Reviewerly/exHarmony huggingface dataset:
Container Mapping and its Impact on Performance in Containerized Cloud Environments

The past few years have witnessed many software companies refactoring their monolithic applications into microservices to take advantage of containers in Cloud-native and DevOps-driven environments. Modern Cloud environments achieve high levels of scalability, flexibility, availability and business agility by combining two different kinds of virtualization: virtual machines and containers. The deployment of complex applications in such environments should be done by taking into account the interactions that may arise from the overlapping of these two different levels of virtualization. In this paper, we first describe a preliminary evaluation of a containerized application that has been obtained by refactoring a monolithic application into microservices. The application was deployed in a private Cloud system that relies on Kubernetes, which is a cluster management software for Docker containers that have been deployed on top of OpenStack-managed KVM virtual machines. Subsequently, in order to systematically investigate the possible interactions that can be produced when multiple containers running different kinds of workloads are activated on top of virtual machines, we present and analyze the results obtained by running different benchmarks in a testbed setup. From the analysis of the benchmark results, we derive some guidelines that should be taken into account by container schedulers when container orchestration platforms, such as Kubernetes, are deployed on top of a virtualized IaaS layer.
Automatic Correction of i/y Spelling in Czech ASR Output

This paper concentrates on the design and evaluation of the method that would be able to automatically correct the spelling of i/y in the Czech words at the output of the ASR decoder. After analysis of both the Czech grammar rules and the data, we have decided to deal only with the endings consisting of consonants b/f/l/m/p/s/v/z followed by i/y in both short and long forms. The correction is framed as the classification task where the word could belong to the "i" class, the "y" class or the "empty" class. Using the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) architecture, we were able to substantially improve the correctness of the i/y spelling both on the simulated and the real ASR output. Since the misspelling of i/y in the Czech texts is seen by the majority of native Czech speakers as a blatant error, the corrected output greatly improves the perceived quality of the ASR system.
Multi-attribute self-attention guided vehicle local region detection based on convolutional neural network architecture

Many pieces of information are included in the front region of a vehicle, especially in windshield and bumper regions. Thus, windshield or bumper region detection is making sense to extract useful information. But the existing windshield and bumper detection methods based on traditional artificial features are not robust enough. Those features may become invalid in many real situations (e.g. occlude, illumination change, viewpoint change.). In this article, we propose a multi-attribute-guided vehicle discriminately region detection method based on convolutional neural network and not rely on bounding box regression. We separate the net into two branches, respectively, for identification (ID) and Model attributes training. Therefore, the feature spaces of different attributes become more independent. Additionally, we embed a self-attention block into our framework to improve the performance of local region detection. We train our model on PKU_VD data set which has a huge number of images inside. Furthermore, we labeled the handcrafted bounding boxes on 5000 randomly picked testing images, and 1020 of them are used for evaluation and 3980 as the training data for YOLOv3. We use Intersection over Union for quantitative evaluation. Experiments were conducted in three different latest convolutional neural network trunks to illustrate the detection performance of the proposed method. Simultaneously, in terms of quantitative evaluation, the performance of our method is close to YOLOv3 even without handcrafted bounding boxes.
Research on the Key Technologies of Big Data Based High-speed Railway Permanent Way Data Asset Collection Platform

It is of great significance to manage high-speed railway permanent way equipment data efficiently for the improvement of operational safety. This paper, by investigating the management status of high-speed railway permanent way equipment data, conducts a demand analysis on exploring big data applications of permanent way and proposes an overall framework for a High-speed Railway Permanent Way Data Asset Collection Platform based on the Railway Data Service Platform. After sorting out and designing the main functions of the platform from a technical and business perspective, this paper also researches some key technologies of the platform including data cleaning and management, full-text retrieval, and cold/warm/hot data storage strategies. Finally, this paper takes one of the highspeed railway permanent way subjects as an example, building a prototype system of the High-speed Railway Permanent Way Data Asset Collection Platform and verifies the feasibility of the overall framework and key technologies. Based on the proposed platform, performing typical permanent way equipment analysis and big data applications can provide data and decision-making support for reasonable guidance towards permanent way equipment maintenance and management.
Classification of existing mobile cross-platform approaches

