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Heterogeneous sensors are equipped with a limited battery source that is concerned with network lifetime problems. However, this problem can be tackled with the effective design of WSN-IoT by clustering and sleep scheduling mechanisms. This paper addresses this issue by presenting novel ideas involved in the WSN operations such as grid construction, cluster head selection, sleep scheduling, and data gathering by intelligent Agents (iAgents). An energy-efficient dual iAgents based Heterogeneous WSN (E2IA-HWSN) is proposed. iAgents are used in this paper to automatically collect the sensed data from IoT sensors. In this E2IA-HWSN, a 3 × 3 grid is built and each cell is sub-divided into four in which cluster heads (CH) are selected in each sub-division, followed by ring partitioning for selecting a CH present at the center. Multi-Objective Harris Hawks optimization (MO-HHO) algorithm is used to select CH and supernode, here to minimize the energy consumption of CH, the supernode takes responsibility to assign sleep schedules to devices. The scheduling slots are assigned only after a sensor reaches below the energy threshold. For scheduling, the Bayes rule-based Markov model (BR-MM) is applied with the determination of residual energy and sensed packet counts. Generator de Bits Pseudo Aleatorios (GBPA) eliminates redundant data in CH and then inter-cluster routing is performed in case of emergency events. If not, then the CH waits for the arrival of iagents, the trajectory of iAgents is dynamically predicted with Deep Policy Gradient (DDPG). The implementation is carried out in NS3.26 and the results show betterment to the well-known methods.

Mobile ad-hoc network (MANET) is a network with two or more number of nodes with restricted energy constraint. The high dynamic nature in MANET attracts needs to consider the energy efficient features in their construct. The routing protocol is an important criterion to be considered for evaluating the performance of the MANET. Energy consumption plays vital role in MANET. Hence designing the scheme that supports energy efficient is much needed for the high dynamic MANET environment concerned. This paper proposes the Energy Efficient Routing (EER) protocol based on efficient route failure detection. The Scope of this paper is to suggest a fresh routing procedure for Mobile Adhoc Network minimizes unsuccessful communication. The projected procedure practices three important criterions to locate the path that assure authentic communication. The channel caliber, connection caliber and node’s residual energy is important reason for the failure of the node in MANET. Hence, the suggested routing mechanism believes these three different parameters to choose the finest node in the route. The reliable transmission and reception are attained by transferring information through route nominated by the suggested system verified by means of NS-2 simulator.

Nowadays energy theft in smart grids has led to severe economic loss to utility companies and countries across the world. Malicious users who illegally steal electricity from the distribution centre without getting noticed lead to unplanned power loss and blackouts in many developing countries. In order to prevent and find the malicious users who steal electricity, a system must be introduced in such a way that the malicious users will be identified easily in a large neighbourhood area network. In this work, to detect energy theft, a decision-making system using the Hadoop multi-cluster model with interpolation search algorithm has been proposed to calculate the actual energy consumed by every user and classify the user as the honest user or malicious user. The proposed interpolation-based approach is lightweight in its operation, which reduces the time complexity for predicting the malicious users in the NAN. The simulation results indicate that the proposed algorithm performs better than the existing approaches in terms of number of inspection steps, execution speed, detection accuracy and false-negative rate.

The purpose of our study focuses to determine the changes in students’ performance, when they are given exposure to areas beyond their curriculum. The study deals with the effects of having departments, where there is minimal contact between students of each department. This lack of connect between students has a negative effect on their skill development. Moreover, the current scenario does not give space for students to collaborate with projects in other departments or projects that involve multiple departments. As students have very restricted access to knowledge, they cannot satisfy the requirements of the industry. Graduates are not able to be technically sound on basic concepts that are beyond their area of study which deprives them of deserving chances. This work, deals with the impact of departmental culture on students who are in such college environments. Also, through the interviews and surveys we conducted, this work proposes solutions and changes that could be made to solve the problems that students face.

