Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Content Based Image Re-Ranking using Semantic Analysis

—A CBIR is an effective way to improve the relevancy rate for image search. Image search engines such as Google and Bing mostly use keyword queried by the user and they rely on surrounding text for searching images. But sometime it is not efficient and results in imprecise output therefor ambiguity of query images is difficult to describe accurately by using keyword for e.g. Sony is query keyword then categories can be “Sony TV”, “Sony laptop”, etc. Given a query keyword, lots of images are first retrieved by the search engine with the help of text information. When user select a query image from the lots of images then the remaining images are re-ranked based on their visual features, A major challenge is that, semantic meanings of images do not well correlated with the similarities of visual features, which interpret user’s search intention. Another challenge is, to characterize highly diverse images by learning a visual semantic space is difficult and insufficient. In this paper, we review the framework in which, a query keyword is first used to retrieve a pool of images based on the given keyword. Framework of image re-ranking automatically learns different visual semantic space for different query keywords by using keyword expansions, at offline stage. For getting the semantic signatures of images, the visual as well as textual features of images are then projected into their related semantic spaces. At online stage, by comparing the semantic signatures of images which are obtained from the visual semantic space with query keywords, images are re-ranked. This concept is significantly improved accuracy as well as efficiency of the re-ranking process. Hence, it is called to be a better approach than the conventional image search techniques.
Quantitative determination of aconitine in Aconitum chasmanthum and Aconitum heterophyllum from Kashmir Himalayas using HPLC

The work presents the estimation of bioactive compound aconitine in two aconitum species (Aconitum chasmanthum, Aconitum heterophyllum) using a simple reversed phase HPLC-UV-DAD method. Peak heights were linear with relation to aconitine concentration with corrrelation coefficient >0.999. This assay is rapid, and highly reproducible. The extraction method described gives high yield of otherwise difficult to extract marker compound aconitine.
ELLIPTICAL FOURIER DESCRIPTOR FOR SHAPE BASED IMAGE CLASSIFICATION

— The amount of images being uploaded to the internet is rapidly increasing. Therefore, an important research topic is how to manage and perform classification of a large amount of images by using the computational resource in a efficient manner. For that purpose, Classification of the images should be based on contents of images. Various image processing methods can be used to classify the images such as color based, shape based, texture based, histogram based etc. In this paper, we have used shape based Elliptical Fourier descriptors for content based image classification. So, efficient algorithm should be designed for classification of images with proper parameters like number of harmonics, sampling points and parameter for classification of images.
An Efficient Energy Balanced Clustering and Routing Protocol for Wireless Sensor Network

-Wireless Sensor Networks (WSN) consists of hundreds of resource constrained sensor nodes. WSN nodes cooperatively monitor various environmental and physical conditions. WSN has gained one of the interested areas in the field of research from last few years. WSN uses various nodes for the communication. To enhance the lifetime of the whole networks energy reduction is the necessary consideration for design and analyze of the clustering and routing protocols. In this paper we present a study of various protocols which are used to enhance energy efficiency of the WSN such as LEACH (Low Energy Adaptive Clustering hierarchy), HEED (Hybrid Energy-Efficient Distributed Clustering), and CEBCRA (Cost Based Energy Balanced Clustering and Routing Algorithm). Finally we conclude this paper with future research and challenges. Key words— Wireless Sensor Network; clustering; Network lifetime; Energy Efficient.
Auxiliary Steering Behaviour for Computer Based Intelligent Agents

— This paper is the next evolutionary step towards steering behaviours through intelligent agents populating virtual or simulated environments. We are now presenting additional or auxiliary steering behaviour which will present a more reliable approach towards automatic path planning of intelligent computer agents and would focus more on group behaviour of intelligent agents. This paper intent to increase the level of accuracy as well as reliability of autonomous locomotion among the group by calculating and acting according to the location of other intelligent agents in such a way that optimum solution or way is available at all times. In this papers different group steering ways are examined. Our approach can be adapted to any place where group behaviour in locomotion is desired or required.
A Reconfiguration Characterization for WSN Optimization

— A sensor network is a critical real time network with specification of distributed nodes. The limited range and energy increase its communication criticality. In this paper, a feature driven characterization method is provided for network communication optimization. The paper has described the network reconfiguration architecture with its characterization. The paper explored the various challenges, requirement and scope of this methodology under sensor network restriction.
The New Quality of Aviation Unmanned Aerial Vehicles (UAV) Prevent Psychological Stress of Military Drone Operators