Nowadays, smartphones occupy a large part of our lives; whether in our personal or professional life, with a variety of mobile applications proposed covering almost every area such as: social media, lifestyle, payment, utility, games and entertainment, productivity, news and information outlet, Enterprise specific-app, etc. The smartphone market has been growing up since 2007 when the first Apple phone appeared. Statistics made by Gartner show that Smartphone sales increased by more than 65% between the first quarter of 2012 and the third quarter of 2018[10]. According to the same statistics, since 2012, android has monopolized the mobile market with a part of 88% in 2018, the rest was taken by IOS and other OS, which cause problems for enterprises, whom want to, target all smartphone users, in other words all smartphone platforms. For that, cross-platform alternative comes to solve this problem with a variety of approaches in depend of the application and the target platform.
Parallel Image Encryption Technology Based on Cellular Automaton

The traditional encryption algorithms such as DES, AES are considered too slow for image encryption, which cannot meet the real-time requirements. Therefore, this paper proposes a parallel image encryption algorithm based on cellular automaton and chaotic map. In the scrambling stage, the keystream generated by 2D-LASM is used to scramble the rows and columns of the image in parallel. In the diffusion stage, the scrambled image is constructed into two identical square bit matrices as the initial state of the cellular automaton based on the balance rule, and the encrypted image is obtained by iterating cellular automaton multiple times. The sub key expansion algorithm adopts a weighted pixel value mechanism, which makes the generation of key stream depend on plain image, so as to resist the attack of chosen plaintext and known plaintext. The algorithm is coordinated by CPU / GPU. With the acceleration of GPU, the efficiency of the algorithm has been significantly improved.
An Application of Fuzzy C-Means, Fuzzy Cognitive Maps, and Fuzzy Rules to Forecasting First Arrival Date of Avian Spring Migrants

In this study, we propose an approach based on the advanced fuzzy techniques such as Fuzzy C-Means and Fuzzy Cognitive Maps to cluster the birds species, based on the information of first arrival date, into more coherent and uniform groups. The birds are very suitable subject for modelling the climate changes. Very popular indicator to forecast bird migration dynamic is the first arrival date. In many reported studies, this indicator is shown as very useful. However, there is still a lack of precise methods grouping the birds into the classes in satisfying manner producing detailed information about species and the relations between them. As evidenced in the experimental series section, the proposed approach enables the researchers and practitioners working with that important area of ecology to observe subtle dependencies between various bird species. Moreover, this work sheds the light on the novel application of both Fuzzy C-Means and Fuzzy Cognitive Maps as the efficient tools to analyse the ecological data collected in changing climatic environment.
Research on Single Image Dehazing Enhancement Method Based on CycleGAN

Although researchers have made great progress in image dehazing recently, there are still great challenges in balancing the suitable universality and dehazing accuracy. In this paper, we propose the dark channel prior cycle dehaze network (DCP-Cycle-Dehaze) to single image dehazing. This network is based on CycleGAN, which adds DCP loss based on dark channel prior knowledge and improved cycle perceptual loss to achieve image dehazing function. DCP-Cycle-Dehaze mainly enhance the dehazing capacity of model by enhancing the sensitivity of network for haze features during training. It further improves the performance of the CycleGAN network framework in image dehazing tasks, and makes the network still reach the accuracy of supervised training without unsupervised training. We conduct simulation experiments on four representative data sets: O-HAZE, I-HAZE, RESIDE and D-Hazy. The experimental results show that DCP-Cycle-Dehaze network we proposed has achieved very good results in outdoor environment, the results on the O-HAZE dataset exceed the best results of NTIRE2018; moreover, it also has better results on the indoor environment. The experimental results prove the effectiveness of our method from a quantitative and qualitative perspective.
How Can We Deal With Adversarial Examples?

