Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Scalability of Energy in Wireless RF Powered Internet of Things

Wireless RF power transfer requires the deployment of multiple energy transmitters (ETs) to cover an entire area of interest. This letter aims at bounding the minimum cumulative power that ETs need to inject into the network, such that the recipient nodes harvest sufficient power to operate. The main findings are that, in the worst case, this scales as O(s1-α/2), where s and α are the number of ETs and the channel path loss. That is, the overall power decreases with the number of ETs. It is also shown that sophisticated design for power transmission can further improve the scalability by s-1.
Journal of Information Technology Education Advancing the Is Curricula: the Identification of Important Communication Skills Needed by Is Staff during Systems Development Advancing the Is Curricula: the Identification of Important Communication Skills

Executive Summary Although research indicates communication is important among information systems (IS) staff, users, and managers to ensure successful development projects, the ineffective communication skills of IS staff are often cited as a possible cause of failed IS projects. To develop effective systems , communication between IS users and systems developers is important throughout the development process, and effective communication supports the collaborative process in system development. As found in the IS 2002 model curriculum, IS experts place interpersonal, communication , and team skills as one of four main categories of exit characteristics of IS graduates (Gorgone et al., 2002). Since effective communication among IS project stakeholders is cited as a critical factor of project success, this study identified specific written, oral, and interpersonal communication skills IS staff, IS users, and IS managers perceived to be the most important for IS staff during the systems development process. To investigate the most important communication skills needed by IS staff during the development process, IS staff, IS users, and IS managers who worked on a system development project together were surveyed. Results of the data obtained from the 324 respondents were analyzed in two ways: first, the most important communication skills needed by IS staff were ranked by each participant category and, second, models of the three communication skill types were developed through confirmatory factor analysis (CFA). Rankings by IS users, IS staff, and IS managers, as presented in this study, indicated that the three stakeholder groups had similar views of critical written, oral, and interpersonal communication skills needed by IS staff in the systems development process. Respondents consistently identified the most important writing skill as coherent writing, the two most important oral communication skills as the ability to ask appropriate questions and have effective oral communication skills, and the two most important interpersonal skills as the ability to accomplish assignments and the ability to work cooperatively in a one-on-one and project team environment. Using CFA, a written communication skill model consisting of 18 items was identified, an oral communication skill model consisting of 6 items was identified, and an interpersonal communication skill model consisting of 7 items was identified. The CFA procedure and comparisons with other studies provided support for the validity and reliability for the proposed constructs developed for important written, oral, and interpersonal communication skills. Both the rankings and confirmatory factor analysis models presented in Material published as part …
Querying Moving Objects in SECONDO

Representing descriptions of movements in databases and querying them is a basic capability required in mobile data management. In this demonstration, we show for the first time a prototype implementing a data model and query language for moving objects (trajectories) completely integrated into a DBMS environment, including query optimization and user interface issues such as animation.
Life cycle assessment of potential biojet fuel production in the United States.

The objective of this paper is to reveal to what degree biobased jet fuels (biojet) can reduce greenhouse gas (GHG) emissions from the U.S. aviation sector. A model of the supply and demand chain of biojet involving farmers, biorefineries, airlines, and policymakers is developed by considering factors that drive the decisions of actors (i.e., decision-makers and stakeholders) in the life cycle stages. Two kinds of feedstock are considered: oil-producing feedstock (i.e., camelina and algae) and lignocellulosic biomass (i.e., corn stover, switchgrass, and short rotation woody crops). By factoring in farmer/feedstock producer and biorefinery profitability requirements and risk attitudes, land availability and suitability, as well as a time delay and technological learning factor, a more realistic estimate of the level of biojet supply and emissions reduction can be developed under different oil price assumptions. Factors that drive biojet GHG emissions and unit production costs from each feedstock are identified and quantified. Overall, this study finds that at likely adoption rates biojet alone would not be sufficient to achieve the aviation emissions reduction target. In 2050, under high oil price scenario assumption, GHG emissions can be reduced to a level ranging from 55 to 92%, with a median value of 74%, compared to the 2005 baseline level.
Efficient power network analysis with complete inductive modeling

In this paper, an efficient method is proposed to accurately analyze large-scale power/ground (P/G) networks, where inductive parasitics are modeled with the partial reluctance. The method is based on frequency-domain circuit analysis and the technique of vector fitting [8], and obtains the time-domain voltage response at given P/G nodes. The frequency-domain circuit equation including partial reluctances is derived, and then solved with the GMRES algorithm with rescaling and precondition techniques. Due to the sparsified reluctance matrix and the frequency-domain based simulation techniques, the proposed method is able to handle large-scale P/G networks with complete inductive modeling. Numerical results show that the proposed method is orders of magnitude faster than HSPICE, and capable of handling the inductive P/G structures with more than 100,000 wire segments.
A 16-Gb/s backplane transceiver with 12-tap current integrating DFE and dynamic adaptation of voltage offset and timing drifts in 45-nm SOI CMOS technology

