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In order to automate the task of correcting handwritten subjective answers, we envision a system that reads such answers and grades them. In this project, we have created a mobile application to achieve this task. We have used the RAKE algorithm to extract key-phrases, which are then embedded using Word2vec model and compared using Word Mover's Distance. Our created model achieved almost human like evaluation accuracy. We have also provided accessibility features to the users with the aim of making this application a one-stop solution to automatic evaluation of handwritten subjective answers.

This position paper proposes CsPI (Cybersecurity Performance Index) as a new way to evaluate the cyber security investment decisions and measure the progresses and effects of the investments and projects. In a conventional way, cyber security budgets are usually evaluated by the return on investment (ROI), which has some undesirable side effects. Since we don't ask for return on investment for other similar expenses (like IT or capital), why should we use ROI for cybersecurity? Here, we propose to adopt a well-established technique in the project management field called "earned value" to the field of cyber security. Specifically, what we propose is to measure the progress of cybersecurity expenses against a plan. The plan should include protection, detect, respond, recover goals that cybersecurity expenses (people, process, technology) attempt to meet – and this has to take into account business objectives and threat/vulnerabilities. Similar to the "Cost Performance Index" or "Schedule Performance Index" in Earned Value, we can define "Cybersecurity Performance Index" (CsPI). The main idea is to show how well current cybersecurity expenses are performing against a plan. At the end, future research directions are presented.

This paper takes a view on the Quality-of-Experience (QoE) perspective of Cyber-Physical System (CpS) applications involving users. We provide an overview of characteristics of CPS applications, their relation to QoE and implications for assessing QoE of such systems. Furthermore, we incorporate eudaimonic aspects such as meaningfulness and personal growth that need to be considered in order to grasp the strongly multi-dimensional aspects of QoE in this context.

The rapid growth of Internet technologies and further dependence on online services, increase the demand for keeping these networks and data secure. The protection of online information is becoming even more vital to the national security and economic stability. Recently, network security has become one of the most concerning subjects in the current research and industry fields. Intrusion Detection Systems (IDSs) are considered as the backbone for network and data protection. Throughout time, different IDS approaches have been implemented to attain maximum detection accuracy. Machine learning IDS is one of the promising IDS techniques that have been created to detect known as well as unknown attacks. This paper investigates various machine learning techniques used to deploy Network-based Intrusion Detection System (NIDS). This survey could provide a more robust understanding of the existing techniques and assists intrigued researchers to identify research opportunities and investigate more in this direction.

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe. A critical challenge to our—and any—autonomous machine is accurate and efficient localization under resource constraints, which has fueled specialized localization accelerators recently. Prior acceleration efforts are point solutions in that they each specialize for a specific localization algorithm. In real-world commercial deployments, however, autonomous machines routinely operate under different environments and no single localization algorithm fits all the environments. Simply stacking together point solutions not only leads to cost and power budget overrun, but also results in an overly complicated software stack.This paper demonstrates our new software-hardware co-designed framework for autonomous machine localization, which adapts to different operating scenarios by fusing fundamental algorithmic primitives. Through characterizing the software framework, we identify ideal acceleration candidates that contribute significantly to the end-to-end latency and/or latency variation. We show how to co-design a hardware accelerator to systematically exploit the parallelisms, locality, and common building blocks inherent in the localization framework. We build, deploy, and evaluate an FPGA prototype on our next-generation self-driving cars. To demonstrate the flexibility of our framework, we also instantiate another FPGA prototype targeting drones, which represent mobile autonomous machines. We achieve about $2 \times$ speedup and $4 \times$ energy reduction compared to widely-deployed, optimized implementations on general-purpose platforms.

Now a days due to the development in color printing technology the rate of counterfeit notes production and distribution is increasing. This is a massive problem, faced by almost all the countries. It affects the economy, sine it compromises the security of the real economy. Such counterfeit currencies are used to fuel nefarious motives, usually involving terrorist activities. According to the research, developing countries like India have been impacted by this very negatively. Even after the steps taken in 2016 to remove the counterfeits, by executing the demonetization of 500 and 1000 rupees bank notes in India the counterfeits of the new notes have begun circulating. This is due to the highly advanced technology adopted by the counterfeiters which makes the tracking of these counterfeit notes hard. This has become a very critical issue and the negative impact due to the counterfeit currency keeps rising. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for a small business or for a common man these machines are not affordable. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion etc. The proposed system will have advantages like simplicity, reliability and costs less.

