Miracle-dz/UniFAR-FTUs huggingface dataset:
DDoS Traffic Control Using Transfer Learning DQN With Structure Information

A DDoS attack is one of the most serious threats to the current Internet. The Router throttling is a popular method to response against DDoS attacks. Currently, coordinated team learning (CTL) has adopted tile coding for continuous state representation and strategy learning. It is suitable for this distributed challenge but lacks robustness. Our first contribution is that we adapt deep network as function approximation for continuous state representation, as a deep reinforcement learning approach is robust in many different Atari games with a little modification of the learning architecture. Furthermore, current multiagent router throttling methods only consider traffic-reading information. Therefore, for a homogeneous team scenario, all agents can share parameters with the same deep network. However, for heterogeneous team scenarios, if all agents still share one deep network, the learning policy may not be sufficiently ideal. Our second contribution is that we add team structure information so that all agents can still share one deep network. However, deep reinforcement learning is a considerably time-consuming task. Transfer learning is an appropriate method because learning policy in a simple scenario allows us to transfer the policy to other different and even complex scenarios. For transfer learning regarding the DDoS control problem, we propose a progressive transfer learning approach, which is our third contribution. Therefore, we can learn a better policy with less time consumption. Moreover, with progressive transfer learning, we can promote our method in a more complex environment. The experimental results validate that our three contributions truly achieve better performance than the existing methods.
An Encryption-based Secure Framework for Data Transmission in IoT

Currently installed frameworks, IoT devices and portable frameworks are exceptionally presented to security issues. These devices must be shielded from unapproved access keeping in mind the end goal to ensure the information with respect to the touchy resources. The high outline multifaceted nature, forceful time– to– showcase, the increment in the computational intensity of devices is the fundamental driver for the presentation of security attacks, mistakes, vulnerabilities, and security secondary passages for on-field bargaining of the device. Associations should be worried about security, which incorporates burglary or loss of mobiles, conceivable infection contamination, conceivable unapproved movement interference and considerably more. Similarly, the IoT devices permit a colossal number of individuals, servers, and systems to interface commonly and share data. As the use of these devices is expanding, the inclined to security attacks have additionally expanded. The IoT devices required privacy, validation, get to control, honesty keeping in mind the end goal to keep a few attacks. In this paper, we give a brief of the current security challenges on cell phones and IoT devices. We presented different security attacks and their prevention techniques in detail with their pros and cons after that proposed encryption algorithm is presented.
A Survey of Deep Learning Techniques in Speech Recognition

The last decade has seen a rapid progress of deep learning techniques in various fields since the proposal of an efficient algorithm for training deep belief networks. Amongst these, speech recognition has seen significant breakthroughs with the application and integration of deep learning architectures. Deep Belief Networks have successfully outperformed GMM based acoustic models within the GMM-HMM hybrid that had dominated the field of speech recognition until now. Recent research with Convolutional Neural Networks and Recurrent Neural Networks for acoustic modeling has shown promising results. Both of these architectures are now being used to solve the problem end-to-end. With this article, a survey is provided on the application of three deep learning architectures in the field of speech recognition, namely, Deep Belief Networks, Convolutional Neural Networks and Recurrent Neural Networks.
Analysis of Process Scheduling Algorithm for Multiprocessor System

The scheduling of tasks in heterogeneous and homogeneous multiprocessor environment has been a topic of interest for the scholars and researcher for a long time. List task scheduling helps to develop various scientific appliances. It is NP- complete problem finding an optimal solution; hence it pairs low complexity with better results. The goal of scheduling task is the minimization of the total computation time. For multiple processor environments, list task scheduling is depicted using Directed Acyclic Graph (DAG) and described in both dynamic and static scheduling algorithms. In this literature review, we have discussed various algorithms to schedule the tasks which are a kind of static task scheduling algorithm for both homogeneous multiprocessor scheduling and heterogeneous multiprocessor scheduling environment. The comparative study of scheduling algorithm is based on the performance matrices termed as efficiency, Scheduling length, load balancing, and Speedup.
Optimization of Electrode Array in Electronic Tongue for Classification of Black Tea

Electronic tongue (ET) involves an array of sensors for accurate qualitative analysis of different food products. In this work, five such electrodes out of nine in an ET have been optimized for obtaining reasonable classification accuracy. Herein, nine different electrodes have been prepared using polymer-graphite composites of different compositional ratio. Cyclic voltammogram (CV) has been employed using a three electrode system, and the corresponding response profile has been used for the purpose of data analysis. Further, principal component analysis (PCA) has been applied on the dataset for selecting the appropriate features. The classification accuracy rate of the suitable five electrodes has been selected using support vector machine (SVM) classifier technique keeping the threshold value of accuracy to 90 %.
Improving Soft Decoding by Spectral Leakage Reduction in Presence of Narrow Band Interference in PLC