Remotely piloted aircraft (RPA; also called drones) constitute a new dimension in aviation. In the past, the military used RPAs extensively in the conflicts in Iraq and Afghanistan. Meanwhile, they are employed worldwide, which illustrates the new way of future complementary military warfare. It is well known that military personnel develops mental health disorders (MHD) during their military services or after deployments, including military pilots or fighting personnel (15.9 per 1,000 person-years). Despite the assumption that operators of drones compared to pilots are not vulnerable to MHD, because they are not physically involved and far away from combat operations and the battlefield, it becomes apparent that there is evidence that they are affected in the same way. During the last years, typical symptoms of PTSD occurred in such individuals and resulted in a severe disturbance of health (25.0 per 1,000 person-years). Currently, to our best knowledge, only in the U.S. Air Force (USAF) RPA operators participate directly in the act of war also using destructive weapons. Especially in this population, increasing and high levels of psychological stress, fatigue, and rates of MH diagnoses were found, without significant difference in the rates of post-traumatic stress disorder, depressive disorders, and anxiety disorders between RPA operators and traditional pilots. The German Air Force has been using drones up to now exclusively for reconnaissance. According to empiric observation, the development of MHD among German RPAs operators was not observed, but it has to be assumed that we will also find these illnesses if we proceed with this technology. Hence, military policymakers and clinicians should be highly sensitive to the fact that RPA operators also have a high probability of developing mental health disorders, particularly when using remotely piloted aircraft as weapons.
A Survey on Malicious Node Detection in Mobile Access WSN under Byzantine Attacks

—In Mobile Access Wireless Sensor Networks, Detecting the Malicious nodes and explores the positive data fusion under Byzantine attacks. The linear q-out-of-m rule, which is more efficient in distributed detection and can achieve a good result between the miss detection probability and the inconvenient alarm rate. The rule in which the mobile access point randomly polls reports from m sensors, however a major limitation with it is that the optimal scheme parameters can only be obtained, making it infeasible for big networks. But first, by exploiting the linear relationship between the scheme parameters and the network size, proposed a simple but effective sub-optimal linear approach. Second, for better performance and expansion, derived a near-optimal closed-form solution. Third, subjecting to a miss detection constraint, proved that the false alarm rate of q-out-of-m diminishes exponentially as the network size grows, even if the percentage of malicious nodes remains fixed.
Electrochemical immunosensor based on carboxylated single‐walled carbon nanotube‐chitosan functional layer for the detection of cephalexin

Abstract In this study, a sensitive and selective electrochemical immunosensor for cephalexin (CEX) determination on a glassy carbon electrode (GCE) surface was modified by a carboxylated single‐walled carbon nanotubes/chitosan (SWNTs‐COOH/CS) composite. The SWNTs‐COOH/CS composite was used to enhance sensor performance and to enlarge the electrochemical response of CEX. The cephalosporin‐ovalbumin coupling (CEX‐OVA) was synthesized using the reactive ester method. The free CEX in solution could be effectively measured based on the competitive immunoreaction between CEX‐antibody and CEX. Under optimal conditions, the electrochemical immunosensor offered an excellent response for CEX. The linear range was 1–800 ng/ml, with a detection limit of 45.7 ng/ml (S/N = 3). This method was applied to determine CEX in six different samples and obtained the recovery range from 80.15% to 94.04%. These results indicated that the fabricated electrochemical immunosensor and sensing method are suitable for quantification of CEX in real samples. These have great potential for wider applications in environmental and agri‐food products industries.
A New Method of Lithium Battery Power Estimation based on Adaptive Filtering

State of charge (SOC) is a key parameter in battery management and vehicle energy management. In recent years, there are problems in the SOC estimation method for electric vehicle(EV) lithium-ion batteries, such as the battery model is too simple, and the sampling noise of voltage and current signals is too large. In this paper, a second-order RC circuit combined with adaptive unscented kalman filter (AUKF) is proposed to estimate the charging state of EV lithium ion batteries. Under the background of unscented kalman filtering (UKF), the adaptive factor is constructed by using the Innovation vector, and the adaptive adjustment of noise covariance in charge estimation is realized. The comparison between AUKF and UKF shows that the proposed method has better estimation accuracy. AUKF algorithm has good performance in estimating battery charging state.
An Integrated and Efficient Approach for Enhanced Medical Image Compression using SPIHT and LZW Coding

— This paper proposes the efficient medical image compression as a combined approach of SPIHT and Lempel Ziv Welch coding. This novel method consists of steps such as splitting the image into bit planes and enhancing each bit plane image using the histogram equalization technique, and then it is decomposed by using 2D Fast wavelet transform and gets compressed by the Set Partitioning in Hierarichal tress algorithm. The SPIHT algorithm results in a large amount of seriate ‘0’ situation. Hence finally it is again encoded with the effective LZW technique to improve the compression ratio and also to increase the picture to signal noise ratio. This combined approach is applied for both colour & gray scale images which also helps in increasing the quality of picture.
The Uncanny Valley Effect in Zoomorphic Robots: The U-Shaped Relation Between Animal Likeness and Likeability

