Miracle-dz/UniFAR-FTUs huggingface dataset:
Hopfield Neural Network Based Cloud Security-State (Hnnbcss) Prediction for Monitoring Data

: Security issue of Cloud Computing (CC) is very significant and it is able to prevent the fast improvement of new methods. This work aims in the direction of calculate the security level with numerous huge-scale attributes in CC. A new classifier based Hopfield Neural Network(HNN) is proposed in this paper for predicting the security state of CC, where the Evidential Reasoning(ER) technique is employed in the direction of combine the various system indicators of real CC system and formulate a practical evaluation in the direction of explain the cloud security state. To show the efficiency of the proposed HNNBCSS prediction model is experimented in engineering, the valuation of security condition in the CC platform will be computed by using logs with 100 days. Ten-fold cross validation is performed to verify the security state of the classifier results further successful and satisfactory; where interval determination is used in order verify the accuracy and security of the CC model.
A Low Power Low Noise Two Stage CMOS Operational Amplifier for Biopotential Signal Acquisition System

Monitoring of the biopotential signals in present scenario attract much attention to the researcher due to increasing demand for better quality of life and to enhance interaction between humans and machines. Biopotential signals are relatively weak amplitude and low frequency in nature. Therefore it is required to design a front circuitry that defines quality of signal for processing to the next block (ADC). This paper presents a design procedure by using the Equivalent Circuit Approach (ECA) for a low power, low noise, and rail-to-rail output swing Operational Amplifier (Opamp) which is used at the front end circuitry of Biopotential signal acquisition systems. A differential pMOS input stage is used for noise cancellation and a common source stage is used at the output for high output swing. The opamp is experimentally validated through TSMC 90nm CMOS process Technology and a comparison is carried out with previous designs. This opamp exhibits input referred noise of 28nV/√Hz, Phase Margin =83, B.W=10.1 kHz and power consumption 91μW for 40μA external bias current. Importantly, all these advantages are achieved at the cost of little Gain deviation. Keywords— Biopotential Signal, Equivalent Circuit Approach (ECA), Low noise, Low power, Opamp, Rail-to-rail
CLOUDSIM: A SOFTWARE FRAMEWORK FOR MODELLING CLOUD COMPUTING ENVIRONMENT

Advances in computing have opened up many potential possibilities. Previously, the main concern of application developers was the consumption and hosting of applications, keeping in mind the acquisition of resources with a fixed capacity to handle the expected traffic due to the demand for the application, as well as the installation, configuration and maintenance of the whole sustaining stack. With the advent of the Cloud, application deployment and hosting has become highly flexible, easier and less expensive because of the pay-per-use chargeback model offered by cloud service providers through online mediums and portals [1]. Cloud computing is a best-fit for applications where users have heterogeneous, dynamic, and competing quality of service (QoS) requirements. Different applications have different performance levels, workloads and dynamic application scaling requirements, but these characteristics, service models and deployment models create a vague situation when we use the cloud to host applications. The cloud creates complex provisioning, deployment, and configuration requirements [1].
Cholangiocarcinoma in a Doe

Cholangiocarcinoma is a rare neoplasm in goats with only four cases reported since 1977 and only one report of cholangioma in 1995. Clinical signs that have been described are non-specific, and included loss of condition and diarrhoea. All the reported cases of cholangiocarcinoma have had similar histological characteristics including the presence of ductules and gland-like structures, a desmoplastic/prominent connective tissue stroma and intrasinusoidal/parenchymal invasion. The aetiology of this neoplasm in humans, dogs and cats is documented but the cause of the disease in goats remains unknown. Very little has been described about the prognosis of these tumors. Three previously reported cases have had intrahepatic metastasis and in one report the neoplasm was found to have led to peritonitis. In this case, a 5-year-old Anglo Nubian mixed doe was submitted for necropsy. The liver was enlarged to approximately two times its normal size, with the left lobe being effaced by multiple, coalescing, irregular moderately circumscribed, firm, whitish, raised foci with 10-15 similar variably sized scattered foci being present in the right lobe. The remaining parenchyma had an accentuated lobular pattern. No metastatic foci were found elsewhere. All of the neoplastic foci had similar histological characteristics consisting of multiple variably sized, well formed to irregular, tubular and acinar structures arranged within a moderately expansive but loose connective
Modern Peculiarities of SWOT Analysis when Taking Management Decisions by Russian Top Managers

The goal of this article is to comprehensively describe all aspects, factors and stages of researching the internal and external environment of the company based on works of national and foreign researchers, and taking into account practical activity of Russian companies. Now there is no complete guidance on making the multifactorial analysis of strengths and weaknesses of the company, opportunities and threats of the market that would take into account the specificity of Russian conditions, and enable the company top management to choose the most correct area of its business development. The authors describe in detail stages of analyzing and formulating variants of strategic goals of the company development. The article will be useful for practical activity of Russian companies when developing and taking variants of management decisions of strategic development.
The use of maternity acupuncture within a New Zealand public hospital: Integration within an outpatient clinic

