Miracle-dz/UniFAR-FTUs huggingface dataset:
A new meta-data driven data-sharing storage model for SaaS

A multi-tenant database is the primary characteristic of SaaS, it allows SaaS vendors to run a single instance application which supports multiple tenants on the same hardware and software infrastructure. This application should be highly customizable to meet tenants’ expectations and business requirements. This paper examined current solutions on multi-tenancy, and proposed a new meta-data driven data-sharing storage model for multi-tenant applications. Our design enables tenants to extend their own database schema during multi-tenant application run-time execution to satisfy their business needs. Experimental results show that our model made a good balance between efficiency and customized.
Image tag recommendation based on novel tensor structures and their decompositions

In this paper, we address the problem of image tagging and we propose automatic methods for image tagging, using tensor decompositions. Tensors are a suitable way of mathematically representing multilink relations. Another, complementary structure that captures the aforementioned high-order relations is the hypergraph. More specifically, three different matrices are derived from the hypergraph, namely, the incidence, adjacency, and affinity matrices. The just mentioned matrices are used to create slices of a novel tensor structure, which combines users' and images' relations. Four methods are exploited to decompose the tensor, i.e., the Higher Order Singular Value Decomposition (HOSVD), the Canonical Decomposition/Parallel Factor Analysis (CANDECOMP/ PARAFAC, CP), the Non-negative Tensor Factor Analysis (NTF), and Tucker Decomposition (TD). Experiments conducted on a dataset retrieved from Flickr demonstrate the potential of the proposed approach.
Immediate acknowledgement for single-channel full-duplex wireless networks

Driven by advances in the signal processing and antenna technology, it has become possible for wireless nodes to simultaneously transmit and receive a packet through self-interference cancellation using multiple antennas. This is known as a full-duplex communication. In this paper, we propose a MAC protocol with immediate acknowledgement (ACK) for single-channel full-duplex wireless networks. The proposed scheme in a full-duplex communication can solve the hidden terminal problem by using the immediate ACK. That is, the proposed scheme ensures that the transmission of a packet that is being correctly received is continued, and prevents the transmission of an already failed packet. To evaluate the performance of the proposed scheme, we carried out extensive simulations and subsequently showed that the proposed scheme significantly improves the network throughput.
Integration adaptive psychometric tests in interactive web environment

Multimedial diagnostics tests can be used for evaluation of user's cognitive functions. Integration of these tests in interactive Web environment, beside diagnostic cognitive capability, also enables collecting data about users, generates user's personal profiles, enables visualization, interpretation and analysis of test results, control over process of testing and finally perform conclusions on collected data.
Choice of Sampling Interval and Extent for Finite-Energy Fields

We focus on the problem of representing a nonstationary finite-energy random field, with finitely many samples. We do not require the field to be of finite extent or to be bandlimited. We propose an optimizable procedure for obtaining a finite-sample representation of the given field. We estimate the reconstruction error of the procedure, showing that it is the sum of the truncation errors in the space and frequency domains. We also optimize the truncation parameters analytically and present the resultant Pareto-optimal tradeoff curves involving the error in reconstruction and the sample count, for several examples. These tradeoff curves can be used to determine the optimal sampling strategy in a practical situation based on the relative importance of error and sample count for that application.
Examination of the Correlation between Dynamic Balance and Leg Strength of 11 and 12-Year-Old Children Who Have Fencing Training.

In this study, it is aimed to examine the relationship between leg strength and dynamic balance of children who have fencing training. 59 epee athletes who are 11 and 12 years old (33 11-year-old, 26 12-year-old) participated in research groups. Star Excursion Balance Test was used to determine balance performances and Baseline brand leg-dynamometer used to determine the leg strength of athletes who had fencing training. First, the Shapiro-Wilk test was used to determine if the data obtained during the study had a normal distribution. Pearson Correlation test was performed for all data displaying a normal distribution. Significance levels were taken as P<0,05 According to the research findings it was determined that there is a meaningful relationship between leg strength and balance test of athletes who have fencing training. As a result, it was seen that balance test values increase as the leg strength increases.
Derivatives of Parsing Expression Grammars

This paper introduces a new derivative parsing algorithm for recognition of parsing expression grammars. Derivative parsing is shown to have a polynomial worst-case time bound, an improvement on the exponential bound of the recursive descent algorithm. This work also introduces asymptotic analysis based on inputs with a constant bound on both grammar nesting depth and number of backtracking choices; derivative and recursive descent parsing are shown to run in linear time and constant space on this useful class of inputs, with both the theoretical bounds and the reasonability of the input class validated empirically. This common-case constant memory usage of derivative parsing is an improvement on the linear space required by the packrat algorithm.
FearNot! demo: a virtual environment with synthetic characters to help bullying

