleted/moved subtrees and updated nodes. Source code differences are detected based on the four kinds of information in GumTree. However, GumTree calculates the difference for each file individually, so that it cannot detect moves of code fragments across files. In this research, we propose (1) to construct a single abstract syntax tree from all source files included in a project and (2) to perform a staged tree matching to detect across-file code moves efficiently and accurately. We have already conducted a pilot experiment on open source projects with our technique. As a result, we were able to detect code moves across files in all the projects, and the number of such code moves was 76,600 in total.
Can Elementary Students Co-design the Learning Content of Educational Apps: The We!Design!Fractions Participatory Design Approach

Although there is a growing interest in engaging children in the design of interactive learning technology, young learners are mostly asked to co-design features and apps that lie on the peripheral of the learning processes, such as websites, e-portfolios or assessment applications. In this article, we will try to investigate whether experienced elementary students can offer useful insights about the design of the content of an educational app, its learning representations and its learning interactions. For this purpose, we have developed a collaborative board design game called We!Design!Fractions, for designing apps about fractions. Fractions constitute a demanding learning domain in elementary education and, hence, an appropriate testbed for our research aims. The design game consists of a game board, cards, dice and pawns and it was developed by exploiting theories about board games, collaborative design games and idea generation. A study was conducted with 27 6th grade students who participated in 7 design sessions of three to four students. Each session lasted for about 3 h. By playing the game, students proposed a total of 199 ideas about the educational app that were later evaluated by themselves regarding their learning usefulness and their innovation. Students claimed that the board game stimulated their idea generation process, offered an enjoyable experience and that their proposals could enable designers develop an educational application about fractions that would fulfil better their needs and preferences.