Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodically Predicting Client's Bandwidth & Cost Acknowledgements Sends to Cloud Server to Optimize Resource Usage

Cloud computing provides users to store their data remotely and enjoy the on demand high quality services and offers it as usage based pricing. Here, bandwidth reduction is an important issue in this paper. Cloud service providers (CSP) are trying to reduce this bandwidth and also applying the judicious use of cloud resources by deploying a TRE system to end-to-end clients. Generally cloud server maintains the client mobility and server migration to cloud elasticity along with this it also maintaining the continuous client's status of data transferring, traffic reduction, it is again a burden to server, So in order to reduce the overhead to server in this paper TRE system is receiver based, by maintaining a chunk store and allowing the client to use newly received chunks to identify previously received chunk chains which in turn sends a predictor message for the subsequent chunks. When redundancy is detected the sender then sends only ACK to the prediction instead of sending data.
ANALYSIS OF THE SELF-REPORTED PHYSICAL ACTIVITY QUESTIONNAIRES INDICATORS

“Global action plan on physical activity 2018–2030” emphasizes the need for weekly moderate physical activity for ensuring human health. Physical activity can be measured by a variety of objective and subjective methods, but one of the prerequisites for understanding the connection between active lifestyle and health certainty that the measuring instrument measures the feature for which it has been created and that it can be used in many countries. The goal of the research is to determine the correlations between measurement properties of fitness club clients’ self-reported physical activity indicators in the Global Physical Activity Questionnaire (GPAQ), International Physical Activity Questionnaire (IPAQ), European Health Interview Survey-Physical Activity Questionnaire (EHIS-PAQ), and to evaluate their concurrent validity. Methods: participants (volunteers) were 70 fitness club visitors (age 18-79). This research is preparative part for the further research of the project “European Physical Activity and Sports Monitoring System (EUPASMOS)”. The type, involvement and amount of physical activity was determined by the GPAQ, IPAQ and EHIS-PAQ adapted into Latvian. The research results showed that questionnaires allowed to determine the respondents’ physical activity indicators in and out of fitness club activities. Statistically significant correlations were determined between indicators obtained in all physical activity questionnaires applied in the research (p<0.05). This confirms that all three physical activity determination questionnaires can be used for physical activity research in Latvian environment.
Gender Classification of Low‐Resolution Facial Image Based on Pixel Classifier Boosting

In face examinations, gender classification (GC) is one of several fundamental tasks. Recent literature on GC primarily utilizes datasets containing high‐resolution images of faces captured in uncontrolled real‐world settings. In contrast, there have been few efforts that focus on utilizing low‐resolution images of faces in GC. We propose a GC method based on a pixel classifier boosting with modified census transform features. Experiments are conducted using large datasets, such as Labeled Faces in the Wild and The Images of Groups, and standard protocols of GC communities. Experimental results show that, despite using low‐resolution facial images that have a 15‐pixel inter‐ocular distance, the proposed method records a higher classification rate compared to current state‐of‐the‐art GC algorithms.
Internet-Based Psychological Experimenting

Internet-based psychological experimenting is presented as a method that needs careful consideration of a number of issues—from potential data corruption to revealing confidential information about participants. Ten issues are grouped into five areas of actions to be taken when developing an Internet experiment (dos) and five errors to be avoided (don’ts). Dos include: (a) utilizing dropout as a dependent variable, (b) the use of dropout to detect motivational confounding, (c) placement of questions for personal information, (d) using a collection of techniques, and (e) using Internet-based tools. Don’ts are about: (a) unprotected directories, (b) public access to confidential data, (c) revealing the experiment’s structure, (d) ignoring the Internet’s technical variance, and (e) improper use of form elements.
L’impact de la culture organisationnelle sur la perception du succès des systèmes ERP dans le contexte marocain

