Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Validation of Real-Time Health Events through Social Media

Twitter, the popular microblogging platform, has more than five hundred million registered users (Tweeters). These Tweeters generate a large amount of information every day. A big challenge and opportunity that is explored in this paper is to use this information to analyse health events -- ideally in real-time. Such real time information is essential for outbreaks of disease and identifying where and who might be affected. In this context however it is essential to verify that the information is accurate and can be compared with other data sources. This paper presents a methodology and infrastructure delivering such capabilities. Unlike other approaches that have been on a small scale, this work exploits large-scale Cloud facilities and much larger collections of data. Specifically, we collected and analysed over 46 million tweets from the three most populated cities in Australia (Sydney, Melbourne and Brisbane) to find patterns related to health events. Five diseases were explored: ebola, dengue fever, flu, H1N1 and hayfever, however the platform can be used for other disease areas. We compared and validated the results with Google Trends data as well as data from the Australian Institute of Health and Welfare. We identified a high and measurable correlation between our data and these other sources. Building on these quantifiable degrees of accuracy, we suggest that social media can indeed be a key approach to alert authorities and the population at large of health disease events, e.g. pandemics, and allow them to track disease spread. At present no such infrastructure or capability exists.
Intrusion detection scheme using traffic prediction for wireless industrial networks

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.
Factors Influencing Job Satisfaction for Employed Adults With Multiple Sclerosis

Given the high unemployment rates of adults with multiple sclerosis (MS) and the importance of job satisfaction as a predictor of turnover intention and job tenure, this investigation examined the relationship of perceived job satisfaction to social cognitive career theory and ecological model constructs. Highly compatible, the two formulations suggest that extent of job satisfaction is a function of personal or demographic, contextual or extrinsic, disability/health status, and self-perceived vocational situation variables. Based on responses to a national survey by 628 employed adults with MS, a four-block, hierarchical logistic regression analysis examined the extent to which the four variable groupings contributed to the prediction of job satisfaction. The following variables were retained in the regression equation: racial/ethnic status, satisfaction with current financial status, satisfaction with housing accessibility, illness duration, extent of job/person match, appropriateness of current work hours, and self-rated job performance level. Consequently, job satisfaction is influenced by multiple factors that counselors must consider in their rehabilitation planning and service delivery.
On the advantages of lifetime and RTT classification schemes for TCP flows

We study classification schemes for TCP flows based on (a) lifetime (short vs. long lived flows) and (b) round-trip-time (RTT) attributes, as well as their combinations. The double objective served by the classification schemes is to satisfy the need for reduced response time, which is the primary concern of short lived flows, while at the same time ensuring fairness among long lived flows. The presented results indicate that the combined classification performs, under all tested case, comparably or better than RED and, as such, it is a reasonable alternative to the cumbersome task of properly parameterizing RED. In fact, the presented combined classification schemes achieve results equal or better than simple RED even when used in conjunction with DropTail queue management.
Impact of DC-Link Voltage Ripples on the Machine-Side Performance in NPC H-Bridge Topology

In this paper, the impact of dc-link voltage ripples on the machine-side spectrum is investigated. The analysis is targeted for a high-power converter using neutral-point-clamped (NPC) H-bridge topology. Due to the high amplitude of second harmonic and additional f0/2 subharmonic that is introduced by a special modulation scheme, their impacts on the machine are at question. In this work, these questions are addressed via mathematical modeling followed by simulations and experimental validations. The conclusions are that the second harmonic in the dc-link leads to a small amount of amplitude distortion of the command voltage, while the f0/2 subharmonic generates small amount of second and fourth even-order harmonics. These voltage ripples do not harm the machine and will not reduce the output voltage capability of the converter.
Subpixel stereo disparity for surface reconstruction by utilising a three-dimensional reaction-diffusion system

