Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Non-interest Income on the Efficiency of China’s Banking Sector

This paper investigates the impact of non-interest income on bank efficiency based on data from China banking sector during the period 1996~2010 by establishing DEA model and Panel-Tobit model. The efficiency levels of China banking sector are estimated by employing traditional DEA model, which only considers loans and investments as the output variables, and alternative DEA model, which considers non-interest income as an additional output variable. The results of parametric and non-parametric univariate tests on the efficiency scores show that there are no significant differences in mean and median efficiency calculated from traditional and alternative DEA models. In other words, the inclusion of non-interest income in output vector does not have a statistically significant influence on the efficiency of China banking sector. Additionally, we normalize each bank’s efficiency score under these two DEA models in order to avoid potential estimation bias due to the fact that the alternative DEA model has one more output variable than the traditional DEA model, and the findings suggest that only a small proportion of banks present an increase in efficiency level with inclusion of non-interest income, while no significant changes are seen on most banks’ efficiency levels. Also, further analysis by establishing Panel-Tobit regression model finds that the relationship between the share of noninterest income to the net operating revenue and the bank efficiency score is not significant, which suggests that the bank efficiency doesn’t increase significantly with the increasing non-interest income share. Furthermore, the bank efficiency also does not present a significant increase with the time. Overall, our findings suggest that the inclusion of non-interest income does not significantly increase the efficiency level of China banking sector. *Corresponding author: Li Li, Assistant Professor, Ph.D. in Accounting, School of Management, Marist College, Poughkeepsie, NY 12601, USA, Tel: 845-575-3000; E-mail: lyanlin2010@gmail.com Received July 07, 2014; Accepted December 23, 2014; Published December 30, 2014 Citation: Li L (2014) The Impact of Non-interest Income on the Efficiency of China’s Banking Sector. J Stock Forex Trad 3: 135. doi:10.4172/2168-9458.1000135 Copyright: © 2014 Li L. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Aspect-Based Opinion Mining Using Dependency Relations

Over the course of recent years, Opinion Mining from unstructured natural language text has received significant attention from the research community. In the context of Opinion Mining from customer reviews, machine-learning approaches have been recommended; however, it is still a very challenging task. In this paper, we have addressed the problem of Opinion Mining, and we propose a Natural Language Processing approach that undertakes Dependency Parsing, Pre-processing, Lemmatization, and part of speech tagging of natural texts in order to obtain the syntactic structure of sentences by means of a dependency relation rule. Speciﬁcally, we employ Stanford dependency relations and Natural Language Processing as linguistic features and present an Aspect-Based opinion mining extraction algorithm from customer reviews. Throughout this paper, we also highlight the importance of subjective clause lexicon. We evaluate our extraction approach using customer product reviews collected from Amazon for nine different products collected by Hu and Liu [1]. Based on empirical analysis, we found that the proposed dependency patterns provided a moderate increase in accurate results than the baseline models. This study also found that the average per cent change for aspect and opinion extraction was significantly improved compared to the baseline models. We show the results of our study and discuss how they relate to comparative experimental results. We end with a discussion that highlights the strong and weak points of this method, as well as direction for future work. Examples are provided to demonstrate the effectiveness of using Dependency Relations for optimizing the problem of Opinion Mining.
Dyslipidemia in Patients of Psoriasis

Background: Psoriasis is a common and recurrent proliferative inflammatory skin disease that has been associated with abnormal plasma lipid metabolism. The high prevalence of atherosclerosis has been reported in psoriatic patients. The aim of our work was to evaluate the development of dyslipidemia in psoriasis and to look for a correlation between their levels and worsening of disease. Methods: We evaluated the fasting lipid profile in 300 clinically diagnosed psoriasis patients (Group II) which were further grouped as 200 plain psoriasis (Group IIA) and 100 psoriasis with mixed complications (Group IIB) and 200 age and gender matched healthy subjects as the controls(Group I). Result: Patient presented risk changes in lipid profile, serum total cholesterol and LDLcholesterol were found to be significantly increased(P<0.01) while there is no significant difference in triglyceride, VLDLcholesterol, HDL-cholesterol levels in Group IIB psoriasis patients with mixed complications than in Group IIA plain psoriasis patients. Conclusion: Our data suggests that high serum lipid profile level especially. Total cholesterol and LDLcholesterol is significantly more common in psoriasis and must be considered as a group at high risk for cardiovascular accidents(CVA). It may be useful to do early screening and treatment of hyperlipidemia in psoriasis to prevent the atherosclerosis and its complications. KeywordsPsoriasis, serum lipids, cardiovascular accident, dyslipidemia, hyperlipidemia. www.jmscr. igmpublication.org Impact Factor 3.79 ISSN (e)-2347-176x
Part-of-Speech (POS) Tagging Using Maximum Entropy Model