The integrated renewable sources and linear load connections are affected more when compared to the stand alone system. This leads to voltage and current changes from grid parameters in uncertain renewable sources. This research work has implemented hybrid control strategies to enhance renewable energy integration. From this technique, it can suppress the reactive power, which introduces unity power factor and harmonic content in the load current. To obtain the required control strategy in an active power filter, novel approaches are implemented b using a PIC microcontroller and then the entire system is simulated.

In this network environment, the different applications of sensor have to generate a huge data traffic which includes basic sensing methods such as air pollution or environment checking. In this huge congested network, the two-standard objective methods, that are objective method 0 (OM0) and objective method 1 OM1 for Congested Networks that has to perform deprived routing paths by picking an already congested parental node and a greater number of retransmissions happened between the networks. So, it is required that to design a new effective Routing Protocol for various congested environments. In our paper, I observe the RPL protocol for various congested networks and developed a new routing protocol technique (QWL-RPL) related with information of queue and node-wise workload. Objective of our developed Routing protocol has to attain an Optimum shortest path in challengeable performance. Our proposed Objective method has to consider connectivity capacity and also representing status of congestion in particular node or device using information of queue in data or packet. Our proposed routing technique was implemented in Cooja environment for comparing with the existing techniques. The results of simulation are shown through Queue status information and balanced Workload routing protocol of RPL that can reduce the data traffic of a various networks as related with OM0 and OM1, exactly specified terms of average delay, ratio of packets transmission and reception, jitter delay and header overhead information. At last, the results shown as on average, 30%–50% reduced header overhead information, 15%–30% reduced in average throughput delay, 10%–30% improvement in PRR and 25%–40% reduced in jitter delay.

This paper describes the performances of STATCOM by adopting various control techniques. In recent time, variety of control techniques are adopted in Power transmission systems. In this paper focused with reactive power control in STATCOM. In this paper both fundamental concepts associated with the reactive power control & its various techniques are studied. Normally harmonics are higher in normal Pulse Width Modulation (PWM) methods; So Space Vector Pulse Width Modulation (SVPWM) is adopted to reduce harmonics. The proposed system enhance the STATCOM performances in reduced harmonics, avoid voltage sag and swell, regulate the linear power flow and maintains healthy transmission and distribution line parameters. Apart from SPWM and SVPWM, Differently Evolution (DE) is used here to reduce harmonics and other parameters in order to enhance the performance and operation of STATCOM.

Digital imaging becomes the foremost important techniques in environmental monitoring and exploration. Underwater images are used to assess variety and profusion of objects are adopted by marine biologists. The quality of underwater image is necessary in the field of deep-sea study for object annotations, identifications and classifications. Automatic processing of analyzing the images working to attain the objectives in reduced price and period. There are many challenges involved such as complex background, deformation, low resolution and light propagation. With the advancement automatic detection, the deep neural network has led in real-time scenarios to detect and recognize the objects. We used the basic image processing operations such as registration and registered and fusion before performing the feature extraction. By using these techniques the proposed system takes advantages and extracted more features. The Harries Detection method is used for detecting the matched points and principal component analysis is used for feature matching. We have implemented with MATLAB and compared results with the existing systems by calculating the Entropy (E), Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR). The proposed system shows 98% of accuracy compared to the existing systems.

Blockchain (BC) methods is one of the best technologies in the Internet of Things (IoT) environments, it is a distributed sharing mechanism and communicate the IoT devices with secure manner. A blockchain is a connection of blocks and each block is linked to its earlier blocks. All block has the security hash code, previous block hash, and its data. The transaction between the BlockChain are the data is transfer between one IoT block nodes to another IoT Block node. The IoT device nodes are various types of physical devices like smart devices with embedded sensors, actuators, and capable of be in touch with other IoT device nodes. The responsibility of BlockChain in IoT is to grant a method to provide protected data transmission through IoT device nodes. In this paper discussed about the blockchain technology is how to secure the IoT transmission data and what are the challenges comes into these technologies. BlockChain is a safe method that can be used widely. IoT needs this type of skill to permit protected communication among IoT device nodes in mixed atmosphere.