This paper presents a 16-Gb/s 45-nm SOI CMOS transceiver for multi-standard backplane applications. The receiver uses a 12-tap DFE with circuit refinements for supporting higher data rates. Both the receiver and the transmitter use dynamic adaptation to combat parameter drift due to changing supply and temperature. A 3-tap FFE is included in the source-series-terminated driver. The combination of DFE and FFE permits error-free NRZ signaling at 16-Gb/s over channels exceeding 30dB loss. The 8-port core with two PLLs is fully characterized for 16GFC and consumes 385mW/link.
Interactions between Fibroblast Growth Factors and Notch Regulate Neuronal Differentiation

The differentiation of precursor cells into neurons has been shown to be influenced by both the Notch signaling pathway and growth factor stimulation. In this study, the regulation of neuronal differentiation by these mechanisms was examined in the embryonic day 10 neuroepithelial precursor (NEP) population. By downregulating Notch1 expression and by the addition of a Delta1 fusion protein (Delta Fc), it was shown that signaling via the Notch pathway inhibited neuron differentiation in the NEP cells, in vitro. The expression of two of the Notch receptor homologs, Notch1 and Notch3, and the ligand Delta1 in these NEP cells was found to be influenced by a number of different growth factors, indicating a potential interaction between growth factors and Notch signaling. Interestingly, none of the growth factors examined promoted neuron differentiation; however, the fibroblast growth factors (FGFs) 1 and 2 potently inhibited differentiation. FGF1 and FGF2 upregulated the expression of Notch and decreased expression of Delta1 in the NEP cells. In addition, the inhibitory response of the cells to the FGFs could be overcome by downregulating Notch1 expression and by disrupting Notch cleavage and signaling by the ablation of the Presenilin1 gene. These results indicate that FGF1 and FGF2 act via the Notch pathway, either directly or indirectly, to inhibit differentiation. Thus, signaling through the Notch receptor may be a common regulator of neuronal differentiation within the developing forebrain.
The Impact of Social Status on Levels of Psychological Well-Being: A Dynamic, Developmental Approach.

The goals of this study are (1) to examine the extent to which African American status is a risk factor for increases in depressive affect during the years immediately following the end of high school – the initial years of early adulthood – and to the extent that African American status is a risk factor (2) to examine those characteristics, both distal and proximal, that mediate the effect. Using data from the Maryland Adolescent Development in Context study (MADICS) – a five-wave longitudinal study consisting of just over 1,600 participants results suggest that African Americans do show increases in depressive affect relative to European Americans after the end of high school, and the effect is largely mediated by (1) the higher levels of non-college status among African Americans, and (2) the fact that non-college status only lead to increases in depressive affect during the post-high school years among African Americans. Lower levels of family income at Grade 7 also proved to be a significant mediator, with lower levels of family income predicting to higher increases in depressive affect after the end of high school. Finally, higher levels of perceived racial discrimination during high school were related to decreases in depressive affect after the end of high school, suggesting that perceived racial discrimination may serve as a distal protective factor.
What Is a Network Community?: A Novel Quality Function and Detection Algorithms

In this study, we introduce a novel quality function for a network community, which we refer to as the communitude. The communitude has a strong statistical background. Specifically, it measures the Z-score of a subset of vertices S with respect to the fraction of the number of edges within the subgraph induced by S. Due to the null model of a random graph used in the definition, our quality function focuses not only on the inside of the subgraph but also on the cut edges, unlike some quality functions for extracting dense subgraphs. To evaluate the detection ability of our quality function, we address the communitude maximization problem and its variants for realistic scenarios. For the problems, we propose a two-phase heuristic algorithm together with some modified versions. In the first phase, it repeatedly removes the vertex with the smallest degree, and then obtains the subgraph with maximum communitude over the iterations. In the second phase, the algorithm improves the obtained solution using a simple local search heuristic. This algorithm runs in linear time when the number of iterations is fixed to a constant; thus, it is applicable to massive graphs. Computational experiments using both synthetic graphs and real-world networks demonstrate the validity and reliability of the proposed quality function and algorithms.
Improved approximation algorthims for scheduling with release dates