— Many different researchers, using a wide variety of techniques, have examined the task of Part-of-Speech (POS) tagging. The task itself consists of assigning basic grammatical word classes such as verb, noun and adjective to individual words, and is a fundamental step in many Natural Language Processing (NLP) tasks. The tags it assigns are used in other processing tasks such as chunking and parsing, as well as more complex tasks such as question answering and automatic summarization systems. Maximum Entropy modeling is one of the techniques that have been used to perform POS tagging, and gives state-of-the-art accuracy.
Manual Lymphatic Drainage Ccombined with Functional Exercise in the Treatment of Lymphedema After Modified Radical Mastectomy

Objective We aim to investigate the effect of manual lymphatic drainage in combination with functional exercise in the recovery from modified radical mastectomy. The outcome measurements are the incidence of lymphedema, function of upper limb, pain and life quality after operation. Methods We included 198 patients diagnosed with breast cancer in pathological examination and having received modified radical mastectomy in the First Affiliated Hospital of Jinan University from January 2016 to December 2017. The patients were randomized into experimental and control groups with 99 cases in each group. The control group was given guidance on functional exercise and health education. The control group, based on the treatment of control group was also given manual lymphatic drainage and the patients or their family members were ensured to master the practice of manual lymphatic drainage so that they could perform it after discharge. The incidence of lymphedema, pain, range of motion of shoulder joint and life quality among patients at three, six, twelve months postoperatively were recorded. Results There was no significant difference in the incidence of lymphedema at three months after operation (P 0.05). Conclusions Manual lymphatic drainage in combination with functional exercise is effective to reduce the incidence of lymphedema, difficulty in motion of shoulder joint and pain and numbness of affected limb, and improve life quality after breast cancer operation.
Survey on Clock Synchronization in WSN

: Wireless ad-hoc sensor network are becoming popular to collect data from the area of interest. But for data collection purpose nodes in the network must be synchronized with each other or with the global clock. Synchronization is needed for media access control (MAC) protocol such as TDMA. Wireless sensor networks have limited battery energy, because they are small in size. All these requirements of wireless sensor networks demand an accurate and energy efficient synchronization protocol. This survey paper reviews clock synchronization, need of synchronization and explains basic synchronization methods and protocols in detail.
Design and in vitro Evaluation of Mucoadhesive Buccal Tablets of Perindopril Prepared by Sintering Technique

The aim of the present study was to prepare and evaluate a mucoadhesive buccal tablet containing antihypertensive drug i.e. Perindopril to avoid the first pass metabolism and to improve its bioavailability with reduction in dose and also dose related side effects. The half life of Perindopril is approximately 0.8 to 1 hrs. The tablets were prepared by direct compression method containing polymer Polyethylene oxide and carnauba wax. The prepared tablets were sintered at various temperatures like 60 C and 70C for 1.5 hr and 3 hr. The sintered tablets were tested for weight variation, hardness, surface pH, drug Content Uniformity, swelling index, bioadhesive strength sand in-vitro drug dissolution study. FTIR studies showed no evidence on interactions between drug, polymers, and excipients. The invitro release of Perindopril was performed under sink conditions (Phosphate buffer PH 6.8, 37±0.5 oC, rpm 50) using USP-XXIV dissolution apparatus. The sintering times and the sintering temperature markedly affected the drug release properties of Perindopril buccal tablets. It is notable that the release rate of Perindopril from buccal tablets was inversely related to the time of sintering and the sintering temperature. This is may be due to increase in extent and firmness of sintering which compacts the mass further, so that the drug release is affected. The best in-vitro drug release profile was achieved with the formulation F4 A (sintered at 60c for 1.5 hr.) which contain the drug, polyethylene oxide and carnauba wax in the ratio of 1:15:10.The surface pH, bioadhesive strength and swelling index of formulation F4 A was found to be 6.27, 34.8 gm and 179.31 (after 12 hr). The tablets (formulation F4 A) containing 4 mg of Perindopril exhibited 8 hrs sustained drug release (98 %) with desired therapeutic concentration. The drug release followed diffusive mechanism with first order release kinetics. The stability studies showed that optimized formulation was considered to be highly stable.
I.D.E.A.S - A Futuristic Thinking Approach to Develop Entrepreneurial Cognition