Nowadays in shopping complexes and departmental stores, we use trolleys to collect things and get it billed in the billing counter. By doing so there exists an inordinate delay when the products are individually checked and billed. This delay may lead to long queues in the billing counter. Especially during rush hours, this wastes our valuable time as well as create discomfort. In the existing system, the shop owners need to monitor customer activities and maintain staff for manual checking of every commodity and its billing. Further, manual stock maintenance is performed. In order to overcome the above issues, we have proposed an advanced retail system using RFID enabled smart-cart with automated billing system. RFID readers are used to read the product tag while dropping into the trolley and taking out of the trolley. When the product is dropped inside the trolley a reader sends an appropriate product ID, quantity and price to the microcontroller. At the same time the updated bill amount will be displayed in the cart LCD. Similarly, when the product is taken out of the trolley another RF reader sends the product ID which is removed from the bill and the updated bill amount is displayed in the LCD. During both events the product and billing details are sent to the remote PC along with cart ID. When the customer finished the purchase, final bill is generated for the cart and the bill amount is displayed to the customer. This system also updates the stock of the product after every billing session. It also contains a smart stick that identifies the object using the same RFID technology for the blind people. Whenever, the stick is kept near the product, the information is announced through voice IC and speaker. Thus the proposed system avoids queuing, and gives us a pleasant shopping experience. In the shop owner’s view, this system reduces the need for surveillance and the manpower.

The prospective competence of WSN theatres a most important role in extensive applications, for instance air pollution, monitoring environment, forest fire, military application, health care and so on. Generally, in WSN enormous number of nodes were deployed and in HWSN all nodes have different energy level. This article offers a survey of HWSN routing algorithms to prolong the battery life time and thereby enhancing the life time of entire network which becomes a serious challenge in many WSN applications.

Helpful interchanges empower productive usage of imparting parties, by permitting hubs or terminals in a correspondence organization. To help the correspondence, the organization enlists the neighboring hubs to team up with one another in data transmission. It is a promising strategy to save the force utilization during the information transmission. We demonstrated an agreeable transmission connect in remote organizations as a transmitter bunch and a recipient group. We at that point proposed an agreeable correspondence convention for foundation of these groups and for helpful transmission of information. The proposed framework utilizes an agreeable systems administration measure by which source hub sends a bundle to the following bounce. The following jump discovers its privilege and left hub to share the bundles for agreeable transmission of information. In Wireless Sensor Networks, hubs have restricted energy assets and, subsequently, conventions intended for sensor organizations should be energy-proficient. The helpful transmission permits the hubs to save the energy significantly contrast with other transmission advances. In helpful transmission, various hubs all the while get, disentangle and retransmit information parcels. The correspondence model is executed in Java and the intensity of the got signal at every center node of the accepting group is insignificantly high. This expanded force prompts generally speaking saving in organization energy and the information misfortune is diminished when contrasted with customary hub to hub correspondence.

We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering. Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents., Comment: To appear in Proceedings of The 16th Conference of the European Chapter of the Association for Computational Linguistics

Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database to create a large corpus of scholarly documents with associated citation information and we propose a new citation prediction model called SChuBERT. In our experiments we compare SChuBERT with several state-of-the-art citation prediction models and show that it outperforms previous methods by a large margin. We also show the merit of using more training data and longer input for number of citations prediction., Comment: Published at the First Workshop on Scholarly Document Processing, at EMNLP 2020. Minor corrections were made to the workshop version, including addition of color to Figures 1,2

The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir.

