Miracle-dz/UniFAR-FTUs huggingface dataset:
AN SSL LOAD BALANCING TECHNIQUE IN CLUSTER WEB SERVERS

As the utilization of Internet fabricates well ordered Information security has transformed into a container neck. In spite of the way that the framework exchange speed continues growing speedier than as far as possible the server ranches/organize servers are obstructions in encouraging framework based organizations. Aggressors' usage of result item is creating and ending up being more present day. Server load adjusting gives adaptability and high openness to applications, Web districts and cloud benefits by watching the quality of servers, consistently circling loads transversely finished servers and keeping up session steadiness and a reliable customer inclusion if no less than one server get the opportunity to be overburdened or dormant. In any case, in late survey it has been watched that framework servers add to around 40 percent of the general delay, and this deferral is presumably going to create with the growing usage of component Web substance. In spite of the way that SSL is the genuine standard for transport layer security, its high overhead and poor flexibility are two significant issues in arranging secure far reaching scale sort out servers. The crucial purpose of this examination is to develop add to throughput under an extended load when customers are added and to enhance the passage of more number of records with greater in estimate. SSL-LB count is used to scatter thusly (load adjusting) the client requests over different servers giving a comparative help of the client, for instance, Internet Information Services (IIS). For giving greater security SSL-LB load adjusting estimation is to vanquish the issues in the present system and decrease the latency time and augmentation the throughput than the present structures.
Replay attack detection by channel frequency response difference enhancement

Compared with the original speech, the replay attack speech passes through a complex channel mainly composed of a recording device and a playback device, and the frequency response of the channel causes a obvious change to the high and low frequency bands of the original speech spectrum. This paper proposed a Channel Difference Enhancement Cepstral Coefficient (CDECC) feature that enhances the channel frequency response difference, and detects the replay attack speech by enhancing the spectral difference caused by the channel frequency response. Experiments based on the ASVspoof 2017 Challenge data set show that the proposed method has a significant improvement in detection performance compared to the baseline system using Constant Q Cepstral Coefficients (CQCC), and the equal error rate (EER) is reduced by 18.20% under the same conditions, indicating that the performance of the CDECC feature is more effective than that of CQCC and MFCC features in detecting replay attack speech.
Impact of wind and solar production on electricity prices: Quantile regression approach

Abstract We study the impact of fuel prices, emission allowances, demand, past prices, wind and solar production on hourly day-ahead electricity prices in Germany over the period from January 2015 until June 2018. Working within a linear regression, ARX-EGARCH and quantile regression framework we compare how different pricing factors influence the mean and quantiles of the electricity prices. Contrary to the existing literature, we find that short-term price fluctuations on the fuel markets and emission allowances have little effect on the electricity prices. We also find that day-of-the-week as well as monthly effects have significant impact on the electricity prices in Germany and should not be ignored in model specifications. Three main factors are found to drive extreme prices: price persistence, expected demand and expected wind production. Our findings contribute to understanding of extreme price movements, which can be used in pricing models and hedging strategies.
Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning

In the cloud-based Internet of Things (IoT) environments, quantifying uncertainty is an important element input to keep the acceptable level of reliability in various configurations. In this paper, we aim to address the pricing model of delivering data over the cloud while taking into consideration the dynamic uncertainty factors such as network topology, transmission/reception energy, nodal charge and power, and computation capacity. These uncertainty factors are mapped to different nodes with varying capabilities to be processed using Artificial Intelligence (AI)-based algorithms. Accordingly, we aim to find a way to calculate and predict the price per big data service over the cloud using AI and deep learning. Therefore, in this paper, we propose a framework to address big data delivery issues in cloud-based IoT environments by considering uncertainty factors. We compare the performance of the framework using two AI-based techniques called Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA) in both centralized and distributed versions. The use of AI techniques can be applied in multilevel to provide a kind of deep learning to further improve the performance of the system under study. The results reveal that the distributed algorithm outperforms the centralized one. In addition, the results show that the GA has lower running time compared to the SAA in all the test cases such as 68% of improvement in the centralized version, and 66% of improvement in the distributed version in case when the size of uncertainty array is 256. Moreover, when the size of uncertainty array increases, the results show 60% speed up in the distributed GA compared to its centralized version. The improvements achieved would help the service providers to actually improve their profit using the proposed framework
Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network

Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can be aware of stages inefficiency before occurrence and prevent them. First, a two-stage structure including deposit collection and loan giving was designed for bank branches using literature review and comments of experts. Human forces and fixed assets were considered as input variables of the first stage, deposit as mediator variable, delayed claims as interim variable, and loan amount as output variable of the second stage. Then, a dynamic network data envelopment analysis model was formulated and stages efficiency were obtained for 16 consecutive periods. Therefore, efficiency trend of stages was obtained during the time. In the following, efficiency of various stages of branches were estimated using artificial neural network and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.
Using collaboration networks to identify authorship bias in meta-analyses

Meta-analytic methods are powerful resources to summarize the existing evidence concerning a given research question, and are widely used in many academic fields. However, meta-analyses can be vulnerable to various sources of bias, which should be considered to avoid inaccuracies. Many of these sources can be related to study authorship, as both methodological choices and researcher bias may lead to deviations in results between different research groups. In this work, we describe a method to objectively attribute study authorship within a given meta-analysis to different research groups by using graph cluster analysis of collaboration networks. We then provide empirical examples of how the research group of origin can impact effect size in distinct types of meta-analyses, demonstrating how non-independence between within-group results can bias effect size estimates if uncorrected. Finally, we show that multilevel random-effects models using research group as a level of analysis can be a simple tool for correcting biases related to study authorship.
Toward Highly Secure Yet Efficient KNN Classification Scheme on Outsourced Cloud Data

Nowadays, outsourcing data and machine learning tasks, e.g., $k$ -nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.
Moving target detection based on OFDM radar

The study combines the general hardware platform of the radar system and wireless communication system, as well as applies it in a real detecting scenario. To detect target, the periodogram-based estimation algorithms based on the signal of orthogonal frequency-division multiplexing (OFDM) is introduced. According to range-Doppler (R-D) spectrum, as an enhanced detection algorithm of constant false alarm rate (CFAR), a radar-target detection method, also referred to as binary successive target cancellation, is presented. This research has important theoretical significance for the communication radar-integrated detection technology, and the research results have essential application value for the technology of radar target detection as well as the fusion and development of wireless communication technology.
A scenario-based approach for the vehicle routing problem with roaming delivery locations under stochastic travel times

We address a stochastic variant of the Vehicle Routing Problem with Roaming Delivery Locations. In this model, direct-to-consumer deliveries can be made in the trunk of the customer’s car, while the vehicle is parked at a location along the customer’s itinerary. The stochasticity arises from the uncertainty in travel times and the problem is formulated as a two-stage stochastic model. We propose a scenario-based sample average approximation to obtain a heuristic solution. Several experiments to assess the effect of our solution approach compared to a pure deterministic solution approach using expected travel times,show that a cost savings of on average more than 30% can be obtained. Furthermore, it is shown that the flexibility provided by using alternative roaming delivery locations as a recourse to avoid missed deliveries can provide, on average, costs savings of 25% compared to a recourse staying with the locations chosen in the a priori first stage plan.
Pre-Training of DNN-Based Speech Synthesis Based on Bidirectional Conversion between Text and Speech

Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments. key words: speech synthesis, generative models, Boltzmann distributions, pre-training methods, deep neural networks
Virtual network function–forwarding graph embedding: A genetic algorithm approach

Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time.
Multi-Task Learning for Acoustic Event Detection Using Event and Frame Position Information

Acoustic event detection deals with the acoustic signals to determine the sound type and to estimate the audio event boundaries. Multi-label classification based approaches are commonly used to detect the frame wise event types with a median filter applied to determine the happening acoustic events. However, the multi-label classifiers are trained only on the acoustic event types ignoring the frame position within the audio events. To deal with this, this paper proposes to construct a joint learning based multi-task system. The first task performs the acoustic event type detection and the second task is to predict the frame position information. By sharing representations between the two tasks, we can enable the acoustic models to generalize better than the original classifier by averaging respective noise patterns to be implicitly regularized. Experimental results on the monophonic UPC-TALP and the polyphonic TUT Sound Event datasets demonstrate the superior performance of the joint learning method by achieving lower error rate and higher F-score compared to the baseline AED system.
Object recognition for a service robot