A Corresponding Author: debra.betts@ nzsao.com B New Zealand School of Acupuncture and Traditional Chinese Medicine C Hutt Hospital Maternity Unit D Graduate School of Nursing and Midwifery, Victoria University Background: In June 2008, a maternity acupuncture service began operating within a Hutt Valley Hospital outpatient department, offering access to free acupuncture care for pregnancy and postnatal related conditions. This was the first and, to date, the only clinic of this type within a New Zealand hospital. Objective: To retrospectively describe and analyse routinely collected data from 2013 and 2014, on treatment delivered in this clinic. Specifically we report on the number of women treated, their presenting condition, referral patterns and patient centred outcomes for those women presenting with back or pelvic/hip pain. Methods: Data on the primary reason for seeking acupuncture and referral pathway were collected on a woman’s initial visit. For those women presenting with back or pelvic/hip pain, a Measure Yourself Medical Outcome Profile (MYMOP) form was also completed, with a follow-up form completed on each subsequent visit until discharge. Findings: Two hundred and sixty-one women received treatment. The majority were referred through Lead Maternity Carer (LMC) midwives and were requesting acupuncture treatment for labour preparation, back or pelvic/hip pain. MYMOP reporting demonstrated a high level of patient satisfaction, with 80% of women reporting a change in their pain that was clinically significant. Conclusion: Data from this clinic illustrate a successful partnership between mainstream and complementary medicine, offering integrated healthcare to women in a public health system. Acupuncture offered a non-pharmaceutical treatment option and for those with back or pelvic/hip pain delivered high levels of patient satisfaction. It is hoped that this article will stimulate further interest in the under-researched area of maternity acupuncture.
11LB40-Butz Intro formatted

The four papers in this themed issue had their first public airing in a session titled “Places Post-Colonialism Forgot: New Examinations of Center and Periphery” at the 2009 Las Vegas AAG Meetings. The session was co-organised by Karen Morin and Tamar Rothenberg, and sponsored by the Indigenous Peoples Specialty Group, the Socialist and Critical Geography Specialty Group, and the Historical Geography Specialty Group; I was one of the discussants.
Information Theoretic Outlier Detection for Categorical Data

-OUTLIER detection means the problem of finding objects in a data set that do not satisfy well-defined criteria of expected behavior. It can be implemented as a preprocessing step earlier to the application of an advanced data analysis method. It can also be used as an effective tool to discover interest patterns such as the expense behavior of to be bankrupt credit cardholder. Outlier detection is a crucial step in a variety of practical applications including intrusion detection, health system monitoring, and criminal activity detection in E-commerce, and can also be used in scientific research for data analysis and knowledge discovery in biology, chemistry, astronomy, oceanography, and other fields.Outlier detection can be done for Numerical as well as Categorical data. Most existing methods are designed for Numerical data but some or many of them encounter problem with real life applications that contain categorical data. Discovering rare events from categorical data is very vital in data mining because of the difficulty of defining a meaningful similarity measure. Also the common difficulty with the existing methods is the lack of a formal definition for the outlier detection problem. Without a formal definition, it is often designed as an ad-hoc process. In general, several user-defined parameters are often needed to define whether an object possesses sufficiently different properties, which separates them from others to be qualified as an outlier. The algorithms or methods which are taking input parameters from users are heavily dependent on suitable parameter settings for their results, which are very difficult to estimate without background knowledge about the data. Also many existing methods suffer from low effectiveness and low efficiency due to high dimensionality and large size of the data set, more complex statistical tests, or inefficient proximity-based measures. Here we are giving formal definition of outliers and an optimization model of outlier detection, via a new concept of holoentropy that takes both entropy and total correlation into consideration. Based on this model, we have defined a function for the outlier factor of an object which is solely determined by the object itself and is updated efficiently. We are implementing two practical, 1-parameter outlier detection methods, named ITB-SS and ITB-SP, which require no user-defined parameters for deciding whether an object is an outlier. Users need to only provide the number of outliers they want to detect. Experimental results show that ITB-SS and ITB-SP are more effective and efficient algorithms for large scale categorical data.
Multi-Pose Face Recognition Using Fuzzy Ant Algorithm and Center of Gravity Search