Living creatures especially human, always aimed to commune every process or incident that take place within the environment, to lead an easy and luxurious life. Everyday a person has to execute certain basic tasks to control their body movements or particular parts of the body. Paralyzed people do not have control over some of their body parts. However, there are persons who are severely paralyzed and they cannot move themselves. They need some assistive technologies to fulfill their needs. A person with disabilities, mainly total paralysis is often unable to exploit the biological communication channels such as voice and action. One such condition was massive Brainstem Lesions, Stupor, Guillain-Baree Syndrome and Traumatic Brain Injury. In these conditions they cannot move their muscles, but they can able to control their eye movement, which leads to a condition called locked in state. In this state the person were unable to control all the motor neural activity which leads to other communication technique to convey their thoughts with others using eye movements. To solve this problem eye controlled interfaces are needed. Human Computer Interfaces help individuals with disabilities to communicate through a computer using a digital channel and make life more prosperous for the paralyzed patients and further enhance their quality of life with the support of bio-based HCI.

Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website: https://ornlcda.github.io/SDProc

We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.

To detect the region and bleeding frame in the wireless capsule endoscopy video, an automatic computer-aided technique is highly demanded to reduce the burden of physicians. The wireless capsule endoscopy (WCE), is an imaging technology which is recently established and doesn’t require any wired device. This device detects abnormalities in GI tract, i.e. (colon, esophagus, small intestine, and stomach). A WCE video consists of 57,000 images. It is very hard to examine by clinicians. To determine bleeding photos out of fifty-seven thousand WCE images makes the task very hard and expensive. The main goal is to develop an automatic obscure bleeding detection method by using superpixel segmentation and naive Bayes classifier. Naive Bayes and superpixel segmentation are used for this problem.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometric, information retrieval (IR), text mining, and natural language processing techniques can assist to address this challenge, but have yet to be widely used in digital libraries (DL). This special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) was compiled after the first joint BIRNDL workshop that was held at the joint conference on digital libraries (JCDL 2016) in Newark, New Jersey, USA. It brought together IR and DL researchers and professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state of the art in scholarly document understanding, analysis, and retrieval at scale. This special issue includes 14 papers: four extended papers originating from the first BIRNDL workshop 2016 and the BIR workshop at ECIR 2016, four extended system reports of the CL-SciSumm Shared Task 2016 and six original research papers submitted via the open call for papers.

We describe the participation and the official results of the 2nd Computational Linguistics Scientific Summarization Shared Task (CL-SciSumm), held as a part of the BIRNDL workshop at the Joint Conference for Digital Libraries 2016 in Newark, New Jersey. CL-SciSumm is the first medium-scale Shared Task on scientific document summarization in the computational linguistics (CL) domain. Participants were provided a training corpus of 30 topics, each comprising of a reference paper (RP) and 10 or more citing papers, all of which cite the RP. For each citation, the text spans (i.e., citances) that pertain to the RP have been identified. Participants solved three sub-tasks in automatic research paper summarization using this text corpus. Fifteen teams from six countries registered for the Shared Task, of which ten teams ultimately submitted and presented their results. The annotated corpus comprised 30 target papers--currently the largest available corpora of its kind. The corpus is available for free download and use at https://github.com/WING-NUS/scisumm-corpus.

Big Science and cross-disciplinary collaborations have reshaped the intellectual structure of research areas. A number of works have tried to uncover this hidden intellectual structure by analyzing citation contexts. However, none of them analyzed by document logical structures such as sections. The two major goals of this study are to find characteristics of authors who are highly cited section-wise and to identify the differences in section-wise author networks. This study uses 29,158 of research articles culled from the ACL Anthology, which hosts articles on computational linguistics and natural language processing. We find that the distribution of citations across sections is skewed and that a different set of highly cited authors share distinct academic characteristics, according to their citation locations. Furthermore, the author networks based on citation context similarity reveal that the intellectual structure of a domain differs across different sections.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sense-making. Bibliometrics, information retrieval (IR), text mining, and NLP techniques could help in these activities, but are not yet widely implemented in digital libraries. The 2nd joint BIRNDL workshop was held at the 40th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017) in Tokyo, Japan. BIRNDL 2017 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the 3rd edition of the CL-SciSumm Shared Task.