IDEAS represents an acronym depicting the Futuristic Thinking approach to develop entrepreneurial cognition resulting in improved new venture ideas. The use of this new approach will solicit a combination of multiple approaches to ensure quality business ideas. This paper is structured into five parts. The first part introduces the study indicating the need to develop a new process. The second part presents a comparison of the existing processes highlighting their shortcomings. The third part focusses on the discussion of research design. The description of the new process and its advantages is presented in the fourth, and evidence from the student reflections is discussed in the fifth part. The paper concludes with future research directions which will assist in enriching the entrepreneurship and innovation education and training experience.
An Integrated Model of Factors Affecting Information Technology Implementation Success in Organizations

The impact of Information Technology on individuals, organization and society is changing everything. The extent of technological revolution that the world is presently witnessing is that Information Technology has become the vehicle or major engine of the information age which has reduce the world to a global village. This new technological epoch is apparent through the intensified investment in computer-processing and data preparation appliance in the manufacturing and service industry and telecommunications infrastructure, and its widespread usage in government agencies, educational organizations, and, more recently, in the households. Owing to these technological progressions, the implementation and application of Information Technology is a significant driving force behind many socioeconomic changes. The Implementation and the subsequent use of IT is a process of interrelated steps, slipping at any of the implementation stages may actually increase inefficiency, ineffectiveness and promote a number of uncertainties. This paper tries to identify different factors which help for a successful IT implementation in organizations. Merging the factors so identified into IT implementation stages the author tries to develop a framework for successful Information technology implementation. Extensive review of literature and expert advice has been sought in developing the framework which we hope to get fee back on publication. The outcome of the study will be a model which helps organizations to understand which are the critical factors for a successful Information technology Implementation and the relation between these factors.
Bitwise Data Compression-Encryption Technique

— Here present is an alternate method for lossless data compression where we can represent our data bits in compressed and encrypted form using a single method. Unlike, where we have to use different methods for data compression [1] and data encryption [2]. Bitwise Data Compression-Encryption Technique can be used for both variable and fixed data bits and also eliminates the use of dictionary for data compression [3].
Visibility Enhancement of Hazy Images using Depth Estimation Concept

- Image Enhancement is used to improve the contrast of the image having luminance. Image enhancement process removes the distortion from the image and improves the quality of the image. The image is captured in the outdoor scene are highly despoiled due to the reduced lighting situation or due to the soil particles. So, due to these particles the irradiation coming from the object is scattered and absorbed and hence the phenomenon of haze and fog occurs. Most of the researchers of the previous decade have proposed various methodologies to improve the visibility of the hazy images under the atmospheric conditions. This work proposes a methodology using depth estimation concept to improve the visibility of hazed images due to atmospheric troubles. The proposed methodology concentrated only on the depth estimation of the surface of the scene to camera lens and gamma correction factor has been applied at the final stage in order to obtain the output image as perfect as visible for the human eyes.
An Improved Version of Big Data Classification and Clustering using Graph Search Technique

: The Big data is categories as its sheer Volume, Variety, Velocity and Veracity. Most of the data is unstructured, quasi structured or semi structured and it is heterogeneous in nature. The volume and the heterogeneity of data with the speed it is generated, makes it difficult for the present computing infrastructure to handle Big Data. Traditional data management, warehousing and study systems fall short of tools to analyze this data. Due to its precise nature of Big Data, it is stored in distributed file system architectures. Hadoop and HDFS by Apache is widely used for storing and managing Big Data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant. Map Reduce is widely been used for the efficient analysis of Big Data DBMS techniques like Joins and Indexing and other techniques like graph search is mainly used for classification and clustering of Big Data. This techniques are being adopted to be used in Map Reduce. Map Reduce is a technique which makes use of file indexing with mapping, sorting, shuffling, reducing etc.
Military Networks by Disruption Tolerant Network Technology

: Mobile nodes in military environments like a field of battle or a hostile region ar seemingly to suffer from intermittent network property and frequent partitions. Disruption-tolerant network (DTN) technologieshave become eminent solutions that enable wireless devices carried by troopers to speak with one another and access the guidance or command faithfully by exploiting secondary storage nodes.a number of the foremost difficult problems during this state of affairs ar the social control of authorization policiesand also the policies update for secure information retrieval. Ciphertext-policy attribute-based secret writing (CP-ABE) could be a promising cryptologic resolution to the access management problems. However, the matter of applying CP-ABE in suburbanised DTNs introduces many security and privacy challenges with respect to the issued fromtotally different authorities. during this paper, we have a tendencyto propose secure information retrieval theme victimization CP-ABE for suburbanised DTNs wherever multiple key authorities manage their attributes severally. we have a tendency to demonstrate a way to apply the projected mechanism and with efficiency manage confidential information distributed within the disruption-tolerant military network . Keyworsds : DTN-Disruption
Review Paper of Identifying Features in Opinion Mining using Bootstrap Methodology and Naive Bayes Classification