The continuous evolution of technology and the fast development of robotic applications has made possible to create autonomous robots that can assist or even replace humans in daily routines and monotonous jobs. Nowadays, with the aging of the world population, it is expected that service robots can be explored to assist elderly or disable people. For this, a service robot has to be capable of avoiding obstacles while navigating in known and unknown environments, recognizing and manipulating objects and understanding commands from humans. The objective of this dissertation is the development of a vision system, capable to detect and recognize household objects, for the service robot CAMBADA@Home. The proposed approach implements two methods for object detection, the first one based on color histograms and the second method using feature detection algorithms (SIFT and SURF algorithms). It uses depth and color information, where the 3D data is used to detect the objects that are found on horizontal planes. Experimental results obtained with the CAMBADA@Home robot are presented and discussed, in order to evaluate the robustness of the proposed system.
Introduction to Probability Models, Eighth Edition

introduction to probability models eighth edition is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to probability models eighth edition is universally compatible with any devices to read
Model for Evaluating Additive Manufacturing Feasibility in End-Use Production

In practical design work, a designer needs to consider the feasibility of a part for a manufacturing using additive manufacturing (AM) instead of conventional manufacturing (CM) technology. Traditionally and by default parts are assumed to be manufactured using CM and using AM as an alternative need to be justified. AM is currently often a more expensive manufacturing method than CM, but its employment can be justified due to number of reasons: improved part features, faster manufacturing time and lower cost. Improved part features means usually reduced mass or complex shape. However, in low volume production lower manufacturing time and lower part cost may rise to the most important characteristics.In this paper, we present a practical feasibility model, which analyses the added value of using AM for manufacturing. The approach is demonstrated in the paper on four specific parts. They represent real industrial design tasks that are ordered from an engineering office company. These parts were manufactured by Selective Laser Meting (SLM) technology and the original design done for conventional manufacturing is also presented and used for comparison purpose.
A Neural Network Approach for Risk Assessment of Asthma Disease

The work in this paper illustrates the deployment of neural network as a machine learning approach for the risk assessment of the asthma disease as indicated by a significant pulmonary function parameter called Tiffeneau-Pinelli index used in the prognosis of obstructive respiratory diseases such as Asthma. The approach is trained and tested on samples taken from SPIROLA dataset. We deploy different neural network types for the prediction of the index and evaluate the performance with respect to their predictive capability, thereby concluding that a few of the neural network types can be relied on for the effective prediction of the disease. The transformed features used in the input features set have proved to yield good prediction results thereby avoiding the need to employ individual raw features for the prediction process.
"Never fry carrots without chopping" Generating Cooking Recipes from Cooking Videos Using Deep Learning Considering Previous Process

Automatic captioning tasks that describe the content of images and moving images in natural language have important applications in areas such as search technology. In addition, captioning can assist with understanding content. Understanding of content can be deepened in a short time by reading captions. Among captioning models that use deep training, the encoder–decoder [1] model has generated considerable results and attracted attention, but many existing studies only consider the consistency of contiguous scenes over short periods. Considering the consistency of video segments as a matter of captioning has high importance. Generating cooking recipe sentences from cooking videos can be considered a captioning problem by treating recipes as captions. In addition, because the cooking video is constituted as a set of fragmentary tasks, a model that considers the consistency of the whole video is considered to be effective.
A Study on the Bead Geometry with Offset based Image Processing Algorithm

During the robotic arc welding process, not only the images taken are often unclear, but also the features are not obvious due to the interference of arc, splash and smoke. Therefore, it is necessary to carefully develop the image process algorithm for the image processing to complete the weld inspection. In this study, the robotic GMA (Gas Metal Arc) welding has been applied to bead-on-plate welding with vertical position. The images with different offsets were obtained based on infrared camera through experiments which might be obtained, and a set of image processing algorithms was designed to detect the geometrical bead images according to the different offsets. In image processing procedure, the first step is to perform the pre-processing to improve the contrast of the image. Then, the edge of the image is detected by the canny operator. Finally, the centre curve equation to predict the bead geometry is obtained by the least square method as well as the angle size is obtained according to the slope of the equation. The bead geometry obtained by the image processing is compared with the bead geometry obtained from the experiment in order to verify the feasibility of the developed algorithm. The experimental results are clear that the error is small so that the developed algorithm can be employed in actual GMA welding process with robot.
A Hybrid Dimensionality Reduction Model for Classification of Microarray Dataset

In this paper, a combination of dimensionality reduction technique, to address the problems of highly correlated data and selection of significant variables out of set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes is proposed. One-WayANOVA is employed for feature selection to obtain an optimal number of genes, Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) are employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classification method. Combining feature selection and feature extraction into a generalized model, a robust and efficient dimensional space is obtained. In this approach, redundant and irrelevant features are removed at each step; classification presents an efficient performance of accuracy of about 98% over the state of art.