A Reaction control system (RCS) is a spacecraft system that uses number of thrusters to provide attitude control of flight vehicles. RCS thrusters give small amounts of thrust in any desired direction or combination of directions and capable of providing torque to allow control of rotation (roll, pitch, and yaw). Reaction Control system consists of many flight critical components arranged in series configuration. Even one subsystem failure, leads to total mission failure. Hence RCS need to be highly reliable system. It is very expensive to conduct more number of system level tests to gather reliability information about system. Hence the data drawn from component level, subsystem level along with expert opinion is used for reliability estimation. In this paper, Bayesian network modelling was carried out for estimating the reliability of RCS system considering the dependency between system and components. Bayesian Inference was carried out using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of RCS system.

In electricity networks where Back to Back (BTB) HVDC systems are connected to EHV AC lines having Fixed Series Compensators (FSCs), ferroresonance has been observed during post fault disturbances in the grid. This causes an undamped exchange of energy between FSC and the saturating magnetic circuits of the converter transformers in BTB HVDC system. This may also result in higher voltages and currents causing detrimental effects on power quality and sustained oscillations. Ferroresonance condition imposes threat to grid causing tripping of EHV lines, adversely affecting the reliability of the power system. In this paper, investigation on real cases of Ferroresonance observed in Southern Regional Grid of India in a BTB HVDC system at Gazuwaka have been discussed. Further, ferroresonace detection methods and techniques for mitigation of the effects of Ferroresonance have been discussed.

Biometrics is a mode of popularity and identification affirmation that uses physical attributes of a particular person that are impossible or as a minimum hard to mask. Finger-Knuckle-Print and ear print are examined to be maximum reliable physiological bio metric methods. These days a Finger Knuckle Print authentication based totally bio metric system has been efficaciously applied in various sectors. These unique Finger Knuckle Print and ear print authentication offers in reveals its user image that matches with its very own template that is stored inside the database for verification. An ear print is a replica of the parts outside the ear which have impressed a particular surface [3]. Many variants of local binary patterns are widely used for feature extraction process due to their satisfactory performance, for recognition purpose. An effective method of extracting feature patterns for Finger Knuckle Print (FKP) and ear has been proposed in this project. After extracting features of ear and FKP separately, they are fused by feature level fusion process which is done registration phase. The fused results of FKP and ear have been compared with the stored fused values in authentication phase. If the level of matching is achieved, that particular person gets authenticated to access the things else the person will be impostor [4]. This system gives more accuracy in less time. Challenges: The accuracy of authentication is improved in less duration. The chances of intruding is reduced.

Diabetes mellitus has become a pandemic in both developed and developing countries. It is estimated that by 2030 diabetes affected people will be around 100 million in India. Diabetes is most common type of disease found in the people of age from 41 to 60 due to inheritance, unhealthy diet causing obesity, reduced Insulin resistance and negative effects caused due to urbanization. Limited knowledge about diabetes causes a various adverse effect in health and it is necessary to spread awareness about Diabetes. To address this problem a Diabetes prediction portal has been developed which is used to get a dichotomous outcome. PIMA India diabetes dataset is used, and machine learning is used to train the data and k nearest neighbours provided highest accuracy was thus used in deployment. Flask web framework was used to handle HTTP requests of the predictions. HTML page was created to display the predictions.

