Miracle-dz/UniFAR-FTUs huggingface dataset:
Geo-location-aware emulations for performance evaluation of mobile applications

This paper presents the design of a simple emulation framework for performance evaluation and testing of mobile applications. Our testbed combines production hardware and software to allow emulation of realistic and repeatable mobility scenarios, in which the mobile user can travel long distances, while being served by an application server. The framework allows (i) geo-location information, (ii) client network conditions such as bandwidth and loss rate, as well as (iii) the application workload to be emulated synchronously. To illustrate the power of the framework we also present the design, proof-of-concept implementation, and evaluation of a geo-smart scheduler for application updates in smartphones. This geo-smart scheduler reduces the average download time by using a network performance map to schedule the downloads when at places with relatively good conditions. Our trace-driven evaluation of the geo-smart scheduler, illustrates the workings of the emulation framework, and the potential of the geo-smart scheduler.
Baseball Prediction Using Ensemble Learning by Arlo Lyle ( Under the direction of Dr

As the salaries of baseball players continue to skyrocket and with the ever-increasing popularity of fantasy baseball, the desire for more accurate predictions of players’ future performances is building both for baseball executives and baseball fans. While most existing work in performance prediction uses purely statistical methods, this thesis showcases research in combining multiple machine learning techniques to improve on current prediction systems by increasing the accuracy of projections in several key offensive statistical categories. By using the statistics of players from the past thirty years, the goal of this research is to more accurately learn from this data how a player’s performance changes over time and apply this knowledge to predicting future performance. Results have shown that using machine learning techniques to predict a player’s performance is comparable to the accuracy seen by some of the best prediction systems currently available. Index words: Machine Learning, Ensemble Learning, Baseball Prediction, Model Trees, Artificial Neural Networks, Support Vector Machines, Bagging, Boosting, Stacking
Componentware - State of the Art 2003 Background Paper for the Understanding Components Workshop of the CUE Initiative at the Univerita Ca' Foscari di Venezia Venice, October 7th-9th 2003

Components and component-based technologies (componentware) are well-known and widely used in software development. There is a large amount of work and research in componentware. The number of available componentware approaches increases steadily and it is quite dicult to keep track of current trends in this area. In this paper, we survey the current state of the art in componentware, introduce and compare several well-known componentware approaches and classify them according to outstanding characteristics. We discuss a list of open issues in resarch and practical use of componentware and oer some proposals for further development. In our practical considerations we focus on embedded systems and business information systems because most of our partners in industry work in one of these two domains. We hope to start a broader discussion on componentware and to get a common understanding, which open issues are most important in research and industry (as a research agenda).
Intermediate and deep water formation in the Okinawa Trough

[1] Water mass formation in the intermediate and deep layers of the Okinawa Trough is investigated using two distinct data sets: a quasi-climatological data set of the water properties of the minimum salinity surface produced from Argo float profiles and historical CTD data, and a velocity data set in the Kerama Gap measured by moored current meters during June 2009 to June 2011. The formation process of Okinawa Trough Intermediate Water is explained on the basis of horizontal advection and mixing of North Pacific Intermediate Water (NPIW) and South China Sea Intermediate Water (SCSIW). The salinity-minimum water intruding into the Okinawa Trough through the channel east of Taiwan is approximately composed of 45% NPIW and 55% SCSIW, while that through the Kerama Gap is 75% NPIW and 25% SCSIW. Salinities of these water masses increase in the Okinawa Trough due to strong diapycnal diffusion; its coefficient is estimated as 6.8–21.5 × 10−4 m2 s−1 based on a simple advection-diffusion equation. On the other hand, deep water in the Okinawa Trough, below the sill depth of the Kerama Gap (∼1100 m), is ventilated by overflow in the bottom layer of the Kerama Gap down to the deepest layer (∼2000 m) in the southern Okinawa Trough. A simple box model predicts that this bottom overflow (0.18–0.35 Sv) causes strong upwelling (3.8–7.6 × 10−6 m s−1) in the southern Okinawa Trough, which must be maintained by buoyancy gain of the deep water due to strong diapycnal diffusion (4.8–9.5 × 10−4 m2 s−1).
Recursive meta-clustering in a granular network

Granular computing represents an object as an information granule. Traditionally the information is derived from the primary source of data by recording events such as transactions, phone calls, user sessions, security breaches, and car trips. Much of the early data mining techniques used information granules generated from primary data sources. Recent data mining techniques such as ensemble classifiers and stacked regression use secondary sources of data obtained from initial data mining activities. Typically, these techniques use preliminary applications of data mining techniques for initial knowledge discovery. The knowledge acquired from the preliminary data mining is then used for more refined analysis. Granular computing can enable us to develop a formal framework for incorporating information from both primary and secondary sources of data. This enhanced granular representation can help us develop integrated data mining techniques. This paper proposes a novel recursive meta-clustering algorithm to demonstrate the versatility of granular computing for developing integrated data mining techniques to exploit primary and secondary knowledge sources.
A Multi-Country Dynamic Heckscher-Ohlin Model with Physical and Human Capital Accumulation