One very important component of the paper making process is the headbox. The inflow of the stock is accumulated up to an appropriate height in the headbox. The regulation of this height is to be strictly regulated to sustain an optimum pressure so that the stock jet is released with the preferred speed. The present work addresses the control system design of headbox consistency. The controllers using ZN and LQG techniques have been developed for it and their performance is compared. Further, the undesired effects of deadtime are compensated by designing Smith predictor. The output response, after deadtime compensation, is compared with the previous responses. All the results have been obtained by MATLAB simulations.

This work is centered on IMC (Internal Model Control) control system design and its robustness analysis for controlling the basis weight of the paper produced by the paper milll. The headbox has a significant role in a paper mill. It maintains the pressure inside the box and discharges stock at an optimum required speed. In this paper, the robustness of the IMC based controllers will be investigated for the two parameters, setpoint tracking and disturbance rejection. The MATLAB software is used for carrying out all simulations done in this paper.

With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. Here, we propose an approach for automatic clustering for text document using a Self-Organizing Map (SOM). It is one of unsupervised artificial neural network that widely used for data analysis, data compression, clustering, and data mining. The quality and accuracy of a SOM algorithm depends on the selection of values for some of its parameters which are its initial learning rate, SOM matrix dimensions, and the number of iterations. Best values are typically selected using trial and error; however, in the current paper we suggest a more systematic approach to parameters optimization using the genetic algorithm. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. Clustering results were validated using the Dunn index.

In today's scenario cloud computing in growing concept in the computer and information technology field for storing the data. Because on the physical storage device whatever the huge amount of data which is sensitive or confidential that cannot be stored. So the cloud computing comes into the picture for storing the data as per their requirement. But if we store the data on cloud it will be easily accessible to all and the confidentiality of that data is not maintained. So for that purpose we are going make the data on the cloud to be secure with the help of cryptography. In this Paper we talk about earlier work in this field of encryption algorithm and the key issues for encrypting the data and decoding the data in cloud environment and proposed the objectives for improving the encryption algorithm and key issues which would help to store the data in cloud environment securely and efficiently. The paper proposed the defined objectives for the secrecy of the data in the storage of cloud computing environment which is the main aspect of our paper.

Cryptography is wide domain with many subs. The objective of this review paper is to give light review for readers and students of the three common used algorithm two in symmetric cryptography DES, AES and one in asymmetric cryptography RSA. Allowing the reader to have simple understanding of the background history of the algorithm in review and the key functional cipher operation of the algorithm. Accordingly summary of strength and weakness of each algorithm under the review will be highlighted. And how the security goals is such confidentiality and integrity are achieve using the mentioned algorithms. The paper will sequentially list and review the said algorithms and clarify the relational between them. Such as the relation between the symmetric and asymmetric algorithm the one with secret key and the ones with key pairs. The paper may insight some of the research window for upcoming scholar research in the field of cryptography.

Under the background of information society, it is a necessary means to protect and inherit Intangible Cultural Heritage to promote the artistic value and economic value of paper engraving art by introducing digital display technology such as virtual reality. This thesis starts with the common features extraction of paper-engraving art in the Han nationality area of China, and puts forward the basic principles of systematization, integrity and extensibility for the design of digital interactive display platform for Intangible Cultural Heritage paper-engraving, and expounds the design strategy of digital interactive display platform from five aspects: user requirement analysis, data classification, two-dimensional and three-dimensional elements reconstruction, platform building and interactive design and user feedback. The Digital Interactive Platform of paper engraving based on virtual reality technology gives the viewer the best immersion and interactive experience, which is helpful to better understand its cultural essence and has very important application value for the inheritance of paper engraving art.