This paper proposes a stereo algorithm utilising a reaction-diffusion system defined in a three-dimensional domain. A previous reaction-diffusion stereo algorithm provides a stereo disparity map by utilising multi-layered reaction-diffusion systems defined in a two-dimensional domain. The previous algorithm assumes that three-dimensional structures of scenes consist of unslanted planar surfaces and approximately describes any slanted or curved surfaces with piecewise unslanted planar surfaces. However, in real scenes there are highly slanted or curved surfaces, which violate the assumption of the previous algorithm and cause inaccurate results of disparity distribution, in particular, with respect to subpixel accuracy. The reaction-diffusion system consists of reaction-diffusion equations, which are described with partial-differential equations and solved with a numerical computation technique such as a finite difference method. Thus, we revise the reaction-diffusion stereo algorithm by utilising a reaction-diffusion system defined in a three-dimensional domain consisting of the two-dimensional domain plus a disparity domain. Discretisation of the disparity domain brings subpixel accuracy and helps the algorithm to detect accurate disparity for slanted or curved surfaces. In addition, this paper proposes a two-stage algorithm for fill-in of disparity undefined areas caused by little image feature. Finally, this paper demonstrates performance of the proposed algorithm for slanted or curved surfaces with the Middlebury stereo vision data-sets.
Locators of mobile sinks for wireless sensor networks

Mobile sinks in wireless sensor networks require an additional communication mechanism of geographic routing. Because the sink's location as the destination in geographic routing is changed dynamically, sinks' location should be propagated continuously though the sensor field for sensor's future data report. However this frequent location updates can drain up the sensor's battery power and increase wireless channel contentions. As a support to the mobile sinks, we proposed locators for mobile sinks that track current sinks' location. If a sensor reports sensed data to sinks later, it can acquire sinks' location from the locators. The locators are uniformly distributed uniformly to the sensor fields by hash bashed structured replication. Sinks update own location only immediate locators and other locators are fed location information by locators self location propagation. We implemented our locator protocol with network simulator-2 and compared previous work TTDD, a two-tier data dissemination. Our results show that locators handle multiple source environments with low overhead of location acquisition process
Physiological and Transcriptional Analyses Reveal Differential Phytohormone Responses to Boron Deficiency in Brassica napus Genotypes

Phytohormones play pivotal roles in the response of plants to various biotic and abiotic stresses. Boron (B) is an essential microelement for plants, and Brassica napus (B. napus) is hypersensitive to B deficiency. However, how auxin responds to B deficiency remained a dilemma for many years and little is known about how other phytohormones respond to B deficiency. The identification of B-efficient/inefficient B. napus indicates that breeding might overcome these constraints in the agriculture production. Here, we seek to identify phytohormone-related processes underlying B-deficiency tolerance in B. napus at the physiological and gene expression levels. Our study indicated low-B reduced indole-3-acetic acid (IAA) concentration in both the shoots and roots of B. napus, and affected the expression of the auxin biosynthesis gene BnNIT1 and the efflux gene BnPIN1 in a time-dependent manner. Low-B increased the jasmonates (JAs) and abscisic acid (ABA) concentrations and induced the expression of the ABA biosynthesis gene BnNCED3 and the ABA sensor gene BnPYL4 in the shoot. In two contrasting genotypes, the auxin concentration decreased more drastically in the B-inefficient genotype ‘W10,’ and together the expression of BnNIT1 and BnPIN1 also decreased more significantly in ‘W10’ under long-term B deficiency. While the JAs concentration was considerably higher in this genotype, and the ABA concentration was induced in ‘W10’ compared with the B-efficient genotype ‘QY10. ’ Digital gene expression (DGE) profiling confirmed the differential expression of the phytohormone-related genes, indicating more other phyohormone differences involving in gene regulation between ‘QY10’ and ‘W10’ under low-B stress. Additionally, the activity of DR5:GFP was reduced in the root under low-B in Arabidopsis, and the application of exogenous IAA could partly restore the B-defective phenotype in ‘W10. ’ Overall, our data suggested that low-B disturbed phytohormone homeostasis in B. napus, which originated from the change of transcriptional regulation of phytohormones-related genes, and the differences between genotypes may partly account for their difference in tolerance (B-efficiency) to low-B.
Influence of chronic exercise on carotid atherosclerosis in marathon runners