We consider the scheduling problem of minimizing a weighted sum of completion times under release dates. We present a simple randomized 2-approximation algorithm for the problem running in O(n log n) time. The algorithm applies to both the off-line and on-line settings with no difference in performance guarantees. In the off-line setting, the algorithm can be derandomized in two different ways, leading either to a deterministic 2-approximation algorithm running in O(n2) time, or to a 2+$approximation algorithm running in O(n log n+lcn) time. In the on-line setting, a specific random choice gives a deterministic 1 + a-competitive algorithm. These bounds improve upon a bound of 2.45 in the off-line settin or in the randomized on-line setting (Chakrabarti et al. 1996]), and upon a bound of 3 + e in p: the deterministic on-line setting (Hall, Schulz, Shmoys and Wein [1996]).
Low-contrast letter acuity testing captures visual dysfunction in patients with multiple sclerosis

Objective: To evaluate concurrent and predictive validity for low-contrast letter acuity (L-CLA) testing as a candidate visual component for the Multiple Sclerosis Functional Composite (MSFC). Methods: L-CLA testing was conducted in two MS patient cohorts. In the MSFC Validation Study, 137 participants from a Phase III trial of inteferon beta-1a (Avonex) for relapsing–remitting MS were followed. A second cohort included 65 patients with secondary progressive MS who participated in a substudy of the International MS Secondary Progressive Avonex Controlled Trial (IMPACT). The total number of letters read correctly at four contrast levels (100, 5, 1.25, and 0.6%) was correlated with Expanded Disability Status Scale (EDSS), MSFC, Sickness Impact Profile, Multiple Sclerosis Quality of Life Inventory, and brain parenchymal fraction (BPF), as determined by MRI. Results: Low- and high-contrast letter acuity scores correlated with BPF at follow-up in the MSFC Validation Study (5%: r = 0.40, p < 0.0001; 100%: r = 0.31, p = 0.0002). L-CLA also correlated with EDSS (5%: r = −0.35, p < 0.0001; 1.25%: r = −0.26, p = 0.0003) and MSFC (5%: r = 0.47, p < 0.0001; 1.25%: r = 0.45, p < 0.0001). In the IMPACT Substudy, change in L-CLA scores from baseline to year 1 predicted subsequent change in the EDSS from year 1 to 2 at the 5% (p = 0.0142) and the 1.25% (p = 0.0038) contrast levels, after adjusting for change in MSFC scores from baseline to year 1. Conclusions: Low-contrast letter acuity (L-CLA) scores demonstrate concurrent and predictive validity in patients with relapsing–remitting and secondary progressive multiple sclerosis (MS). L-CLA testing provides additional information relevant to the MS disease process that is not entirely captured by the Multiple Sclerosis Functional Composite.
Annotation of Information Structure: an Evaluation across different Types of Texts

We report on the evaluation of information structural annotation according to the Linguistic Information Structure Annotation Guidelines (LISA, (Dipper et al., 2007)). The annotation scheme differentiates between the categories of information status, topic, and focus. It aims at being language-independent and has been applied to highly heterogeneous data: written and spoken evidence from typologically diverse languages. For the evaluation presented here, we focused on German texts of different types, both written texts and transcriptions of spoken language, and analyzed the annotation quantitatively and qualitatively.
Neural Classification of Linguistic Coherence using Long Short-Term Memories

Given a set of sentences, a sentence orderer permutes the sentences in a way that the final text is linguistically coherent and semantically understandable. In this work, we focus on the binary and ternary tasks of ordering a pair of sentences regarding their linguistic coherence. We propose a methodology to automatically collect and annotate sentence ordering corpora in the news domain for English and German documents. Furthermore, we introduce a data-driven end-to-end neural architecture to learn the order of a pair of sentences and also recognize the cases where no ordering can be determined due to missing context.
The FlyCatwalk: A High-Throughput Feature-Based Sorting System for Artificial Selection in Drosophila

Experimental evolution is a powerful tool for investigating complex traits. Artificial selection can be applied for a specific trait and the resulting phenotypically divergent populations pool-sequenced to identify alleles that occur at substantially different frequencies in the extreme populations. To maximize the proportion of loci that are causal to the phenotype among all enriched loci, population size and number of replicates need to be high. These requirements have, in fact, limited evolution studies in higher organisms, where the time investment required for phenotyping is often prohibitive for large-scale studies. Animal size is a highly multigenic trait that remains poorly understood, and an experimental evolution approach may thus aid in gaining new insights into the genetic basis of this trait. To this end, we developed the FlyCatwalk, a fully automated, high-throughput system to sort live fruit flies (Drosophila melanogaster) based on morphometric traits. With the FlyCatwalk, we can detect gender and quantify body and wing morphology parameters at a four-old higher throughput compared with manual processing. The phenotyping results acquired using the FlyCatwalk correlate well with those obtained using the standard manual procedure. We demonstrate that an automated, high-throughput, feature-based sorting system is able to avoid previous limitations in population size and replicate numbers. Our approach can likewise be applied for a variety of traits and experimental settings that require high-throughput phenotyping.
Measuring Training Effectiveness: Evidence from Malaysia