We study a multi-country endogenous growth model in which the long-run growth is propelled by the accumulation of physical and human capital. In the integrated world economy, we illustrate that a unique balanced growth equilibrium exists which is locally saddlepoint stable. We also found that incorporating Penrose e¤ect in the human capital accumulation process leads to a lower long-run growth rate. In the 2-country world economy with international trade, we conclude that asymmetric balanced growth path can be pinned down in the present 2-country framework. Lastly, we build the dynamic Heckscher-Ohlin theorem of international trade pattern.
Data-driven generation of text balloons based on linguistic and acoustic features of a comics-anime corpus

Most automatic speech recognition systems existing today are still limited to recognizing what is being said, without being concerned with how it is being said. On the other hand, research on emotion recognition from speech has recently gained considerable interest, but how those emotions could be expressed in text-based communication has not been widely investigated. Our long-term goal is to construct expressive speech-to-text systems that conveys all information from acoustic speech, including verbal message, emotional state, speaker condition, and background noise, into unified text-based communication. In this preliminary study, we start with developing a system that can convey emotional speech into text-based communication by way of text balloons. As there exist many possible ways to generate the text balloons, we propose to utilize linguistic and acoustic features based on comic books and anime films. Experimental results reveal that expressive text is more preferable than static text, and the system is able to estimate the shape of text balloons with 87.01% accuracy. Index Terms: data-driven approaches, expressive text generation, linguistic and acoustic features
Gender Differences in Internet Use: A Logistic Regression Analysis

In today’s higher education, Internet technologies play increasingly essential roles in creating, storing, and disseminating information and knowledge. This study investigates gender differences in Internet usage patterns and perceptions of Internet technologies using data gathered from 805 business students. A three-variable logistic regression model is assessed in terms of the overall model fit, as well as the direction of variable association and its magnitude among the research variables. It is found that gender differences of business students do exist in terms of Internet usage patterns and perceptions of Internet technologies. Such differences are reflected in perceptions of Internet self-efficacy, experience, and information overload. Our findings indicate that the gender neutrality among business students in the Internet era is yet to be a social reality. Research implications and methodological issues of logistic analysis are also discussed.
Design of expanded adaptive fuzzy sliding mode controller using FBFE for UFV depth control

Generally, the underwater flight vehicle (UFV) depth control system operates with the following problems: it is a multi-input multi-output (MIMO) system, it requires a robustness, it requires a continuous control input, and further, it has the speed dependency of controller parameters. To solve these problems, an expanded adaptive fuzzy sliding mode controller (EAFSMC), which is based on the decomposition method designed by using an expert knowledge and the decoupled sub-controllers and composition method designed by using the fuzzy basis function expansions (FBFEs), is proposed. To verify the performance of the EAFSMC, the depth control of UFV in various operating conditions is performed. Simulation results show that the EAFSMC solves all problems experienced in the UFV depth control system online.
Different Channel Estimation and ICI Cancellation Techniques in MIMO-OFDM Wireless Communication Systems

MIMO OFDM is one of the prominence communication schemes with multi-carrier modulation. The MIMO combined with OFDM modulation technique provides the reliable high data rate transmission over the broadband wireless channels. They have been widely studied and employed for 4G systems such as Wi-Fi, DVB- T, Wi-MAX and LTE-A. The major challenge in MIMO-OFDM systems is how to estimate the channel. The estimation of the channel is done by various algorithms such as Least Square (LS) and Minimum Mean Square Error (MMSE). The main issue in OFDM systems is the problem of ICI (Inter carrier Interference) which is due to the loss of the orthogonality between the sub-carriers and the signal transmitted.ICI self-cancellation is one of the reduction methods to reduce the ICI effect in the signal.
MicroRNA-Dependent Regulation of Transcription in Non-Small Cell Lung Cancer