The uncanny valley effect denotes a dip in the positive relation between a robot’s human likeness and likeability. This paper provides first evidence that this design-guiding effect is not limited to humanoids, but extends to zoomorphic robots. In a first online survey, a diverse group of 235 participants rated the animal likeness of 140 robots. Three predictors for high or low animal likeness emerged: surface properties, such as joint visibility; facial properties, such as presence of a pupil; animal-specific properties, such as presence of snout. In a second online survey, 187 participants rated the likeability of 53 robots of varying degrees of animal likeness drawn from the first study. The relation between animal likeness and likeability followed a U-shaped function and showed an uncanny valley effect: robots high and low in animal likeness were preferred over those mixing realistic and unrealistic features. Besides theoretical implications tentative guidelines for the design of zoomorphic robots are discussed. Acm Reference Format: Diana Löffler, Judith Dörrenbächer and Marc Hassenzahl. 2020. The Uncanny Valley Effect in Zoomorphic Robots: The U-Shaped Relation between Animal Likeness and Likeability. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI’20), March 23–26, 2020, Cambridge, UK. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374788
Closed Loop Control of LLC Resonant Converter Incorporating ZVS Boost Converter

The modeling and closed loop control of LLC type series-parallel resonant converter is presented in this paper. The LLC resonant converter provides high efficiency with high power density, which leads to their applications in most advance technology. The state – space modeling is analyzed to know the stability of the converter. The common closed loop PI controller is used on both the side of the LLC resonant tank which eliminates the steady-state error and decrease the start-up time of the output voltage. The input of 40V LLC resonant converter is built to produce 220V output with the help of ZVS boost converter. The simulation result is built for this proposed system with 140 kHz resonant frequency. The comparison of simulation result with the open loop and closed loop converter shows the output performance and reliability of a controller. sivakumaran1969@gmail.com Keyword – LLC resonant converter, PI controller, ZVS boost converter, State – space modeling.
Data Mining and Information Security in Big Data Using HACE Theorem

- Big Data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. Big Data might be petabytes (1024 terabytes) or exabytes (1024 petabytes) of data consisting of billions or trillions of records. Big Data are now rapidly expanding in all science and engineering domains, including biological, physical and biomedical sciences. Here presented the HACE theorem that characterizes the features of the Big Data revolution and proposes a Big Data processing model from the data mining perspective. Search in Big Data is cumbersome practice due to the large size and complexity of Big Data. The Big Data challenges are broad in the case of accessing, storing, searching, sharing, and transfer. Managing Big Data is not easy by using traditional relational database management systems; it requires instead parallel computing of dataset. Big data mining and analysis is parallel computing method which uses MapReduce framework of Hadoop and uses the k-means or Nave Bayes algorithm for mine the data. This paper represents the use of MapReduce function of Hadoop and demand driven aggregation of big data which reduces computational cost. This paper also focuses on security and privacy issues in big data mining. Here it gives the privacy to data with AES algorithm.
The Effects of Employment Opportunity on Organizational Commitment and Intention to Quit in China's IT Outsourcing Industry

Although India has dominated the global IT outsourcing market over the past few decades, China has picked up momentum and is rapidly catching up. As China threatens the Indian monopoly on IT outsourcing, the Chinese success in this area is threatened by high employee turnover. To provide insights to the key factors that impact turnover intention, this research examined the moderating effects of employment opportunity on the impact of organizational commitment on intention to quit of IT outsourcing professionals in China.A quantitative methodology was employed to collect and analyze data from 292 participants who completed a questionnaire through a self-administrated Internet survey. The questionnaire design comprised organizational commitment items from Wang’s (2004) five-component commitment model, and items for employment opportunity and intention to quit from Peters et al. (1981). Findings suggest that organizational commitment have a negative and significant influence on intention to quit, while employment opportunity has a positive and significant influence on intention to quit. However, the regression analysis results revealed that employment opportunity does not have a significant moderating effect on the impact of organizational commitment on intention to quit.Other findings are that Wang’s (2004) five-component commitment model can be successfully extended to China’s IT outsourcing sector.The research adds to the existing body of knowledge in the field by providing a greater understanding of the key factors that impact turnover from the perspective of IT outsourcing professionals in China. However, the sample size relative to China’s huge IT outsourcing population may be considered insufficient to generalize the study’s findings to other jurisdictions. Accordingly, it is recommended that future related research should use a larger sample size to conduct a cross-cultural study. And, since there might be relationships among the variables other than those identified in this research, it is further recommended to develop a more comprehensive turnover model for IT outsourcing professionals in China.
Picture Management of Power Supply Safety Management System Based on Deep Learning Technology