This demo features FearNot! , a school-based Virtual Learning Environment (VLE) populated by synthetic characters representing the various actors in a bullying scenario. FearNot! uses emergent narrative to create improvised dramas with those characters. The goal is to enable children to explore bullying issues, and coping strategies, interacting with characters to which they become affectively engaged. Through their appearance, behaviours and affect, these characters are able to trigger empathic relations with the user. FearNot! is used for Personal and Health Social Education (PHSE) for children aged 8--12, in the UK, Portugal and Germany.
Acoustic fMRI Noise: Linear Time-Invariant System Model

Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise, the MR scanner is modeled as a linear electroacoustical system generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications: 1) by using the gradient waveform calculated by the scanner software and 2) by using a recording of the gradient current. Up to 4 kHz, the first method is shown as reliable as the second one, and its use is encouraged when direct measurements of gradient currents are not possible. Additionally, the linear order and average damping properties of the gradient coil system are determined by impulse response analysis. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction ability can be obtained by calculating the acoustic output for the EPI sequence. We found a predicted sound pressure level (SPL) for the EPI sequence of 104 dB SPL compared to a measured value of 102 dB SPL. As yet, the predicted EPI pressure waveform shows similarity as well as some differences with the directly measured EPI pressure waveform.
Creating a Mobile Head-mounted Display with Proprietary Controllers for Interactive Virtual Reality Content

A method to create a mobile head-mounted display (HMD) a proprietary controller for interactive virtual reality (VR) content is proposed. The proposed method uses an interface cartridge printed with a conductive pattern. This allows the user to operate a smartphone by touching on the face of the mobile HMD. In addition, the user can easily create a mobile HMD and interface cartridge using a laser cutter and inkjet printer. Changing the form of the conductive pattern allows the user to create a variety of controllers. The proposed method can realize an environment that can deliver a variety of interactions with VR content.
Real-time recognition of personal routes using instance-based learning

Predicting routes is a critical enabler for many new location-based applications and services, such as warning drivers about congestion- or accident-risky areas. Hybrid vehicles can also utilize the route prediction for optimizing their charging and discharging phases. In this paper, a new lightweight route recognition approach using instance-based learning is introduced. In this approach, the current route is compared in real-time against the route instances observed in past, and the most similar route is selected. In order to assess the similarity between the routes, a similarity measure based on the longest common subsequence (LCSS) is employed, and an algorithm for incrementally evaluating the LCSS is introduced. The feasibility of the proposed approach is empirically evaluated using real-world data; the obtained results indicate that the routes can be accurately recognized with a delay of 11 turn-points.
Post implementation framework for ERP systems with special reference to Sri Lanka

Over the past decade global competition and high levels of IT enablement in business processes changed how the businesses were managed. Introduction of Enterprise Resource Planning (ERP) and rapid adoption of such was a result of increased competition and higher concern on efficient practices in doing the normal functions of a business. When many large Sri Lankan organizations have adopted ERP systems in order to improve their existing performance measurements, most of the organizations have failed to acquire the true benefits expected mainly due to poor post implementation practices. The main purpose of the research would be to develop a framework for successful ERP post implementation by analyzing the best practices, procedures and success factors based on eight selected large scale manufacturing organizations. A qualitative approach based on case studies has been used in order to derive the importance of each variable to the successfulness of the ERP usage. Furthermore the research was supported by exhaustive interviews and participative observation. The findings identified that ERP post implementation failure was not just caused by technical issues of the system, but more importantly was also attributed to critical problems related to top management, change management and effective user training. The proposed framework will assist Sri Lankan companies in utilizing their ERP usage in the post implementation phase. Furthermore the proposed framework can be used as a guideline in successful ERP implementations. The current research community will be benefited by extending the knowledge in to greater paradigm.
Lobbying for Power : A Structural Model of Lobbying in the Energy Sector