Squamous cell lung cancer (SCC) and adenocarcinoma are the most common histological subtypes of non-small cell lung cancer (NSCLC), and have been traditionally managed in the clinic as a single entity. Increasing evidence, however, illustrates the biological diversity of these two histological subgroups of lung cancer, and supports the need to improve our understanding of the molecular basis beyond the different phenotypes if we aim to develop more specific and individualized targeted therapy. The purpose of this study was to identify microRNA (miRNA)-dependent transcriptional regulation differences between SCC and adenocarcinoma histological lung cancer subtypes. In this work, paired miRNA (667 miRNAs by TaqMan Low Density Arrays (TLDA)) and mRNA profiling (Whole Genome 44 K array G112A, Agilent) was performed in tumor samples of 44 NSCLC patients. Nine miRNAs and 56 mRNAs were found to be differentially expressed in SCC versus adenocarcinoma samples. Eleven of these 56 mRNA were predicted as targets of the miRNAs identified to be differently expressed in these two histological conditions. Of them, 6 miRNAs (miR-149, miR-205, miR-375, miR-378, miR-422a and miR-708) and 9 target genes (CEACAM6, CGN, CLDN3, ABCC3, MLPH, ACSL5, TMEM45B, MUC1) were validated by quantitative PCR in an independent cohort of 41 lung cancer patients. Furthermore, the inverse correlation between mRNAs and microRNAs expression was also validated. These results suggest miRNA-dependent transcriptional regulation differences play an important role in determining key hallmarks of NSCLC, and may provide new biomarkers for personalized treatment strategies.
Network Safety Monitoring System with Suspicious Hosts Early Localization

This paper is devoted to the new monitoring mechanism with suspicious hosts early localization that allows detect the network attacks with low level of incorrect detections. The structure of the monitoring system, based on suggested mechanism is designed. Also is described the experimental researches of the different types network monitoring systems and is analyzed their effectiveness for the attacks detection in the computer networks.
Application of RFID Technology and Ontology Model in Designing Smart Home System

The purpose of building smart home system is some home devices (audio and video equipment, lighting systems, air conditioning control, security systems, digital cinema systems, network appliances, etc.) through smart home network automation, broadband and wireless networks, and we can achieve remote control home devices. The paper proposes application of RFID technology and ontology model in designing smart home system. RFID system is composed of two parts: the read / write unit and electronic transceiver. RFID technology can identify the moving objects and can also identify multiple tags; the operation is fast and convenient. Ontology is the concept of shared explicit and formal specification; it can be used to describe the concepts and relationships between concepts. This paper uses RFID embedded platforms and other types of home sensor ontology model to design a smart home control system. Copyright © 2013 IFSA.
A simple and efficient security scheme for vehicular ad hoc networks

In order to enhance the security of vehicular ad hoc networks (VANETs). By dividing the roadside units (RSUs) into application-RSUs (AP-RSUs) and authentication-RSUs (AU-RSUs) in application layer, we give a new security scheme for VANETs. This functional division of RSUs makes VANETs more logically clear. AU-RSUs record vehicles' real identities and assign temporary certificates to them. The temporary certificates can help vehicles hide their real identities. Hence, the scheme not only can guarantee the vehicle anonymity, but also can realize the vehicle traceability.
Reconstruction of handwritten digit images using autoencoder neural networks

This paper compares the performances of three types of autoencoder neural networks, namely, the traditional autoencoder with restricted Boltzmann machine (RBM), the stacked autoencoder without RBM and the stacked autoencoder with RBM based on the efficiency for reconstruction of handwritten digit images. Experiments are performed to determine the reconstruction error in all the three cases using the same architecture configuration and training algorithm. The results show that the RBM stacked autoencoder gives better performance in terms of the reconstruction error compared to the other two architectures. We also show that all the architectures outperform PCA in terms of the reconstruction error.
A Decidable Logic for Time Intervals: Propositional Neighborhood Logic

Logics for time intervals provide a natural framework for representing and reasoning about timing properties in various areas of artificial intelligence and computer science. Unfortunately, most time interval logics proposed in the literature are (highly) undecidable. Decidable fragments of these logics have been obtained by imposing severe restrictions on their expressive power. In this paper, we focus our attention on the propositional fragment of Neighborhood Logic (PNL for short). We show that PNL is expressive enough to capture meaningful timing properties and that it is decidable. Decidability is proved by developing an original tableau decision method for PNL. We conclude the paper by pointing out interesting relationships between PNL and compass logics for spatial reasoning.
High plasma N-terminal pro-brain natriuretic peptide level found in diabetic patients after myocardial infarction is associated with an increased risk of in-hospital mortality and cardiogenic shock.