Nowadays, there is a rapidly increasing number of conferences and journals in computer science that make a lot of challenges for researchers to find an appropriate venue to submit their scientific work. There is a need for a recommendation system that can support researchers for a better process of paper submission. In this paper, we present an efficient approach for building such a recommendation model by using embedding methods, Global Vector (GloVe) 1 created by Pennington et al. [1] and FastText 2 proposed by Facebook [2], Convolutional Neural Network (CNN) [3], and LSTM. The experimental results show that the combination of CNNs and FastText, CNN + FastText, can achieve the best performance in terms of the Top 1 Accuracy compared with other techniques, including the S2RSCS model, as presented in [4]. Moreover, the performance by using GloVe or FastText is much better, faster, and more stable than S2RSCS in most cases.

The paper mill is one of the major industries of India. The various processes that are present in paper mill are not simple to model and control because of presence of nonlinearities and the dead time. The present work is focused on a prime section of a paper mill that is the headbox. The regulation of its consistency is a major concern. In this paper, first of all, the FOPDT model of the head box consistency dynamics is approximated as a polynomial model using the Pade approximation and hence control system is designed for it. The Simulated Annealing (SA) technique is used for the performance optimization of this control system. MATLAB software has been used to obtain all simulation results.

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams from several research papers. Retrieving such information poses significant challenge as most of the existing academic search engines are primarily based on only the text content. In this paper, we introduce Diag2Graph, an end-to-end framework for parsing deep learning diagram-figures, that enables powerful search and retrieval of architectural details in research papers. Our proposed approach automatically localizes figures from research papers, classifies them, and analyses the content of the diagram-figures. The key steps in analyzing the Figure content is the extraction of the different components data and finding their structural relation. Finally, the extracted components and their relations are represented in the form of a deep knowledge graph. A thorough evaluation on a real-word annotated dataset has been done to demonstrate the efficacy of our approach.

The increasing number of research papers from Indonesia and the higher the need to manage large amounts of documents, including memory capacity and the availability of time to manage research documents, keyphrase can provide a picture representing a text document. Thus, the management of documents in large quantities will become easier to prepare and find a collection of documents. For this reason, proper and correct keyphrase extraction can assist in managing paper documents. This study tried to compare three extraction methods using unsupervised graph-based techniques, including TextRank, TopicRank, and PositionRank. We compare the extraction results with the keyphrase made by the authors of the paper to see the three method’s performance. From the results of our research, we found that the method TextRank Providing the highest performance compared to other methods using the ROUGE test with average Recall, Precision, and F1-Score are 0.558, 0.71, and 0.225. However, the extracted phrases are longer compared to others. The performance of TopicRank, although it does not provide higher results with TextRank, the extracted keyphrase is more compact than TextRank and PositionRank methods.

A robustness analysis of the control system, designed for the stock consistency of the paper machine subsystem, using PSO based conventional controllers has been presented in this paper. The robustness of the designed control system has been assessed through worst case sensitivity analysis. The subsystem taken into consideration for this work is the headbox. Tuning parameters of conventional PI and PID controllers have been determined through FL-PSO algorithm. Worst case sensitivity has been assessed by evaluating the peak values of sensitivity function and complementary sensitivity function for the perturbed consistency models. The aforesaid peak values have been determined through MATLAB. A comparative analysis of both PI and PID controllers for all five stock consistency models have also been presented. From the comparative analysis, it has been observed that the PI controller exhibits better robustness as compared to PID controller.

In recent years, with the increasing investment in scientific research, the number of papers has proliferated, and the development of scientific research has also received widespread attention from scholars. Paper citations as an indicator to measure academic influence have essential reference value for future research directions. Aiming at the future paper citation number prediction problem, we propose a citation number prediction model based on the recurrent neural network method with gated recurrent unit(GRU-CPM). In this paper, we first extract features from real data sets that are useful for predicting the number of citations in papers, and then the features were input into GRU-CPM for prediction. Finally, the prediction results are compared with other regression models. Experimental results demonstrate that the GRU-CPM has higher prediction accuracy and faster convergence speed. The time series prediction of citation count is better than existing methods.

In this paper, machine learning approaches have been used to predict whether a scientific paper will be accepted in a top-tier AI conferences or not. This shall help authors identify the likelihood of their paper getting accepted in a top-tier AI conference. We have used the PeerRead dataset containing papers collected from major AI conferences that are publicly available. We have achieved an accuracy of 81% using Random Forest classifier. The novelty of the paper lies in accurately predicting whether a scientific paper will be accepted in the top AI conference.