With the advent of the era of big data, power supply security management systems will get a lot of picture data. In the face of massive image data, this paper studies the image management technology based on convolutional neural network. Aiming at the high repetition rate of self built image database samples and the problem that many sample classes contain uncorrelated images, two algorithms are proposed to improve the quality of the database: de duplication and de uncorrelation. By using the depth convolution neural network, the Embedding represented by the corresponding image is taken, and the distance between Embedding is calculated in the Euclidean space to achieve the purpose of de duplication and de uncorrelation. In this paper, "time" and "accuracy" are used to evaluate the performance of de duplication and de uncorrelation algorithms. The comparison examples of some sample classes before and after removing repetition and before and after removing uncorrelation are shown. The Recall-value of the database after removing duplicate and uncorrelated is tested based on the GoogLe Netplus-model respectively, which proves the effectiveness of the two filtering algorithms and overcomes the complexity of the traditional filtering process.
Text Extraction and Recognition Using Median Filter

- Text extraction is difficult process due to noise present in image, also various size, complex background and font. Text extraction and recognition is important process due to extracted text should preserve data formatting, Extracted text using system should be easily posted in another application and it should maintain quality of text. Digital English Comic Image is very complicated image so text extraction from it is very vital work. In existing work, Manga Comic image is used for text extraction. From Manga comic image text is extracted and recognized vertically using Blob extraction function. This paper contain method of extraction and recognition from comic image using median filter for preprocessing , CCL algorithm for balloon detection and OCR with image centroid zone concept for text extraction and recognition.
Hepatoprotective Effects of Metformin on Fructose Induced Non-Alcoholic Steatohepatitis in Rats

The most known risk factor for nonalcoholic fatty liver disease (NAFLD) is the metabolic syndrome. In this study, we characterized changes in liver pathology, hepatic lipid composition, and plasma biochemistry occurring in rats given fructose-enriched diet 10% (FED). Rats were given FED or standard rat chow for 5 weeks. Rats on FED were divided into 2 groups: One group of rats was fed FED only for 5 weeks and another group of rats was received metformin 50 mg/kg for the last 2 weeks (3 weeks FED + 2 weeks FED and metformin). FED rats had developed hepatic macrovesicular and microvesicular fat deposits, with increase in hepatic triglycerides (+198%) and hepatic cholesterol (+89%), but a decrease in hepatic phospholipids (-36%), hypertriglyceridemia (+223%), and hypertension (+15%). Also, in FED rats there was significant increase in serum cholesterol and serum glucose (100.6±2.5, 9.0±0.4 respectively), and significant increase in hepatic MDA and TNF-α (209.9± 43.9, <12.5, respectively). Metformin reduced blood pressure ( -24%), serum triglycerides ( -36%), hepatic triglycerides (-51%), hepatic macrovesicular fat (51%) and increased Hepatic phospholipids (+37%). Also, significant decrease in hepatic MDA and TNF-α (150.1± 27.0, <10.5 respectively). In conclusion: Metformin could reduce most of biochemical and tissue parameters and also improve the histopathological features of liver associated with non alcoholic steatohepatitis in rats. [Ahmed MF and Mahmoud MA Hepatoprotectine Effects of Metformin on Fructose Induced Non-Alcoholic Steatohepatitis in Rats. J Am Sci. 2012; 8(5): 67-73]. (ISSN: 1545-1003). http://www.americanscience. org.10
Indoor Navigation Approach for the Visually Impaired

Navigation in indoor environments is difficult for the person who suffers from visual impairment, especially in spaces visited for the first time. Several solutions have been proposed to deal with this challenge. Although some of them have shown to be useful in real scenarios, they involve an exorbitant cost or use artifacts that are not natural for blind users. According to WHO survey about 90% of the worlds visually impaired live in developing countries. Thus our work aims to develop an inexpensive visual assistance prototype which detects an obstacle and gives auditory response. We propose a solution which holds image processing methodology & low cost hardware for obstacle detection. Here in a vision assistance prototype a camera captures the image frames directly in front of the user and it is fed to MATLAB for the processing. Processing unit processes the captured image and enhances the significant vision data. Also a connection between ARM7 and PC is made using USB to RS232 connector to use stored procedures of audio annotations. Upon detection of obstacle the person gets auditory feedback to his/her ears.
The Study of Various Approaches for Removal of Blocking Artifacts in Spatial Domain

— The Block Transform Coded lossy image and video compression schemes such as those used in the Joint Photographic Expert Group (JPEG) and Moving Picture Experts Group (MPEG) formats to keep storage and bandwidth requirements of digital image and video at practical levels have a significant importance. However, these lossy compression schemes may exhibit blocking artifacts in smooth/monotone areas of an image, caused by the coarse quantization of Discrete Cosine Transform (DCT) coefficients. In highly compressed images using block coding as in JPEG standard suffer noticeable image degradation because each block is individually quantized. Several post processing algorithms have been proposed till date. We study and analyze some popular approaches for reduction of blockiness in spatial domain