AIMS No studies have yet been conducted concerning plasma N-terminal pro-brain natriuretic peptide (Nt-pro-BNP) levels after Myocardial Infarction (MI) and their relationship with short-term outcomes in diabetic patients. METHODS AND RESULTS Five hundred and sixty patients hospitalized for MI from the RICO survey, including 199 diabetic and 361 non-diabetic subjects, were included in the study. Plasma Nt-pro-BNP levels were measured on admission. Median Nt-pro-BNP levels were significantly higher in diabetic patients compared with non-diabetic patients [245 (81-77) vs. 130 (49-199) pmol/L, P<0.0001]. This difference remained highly significant after adjustment for age, female gender, creatinine clearance, left ventricular ejection fraction (LVEF), plasma peak troponin, anterior wall necrosis, and hypertension. In multivariable analysis, Nt-pro-BNP levels were negatively associated with creatinine clearance (P<0.0001) and LVEF (P<0.0001) and positively associated with plasma peak troponin (P<0.0001), age (P=0.0029), diabetes (P=0.0031), and female gender (P=0.0102). Diabetic patients showed a 4.7-fold increase in hospital mortality (15.6 vs. 3.3%, P<0.0001) and a 2.2-fold increase in cardiogenic shock (17.6 vs. 7.7%, P=0.0004). In multivariable analysis, diabetes was an independent factor for mortality [OR: 1.79 (1.45-2.20); P=0.0064] and cardiogenic shock [OR: 1.45 (1.22-1.72); P=0.0364] when the variable Nt-pro-BNP level was not introduced into the model, but was less significantly associated with mortality [OR: 1.73 (1.39-2.16); P=0.0107] and no longer associated with cardiogenic shock when Nt-pro-BNP was in the model. CONCLUSION After MI, diabetes is independently associated with high plasma Nt-pro-BNP levels. This elevated Nt-pro-BNP is strongly associated with the increased incidence of in-hospital mortality and cardiogenic shock observed in diabetes. Our findings clearly indicate that plasma Nt-pro-BNP provides highly valuable prognostic information on in-hospital outcome after MI, in particular in diabetic patients.
USENIX Association Proceedings of FAST ’ 03 : 2 nd USENIX Conference on File and Storage Technologies

This paper explores the effect of the current generation of hardware support for IP storage area networks on application performance. In this regard, this paper presents a comprehensive analysis of three competing approaches to build an IP storage area network that differ in their level of hardware support: software, TOE (TCP Offload Engine) and HBA (Host Bus Adapter). The software approach is based on the unmodified TCP/IP stacks that are part of a standard operating system distribution. For the two hardware-based approaches (TOE, HBA), we experimented with a range of adapters and chose a representative adapter for the current generation of each of the hardware approaches. The micro-benchmark analysis reveals that while hardware support does reduce the CPU utilization for large block sizes, the hardware support can itself be a performance bottleneck that hurts throughput and latency with small block sizes. Furthermore, the macro-benchmark analysis demonstrates that while the current generation of the hardware approaches may have the potential to provide performance improvements in CPU-intensive applications, overall the analysis does not demonstrate any performance benefits in database, scientific and email benchmarks. The analysis in this paper points out that a disparity in the processing power between the host and the adapter is the primary cause of the performance bottleneck in the current generation of the hardware approaches. The paper aims to guide the designers of the next generation of hardware-assisted adapters to better leverage the increasing processing power in the host. In particular, future adapters should be capable of handling small operations at wire speed.
An Empirical Study on Consumers’ Continuance Intention Model of Online Group-buying

This paper examines the factors influencing consumers’ intention to continue using a new pattern of online group-buying, which benefits consumers from high discounts and is prevalent in many countries of the world since 2009. A theoretical model is proposed based on Expectation-Confirmation Model of IS Continuance (ECM-ISC) from the previous IS continuance literature, integrating with e-recovery service quality, perceived risk and two external variables. Data collected from a questionnaire survey of OGB consumers provides empirical support for the proposed model. The results indicate that consumers’ satisfaction with prior use and perceived usefulness significantly influence consumers’ continuance intention. Consumers’ satisfaction is determined primarily by consumers' confirmation of expectation from prior use and secondarily by perceived erecovery service quality. Further, confirmation also has a significant influence on post-acceptance perceived usefulness. Confirmation, in turn, is significant influenced by both product quality and information quality. Some suggestions for practitioners are also offered.
A modified RPL for Wireless Sensor Networks with Bayesian inference mobility prediction

Wireless Sensor Networks (WSNs) become one of the most common technologies that can be deployed in various domains. However, the networks suffer from many problems caused by the limited sensor resources and the harsh environments where these networks are deployed. Many algorithms have been proposed to manage data routing while respecting the specificity of such networks. One of the known proposed routing protocols is RPL (IPv6 Routing Protocol for Low power and Lossy networks). Nevertheless this protocol is designed with consideration of the limited sensor energy, it cannot be integrated in may WSN applications. In fact, RPL, as it has been proposed, assume that the sensor nodes are static and don't manage any type of mobility. In this paper, we propose a new approach, called BMP-RPL (Bayesian model Mobility Prediction RPL for wireless sensor networks), that aims to adapt native RPL to nodes' mobility scenarios. This approach is based on nodes' identification and velocity prediction as well as the estimation of the link duration. Thus, we introduce a new metric which constructs routes according to the node status and within information lost. The performance of our approach is proved through simulation.