Soft food grasping in the food processing field is challenging for traditional robot hands because of the large demand, special hygiene cleanness and damage-free requirements. The paper-made grippers are low-cost and disposable to solve the crucial cleanness problem and they have soft structures which can adapt to the object shapes to avoid possible grasping damages. In this paper, we propose two paper-made grippers for the increasing soft object grasping requirements in the food processing field. One gripper uses a biomimetic mechanism of human hand to provide a force-closure grasping capability, and the other one uses a scoop mode to achieve a shape- closure grasping capability. Both grippers are low-cost, easy- fabrication and with simple actuation mechanism to enable a mass production. Experiments with different gripper designs were carried out to compare the two grippers performance, and preliminary results proved the possibility of applying these paper grippers for the food processing field.

This project represents the design of a safety system for conveying and storing valuable documents while restricting unauthorized access. The system consists of a couple of security lair to prevent any leakage from important documents such as question paper, banknotes, and statements, etc. from collection to distribution. The system has been made with a Memory module, Ultrasonic sensor, Fingerprint security, RFID module, GPS and GSM module, etc. This proposed security system has been featured in such a way that the navigation box where we will put our question paper can be opened only by an authorized person in specific places and in a specific time. Education is the most important aspect of our life and through the exams, we can judge people's outlook on their respective fields of study. But question paper leak is a major problem in several countries and this project is here to end it.

In worldwide, security plays a significant role in preventing different assets and crimes. Education has become an essential right of human life as it has been transferred from one era to next. The examination is a significant part of the coaching structure. The key objective of the examination would be to select the suitable applicants for different positions. Every year the updates that exams have been postponed or canceled due to inquiry paper leakage are occurring. So, an innovative system is implemented to prevent this spillage of examination papers. This system is designed on the embedded system. The main types of equipment of this advanced system are Arduino Mega, solar panel, fingerprint scanner, GSM module, and GPS module.

Electronic examinations provide numerous advantages over traditional examination carried out on paper, such as more streamlined logistic and savings of cost, manpower and time. However, when solving examination problems in the field of engineering, the use of pencil and paper is frequently preferred to using the most common computer input devices, the mouse and the keyboard. The traditional paper approach offers the students flexibility in expressing their thoughts in a free form, experimenting and visualizing their approaches while working out their solutions. The focus of this work, which is in progress, is to modify and adapt certain aspects of traditional engineering exam problems to make them more suitable for electronic examinations. This allows the students to continue use the traditional tools of pencil and paper, but the electronic exam will extract the information needed for automatic grading, combining the best of both methods.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

In the contemporary world, security plays the major role to prevent various assets and thefts. Education is the integral part of every human life as it delivers from one age then onto the next. Examination is the heart of the training structure. The main aim of the examination is to pick the qualified applicants for various positions. We hear the news of postponed or cancellation of exams each year due to question paper spillage. So, to stop this leakage of question papers a smart system is proposed. This system is designed on the embedded system. The key components of the device are NodeMcu, Biometric Sensor and GPS Module.

Mobile augmented reality (AR) has been attracting considerable attention from industry and academia due to its potential to provide vibrant immersive experiences that seamlessly blend physical and virtual worlds. In this paper we focus on creating contextual and personalized AR experiences via edge-based on-demand provisioning of holographic content most appropriate for the conditions and/or most matching user interests. We present edge-based hologram provisioning and pre-provisioning frameworks we developed for Google ARCore and Magic Leap One AR experiences, and describe open challenges and research directions associated with this approach to holographic content storage and transfer. The code we have developed for this paper is available online.

On average, 2.5 quintillion bytes of data are generated on the internet every day. So, the demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. A revolution in the field of data storage is the need of an hour. This paper surveys a very unique technique of storing digital data in DNA sequences. The idea is to replicate nature’s way of storing information. It has been around with us for ages. This paper elaborates on the procedure, applications, and challenges that are associated with this fictitious idea of data storage. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 Exabyte/mm3 (109 GB/mm3), and long-lasting, with an observed half-life of over 500 years.