Recently, deep neural network has been widely used in computer vision, machine translation and Go playing and has become the important part of artificial intelligence applications. However, the existence of adversarial examples designed by adversaries could let deep neural networks make incorrect outputs, and fail to work in their applications. Indeed, a considerable amount of work on generating adversarial examples has emerged. Thus, it’s important to investigate adversarial examples in deep learning community. Throughout this paper, we introduce some basic knowledge about attacks on DNNs. Then, we discuss several attack methods to generate adversarial examples. Subsequently, we introduce some defense mechanisms to classify or detect adversarial examples correctly. Finally, we review the challenges in this field, along with the corresponding future directions towards dealing with adversarial examples.
Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.
Deep Learning Framework for Fingerspelling System using CNN

Sign language is used for communication among the hearing-challenged-cum-speech-challenged community. It is the language that uses facial expressions, movements of hands and other body parts. Indian Sign language (ISL) is commonly used sign language, in India. These days, online interpreters are available for translating the sign language to common language and vice versa. But it requires an expert who can translate in both ways and also they charge. Thus, the communication between the hearing-challenged-cum-speech-challenged community and the rest has become difficult and costly. This causes isolation of the hearing-challenged-cum-speech-challenged community from the rest of society. Hence, this work focuses on bridging the communication gap between the two. One important aspect of sign language is finger spelling. Through finger spelling, a word that lacks a particular sign in sign language can be expressed by spelling each alphabet of that word. Here, a vision based finger spelling system using Convolutional Neural Networks (CNN) is created. The proposed CNN model performs as par with the InceptionV3 and better than ResNet and VGG16 models.
An Oracle-Based On-Chain Privacy

In this work, we demonstrate how the blockchain and the off-chain storage interact via Oracle-based mechanisms, which build an effective connection between a distributed database and real assets. For demonstration purposes, smart contracts were drawn up to deal with two different applications. Due to the characteristics of the blockchain, we may still encounter severe privacy issues, since the data stored on the blockchain are exposed to the public. The proposed scheme provides a general solution for resolving the above-mentioned privacy issue; that is, we try to protect the on-chain privacy of the sensitive data by using homomorphic encryption techniques. Specifically, we constructed a secure comparison protocol that can check the correctness of a logic function directly in the encrypted domain. By using the proposed access control contract and the secure comparison protocol, one can carry out sensitive data-dependent smart contract operations without revealing the data themselves.
One-Phase Temporal Fuzzy Utility Mining

Temporal utility mining is complicated than utility mining due to the time consideration. The former discusses the situation that different items may have different profit values and on-shelf time periods. In this paper, we handle the problem of temporal fuzzy utility mining, which simultaneously considers the temporal factor, purchased quantities, item profits and linguistic terms from a transaction database. In the past, we proposed a two-phase tree-based approach to solve this problem. In this paper, we further design a tree structure with less memory to store all the required information and with faster execution speed than the previous one. The experimental results show that the proposed method can get good performance on the execution efficiency and consumed memory size.
Data Balancing to Improve Prediction of Project Success in the Telecom Sector

Investments in the telecom industry are often conducted through private participation projects, allowing a group of investors to build and/or operate large infrastructure projects in the host country. As governments progressively removed the barriers to foreign ownership in this sector, these investment consortia have become increasingly international. Obviously, an accurate and early prediction of the success of such projects is very useful. Softcomputing can certainly contribute to address such challenge. However, the error rate obtained by classifiers when trying to forecast the project success is high due to the class imbalance (success vs. fail). To overcome such problem, present paper proposes the application of classifiers (Support Vector Machines and Random Forest) to data improved by means of data balancing techniques (both oversampling and undersampling). Results have been obtained on a real-life and publicly-available dataset from the World Bank.
SAR Image Data Enhancement Method Based on EMD

In order to solve the problem of poor processing of complex image, a method of SAR image data enhancement based on EMD (empirical mode decomposition) is proposed. Through the principle of feature mining, the image feature data is collected, and the collected image data is optimized and integrated, and then the corresponding image data processing model is established by combining the normalization algorithm. Finally, the effective enhancement of SAR (Synthetic Aperture Radar) image data is realized. Finally, the experimental results show that the method of SAR image data enhancement based on EMD has high practicability and fully meets the research results.
Fusing Edge-information in Image Denoising Based on CNN