Objectives The effect of habitual, high-intensity exercise training on the progression of atherosclerosis is unclear. We assessed indices of vascular health (central systolic blood pressure (SBP) and arterial stiffness as well as carotid intima-medial thickness (cIMT)) in addition to cardiovascular risk factors of trained runners versus their untrained spouses or partners to evaluate the impact of exercise on the development of carotid atherosclerosis. Setting field study at Boston Marathon. Participants 42 qualifiers (mean age±SD: 46±13 years, 21 women) for the 2012 Boston Marathon and their sedentary domestic controls (46±12 years, n=21 women). Outcomes We measured medical and running history, vital signs, anthropometrics, blood lipids, C reactive protein (CRP), 10 years Framingham risk, central arterial stiffness and SBP and cIMT. Results Multiple cardiovascular risk factors, including CRP, non-high-density lipoprotein cholesterol, triglycerides, heart rate, body weight and body mass index (all p<0.05), were reduced in the runners. The left and right cIMT, as well as central SBP, were not different between the two groups (all p>0.31) and were associated with age (all r≥0.41; p<0.01) and Framingham risk score (all r≥0.44; p<0.01) independent of exercise group (all p>0.08 for interactions). The amplification of the central pressure waveform (augmentation pressure at heart rate 75 bpm) was also not different between the two groups (p=0.07) but was related to age (p<0.01) and group (p=0.02) in a multiple linear regression model. Conclusions Habitual endurance exercise improves the cardiovascular risk profile, but does not reduce the magnitude of carotid atherosclerosis associated with age and cardiovascular risk factors.
Variable dependency analysis of a computer program

To keep pace with the advancement of technology, software products are overlooking the chances of soft errors in the program. Program analysis to check program execution flow is an effective way to detect soft errors. High level language computer program execution can be analyzed on the basis of the dependence of the variables used in the program. This paper illustrates a novel method to analyze variable dependencies of program based on automated generation of dependence graph. Dependence graph depicts the connectivity between the program variables where variables works as the vertices and dependence between variables perform as edges. The automated generated dependence graph also discovers the critical variables of a program and these critical variables perform as the key to detect the sequential execution of a program outperforming the existing methods of program analysis.
Energy-closed AC asynchronous drive test system based on field-oriented vector control

This paper presents a “Two Inverter-Motor” energy-closed AC asynchronous drive test system which uses the asynchronous machine as the dynamic emulation load. The common DC bus of both inverters are connected to each other to circulate the energy and improve efficiency of the system. In this paper, the common DC bus technique and the dynamic loading technique are discussed, and direct field-oriented vector control strategy are used to control speed and torque of the induction machine. Then, we introduce the hardware realization of the control system. At last, experimental results show that this drive test system has excellent dynamical and static performances and high control effect.
Fast and Accurate Maximum Inner Product Recommendations on Map-Reduce

Personalization has become a predominant theme in online advertising; the internet allows advertisers to target only those users with the greatest chances of engagement, maximizing the probability of success and user happiness. However, a na{\"i}ve approach to matching users with their most suitable content scales proportionally to the product of the cardinalities of the user and content sets. For advertisers with large portfolios, this quickly becomes intractable. In this work, we address this more general {\em top-$k$ personalization} problem, giving a scalable method to produce recommendations based on personalization models where the affinity between a user and an item is captured by an inner product (i.e., most matrix factorization models). We first transform the problem into finding the $k$-nearest neighbors among the items for each user, then approximate the solution via a method which is particularly suited for use on a map-reduce cluster. We empirically show that our method is between 1 and 2 orders of magnitude faster than previous work, while maintaining excellent approximation quality. Additionally, we provide an open-source implementation of our proposed method, this implementation is used in production at Etsy for a number of large scale personalization systems, and is the same code as used in the experiments below.
Linear discriminant analysis with a generalization of the Moore–Penrose pseudoinverse

The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this paper, we propose improving LDA in this area, and we present a new approach which uses a generalization of the Moore-Penrose pseudoinverse to remove this weakness. Our new approach, in addition to managing the problem of inverting the covariance matrix, significantly improves the quality of classification, also on data sets where we can invert the covariance matrix. Experimental results on various data sets demonstrate that our improvements to LDA are efficient and our approach outperforms LDA.
Reliable code generation and execution on unreliable hardware under joint functional and timing reliability considerations

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness, i.e., vulnerability indexes, and timing correctness, i.e., the deadline misses. This paper considers the optimization of the Reliability-Timing (RT) penalty, defined as a linear combination of the vulnerability indexes (reliability penalties) and the deadline misses. We propose a multi-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions, for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. Based on the reliability and execution time profiling of these versions, our reliability-driven system software employs dynamic version selections to dynamically select a suitable version of a function according to the execution behavior of the previous functions. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions properly. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution. The reliability analysis and evaluation is performed on a reliability-aware processor simulator.
HAL: a hierarchical format for storing and analyzing multiple genome alignments