Global issues have emphasized the importance of training effectiveness as the essence of developing and managing quality human resources. Hence, to reinforce the management and implementation of national public policy for Malaysia, an easier way of measuring training effectiveness should be determined. Unfortunately, the information of psychometric properties for general instrument to measure training effectiveness was limited. Therefore, the objective of this article is to discuss research findings on constructing a valid and reliable instrument to measure training effectiveness or the development of General Training Effectiveness Scale (GTES). Research was done through three studies including jury validation, pilot study, and actual study*. The development of GTES’s psychometric properties have included face validity, content validity, and constructs validity (including convergent, discriminant and nomological validity), as well as reliability and construct reliability. Data was analysed using reliability analysis, confirmatory factor analysis (CFA), and structural equation modelling (SEM) through SPSS and AMOS software. Significantly, the GTES as produced in this research can be used as an instrument to measure the effectiveness of training in private and public sector. Subsequently, this effort will support the improvement of quality human resources in public sector for effective national policies implementation.
Measuring User Influence on Twitter Using Modified K-Shell Decomposition

Social influence can be described as power - the ability of a person to influence the thoughts or actions of others. Identifying influential users on online social networks such as Twitter has been actively studied recently. In this paper, we investigate a modified k-shell decomposition algorithm for computing user influence on Twitter. The input to this algorithm is the connection graph between users as defined by the follower relationship. User influence is measured by the k-shell level, which is the output of the k-shell decomposition algorithm. Our first insight is to modify this k-shell decomposition to assign logarithmic k-shell values to users, producing a measure of users that is surprisingly well distributed in a bell curve. Our second insight is to identify and remove peering relationships from the network to further differentiate users. In this paper, we include findings from our study.
Method to Protect Passwords in Databases for Web Applications

Trying to make it more difficult to hack passwords has a long history [3, 14, 20, 16]. However the research community has not addressed the change of context from traditional Unix mainframe systems to web applications which face new threats (DoS) and have fewer constraints (client-side computation is allowed). In absence of updated guidance, a variety of solutions are scattered all over the web, from amateur to somewhat professional. However, even the best references have issues such as incomplete details, misuse of terminology, assertion of requirements that are not adequately justified, and too many options presented to the developer, opening the door to potential mistakes. The purpose of this research note is to present a solution with complete details and a concise summary of the requirements, and to provide a solution that developers can readily implement with confidence, assuming that the solution is endorsed by the research community. The proposed solution involves clientside processing of a heavy computation in combination with a server-side hash computation. It follows a similar approach to a few other proposals on the web, but is more complete and justified than any that we found.
Rapid stereo-vision enhanced face detection

This paper presents a real-time face detection algorithm. It improves state-of-the-art 2D object detection techniques by additionally evaluating a disparity map, which is estimated for the face region using a calibrated stereo camera setup. First, faces are detected in the 2D images with a rapid object classifier based on haar-like features. In a second step, falsely detected faces are removed by analyzing the disparity map. In the near field of the camera, a classifier is used, which evaluates the Eigenfaces of the normalized disparity map. Thereby, the transformation into Eigenspace is learned off-line using a principal component analysis approach. In the far field, a much simpler approach determines false-positives by evaluating the relationship between the size of the face in the image and its distance to the camera. This novel combination of algorithms runs in real-time and significantly reduces the number of false-positives compared to classical 2D face detection approaches.
Evaluating Machine Reading Systems through Comprehension Tests

This paper describes a methodology for testing and evaluating the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. The methodology is being used in QA4MRE (QA for Machine Reading Evaluation), one of the labs of CLEF. The task was to answer a series of multiple choice tests, each based on a single document. This allows complex questions to be asked but makes evaluation simple and completely automatic. The evaluation architecture is completely multilingual: test documents, questions, and their answers are identical in all the supported languages. Background text collections are comparable collections harvested from the web for a set of predefined topics. Each test received an evaluation score between 0 and 1 using c@1. This measure encourages systems to reduce the number of incorrect answers while maintaining the number of correct ones by leaving some questions unanswered. 12 groups participated in the task, submitting 62 runs in 3 different languages (German, English, and Romanian). All runs were monolingual; no team attempted a cross-language task. We report here the conclusions and lessons learned after the first campaign in 2011.