Firms systematically lobby the U.S. Congress to in uence policy-making. To what extent do lobbying expenditures a ect the probability that a policy is enacted? What are the private returns from lobbying expenditures? To answer these questions, I construct a novel dataset comprised of federal energy legislation and lobbying activities by the energy sector during the 110th Congress (2007 2008). A unique feature of this dataset is that it focuses on speci c policy issues rather than entire pieces of legislation (bills). I develop and estimate a game-theoretic model where heterogeneous players choose lobbying expenditures to a ect the probability that a policy is enacted. I nd that the e ect of lobbying expenditures on a policy's equilibrium enactment probability is small. However, the average returns from lobbying expenditures are estimated to be large. As the estimated average value of a policy is quite large, even a small change in its enactment probability can lead to large returns. ∗I am greatly indebted to my advisor, Antonio Merlo, and my dissertation committee members, Kenneth I. Wolpin, Hanming Fang, and Flávio Cunha for their guidance, support, and insight. I have greatly bene ted from discussions with Xu Cheng, Camilo García-Jimeno, Áureo De Paula, Holger Sieg, Xun Tang, and Petra Todd. I thank the participants of the 2011 Asian Meeting of the Econometric Society, the 25th Annual Meeting of European Economic Association, the 10th World Congress of the Econometric Society, and Penn's Empirical Micro Seminar and Empirical Micro Lunch. I also thank John Chwat of Chwat & Company and the sta in the Center for Responsive Politics, especially Jihan Andoni. Lastly, I thank Douglas Hanley for computerizing policy identi cation and also thank Mahuhu Attenoukon, Audrey Boles, Eric Sun, Jennifer Sun, and Yi Yi for providing excellent research assistance for data collection. The research reported here was supported by the National Science Foundation through Grant SES-1023855. All errors are mine. †Department of Economics, University of Pennsylvania. E-mail: karam2@econ.upenn. edu
Design of Multi Bit Flip Flop in FIR Application Using Clustering Algorithm

In Integrated Circuit industry power has become a major contribution. The main attribute is the clock power in circuits of VLSI. In today’s VLSI design scenario, power utilization by clocking takes up a vital role especially in design that uses deeply scaled CMOS technology. Proficient power utilization tends to be an important constraint in modern IC design. The underneath idea of multi bit flip flop is to reduce the inverter number by sharing among flip flop. Indulging multi bit flip flop in synchronous design is becoming a considerable method for reducing clock power. The single bit flip flop cells uses a mutual number of inverter that possess high driving capability to drive over clock signal. Grouping of such cells to form multi bi flip flop can spare drive strength, dynamic power and area of common inverter where there is no compromise among the necessary constraint among area and power. In this paper, a Hausdorff clustering algorithm is utilized to obtain nearest clustering for merging flip flops. The multi bit technique is introduced in FIR circuit to lessen power as well as area. This satisfies with the above given constraints. According to the experimental results, our algorithm significantly reduces clock power by 25.8% and it is found that total gate count is reduced from 186 to 128. The delay is curtailed upto 1.19 ns which increases the speed. KeywordsClock Power, Cluster, Delay, Manhattan Distance, Merging, Multi Bit Flip Flop
D2D-enabled wireless caching using Stackelberg game

Evidence indicates that wireless video traffic has played an important role in cellular networks. Device-to-device (D2D) enabled caching technology has been proposed to alleviate network backhaul traffic pressures, especially in peak hours. In this paper, a Stackelberg game is proposed to solve the interests' conflict in D2D-enabled wireless caching networks, where exist two kinds of users including subscribers and helpers. System model is characterized by a hierarchical structure, where the macro base station (MBS) optimizes its strategy based on the prices of helpers. Then, the optimal price and optimal power are derived in closed-forms. Simulations are carried out in three environments which include one helper, two helpers and three helpers in the network, respectively. In one-helper situation, the utilities of MBS and helper decrease as the distance increases. The tradeoffs between power and prices are presented in the simulation results of two-helpers and three-helpers situations. It can be seen that the utility of MBS is concave over power for the reasons that the increased profits are dominated firstly and then the cost substantially increases.
A sliding-mode based smooth adaptive robust controller for friction compensation

A new approach employing both adaptive and robust methodologies is proposed for stick-slip friction compensation for tracking control of an one-DOF DC-motor system. It is well-known that the major components of friction are Coulomb force, viscous force, exponential force (used to model the downward bend of friction at low velocity) and position-dependent force. Viscous force is linear and Coulomb force is linear in parameter; thus, these two forces can be compensated by adaptive feedforward cancellation. Meanwhile, the latter two forces, which are neither linear nor linear in parameters, can only be partially compensated by feedforward cancellation. Therefore, a robust compensator with an embedded adaptive law to online "learn" the upper bounding function is proposed to compensate the uncanceled exponential and position-dependent friction. Lyapunov's direct method is utilized to prove the globally asymptotic stability of the servo-system under the proposed friction compensation method.
Risk factors for detecting germline BRCA1 and BRCA2 founder mutations in Ashkenazi Jewish women with breast or ovarian cancer