This present work consists of evaluating the extent to which organizational culture aspects influence the perception of the success of the ERP system. To do this, we conducted a survey on 94 companies operating in different business areas in Morocco. We have applied the method of structural equations to analyze the data collected. The results of this investigation show that culture oriented to employees emphasizing on involvement, collaboration , teams working , mutual trust, decentralized decision making and power sharing influenced perceived success of ERP system in term of system quality and net benefits. The results also show that perception of ERP success toward information quality and net benefits are influenced by hierarchical culture which focuses on formalization.
Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined in the AI field the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” i.e. non optimal solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems.
Automatic Web site classification in a large repository under information filtering and retrieval techniques

Nowadays, there is a huge volume of information on the Web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce sites according their Web-content. This system can be implemented for automatic knowledge segmentation in a portal or in a search engine repository. The system performance reached 96% in the first test sets, after the learning phase. However, the performance significantly increases (up to 98%) as the number of the test sets increases.
of the 23 rd Annual EMBS International Conference , October 25-28 , Istanbul , Turkey FILTERING MEDICAL IMAGE USING ADAPTIVE FILTER

To date various image filtering approaches are reported, most of them enhance the signal-to-noise ratio(SNR) in different levels with loss of some useful information. In this paper, an adaptive template filtering method is described which can be used to increase SNR and keep the important edge information of medical images. In the proposed method, for each pixel, an optimal template of different shape and coefficients is selected automatically depending on its neighbor pixels. Not being simple extension of the' 2D method, sequential filtering of slices in three orthogonal directions implemented the 3D algorithm, which improved existing adaptive template filtering technique, avoiding a burden of enormous search. Simulation and MRI image tests ,both 2D and 3D, show that the new adaptive template filter provides higher SNR and sharper edges.
A multi-mechanism rule-extraction pipeline for use on unannotated datasets

We outline a hybrid methodology (incorporating both supervised and unsupervised-learning components) for rule-based knowledge discovery from unannotated data i.e. when the classification information is unknown. The motivation for our work stems from the individual effectiveness of various data mining mechanisms i.e.: (1) class identification via unsupervised datavector cluster formation, (2) datavector simplification and feature selection via attribute discretisation, and (3) symbolic rule extraction via the association of symbolic rules with the structural parameters of a trained neural network (NN). The basic operational concept involves the pipelined application of various unsupervised and supervised mechanisms i.e.: (1) k-means, (2) Chi-2, (3) local cluster (LC) network training, and (4) rule extraction from a trained LC network. The methodology will be tested and analysed using several well-known datasets.
An Improved Key Agreement Protocol Based on Fractal Theory

A key agreement protocol is a key establishment technique which enables two or more communicating parities to agree on a key or exchange information over an open communication channel. Due to the complicated mathematical structure and deterministic nature of the fractal functions that meet the cryptographic requirements, and taking the security threats and privacy issues into consideration, a new key agreement protocol based on Iterated Function Systems (IFS) is proposed to provide techniques and tools that may be useful for developing cryptographic protocols. The proposed protocol is a generalization of the Diffie Hellman (DH) protocol. It is designed to overcome some of the drawbacks of se veral previously proposed key agreement protocols. The experimental results and security analysis shows that the proposed scheme provides an essential security requirement, where their efficiency makes it easier to be applied alone or hybrid with other security methods.
A Secured Control Data Transmission using Hop by Hop Encryption Protocol Over Public Networks

Generally during data transmission data has to pass through the public networks but public networks are not secure and vulnerable and hence data can be theft so preserving both the security and privacy of the data and maintaining the consistency and effectiveness of applications becomes essential. But in spite of its importance they are discoursed. So effective encryption and decryption are needed existing encryption technologies are not effective and are incapable to provide secure data transmission. So an hop by hop encryption protocol is introduced in this paper which can provide security, privacy and integrity to the data traveling over public networks using SoR infrastructure. A special kind of routers called Service Oriented Routers(SoR) is introduced. It shifts the present Internet infrastructure to an open-innovation platform and
Text Mining Technique for Data Mining Application

Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In decision tree approach is most useful in classification problem. With this technique, tree is constructed to model the classification process. There are two basic steps in the technique: building the tree and applying the tree to the database. This paper describes a proposed C5.0 classifier that performs rulesets, cross validation and boosting for original C5.0 in order to reduce the optimization of error ratio. The feasibility and the benefits of the proposed approach are demonstrated by means of medial data set like hypothyroid. It is shown that, the performance of a classifier on the training cases from which it was constructed gives a poor estimate by sampling or using a separate test file, either way, the classifier is evaluated on cases that were not used to build and evaluate the classifier are both are large. If the cases in hypothyroid.data and hypothyroid.test were to be shuffled and divided into a new 2772 case training set and a 1000 case test set, C5.0 might construct a different classifier with a lower or higher error rate on the test cases. An important feature of see5 is its ability to classifiers called rulesets. The ruleset has an error rate 0.5 % on the test cases. The standard errors of the means provide an estimate of the variability of results. One way to get a more reliable estimate of predictive is by f-fold –crossvalidation. The error rate of a classifier produced from all the cases is estimated as the ratio of the total number of errors on the hold-out cases to the total number of cases. The Boost option with x trials instructs See5 to construct up to x classifiers in this manner. Trials over numerous datasets, large and small, show that on average 10-classifier boosting reduces the error rate for test cases by about 25%. Keywords—C5.0, Error Ratio, text mining, training data, test
PENGARUH KEPEMILIKAN INSTITUSIONAL, KEPEMILIKAN MANAJERIAL, RETURN ON ASSETS (ROA) DAN UKURAN PERUSAHAAN TERHADAP PENGUNGKAPAN CORPORATE SOCIAL RESPONSIBILITY (CSR) PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011-2013

Penelitian ini bertujuan untuk memperoleh bukti empiris mengenai pengaruh Kepemilikan Institusional, Kepemilikan Manajerial, Return On Assets (ROA) dan Ukuran Perusahaan Terhadap Pengungkapan Corporate Social Responsibility (CSR) pada perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia periode 2011-2013. Penelitian ini menggunakan 36 sampel perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia dan pengambilan sampel menggunakan teknik purposive sampling . Metode analisis yang digunakan adalah analisis regresi linear berganda. Hasil pengujian menunjukkan: (1) Kepemilikan Institusional berpengaruh negatif terhadap pengungkapan corporate social responsibility , (2) Kepemilikan Manajerial tidak berpengaruh secara signifikan terhadap pengungkapan Corporate Social Responsibility (3) Return On Assets berpengaruh positif terhadap pengungkapan corporate social responsibility , dan (4) Ukuran Positif berpengaruh positif terhadap pengungkapan corporate social responsibility .
Combined use of space and polarization diversity on mobile cellular networks

Diversity schemes are being used on mobile cellular networks to combat the effect of multipath fading mainly at the base station site. This work investigates the use of both space diversity (where two receiving antennas are installed at the base station on each one of the coverage sectors) and polarization diversity (a dual polarization antenna is used and antennas with two /spl plusmn/45/spl deg/ linear polarization are used) at the base station in order to verify experimentally the real gain that can be obtained with this proposition. Each scheme was first tested isolated before the use of the combined technique. Results are presented in dB related to the 10% multipath fading probability occurrence improvement.
Lecture capture:making the most of face-to-face learning

Abstract The deployment of a new virtual learning environment (VLE), Blackboard, at the University of Leeds came with a vision of ‘the use of technology enabling better use of […] contact time’. This paper describes how a programme of lecture capture (in which the audio of lectures, video of the presenter and on-screen projected content were recorded) impacted on the delivery and student perception of face-to-face teaching and associated pedagogies in the School of Electronic and Electrical Engineering. Two modules were involved in the pilot study: one at level 1 (on which 74 students were enrolled) and the other at level 2 (45 students). Approximately 30 lectures were captured in total, the majority of these in the level 1 module. Students were regularly polled about their interactions with the captures and their experiences were overwhelmingly positive. In addition to the teaching resources that were created and evaluation that was conducted, the project also produced a series of guides to support the use of the hardware and software used in the process. This paper describes how the project has informed attitudes to lecture capture and practices of lecture delivery within the School against a background of current interest within higher education in this area. The paper discusses the benefits of the approach taken (using simple lecture capture technology) and some of the challenges of the project (including cost, support, training, reliability and scalability). Finally, the paper considers the reaction to calls for wider use of this technology and the implementation of a more scalable, automated lecture capture system.
The four horsemen of innovation: learning styles, entrepreneurship, attitudes, and knowledge network