Summary In this paper, we present the novel technique to solve the recognition errors and minimize memory size of invariant range image multi-pose face recognition. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-pose. The fuzzy ant clustering algorithm is used to classify and find the number of clusters for reduced recognition time. RIFD will be transformed by the gradient transformation into significant feature and matching by using Membership Matching Score (MMS) and Center of Gravity (CG) search. The proposed method was tested using facial range images from 130 persons with normal facial expressions. The processing time of the recognition system is better than 3LMS. Moreover, it is 6 times faster without any change of recognition rate. Memory size of this experimental was about 136,890 bytes which show that the memory sizes decrease to extremely small-scale knowledge base.
COLLABORATIVE FILTERING APPROACHES FOR MOVIE RECOMMENDATION SYSTEM USING PROBABILISTIC RELATIONAL MODEL

:- Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web in terms of the information space and the amount of the users in that space. However, in today's world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. This Paper focuses on the development and evaluation of a web based movie recommendation system.
Survey of Soft Computing based Speech Recognition Techniques for Speech Enhancement in Multimedia Applications

: Speech is the fundamental, most effective, reliable and common medium to communicate in real time systems. There are so many applications of speech still to be far from reality just because of lack of efficient and reliable noise removal mechanism and techniques for preserving or improving the intelligibility for the speech signals. In this paper attempt has been stepped towards surveying the methodologies for soft computing based speech recognition techniques for speech enhancement in multimedia applications. Finally we can conclude that to improve the performance of beamforming based speech recognition system, evolutionary computational algorithm (GA) optimization can be used in multimedia applications.
Performance Analysis of Semi-supervised Intrusion Detection System

Supervised learning algorithm for Intrusion Detection needs labeled data for training. Lots of data is available through internet, network and host. But this data is unlabeled data. The availability of labeled data needs human expertise which is costly. This is the main hurdle for developing supervised intrusion detection systems. We can intelligently use both labeled and unlabeled data for intrusion detection. Semi-supervised learning has attracted the attention of the researcher working in Intrusion Detection using machine learning. Our goal is to improve the classification accuracy of any given supervised classifier algorithm by using the limited labeled data and large unlabeled data. The key advantage of the proposed semi-supervised learning approach is to improve the performance of supervised classifier. The results show that the performance of the proposed semi-supervised algorithm is better than the state-of the-art supervised learning algorithms. We compare the performance of our DS-AdaBoost algorithm as
Isolation Enhancement in a Printed UWB-MIMO Antenna System

UWB printed antennas are essential part of most of the wireless portable devices. Due to the induction of 4G and upcoming 5G technologies, the demand of higher data rate and larger channel capacity has become a serious issue. Multiple Inputs Multiple Output (MIMO) technology find its place among various existing wireless technologies as a solution to various short comings of UWB like multi path fading. The isolation performance of a previously proposed UWB-MIMO antenna is improved using meandered line parasitic monopole and a reflector composed of hexagonal ring cells. The proposed antenna works over 3.1-10.6GHz band with mutual coupling less than -21 dB. All simulations are done in ANSOFT HFSS 13.0.
Audio Tagging System using Deep Learning Model

Deep learning has been getting more attention towards the researchers for transforming input data into an effective representation through various learning algorithms. Hence it requires a large and variety of datasets to ensure good performance and generalization. But manually labeling a dataset is really a time consuming and expensive process, limiting its size. Some of websites like YouTube and Freesound etc. provide large volume of audio data along with their metadata. General purpose audio tagging is one of the newly proposed tasks in DCASE that can give valuable insights into classification of various acoustic sound events. The proposed work analyzes a large scale imbalanced audio data for a audio tagging system. The baseline of the proposed audio tagging system is based on Convolutional Neural Network with Mel Frequency Cepstral Coefficients. Audio tagging system is developed with Google Colaboratory on free Telsa K80 GPU using keras, Tensorflow, and PyTorch. The experimental result shows the performance of proposed audio tagging system with an average mean precision of 0.92 .
The Views of Educational Supervisors on Clinical Supervision

Contemporary educational supervision expresses democratic and leadership focused supervisory approach which consists of collaboration, trust, sharing and improving. The aims of the study are to investigate the answer of how current teacher supervision in Turkey is conducted according to the views of educational supervisors, and to unearth what the general attitudes and evaluations of educational supervisors regarding clinical supervision. A phenemological research design was used in the study. The study group consists of 53 educational supervisors working in the province of Antalya. A structured form consists of both open and close ended questions was delivered to the supervisors participated in the study. Data was gathered oral and written via interviews. A conference lasting six hours on clinical supervision’s aim, processes, techniques, observation and data collecting tools was held for the supervisors within the study. According to the results of the study, current supervision may be categorized under four stages; the stage before reaching the school setting, pre-observation, observation, and post-observation. That the views of educational supervisors are positive regarding the applicability of clinical supervision. The participants, mostly, stressed that the supervisory duties must be divided from the duties of investigation, and that the supervisors must be trained on clinical supervision. Keywords: clinical supervision, educational supervisors, evaluation, schools
Meth Hunter: an Extension of Automated Intelligence Systems via Graph Database