Sentiment Analysis is done by gathering the data, analyzing the text and aggregating the words in the text. The main focus of this survey is on the evaluation methods used and their respective core solutions. In this paper we are going to study the aspect based sentiment classification, which is classified into three. Those are Implicit, Explicit and Coreferential aspect based sentiment classification. In this paper we are also going to elaborate the polarity of the reviews into positive and negative sentiments. Aspect Based Classification not only promises to suppress the noisy data but also provides high efficiency in identifying the aspects with an addition of performing classification task with higher accuracy. This accuracy can be achieved by using Neural Network algorithms.

Sentiment analysis and opinion mining have developed significantly throughout the last decades. This study aims to assess, amongst many other aspects, people's feelings, opinions, and emotions of someone or something. This review's principal objective is to automatically assess the direction of the review and contrast this with the evaluation made by the reviewers. This will allow scientists to categorize and evaluate reviews cross-sectionally and encourage a scientific article's overall evaluation more critically. An EM-VC ensemble approach has been proposed for analyzing the reviews. This approach uses POS to achieve a syntactic sentence structure. With dictionaries, this syntactic structure enables the semantic orientation of the analysis via the scoring algorithm to be determined. To test the proposed approach's efficiency and performance about the baseline method, some tests have been performed using typical performance metrics like precision, accuracy, F1-score, and recall. The findings reveal that improvements are achieved in binary classification, ternary classification, & five-point classification compared to the existing HS-SVM method. Still, it provides a slight improvement in accuracy in the context of multiclass classification compared to others.

Facing the difficulty and inefficiency of creating and maintaining High-Definition (HD) maps in our commercial deployments, we have developed an edge-based crowdsourcing task distribution framework for HD Map in autonomous driving. Our key observation is that: HD map data crowdsourcing exhibits the diminishing marginal utility thus there exists an inflection point for maximum utility, meanwhile its premature convergence of utility will leave some map updates not notified in time. Based on this observation, we develop a periodic crowdsourcing task distribution framework. It discretizes the demands for collecting source data into different periods and uses an optimal stopping rule to terminate the data collection for the maximum crowdsourcing utility. The experimental results verify that our crowdsourcing framework can achieve high time coverage and high efficiency with lower cost.

In autonomous driving, 3D object detection is es-sential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support realtime 3D object detection on edge-computing devices in selfdriving cars with limited computation and memory resources. To facilitate this, we propose a compiler-aware pruning search framework, to achieve real-time inference of 3D object detection on the resource-limited mobile devices. Specifically, a generator is applied to sample better pruning proposals in the search space based on current proposals with their performance, and an evaluator is adopted to evaluate the sampled pruning proposal performance. To accelerate the search, the evaluator employs Bayesian optimization with an ensemble of neural predictors. We demonstrate in experiments that for the first time, the pruning search framework can achieve real-time 3D object detection on mobile (Samsung Galaxy S20 phone) with state-of-the-art detection performance.

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.

We give an introduction to the MARS toolchain for formal modeling and verification of hybrid systems. It consists of translators from Simulink/Stateflow models to Hybrid Communicating Sequential Processes (HCSP), and tools for simulation, code generation, and deductive verification of an HCSP model. We apply the toolchain to model the descent guidance control phase of the recently launched Tianwen I mars lander, and verify that it correctly controls the velocity of the lander.

As a fundamental component in the autonomous driving technology stack, High Definition Maps (HD map) provide high-precision descriptions of the environment. It enables extremely accurate perception and localization while improving the efficiency of path planning. However, the HD map’s extremely large data volume poses great challenges for the real-time and safety requirements of autonomous driving. Based on our real-world deployment experiences, we first demonstrate how the existing data transmission mechanism is weak in supporting HD map services. To address this problem, we propose an HD map data service mechanism on top of Vehicle-to-Infrastructure (V2I) data transmission under a tight time and energy budget. By this mechanism, the selected road side unit (RSU) nodes cooperate on map provisioning tasks and transmit HD map data proportionately. Furthermore, we model the real-time map data service into a partial knapsack problem and develop a greedy data transmission algorithm. Experimental results confirm that the proposed mechanism can ensure the real-time HD map data service meanwhile meeting the energy limits.