Motivation: Large multiple genome alignments and inferred ancestral genomes are ideal resources for comparative studies of molecular evolution, and advances in sequencing and computing technology are making them increasingly obtainable. These structures can provide a rich understanding of the genetic relationships between all subsets of species they contain. Current formats for storing genomic alignments, such as XMFA and MAF, are all indexed or ordered using a single reference genome, however, which limits the information that can be queried with respect to other species and clades. This loss of information grows with the number of species under comparison, as well as their phylogenetic distance. Results: We present HAL, a compressed, graph-based hierarchical alignment format for storing multiple genome alignments and ancestral reconstructions. HAL graphs are indexed on all genomes they contain. Furthermore, they are organized phylogenetically, which allows for modular and parallel access to arbitrary subclades without fragmentation because of rearrangements that have occurred in other lineages. HAL graphs can be created or read with a comprehensive C++ API. A set of tools is also provided to perform basic operations, such as importing and exporting data, identifying mutations and coordinate mapping (liftover). Availability: All documentation and source code for the HAL API and tools are freely available at http://github.com/glennhickey/hal. Contact: hickey@soe.ucsc. edu or haussler@soe.ucsc. edu Supplementary information: Supplementary data are available at Bioinformatics online.
A Cognitive CSMA-based Multichannel MAC Protocol for Cognitive Radio Networks

—Multichannel media access control (MAC) protocols have been widely considered for future mobile ad hoc networks and various wireless data networks. However, ALOHA-based protocols have been extensively studied in current researches for their simplicities both in design and analysis. To cope with co-existing multiple systems under the cognitive radio (CR) paradigm, we introduce the cognition capability and CSMA into multichannel MAC protocol, along with learning functionality of CR and a multichannel stabilization mechanism. Via facilitating spectrum sensing, cognition and adaptation for probability of transmission attempt optimize the proposed cognitive CSMA-based multichannel MAC protocol. Simulation results validate our analysis of steady-state approximation and suggest a pertinent throughput improvement over existing multichannel MAC protocols.
Indoor localization system using wireless sensor networks for stationary and moving target

The recent research in localization technique has been supported by the emerging of wireless sensor network (WSN) technology. The issues of the estimated location accuracy have becoming the main research topics in WSN-based localization technique. In this paper, the application of WSN for localization technique using ZigBee standard is proposed. As IEEE 802.15. 4 standard, ZigBee is widely used because of its advantages. It specially provides useful parameters for location estimation, i.e. received signal strength indicator (RSSI) and link quality indicator (LQI). In this paper, the simple but effective localization system is proposed. Range-based and location fingerprint-based are implemented. The different set of the reference nodes is applied. The effectiveness of this method is verified by data obtained from an indoor experiment in the 5m × 5m observed area. The error from estimated locations using both techniques is analyzed. The estimated location results from different amount of reference nodes are compared. The results show that the error from the observed area is less than 1.2 m for both localization techniques, respectively. The GUI is created to help the experiments in the visualization and for showing the estimated location. From this result, the simple but effective system is really beneficial for the real application.
iDedup: latency-aware, inline data deduplication for primary storage

Deduplication technologies are increasingly being deployed to reduce cost and increase space-efficiency in corporate data centers. However, prior research has not applied deduplication techniques inline to the request path for latency sensitive, primary workloads. This is primarily due to the extra latency these techniques introduce. Inherently, deduplicating data on disk causes fragmentation that increases seeks for subsequent sequential reads of the same data, thus, increasing latency. In addition, deduplicating data requires extra disk IOs to access on-disk deduplication metadata. In this paper, we propose an inline deduplication solution, iDedup, for primary workloads, while minimizing extra IOs and seeks. Our algorithm is based on two key insights from real-world workloads: i) spatial locality exists in duplicated primary data; and ii) temporal locality exists in the access patterns of duplicated data. Using the first insight, we selectively deduplicate only sequences of disk blocks. This reduces fragmentation and amortizes the seeks caused by deduplication. The second insight allows us to replace the expensive, on-disk, deduplication metadata with a smaller, in-memory cache. These techniques enable us to tradeoff capacity savings for performance, as demonstrated in our evaluation with real-world workloads. Our evaluation shows that iDedup achieves 60-70% of the maximum deduplication with less than a 5% CPU overhead and a 2-4% latency impact.