- Extracting opinion characteristics are based only on models of a single review corpus, ignoring nontrivial disparities in the characteristics of word distribution of the characteristics of opinion across different Corpus. In this article we propose a new method to identify the characteristics of the opinions of online journals by exploiting the difference in opinion statistics through two corpus, a specific corpus of the domain and an independent corpus of the domain the contrasted corpus. We grasp this disparity via a measure called domain relevance (DR), which characterizes the relevance of a term to a collection of texts. First we extract a list of the characteristics of the candidate's opinion of the domain review corpus by defining a set of syntactic dependency rules. For each extracted candidate feature, we then estimate the intrinsic domain (IDR) and extrinsic domain relevance (EDR) scores in the domain-dependent and independent corpus domain, respectively. Candidate characteristics that are less generic (EDR score below a threshold) and more specific for the domain (IDR score higher than another threshold) are confirmed as characteristics of the opinion.
ENERGY EFICIENT METHOD USING ENERGY BALANCED ROUTING PROTOCOL FOR WSN

— Wireless sensor network is a wireless network consisting of widely distributer autonomous devices using sensors to monitor physical or environmental conditions. In WSN large number of node organized into a cooperative network. In WSN nodes are battery powered devices, they have limited transmission power, communication ability storage capacity. For effective transmission of data to receiver we go for design of routing protocol. Since energy conservation is a key issue in WSN, data aggregation is exploited to save the energy. This paper proposes energy efficient method using energy balanced routing protocol. This method is based on forward aware factor technique. In FAF, next hop node is selected based on forward energy density and link weight. Simulation results shows that the proposed method balances the energy consumption, increase the network lifetime and provide QoS .
An Enhanced Intrusion Detection System for Multitier Dynamic Web Applications

--------------------------------------------------------------------ABSTRACT-------------------------------------------------------------- We present an efficient approach, a system used to detect attacks in multitiered web services and classify through Hierarchal clustering Algorithm. Our approach can create normality models of isolated user sessions that include both the web front-end (HTTP) and back-end (File or SQL) network transactions with respect to Data volumes and Classify them. Implements a lightweight virtualization technique to assign each user’s web session to a dedicated container, an isolated virtual computing environment. We use the cluster algorithm to accurately associate the web request with the subsequent DB queries. DoubleGuard can build a causal mapping profile by taking both the webserver and DB traffic into account. Internet services and applications have become an inextricable part of daily life, enabling communication and the management of personal information from anywhere. To accommodate this increase in application and data complexity, web services have moved to a multitiered design wherein the webserver runs the application front-end logic and data are outsourced to a database or file server. In this paper, we present DoubleGuard, an IDS system that models the network behavior of user sessions across both the front-end webserver and the back-end database. By monitoring both web and subsequent database requests, we are able to ferret out attacks that an independent IDS would not be able to identify. Furthermore, we quantify the limitations of any multitier IDS in terms of training sessions and functionality coverage. We implemented DoubleGuard using an Apache webserver with MySQL and lightweight virtualization. --------------------------------------------------------------------------------------------------------------------------------------------------
Quantify the intersection of behavior in the work environment

—This is the study of intersection of behaviour to analyze the behaviour of users in the social networking environment. We have so many opportunities and challenges to analyze the behaviour on a large scale. A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media. Day by day the clicks are increasing in a particular network. To know the interest and requirement of users we propose an edge-centric clustering scheme to extract sparse social dimensions. Even though there are thousands and lacks of users involved in the social network, the proposed approach can efficiently analyze the even behaviour of the users. This helps to improve the odd areas as well as even areas where the people stick on to their desires.
Effect of local binary pattern on performance of iterative quantization in large-scale image retrieval

: Hashing is an efficient algorithm in order to approximate nearest neighbor in large-scale image datasets. Learning Binary Code is one of the key steps to improve the performance of these algorithms, however, it is still a challenge in this area. This article has improved hashing algorithm performance with the use of appropriate inputs. In the proposed method,image features extract by local binary pattern Then, thay use as input vector of iterativa quantization (ITQ) that leads to more compact codes, less memory and lower computational cost. The reasons behind these achivements are the binary nature and proper functioning of the local binary pattern. Finally in order to rerank results based on most similarity to query image, bit importance reranking method is used and then big weights are assigned to important bits and small weights are assigned to minor bits and weighted hamming distance is used to compute similarity between query image and top results.To evaluate performance of proposed method we use CIFAR-10 and MNIST as dataset and precision vs recall curve as evaluation criteria. The simulations compare the new algorithm with three state of the art and along the line algorithms from three points of view; the hashing code size, memory space and computational cost, and the results demonstrate the effectiveness of the new approach.