The study aimed to realize the automatic production scheduling of waste paper in paper mills. Based on the quantities of the field data (mixing ratios of waste paper and pulp properties) stored in a paper mill, the methods of back propagation neural networks (BP-NN) and support vector machine (SVM), the all data set and the average data set, were firstly applied to develop the prediction models of the pulp properties; and then the genetic algorithm was used to search for the suitable scheduling solutions. The simulation results revealed that: demonstrating its acceptable prediction accuracy and fast training time, the prediction model with SVM method and the average data set was good enough to be used in the genetic algorithm to search for the optimal scheduling ratios of waste paper, the obtained optimal scheduling solution not only had the lowest purchase cost of waste paper, but also met the requirements of the mill: specified types of waste paper, specified ratio of some kind of waste paper and the required pulp brightness.

Pervasive Social Networks of Strangers are available constantly, connecting users directly with no preexisting relationships. Such networks demand a high level of privacy, as users should be anonymous and their behaviour unlinkable. However, the anonymity of users could lead to abusive messages, spam, or fraud. A possible solution is to keep track of users' reputations, based on feedback from other users. However, the need to collate feedback on user behaviour to form reputations is at odds with the need for privacy. Anonymous and unlinkable feedback is also required, whilst multiple feedbacks given on the same item should be detected. To resolve this, we use group signatures and direct anonymous attestation, to give a reputation-based messaging scheme.

U.S. Department of Defense regulations intended to improve cybersecurity within the Defense Industrial Base may cause degradation of critical defense infrastructure. Four impediments to 'clean' compliance, environmental and internal, are detailed. Inability to functionally comply, or to correctly insure, will impact the DIB's cybersecurity decision-making at the enterprise level. Economic impact to the DIB may drive withdrawal and attrition from the sector, or aversion to R&D, which negatively affects mission resilience with concomitant capability loss to the DoD.

An important branch in quantum computing involves accurate resource estimation to assess the cost of running a quantum algorithm on future quantum hardware. A comprehensive and self-contained workflow with the quantum program in its center allows programmers to build comprehensible and reproducible resource estimation projects. We show how to systematically create such workflows using the quantum programming language Q#. Our approach uses simulators for verification, debugging, and resource estimation, as well as rewrite steps for optimization.

In this research, PI Classical Controller, LQG (Linear Quadratic Gaussian) Controller and FLC (Fuzzy Logic Controller) is explored and employed for the performance analysis of the blend chest level control in a Paper Mill. This work has been done on the MATLAB platform with Simulink. The comparative results infer that Fuzzy Logic Controller depicts improved set point tracking as the settling time is found to be minimum, which is an important steady state characteristic for FLC. Also Fuzzy Logic Controller gives the least maximum percentage overshoot among all three Controllers, which is an important transient characteristic.

In the present work, the 48 input-output data points of the paper mill process have been considered with the desired machine speed as the input and required total head as the output. The data has been taken from the literature. Two clustering techniques are applied on this data using MATLAB. One is the FCM (fuzzy C-means clustering), another is the subtractive clustering. In present work, initial FIS (fuzzy inference system) developed by subtractive clustering is further optimized to improve its performance, and finally their responses are compared.

Currently, anomaly detection is receiving a great deal of attention due to its importance in many real-world applications especially in data analytics. Anomaly detection has two important purposes in data pre-processing stages: one is to detect and attempts to eliminate them if and only if it is really outlier, while the other requires attention be paid to valid outlier (extreme value) because extreme value themselves carry the significant and critical information, such as extreme weather events. It will lead to significant harm if not detected and treated properly. This paper begins with the summary of anomaly detection, challenges, type of anomalies, extreme value followed by machine learning algorithms approach that deal with anomaly detection. Finally, we provide a conclusion that discusses about the important of detecting clearly between outlier or extreme value in data pre-processing stage, so that an effective and efficient prediction can be achieved later in modelling stage.