— Automated intelligence analysis was born into a skeptical community dealing with high stakes dilemmas, where often outcomes are measured in loss of human life. The massive amount of data require analysts to be masters of identifying “indicators” intuitively foreshadowing intelligence targets and masters of modeling the indicators in analysis systems. The paper explores the application of graph theory through graph databases to determine how the index-free adjacency of graph databases can be exploited to improve processing time. The study utilized the Meth Hunter, an analytic machine designed to identify methamphetamine conspirators by analyzing pseudoephedrine purchase records as a case study. The graph database (Neo4j) was compared to a SQL relational database (WAMP). Neo4j demonstrated superior performance in identifying and retrieving relationships between data points. Neo4j successfully demonstrated the ability to implement such a strategy and extend the horizons of traditional data mining systems.
An Investigation of the Effect of Copper Oxide and Silver Nanoparticles on E. Coli Genome by Rapd Molecular Markers

Regarding the bacterial resistance of current antibiotics, many studies have been conducted to evaluate the antimicrobial properties of metal nanoparticles such as silver and copper oxide. This study was carried out to compare the effects of these nanoparticles on the genome of Escherichia coli strain O157: H7 as a model for gram-negative bacteria. For this purpose, the bacteria were first treated by 30 and 60 μg/ml the nanoparticles and the growth of bacteria was controlled at certain time intervals by spectrophotometry to evaluate the antimicrobial activity. Then the DNA was extracted to investigate the effects of the nanoparticles on the genomes 4 hours after treatment using RAPD-PCR. The bands obtained from electrophoresis of PCR products on agarose gel 2% were analyzed. The results of the study revealed that silver and copper oxide nanoparticles not only inhibit the growth of bacteria, but also change the genomic DNA sequences and cause genetic differences between control and treated samples. Metal nanoparticles are antibacterial compounds, and copper oxide was more effective than silver on E. coli genome as a model for gram-negative bacteria.
A Novel Energy Efficient Cluster Based Routing Protocol for MANET with Related Metrics

With the growth of Mobile Ad-hoc network communications, the need for recent research to moving towards the better and capable routing protocols. The efficiency of the routing protocols depends on the metrics as communication cost, packet delivery, energy consumption, delay, throughput, packet loss. Nevertheless, due to the nature of battery power devices in the network for MANET and WSN, the power consumption of the routing protocols also to be consider for the betterment of the routing algorithms. Henceforth the applicability of the energy efficiency became the one of the most important criteria for performance evaluation. Thus, this work evaluates the performance of fish-eye state routing algorithm, order-one Manet protocol and cluster based routing protocol. The outcome of this work is to advise a novel cluster based algorithm with low energy consumption and evaluate improvement over the earlier system with all the above said metrics.
METAMATERIAL PROPERTIES AND APPLICATIONS

This paper discusses fundamental properties of metamaterials. It also discuss the recent research activities on metamaterials in various areas such as antenna design, design of high frequency components & devices, microwave engineering etc. The metamaterials are engineered media whose electromagnetic responses are different from those of their constituent components. Here general advantages of metamaterials are pointed out. The main research directions related to metamaterials are discussed.
An Efficient Technique for Night Time Vehicle Detection with Fusion Based Image Enhancement

: Vehicle detection at night time is a challenging problem which deals with inadequately illuminated images of low contrast and reduced visibility. In this paper, an efficient region of interest (ROI) extraction approach which combines vehicle light detection and object proposals together with fusion-based score-level feature technique is proposed. The score-level multi-feature fusion method involves seven complementary features such as difference of Gaussian (DOG) , Scale Invariant Feature Transform (SIFT) ,local binary pattern (LBP), histogram of oriented gradients (HOG), four-direction features (FDF), HSV color histogram and edge HOG which represent different attributes of the vehicle and demonstrate different accuracy for vehicle recognition. In addition Elastic Edge Boxes is utilized on enhanced images to generate original object proposals. Eventually, the versatile AdaBoost classifiers are used to classify the corresponding features extracted from the ROIs for improving the accuracy of nighttime vehicle detection.
Survey on Ontology Construction Tools

— This paper presents a detailed survey on ontology development tools. Ontology development tools used for building a new ontology from scratch or reusing existing ontologies. Some of the popular ontology construction tools are Ontolingua Server, WebOnto, OilEd, OntoSaurus, Protégé, SWOOP, TopBraid Composer, WebODE, OntoEdit and NeOn toolkit. This survey article briefly describes the ontology development and it presents comparison summary of the ontology tools with respect to their features.