Due to the good performance, image denoising based on Convolutional Neural Network (CNN) has been widely studied. However, most of existing methods use a single neural network for image denoising. The denoised images occur smooth edges (missing details) at a high noise level. To address this problem, we propose a dual convolutional neural network for image denoising, which is termed as Fusing Edge-information in image Denoising based on CNN (FEDnets). It consists of two parallel network branches, which respectively get the denoised image and edge details in an end-to-end manner. In addition, the edges are fused with the denoised image to get a clearer and more detailed image. Experimental results show that FEDnets can be effectively applied to noise removal tasks and recover clearer images with more edge details and textures features.
ICTs for Community Development: Bridging Conceptual, Theoretical, and Methodological Boundaries

The aim of this special issue is to collect and put into dialogue theoretical, methodological, critical, and applied contributions dealing with the variety of Information and Communication Technology (ICT) uses in community development. In this introduction to the special issue, we first provide the readers with a quick overview of the field, conducted by analyzing the lexicon of titles and abstracts published in the past few years in information and communication technology for development journals. We will then briefly elaborate on the concept of “users” as a pivotal dimension that can shed light on the complex interlinkages between technological artifacts and sociocultural factors involved in ICT usage. The focus on user, in fact, highlights some relevant issues concerning the relation between ICT, people and communities. Drawing on these premises, the papers collected in the special issue will then be briefly described. These contributions come from a variety of theoretical and methodological approaches, include the uptake of innovative technologies as well as top-down interventions, and focus on different regions of the world. The cross-fertilization and the range of disciplines included in this special issue result in a kaleidoscope in which different facets can be mixed, showing the individual, community, and societal aspects of community use of ICT for development.
A Hamiltonian Engine for Radiotherapy Optimization

We apply a new hardware and software platform called the Hamiltonian Engine for Radiotherapy Optimization (HERO) to the problem of Intensity-Modulated Radiation Therapy (IMRT) treatment planning. HERO solves large general-form binary optimization problems by decomposing them into sub-problems and approximating them using a quadratic pseudo-boolean function. Optimizing the resulting function becomes a quadratic unconstrained binary optimization (QUBO) problem, which has been widely studied and has numerous applications in various fields. A Quantum Annealer (QA) approach has been previously investigated to solve QUBO problems, including IMRT optimization. However, the QA can only accommodate a small number of variables and requires several hours to obtain optimized plans. HERO acts as an optimizer for QUBO problems, which not only addresses these shortcomings but also relies solely on conventional hardware design while operating at room temperature. We evaluate HERO on seven prostate IMRT cases with clinical objectives, each using approximately 6000 beamlets. Our method was compared to the commercial treatment planning software, Eclipse, for both time-to-solution and plan quality. HERO solves most cases in about 30 seconds, with significantly lower objective function scores than Eclipse. The results indicate that HERO is promising for radiation therapy optimization problems. Additionally, HERO has the potential to be applied to Volumetric-Modulated Arc Therapy (VMAT) and other complex types of treatment planning.
An Empirical Study on Supervised and Unsupervised Fuzzy Measure Construction Methods in Highly Imbalanced Classification

The design of an ensemble of classifiers involves the definition of an aggregation mechanism that produces a single response obtained from the information provided by the classifiers. A specific aggregation methodology that has been studied in the literature is the use of fuzzy integrals, such as the Choquet or the Sugeno integral, where the associated fuzzy measure tries to represent the interaction existing between the classifiers of the ensemble. However, defining the big number of coefficients of a fuzzy measure is not a trivial task and therefore, many different algorithms have been proposed. These can be split into supervised and unsupervised, each class having different learning mechanisms and particularities. Since there is no clear knowledge about the correct method to be used, in this work we propose an experimental study for comparing the performance of eight different learning algorithms under the same framework of imbalanced dataset. Moreover, we also compare the specific fuzzy integral (Choquet or Sugeno) and their synergies with the different fuzzy measure construction methods.
Local Distortion Hiding (LDH) Algorithm: a Java-based prototype

Data sharing among organizations has become an increasingly common process, but any part will most likely try to hide some sensitive patterns before it shares its data with others. In this article, we present a java application based on Local Distortion Hiding (LDH) algorithm that is being used to hide decision tree (DT) rules using Java, integrated with the Waikato Environment for Knowledge Analysis (Weka) data mining software library. Users may select a data set, and by visualizing it as a J48 decision tree could perform the hiding procedure of the LDH algorithm and consequently produce the modified data set, which ultimately leads to a DT that the sensitive pattern has been successfully hidden.