This Embedded Graphics libraries have evolved from OpenGL to next gen Vulkan libraries. OpenCL and CUDA have been used for many years for GPU compute on a range of SoC devices with powerful embedded GPU chipsets. OpenCL and CUDA were never able to be certified, due to the contributions from many developers. Next generation autonomous vehicles, unmanned aircraft, transportation and even military vehicles will require both graphics, as well as GPU compute to implement DSP or FPGA logic is software, as well as Deep Machine Learning. Vulkan libraries are available now and are defined by the Kronos Group, as well as this, a next generation of Vulkan safety critical library for safe Shader and GPU Compute is now available. This white paper will look at Vulkan architectures, embedded system use and the implications for using GPU Compute, where certification may be a critical requirement for ISO 26262, EN50182, IEC 61508, DO178C certifiable embedded systems.

Academic advising of the postgraduate students is considered as a fundamental rule and one of the significant positive services and responsibilities for the academic staff follow up in most of the e-leading educations. The expert system is considered as one of most achievement areas in artificial intelligence and highly appreciated for application with e-learning and teaching methods. The aim of this paper, is to survey the most expert systems used in advisory model and replaced with the manual registration and advising system of face to face communication and discussion in Instructor office. The method used of determined the key factors related to student advising and expert system models though open access databases. The finding of this section reviews out nine selected expert systems in order to explore the strengths, and limitations of existing expert system approaches to advising undergraduate students.

On internet, there are plenty of social networking sites on which people express their perception regarding a topic every day which can be helpful for sentiment analysis. In this paper, we have discussed the general procedures in sentiment analysis and how we are going to use python and its libraries for performing sentiment analysis on data retrieved from twitter’s website so we can get the view of what public thinks of last ten government decisions like Demonetization, Goods and Service Tax (GST), Bharat Interface for Money (BHIM), Citizen Amendment Act (CAA).

Security is becoming an important issue everywhere nowadays. Every person wants his house, factory, bank etc. to be secured. House security is becoming necessary as the possibilities of intrusion are increasing day by day. Here in this paper, we are trying to present research made on various door locking systems that have been used at homes, offices, shops, industries, etc. The research will also put some light on the pros and cons of the various door lock systems and the technology used in the respective systems. The research will include traditional door lock system, RFID based system, gesture-based systems, Bluetooth and GSM technology- based and various other technologies used in the door lock security systems. The advance in technology in each passing day can help us come across various technologies that can be used in the door lock security system. Since security is the major concern of the majority of the people at present, we feel research made on the various system relevant to the topic can help people to choose the best technology for their upcoming projects or make changes in the past projects and use effective door lock systems for their needs.

Technology grows at a rapid rate and trust has involved in a risk judgment between different parties. Authentication and Authorization generally determines the trust while dealing with transactions of data over different channels. Blockchain has become a digital backbone due to its security, time-management, and transparency on data decentralization ledger. This survey paper studies the existing blockchain structure, analyses the research gaps and proposes a new theoretical approach based on neighbor monitoring and updating model. This model may help the patients to decentralize real data and results in the blockchain network. This work discusses in detail about the various security challenges in block chain against 51% attack and double-spend attack by proposing a theoretical framework for the problem.

This paper introduces the time stamped bijective MAC algorithm (bMAC), which aims at proving firmware integrity in embedded systems. bMAC computes a MAC over a set of memories, in a pseudo random way relying on a permutation. It is based on exponential permutations that induce computing time distributions. Because bMAC performs calculations on SRAM content, it can be bound to physical chips, by means Physical Unclonable Functions (PUF), extracted from SRAM.

Cloud computing is the emerging way of computing that is being used by wide range of users. At present 80 % of workload is on cloud. There are many safety risks in cloud computing, “Cloud Security Alliance” (CSA) listed top 7 security threats as Maltreat and Obnoxious utilization of cloud, Uncertain API, Vicious Infiltrators, Distributed mechanism challenges, Information drop, Unidentified Risk Report and Account, Service and Traffic Hijacking. An IT company spends 42 percent of its funds for the security of cloud and 80 percent of the funds will be bounded to attain the solution to security issues. Researchers have proposed no. of solutions to cloud security. Proposed solutions fall under three primary categories, Cryptographic, data storage and data semantics. In this paper we have reviewed the concerns in cloud computing with some of the subsequent proposed models to tackle the security issues in cloud computing.

During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.