We ascertained 184 Ashkenazi Jewish women with breast/ovarian cancer (171 breast and 13 ovarian cancers, two of the former also had ovarian cancer) in a self-referral study. They were tested for germline founder mutations in BRCA1 (185delAG, 5382insC, 188del11) and BRCA2 (6174delT). Personal/family histories were correlated with mutation status. Logistic regression was used to develop a model to predict those breast cancer cases likely to be germline BRCA1/BRCA2 mutation carriers in this population. The most important factors were age at diagnosis, personal/family history of ovarian cancer, or breast cancer diagnosed before 60 years in a first degree relative. A total of 15.8% of breast cancer cases, one of 13 ovarian cancer cases (7.7%), and both cases with ovarian and breast cancer carried one of the founder mutations. Age at diagnosis in carriers (44.6 years) was significantly lower than in non-carriers (52.1 years) (p<0.001), and was slightly lower in BRCA1 than BRCA2 carriers. Thirty three percent of carriers had no family history of breast or ovarian cancer in first or second degree relatives. Conversely, 12% of non-mutation carriers had strong family histories, with both a first and a second degree relative diagnosed with breast or ovarian cancer. The predicted values from the logistic model can be used to define criteria for identifying Ashkenazi Jewish women with breast cancer who are at high risk of carrying BRCA1 and BRCA2 mutations. The following criteria would identify those at approximately 10% risk: (1) breast cancer <50 years, (2) breast cancer <60 years with a first degree relative with breast cancer <60 years, or (3) breast cancer <70 years and a first or second degree relative with ovarian cancer.
Experimenting with Anomaly Detection by Mining Large-Scale Information Networks

Social networks have formed the basis of many studies into large networks analysis. Whilst much is already known regarding efficient algorithms for large networks analysis, data mining, knowledge diffusion, anomaly detection, viral marketing, to mention. More recent research is focussing on new classes of efficient approximate algorithms that can scale to billion nodes and edges. To this end, this paper presents an extension of an algorithm developed originally to analyse large scale-free autonomic networks called the Global Observer Model. In this paper, the algorithm is studied in the context of monitoring large-scale information networks. Hence, taking into account the size of such networks, the proposed algorithm starts by partitioning the graph using structural network metrics. This is followed by a calculation of the graph nodes’ metrics, which are used in the selection from the original graph a subset of nodes to be monitored. The paper is organised as follows: it will outline the problem definition and algorithm, then will proceed to a brief description of an event and signature based model used to instrument monitored nodes. Finally, the paper will conclude with an evaluation using an infection detection scenario, which will be followed by a general discussion and proposed further work.
The degree of an Internet addiction among school youth

Abstract Introduction. The development of mass media, including an Internet has a significant impact on the lifestyle of young people. Despite of universal access and countless opportunities offered via Internet, an increasingly common phenomenon observed for several years is the excessive use of networks by young people, which can lead to Internet addiction. Aim. The aim of this study was to determine the degree of the Internet addiction in a group of junior high school students and high school students and selected conditions of this state. Material and methods. The applied research method was a diagnostic survey, and the following tools: the Kimberly Young’s Internet Addiction Test and the authors’ self-designed survey. The study covered 263 junior high school and high school students. The research was carried out from April to June 2018. Results. The Internet addiction was observed in 11% of the surveyed students. The use of social networking sites was the most common reason for using the Internet among surveyed (95%). The health problems associated with the long time of using the network of addicted students were general fatigue (61%) and lack of sleep (61%). Students addicted to the Internet much more often felt lonely (61%), depressed, upset or sad (n = 22, 78.6%) in contrast to other respondents. Conclusions. Demographic variables (such as: sex, type of school, place of residence) have no significant impact on the development of Internet addiction. Psychosocial functioning has an impact on the development of the Internet addiction.
Proliferation Rates of Bovine Primary Muscle Cells Relate to Liveweight and Carcase Weight in Cattle

Muscling in cattle is largely influenced by genetic background, ultimately affecting beef yield and is of major interest to the beef industry. This investigation aimed to determine whether primary skeletal muscle cells isolated from different breeds of cattle with a varying genetic potential for muscling differ in their myogenic proliferative capacity. Primary skeletal muscle cells were isolated and cultured from the Longissimus muscle (LM) of 6 month old Angus, Hereford and Wagyu X Angus cattle. Cells were assessed for rate of proliferation and gene expression of PAX7, MYOD, MYF5, and MYOG. Proliferation rates were found to differ between breeds of cattle whereby myoblasts from Angus cattle were found to proliferate at a greater rate than those of Hereford and Wagyu X Angus during early stages of growth (5–20 hours in culture) in vitro (P < 0.05). The proliferation rates of myoblasts during early stages of culture in vitro were also found to be positively related to the liveweight and carcase weight of cattle (P < 0.05). Gene expression of MYF5 was also found to be significantly down-regulated in WagyuX compared with Angus cattle (P < 0.05). These findings suggest that early events during myogenesis are important for determining liveweight and caracase weights in cattle.