The objective of our paper is to design and model a mobile application-controlled automobile screw jack using android programming as an Internet of Things platform. Today’s importance on technology expansion has projected a good design and quality product in a lesser time to ease the complicated process of human being. The development of software has become a vital in the digital world to satisfy a number of demands of the user or applications. In order to accomplish the demand of current era, the article has been modelled a design for a mobile enabled hydraulic screw jack system. The description of evolving design and signification of its industrial implication has been shown here. Screw jack used for automobiles is emerging as necessary technology expansion in the industry of automobiles to produce application that allows lifting heavy load on applying less effort. The primary importance of the design of automobile screw jack is to reduce human involvement and their contribution to lift a load. Further there is a significance increases in the efficiency of screw jack model and its designs as ergonomically, (i.e.) neglecting of the complex operating as well as positioning of the screw jack by the operators. On usage of hydraulic system and pneumatic system, the system reduces involvement to lift load on basis of the computation calculation. But in this case also complexity in the posture and position of worker during the operation remains the same here. Hence, we have to design a jack for car which is motorised for easiness of use. But somehow in any case of failure in this electronic system, then the jack will be not able to work further manually. In this work, the entire setup is administrated by mobile application which is constructed as MIT app maker and the important functionality of this project is controlled using Arduino Uno which controls all the motors by obtaining signals from the app with help of a wifi module (ESP8266), Arduino builds the code which is encoded into it by Arduino encoder. Entire assembly is propagated by 60 RPM DC motors. Importance of this model is to make system as effortless and fast response in weight lifting, further it reduces the physical fatigue felt by the weight lift operating employee. Experimental results prove that proposed model outperforms the existing Bluetooth based model on terms of the efficiency and accuracy.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Information retrieval~(IR), bibliometric and natural language processing (NLP) techniques could enhance scholarly search, retrieval and user experience but are not yet widely used. To this purpose, we propose the third iteration of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL). The workshop is intended to stimulate IR, NLP researchers and Digital Library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop will incorporate multiple invited talks, paper sessions, a poster session and the 4th edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.

Internet is a huge platform where network communication and cloud computing plays a major role, in which sharing of digital objects in multimedia applications creates more similarities in recent years. Due to lack of security, the transfer of two dimensional data among the users is being compromised. In this paper both network as well image security is considered. In wireless network seurity, ARP Spoofing attack analysis and anti-attack using tunnel are carried out. For 2D data one of the best solutions is chaos based crypto system model for image encryption. In this paper, image encryption employs confusion and two stages of diffusion processes with key stream of chaotic sequence generation using logistic map. Performance of the the proposed algorithm is tested through the metrics and comparison with the existing literatures shows the proposed approach is good to resist the statistical and differential attacks.

We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2% in F1 and 24.4% in recall on average.

New learning resources are created and minted in Massive Open Online Courses every week – new videos, quizzes, assessments and discussion threads are deployed and interacted with – in the era of on-demand online learning. However, these resources are often artificially siloed between platforms and artificial web application models. Facilitating the linking between such resources facilitates learning and multimodal understanding, bettering learners’ experience. We create a framework for MOOC Uniform Identifier for Resources (MUIR). MUIR enables applications to refer and link to such resources in a cross-platform way, allowing the easy minting of identifiers to MOOC resources, akin to #hashtags. We demonstrate the feasibility of this approach to the automatic identification, linking and resolution – a task known as Wikification – of learning resources mentioned on MOOC discussion forums, from a harvested collection of 100K+ resources. Our Wikification system achieves a high initial rate of 54.6% successful resolutions on key resource mentions found in discussion forums, demonstrating the utility of the MUIR framework. Our analysis on this new problem shows that context is a key factor in determining the correct resolution of such mentions.

In imaging science, the photo editing software packages can alter the original images without any detecting traces of tampering. Hence, the image forgery detection technique plays an important role in verifying the integrity of digital image forensics for authentication. The techniques such as watermarking are used for authentication but it can be modified through third parties attack through extraction. Malicious and digital imaging (digital products) tamper detection is the subject of this article. In particular, we focus on a special type of digital forgery detection - copy attack campaign, in which part of the image is copied and pasted into the image and the cover features a large image of intentions another. In this paper, we investigate the dynamic forged copy detection problem, and describes a highly efficient and reliable detection method that based on image source ANN identification.. Even when the region is enhanced copy / retouching and background merger, and the method can successfully identify counterfeit forgery when images are saved in a lossy format (such as JPEG). The performance of the method's performance several forged images.