Communications over power lines (PLC) is a promising technology for a variety of applications. The use of a single network for power and data transmission with PLC provides weight, space, and cost savings for vehicular communications. Current work focuses on broadband PLC (BPL) systems compliant with the IEEE 1901 standard. Given that transmissions occur on a physical medium not designed for data communication, the channel and noise characteristics are mostly unfavorable. To deal with these conditions, turbo codes have been widely employed due to their high performance. However, their performance strongly depends on the accuracy of the noise estimation. This work addresses the estimation of the noise power spectral density (PSD) to perform soft-decoding. A general background noise (GBN) model and a narrowband interference (NBI) model is considered. To the best of our knowledge, this is the first work that analyzes the effects of the NBI leakage on noise estimation when it is performed during the reception of the data frame. We propose the use of the frame control (FC) symbol to reduce interference leakage on noise estimation. In addition, we investigate the use of payload symbols to further reduce spectral leakage. Finally, we show that system performance considerably improves by using the FC-based estimation in soft-decoding procedures.
High Performance 3-2 Compressors Architectures for High speed Multipliers

The 3-2 compressor is widely used in broad range of applications. Its main application is in partial product addition of multipliers. In this paper new architectures for 3-2 compressors which are having less power consumption, less time delay and less number of transistors than the conventional architectures, are presented. The proposed architectures use pass transistors, transmission gates and domino logic circuits. All the circuits used in the proposed architectures have better performance characteristics than that of CMOS circuits and existing compressors. The loss of voltage swing caused by the use of pass transistors is negated by the use of transmission gates and domino logic circuits after the pass transistors. The proposed 3-2 compressors offer 66–82% less power and 52 to 64% reduction in propagation delay. Simulation are done with Synopsis HSPICE tool. Therefore, high performance of the proposed 3-2 compressors makes them a good option for efficient multiplier designs.
Coping with uncertainties in estimating requirements development effort

Estimating requirements development effort is a critical activity in the software development process. Producing an accurate estimate, however, is extremely difficult, especially in the early stages of the software development process, where no clear and no complete description of the desired software behavior is available, hence leading to an estimate process with a high level of uncertainty. To handle this issue, in this paper, we present REEE (Requirements Elicitation Effort Estimation) – a framework for incrementally estimating software requirements elicitation efforts. We first categorize a project in terms of project’s domain, different types of project parameters and requirements – functional requirements (FRs), non-functional requirements (NFRs), constraints and domain properties (DPs), while taking into consideration relationships among them. We then present an incremental approach to software requirements elicitation effort estimation that uses similar past projects in a case-based reasoning (CBR) model. In this approach, semantic similarity is quantified, once classification and normalization of requirements are done. To see the strengths and limitations of REEE, a dataset of 36(students’) projects have been analyzed. Experimental results show that the use of REEE is a viable alternative to other cost estimation techniques, and can provide better results, with less uncertainty, for requirements elicitation effort estimation.
Detection and Prediction of Breast Cancer Using CNN-MDRP Algorithm in Big Data and Machine Learning: Study and Analysis

The rapidly growing sector of big-data analysis [1] shows primal function in devolution of recitations and an inquiry in the health sector. It has provided tools to roll up, succeed, examine and absorb large mass of integrated and ambiguous data which is, produced by incumbent health care systems. Big Data Analysis has recently been applied to help in the process of attention and disease exploration. However, the degree of acceptance and development of research in this space has been hampered by some underlying problems inherent the big data paradigm. Here, we have optimized machine erudition-formula for the impressive prevision of epidemics of prolonged diseases in communities of predominant diseases. Here the newfound multimodal disease prediction algorithm for convolutional neuronal networks (CNN-MDRP) that uses structured and unstructured hospital data.
Block Level based Data Deduplication and Assured Deletion in Cloud