Although previous research programs have yielded valuable knowledge that can help sugar beet growers to innovate farming processes, actual transfer of this knowledge to the growers so far is lacking. Currents ways of knowledge transfer do not match learning styles, personal traits or the social environment of previously identified groups of growers. The current research was designed to asses to which level new means of knowledge transfer are suitable: using both digital means, e.g., decision support systems, and other means, e.g. study groups, knowledge transfer can be re-assessed to form specific inspiring learning environments. A survey study assessed learning styles, attitudes toward innovation, personality traits related to entrepreneurship and the social network growers use to obtain new knowledge. These data were linked to the crop yield data over the previous five years, to be able to compare the influence of learning styles, attitudes, network and individual differences on the occurrence and effectiveness of certain types of innovative behaviour. Results indicate that different learning styles correlate with different ways of using one's knowledge network: for instance, people who are more prone to seek help, have significantly more contacts and exchange more knowledge within their networks. Growers whom significantly participate more in meetings and interactions with colleagues, produce an above average crop yield, as compared to other groups. The innovation attitude appeared to predict the innovation intention of growers; people with more positive attitudes are more willing to try new ideas and implement not fully tested techniques than growers with less positive attitudes toward innovation. Knowledge networks are comprised of fellow growers, friends, family, but mostly the growers receive their knowledge from advisors, suppliers and study groups. Preferences for learning and innovating correlate with the size of the network, and how intensively it is used.
Automatic Test Generation From Statecharts Using Model Checking

This paper describes a method for automatic generation of tests from specifications written in Statecharts. These tests are to be applied to an implementation to validate the consistency of the implementation with respect to the specification. For test coverage, we adapt the notions of control-flow coverage and data-flow coverage used traditionally in software testing to Statecharts. In particular, we redefine these notions for Statecharts and formulate test generation problem as finding a counterexample during the model checking of a Statecharts specification. The ability to generate a counterexample allows test generation to be automatic. To illustrate our approach, we show how to translate Statecharts to SMV, after defining the semantics of Statecharts using Kripke structures. We, then, describe how to formulate various test coverage criteria in CTL, and show how the SMV model checker can be used to generate only executable tests.
A 763 pW 230 pJ/Conversion Fully Integrated CMOS Temperature-to-Digital Converter With +0.81 °C/−0.75 °C Inaccuracy

A sub-nW fully integrated temperature sensor is presented that digitizes temperature via a capacitive charging time feedback loop controlled by a least significant bit (LSB)-first algorithm. Specifically, an ultra-low-power current reference generator charges two metal–insulator–metal (MIM) capacitors <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ top}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ bot}}$ </tex-math></inline-formula>, generating <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ ramp,top}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ ramp,bot}}$ </tex-math></inline-formula> which are then compared to a constant with temperature (CWT) voltage and a proportional to absolute temperature (PTAT) voltage, respectively. Temperature is then digitized by matching the charging time between the <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ ramp,top}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ ramp,bot}}$ </tex-math></inline-formula> via feedback tuning of <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ top}}$ </tex-math></inline-formula> driven by an energy-efficient digital processing unit (DPU) for direct ultra-low-power digital readout. The design is fabricated in 65 nm complementary metal–oxide–semiconductor, and measurement from 12 samples reveals a maximum temperature error of +1.61 °C/−1.53 °C (+0.86 °C/−0.83 °C) and +0.81 °C/−0.75 °C when operating from 0 °C to 100 °C after two-point (three-point) calibration without and with trimming, respectively. Operating from a 0.5 V supply, the 12 samples consumed an average power of 763 pW at 20 °C, which after a 0.3 s conversion time results in 230 pJ/conversion.