UAV's having four propellers are always a threat to the society and having some security disadvantages. The main objective of this paper is to present the safest drone. Orbot is the modern method of copters and having single propeller powered with a brushless motor. The mono copter will be controlled and monitored through a GUI which is specially built for this. An onboard computer is used to control the copter powered by an ATmega microcontroller. The small, highly manageable system will receive the data through the camera, sensors and transfers it to the database. Ducted fan technology is the methodology used to lift the copter. In this system, an IOT based controller is used with the cloud for data storage and monitoring the status. It will have the ability to help the people thinking about their safety. We also designed to save some additional commands to work in offline. Four valves are used to control the motion, which is connected to servo motors each. The copter is fully constructed by fiber coverings and can be able to work in little drizzles.

Breast Cancer is one of a major issue that some of the women are facing today. Earlier detection of cancer by performing detailed analysis based on the existing records which may assist the physicians in providing a better treatment to their patients. Data to analyze and predict the breast cancer are obtained from UCI Machine Learning Repository (Wisconsin Breast Cancer). The main objective is to classify whether the type of cancer is benign or malignant. Based on the available data set and the patient record, whether the disease is curable or non-curable is predicted. Thus the success rate of classification is 84.14% and the prediction percentage is 88.14%.

Reactive power and voltage support is an essential service to ensure secure power system operations. Low voltages in the transmission grid pose threat to operational security of the grid. Certain control areas in the Southern regional grid in India experience severe low voltages during peak demand period. This paper provides an overview of the different methods being used by the grid operator to access the voltage stability of 400kV buses which experience low voltage in Southern regional grid in India, discusses various short term and long term measures which have been taken up for mitigating the issues related to the management of reactive power and voltage support.

Diabetes self-management offers an opportunity to patients to be actively involved in managing their conditions and modifying lifestyle behaviors to attain positive health outcomes. With the unprecedented growth of mobile technology, smartphone plays a role in supporting diabetes self-management. Nonetheless, selecting appropriate mobile applications (apps) is challenging for patients. Thus, this study aimed to evaluate and compare the contents and features of mobile medical apps for diabetes self-management. Of 346 commercial apps, 16 (16%) and 19 (7.72%) of the diabetes apps found in Apple and Google Play stores, respectively, were included based on the selection criteria and individually scored for the availability of 8 main features of diabetes self-management. The apps supported self-management by offering features such as free installation, less than 50 MB space used, offline use, automated data entry, data export and sharing, educational tool, and advice. Of the 8 evaluated features, only 11 (31.4%) apps had a score of 5 whereas 7 (20%) apps scored the lowest, with a score of 3. The majority of apps were free, required no Internet connectivity to use and were less than 50 MB in size. Our findings showed that the design of diabetes mobile apps focused on reporting and setting reminders, rather than providing personalized education or therapeutic support. In the future, the design of apps could be improved to integrate patients’ needs, usability for disease management, and lifestyle modifications.

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task., Comment: 2 pages, workshop paper accepted at the SIGIR 2017

Intelligence is an interdisciplinary research area which aims at making the machines more human. Extensive research is going on to teach them to perform the tasks. Deep learning is a collection of algorithms in Machine Learning. In this paper we implement deep learning for learning and gaining the knowledge of the text from real time images. An algorithm namely word localization is proposed to able to make the machine to understand the words extracted from the images. In comparison with traditional Optical character recognition (OCR) it has many advantages over it which is been analyzed.

The implementation of custom power devices (CUPD) such as dynamic voltage restorer (DVR), distribution static compensator (DSTATCOM) and unified power quality conditioner (UPQC) has become essential for reducing the impact of PQ problems. More specifically, DVR plays vital role for proving solution to the voltage sag problems and to restore the load voltage with in few milliseconds. The performance of the DVR depends on the control strategy adopted with the algorithm. Synchronous reference frame theory (SRFT)-based ANFIS controller is proposed in this paper. The performance of the proposed control strategy is simulated for various conditions like line to ground, double line to ground and three phase fault in MATLAB/Simulink. From the simulation study, it is found that the ANFIS-based DVR helps to mitigate the voltage sag and reduces the harmonics. The proposed ANFIS-based DVR compared with PI, fuzzy and ANN-based controllers.