With the progression of web, an ever increasing number of individuals have begun to outsource information on cloud. However, sending information to an outsider creates opportunity for attackers to easily retrieve data. Because of simplicity, individuals regularly tend to store excess information on the cloud. This leads to wastage and increase in the cost of storage space. As nobody knows where the information is put away on cloud; it is likewise vital to certainly erase information from cloud. Hence along with security, data deduplication and assured deletion are the major concerns in cloud storage. In this paper, we put-forth an application based on block level based file deduplication scheme over fragmented blocks that addresses the issue of duplication in the cloud storage platform. We also focus on guaranteed erasure using user defined policies. Thus on expiry of a policy, the keys are deleted therefore making the file inaccessible to anyone including the proprietor.
On the Efficiency of Transactional Code Generation: A GCC Case Study

Memory transactions are becoming more popular as chip manufacturers are building native support for their execution. Although current Intel and IBM microprocessors support transactions in their instruction set architectures, there is still room for improvement in the compiler and runtime front. The GNU Compiler Collection (GCC) has language support for transactions, although performance is still a hindrance for its wider use. In this paper we perform an up-to-date study of the GCC transactional code generation and highlight where the main performance losses are coming from. Our study indicates that one of the main source of inefficiency is the read and write barriers inserted by the compiler. Most of this instrumentation is required because the compiler cannot determine, at compile time, whether a region of memory will be accessed concurrently or not. To overcome those limitations, we propose new language constructs that allow programmers to specify which memory locations should be free from instrumentation. Initial experimental results show a good speedup when barriers are elided using our proposed language support compared to the original code generated by GCC.
Software Development Approaches Significant for Runtime Software Evolution: A Review

Dynamic adaptability in software is a challenging task in recent software development scenario. This still remains to be a continuous process as a result of incorporating new and ever changing requirements of user as one of the major factors among others. An evolutionary system can be a novel approach that should provide an intelligent solution to the problem of dynamic adaptability. Such a system should evolve by learning to reconfigure itself according to the emerging requirements and changed situation. In the context of current scenario, this paper summarizes the features of some runtime software evolution approaches. The study also presents a brief summary of some proposed software architectures and frameworks that are being used or have potential to be used for the development of such evolutionary software.
Analysis of Variants of KNN Algorithm based on Preprocessing Techniques

K-nearest neighbor (KNN) algorithm is a nonparametric classification method often used in pattern classification for handling binary and multiclass problems in different domains. Although KNN algorithm is a simple data mining approach but it may give inaccurate results when training datasets are imbalanced and incomplete. Different variants of KNN algorithm are developed to handle the discrepancy and inaccuracy in the results. To further improve the efficiency and accuracy, preprocessing can be done to handle imbalanced and incomplete datasets. This paper presents concept of KNN algorithm and brief analysis of variants of KNN algorithm based on their preprocessing techniques.
Compliant Impedance Control for a Redundant Manipulator During Human Robot Interaction

Robot control with a compliant motion behaviour is important to guarantee the safety of human robot interaction. In this paper, we aim to guarantee the desired task impedance of the end-effector and to ensure a compliant behaviour of a redundant robot during human robot interactions. A task space impedance control scheme is proposed by designing a transferred impedance error, such that the robot end-effector can be governed to follow a target impedance model. Additionally, a low-priority controller is designed using the null space projection, such that compliant joint motion is guaranteed without affecting the main task. The stability of the whole system is illustrated using a conditional Lyapunov theorem. Simulation studies based on a plane redundant robot is carried out to demonstrate the validity of the proposed controller.
Efficient PRNU Extraction Using Joint Edge-Preserving Filtering for Source Camera Identification and Verification

The photo response non-uniformity (PRNU) of a digital image is a kind of sensor pattern noise (SPN) by which one can distinguish the gadget which is utilized to take the image. It has been extensively used for source camera identification and verification in the past decade. The useful data that the SPN passes makes it unique, and thus sensible for perceiving the source camera and recognizing image forgeries. In any case, the PRNU extraction process is unavoidably looked with the contamination of image dependent features and furthermore other non-unique artifacts. In this work, we have presented joint edge preserving filtering technique for Π extraction. The proposed method is computationally efficient and PRNU separated by utilizing the proposed method contains an insignificant measure of scene details of the image. Hence the PRNU obtained from the proposed method is more efficient for source camera identification. Exploratory outcomes on publicly available database by different cameras have demonstrated a significant gain acquired and the predominance of the proposed framework over related best in class frameworks.
Firefly Algorithm and Particle Swarm Optimization for Optimal IIR System Identification