Objective: The aim of this work is to study about the behavior of seven storey single bay infilled frame with pneumatic interface under various levels of pressures with lateral loads.Method: Linear analysis was carried out using a finite element based software by modeling various 2D frames. The dimension and reinforcement details for the frames are chosen based on the experimental study. For infilled frame with pneumatic interface different pressure configuration at different levels shall be considered in the study. Findings: The study of behavior includes stiffness, bending moment, axial force and shear force. The analysis of infilled frame with cement mortar interface and bare frame was also carried out to compare the frame stiffness and member forces of these frames.Interface characteristics have also influence the structural behavior of infilled frame. It has effect on overall stiffness and of the in-plane moment of inertia of composite frame and modifies energy dissipation. Applications: The research idea can be implemented in construction field so that the structural failure during earthquake will be minimized. The research work has been done relevant to study the behavior of infilled frame and to adopt new technique which can improve its performance. This study can be the basis of further studies which can increase the number of parameters under various conditions.

International audience; The large scale of scholarly publications poses a challenge for scholars in information-seeking and sensemaking. Bibliometric, information retrieval (IR), text mining and NLP techniques could help in these activities, but are not yet widely used in digital libraries. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric and recommendation techniques which can advance the state-of-the-art in scholarly document understanding, analysis and retrieval at scale.

In this paper, a system is designed especially to communicate between vehicles at night time. This system providing a security for the driver by giving him the luxury of communicating with other vehicles without getting out of his vehicle and another important feature of this system is that for the communication purpose no service provider is needed. In this system visible light is used for communication, which does not require any service provider for communication. LED's are used in this system whose switching frequency is high and for its longer life.

Cloud computing being promising and emerging technology for the next generation of IT applications which provides the storage and supports for outsourcing of data without having the native copy of data or files. Cloud computing service providers require a system which can handle a large number of requests at a time and is needed to be highly available. Major issue faces in cloud computing is Security and data retrieval. In data security an important problem is to prevent illegal modifications and data loss. But, for this it requires either data replication or on-demand computation of a function over the entire contract out data. Generally system keeps multiple copies of the blocks of data on different nodes by replication. Another issue is retrieval of data, whereas in the cloud computing environment it becomes particularly serious because the data is located in different places even in the entire globe. Load on system may increase when there is high demand of a data. In this, the main focus is on the problems of security, replication and availability of data. The proposed system use a fragmentation and sole replication (FASR) concept in the cloud to improve the security and data retrieval time rather than using traditional cryptographic techniques. It fragments the file, place every single fragment in distinct node and perform sole replication that is every fragment is replicated only once, to provide security. Particularly selection of primary node is done through hotbed measure, selection of node and placement of other fragments is done through T-coloring technique, which limits tampering of data. Thereby improve retrieval time for easy accessing of the fragments for reconstruction of original file on user request. In this user can download, update and upload the file again. User can also request for required fragments and can update, upload it to cloud. Another automatic updating technique is proposed which allow uploading of only updated fragment in the cloud node. FASR concept provides security; save time and automatic updating technique reduce the resource utilized. Non-cryptographic nature makes the system even faster.

processor allocation in multiprocessors can be accomplished efficiently for reducing the required number of processors for the given task set, accounting reduced power consumption with maximum processor utilization. This work is based on next fit algorithm using Rate Monotonic Algorithm (RMA) for a fixed priority system. The work proposes a minimal task allocation algorithm for multiprocessor environment. The proposed method reduces the number of processors required for a given task set using improved next fit algorithm and the same has been evaluated and tested. The proposed algorithm gives better results when there is large number of tasks in the system.