Adaptive filtering has been used in recent years in many areas such as control systems, prediction, system identification and modeling, signal processing, speech processing and communication systems. In order to implement the adaptive infinite impulse response (IIR) system identification problem efficiently, it is essential to calculate the optimal system parameters and select the identity structure. In this study, Firefly and Particle Swarm Optimization are used to calculate IIR optimal system parameters. In these algorithms, Integral Absolute Error (IAE) is chosen as the error criterion. The performance analysis was carried out by implementing the proposed methods in three different IIR systems. (Abstract)
Predicting Cryptocurrencies Prices with Neural Networks

A cryptocurrency is a digital asset designed to work as a medium of exchange that uses cryptography to secure its transactions, to control the creation of additional units, and to verify the transfer of assets.Cryptocurrencies are a type of digital currencies, alternative currencies and virtual currencies. Cryptocurrencies use decentralized control as opposed to centralized electronic money and central banking systems. The decentralized control of each cryptocurrency works through a blockchain, which is a public transaction database, functioning as a distributed ledger.Neural Networks field has many techniques to perform predictions. They are widely used to predict the future values of stock exchange indicators variables. In this paper we will try to use Artificial Neural Network to predict cryptocurrencies close prices, and we’ll study the difference in price change with the normal stock exchanges.
Information Retrieval on Tweets collected during Disaster Situations

Microblogging sites like Weibo, Tumbler, Pinterest, and Twitter have been identified as significant resources for current events including social, political and emergency cases. During disaster situations, these sites are very advantageous for collecting information in real-time. During these events, many people make way to microblogging sites but very few of microblogs published are relevant to the information needed. Hence, it is important to make useful methodologies that can extract relevant microblogs from the huge chunk of data posted on these sites. In this work, one of the methodologies is applied for retrieving tweets for a distinct situation. The accuracy of retrieval and the relevance of tweets retrieved have also been assessed as a portion of this work.
Optimization of Gate Stack MOSFETs with Quantization Effects

In this paper, an analytical model accounting for the quantum effect. in MOSFETs has been developed to study the behaviour of high-k dielectric. and to calculate the threshold voltage of the device considering two dielectrics gate stack. The effect of variation in gate stack thickness and permittivity on surface potential, inversion layer charge density, threshold voltage, and In-Vo characteristics have also been studied. This work aims at presenting a relation between the physical gate dielectric thickness, dielectric constant and substrate doping concentration to achie"e targeted threshold voltage, together with minimizing the effect of gate tunneling current. The results so obtained are compared with the available simulated data and the other models available in the literature and show good agreement. Index Terms-Quantization effects (QEs), Triangular Potential Well (TPW), Gate stack, Equivalent oxide thickness (EOT)
Location-Assisted Beam Alignment for Train-to-Train Communication in Urban Rail Transit System

Train-to-train (T2T) communication is expected to increase efficiency of train operation and reduce life-cycle cost in construction for the urban rail transit system. In this paper, we adopt the millimeter wave (mmWave) band to achieve T2T communication and focus on the alignment problem of narrow beams between trains in the turning scene. We first investigate the beam coherence time related to a number of antennas and train speed to avoid misalignment problems and then propose a location-assisted beam alignment method and obtain analog beamforming and combining vectors by comparing the estimated beam deflection angle with the reference phase angle. To validate the proposed algorithm, the numerical results are presented and discussed, where the received signal to noise ratio (SNR), the computational complexity, and the loss rate of the received SNR are assessed. Compared with other search and alignment methods, the proposed algorithm shows significant superiority.
Contribution onto Prognosability Based on Discrete Events Systems

Developing a method of prognosis, allows to avoid any accidental stop of the system, by predicting the future failure occurrence. Which makes it possible to preserve performance, availability and safety of both operators and equipment. To strengthen this technique, various research works have developed the prognosability, as a necessary and sufficient condition for prediction of a failure without missed detection and false alarm. This paper is a continuation of the works that has been realized on the prognosis of failure on extended Petri Net (PN) based on discrete events systems (DES); a prognostic method based on time, to predict the future failure occurrence at the earliest and at the latest. The aim is to prove that prognosability is possible on extended PN, while respecting these necessary and sufficient conditions.
A Fast Similarity Search kNN for Textual Datasets

The k nearest neighbors (kNN) is an algorithm for finding the closest k points in metric spaces. Due to its high computational costs, many parallel solutions have been proposed, including some implementations targeted at modern accelerators. However, most approaches assume relatively low dimensionality and dense data. Such conditions do not apply to textual datasets, which are known for their high dimensionality and sparsity. This work presents a fine-grained parallel algorithm that applies filtering technique based on most common important terms of the query document using an inverted index and its implementation on GPU. Our method improves the top k nearest neighbors search in textual datasets by up to 37x with a single GPU.