Miracle-dz/UniFAR-FTUs huggingface dataset:
iTracking: Accurate light-based location-tracking in wireless sensor networks

Most previous localization and tracking systems in wireless sensor networks are based on RF signals, ultrasounds, and UWB. However, systems using RF signals suffer accuracy fluctuations and systems using ultrasound or UWB need extra hardware. In our paper, we propose to utilize light, an easily accessible and pervasive resource in our daily life, and off-the-shelf TelosB Motes to achieve the goal of stability and high accuracy in localization and tracking. The iTracking1 system is a mobile light source location-tracking system based on light intensity. Our main contribution is the first demonstration to track mobile light sources based on light intensity with high accuracy, which may provide an alternative method for localization and tracking or inspire game designers. We examine point light based tracking and flashlight based tracking in our demonstrations, which proves these two main sources of light in our daily life can achieve centimeter-level location-tracking requirements. Our future work will focus on enabling the iTracking system to writing Chinese characters in flashlight.
Fast Detour Computation for Ride Sharing

Ride sharing becomes more and more popular not least because internet services help matching offers and request. However, current systems use a rather simple-minded functionality allowing to search for the origin and destination city, sometimes enriched with radial search around the cities. We show that theses services can be substantially improved using innovative route planning algorithms. More concretely, we generalize previous static algorithms for many-to-many routing to a dynamic setting and develop an additional pruning strategy. With these measures it becomes possible to match each request to $n$ offers using $2n+1$ exact travel time computations in a large road network in a fraction of a microsecond per offer. For requests spread over Germany according to population density, we are able to reduce the number of failing entries substantially. We are able to find a reasonable match for more than 60% of the failing entries left by contemporary matching strategies. Additionally, we halve the average waste of resources in the matches found compared to radial search.
Towards Energy-Optimal and Reliable Data Collection via Collision-Free Scheduling in Wireless Sensor Networks

We consider the problem of supervision or periodic data collection for stationary wireless sensor networks and present a practical, energy-efficient, and reliable solution. Energy-efficiency is achieved by combining three design methods: (a) adopting network flow optimization techniques, the optimal scheme for balancing the communication load among all the nodes in the network is calculated. This gives the lower bound for the energy required for data collection process, (b) instead of using a fixed network topology (communication tree), a set of optimized trees is constructed and the communication tree varies over different data collection cycles. We show that this method achieves an average energy consumption rate very close to the optimal value, (c) the packet exchange procedure is designed based on collision-free schedules, to minimize the number of packets and the transmission and reception times for each node. Reliability of the process is guaranteed by including many retransmission opportunities in the schedules. The performance is evaluated through simulations.
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network

Speech conveys different yet mixed information ranging from linguistic to speaker-specific components, and each of them should be exclusively used in a specific task. However, it is extremely difficult to extract a specific information component given the fact that nearly all existing acoustic representations carry all types of speech information. Thus, the use of the same representation in both speech and speaker recognition hinders a system from producing better performance due to interference of irrelevant information. In this paper, we present a deep neural architecture to extract speaker-specific information from MFCCs. As a result, a multi-objective loss function is proposed for learning speaker-specific characteristics and regularization via normalizing interference of non-speaker related information and avoiding information loss. With LDC benchmark corpora and a Chinese speech corpus, we demonstrate that a resultant speaker-specific representation is insensitive to text/languages spoken and environmental mismatches and hence outperforms MFCCs and other state-of-the-art techniques in speaker recognition. We discuss relevant issues and relate our approach to previous work.
THE FIRST PSYCHOMETRIC PROPERTIES OF TURKISH VERSION OF THE NEW ECOLOGICAL PARADIGM (NEP) SCALE FOR CHILDREN AND GIFTED STUDENTS’ ECOLOGICAL BELIEFS

The New Ecological Paradigm (NEP) scale has been extensively used to measure adults’ environmental values. However, it has only recently been adapted for use with children. This paper presented the Turkish version of the NEP Scale for Children, examined its pre-psychometric properties. Besides, gifted and normal children’s ecological beliefs were compared. Exploratory factor analysis was conducted, followed by concurrent validity. Primary School Students' Attitudes towards Environment Scale was used for concurrent validity. In addition, the instrument’s internal consistency and re-test reliability were studied. Results explanatory factor analyses demonstrated that 10 items yielded three-factors, as original form and that the three-dimensional model was explained 50.21% of total variance. Factor loadings ranged from . 45 to . 73. In the concurrent validity significant positive relationship was found between NEP Scale for Children and Primary School Students' Attitudes towards Environment Scale. Cronbach Alpha internal consistency coefficients were found as . 84 for whole scale. Moreover, test-retest reliability coefficient was . 71 and corrected item-total correlations ranged from . 37 to . 69. In second study, gifted students’ ecological paradigm scores were higher than non-gifted students’. The results were discussed in the light of the related literature.
Assessing the probability that a positive report is false: an approach for molecular epidemiology studies.

Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below.05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or "noteworthy. " We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators--that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.
Experiments Dynamics in Hippocampal Neurons : Theory and Geometry of Dendritic Spines Affects Calcium

[PDF] [Full Text] [Abstract] , October 12, 1999; 96 (21): 12068-12072. PNAS E. Korkotian and M. Segal hippocampal neurons Release of calcium from stores alters the morphology of dendritic spines in cultured [Full Text] [Abstract] , January 1, 2001; 21 (1): 186-193. J. Neurosci. M. Goldin, M. Segal and E. Avignone Hippocampal Networks Functional Plasticity Triggers Formation and Pruning of Dendritic Spines in Cultured [PDF] [Full Text] [Abstract] , August 15, 2001; 21 (16): 6115-6124. J. Neurosci. E. Korkotian and M. Segal Regulation of Dendritic Spine Motility in Cultured Hippocampal Neurons [PDF] [Full Text] [Abstract] , July 1, 2004; 87 (1): 81-91. Biophys. J. D. Holcman, Z. Schuss and E. Korkotian Calcium Dynamics in Dendritic Spines and Spine Motility [PDF] [Full Text] [Abstract] , August 30, 2006; 26 (35): 8881-8891. J. Neurosci. M. Haber, L. Zhou and K. K. Murai Cooperative astrocyte and dendritic spine dynamics at hippocampal excitatory synapses.
SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks

Hop-by-hop data aggregation is a very important technique for reducing the communication overhead and energy expenditure of sensor nodes during the process of data collection in a sensor network. However, because individual sensor readings are lost in the per-hop aggregation process, compromised nodes in the network may forge false values as the aggregation results of other nodes, tricking the base station into accepting spurious aggregation results. Here a fundamental challenge is: how can the base station obtain a good approximation of the fusion result when a fraction of sensor nodes are compromised.To answer this challenge, we propose SDAP, a Secure Hop-by-hop Data Aggregation Protocol for sensor networks. The design of SDAP is based on the principles of divide-and-conquer and commit-and-attest. First, SDAP uses a novel probabilistic grouping technique to dynamically partition the nodes in a tree topology into multiple logical groups (subtrees) of similar sizes. A commitment-based hop-by-hop aggregation is performed in each group to generate a group aggregate. The base station then identifies the suspicious groups based on the set of group aggregates. Finally, each group under suspect participates in an attestation process to prove the correctness of its group aggregate. Our analysis and simulations show that SDAP can achieve the level of efficiency close to an ordinary hop-by-hop aggregation protocol while providing certain assurance on the trustworthiness of the aggregation result. Moreover, SDAP is a general-purpose secure aggregation protocol applicable to multiple aggregation functions.
The Impact of Aging on Financial Markets and the Economy: A Survey

All major industrial countries will experience significant population aging over the next several decades. In both academic circles and the business press it is widely believed that population aging will have important effects on financial markets because of its expected impact on saving rates and the demand for investment funds. This paper reviews the literature on the macroeconomic and asset market effects of population aging, focusing on four related issues: (a) The impact of population age structure on aggregate household saving; (b) The effect of population aging on investment demand; (c) Evidence on the influence of population age structure on financial market asset prices and returns; and (d) Effects of globalization on our interpretation of the impact of demographic change.
KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking

Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using dynamic programming methods that minimize ad hoc cost functions. The goal of the current work is to provide both point estimates and associated uncertainties of center frequencies and bandwidths in a statistically principled state-space framework. Extended Kalman (K) algorithms take advantage of a linearized mapping to infer formant and antiformant parameters from frame-based estimates of autoregressive moving average (ARMA) cepstral coefficients. Error analysis of KARMA, wavesurfer, and praat is accomplished in the all-pole case using a manually marked formant database and synthesized speech waveforms. KARMA formant tracks exhibit lower overall root-mean-square error relative to the two benchmark algorithms with the ability to modify parameters in a controlled manner to trade off bias and variance. Antiformant tracking performance of KARMA is illustrated using synthesized and spoken nasal phonemes. The simultaneous tracking of uncertainty levels enables practitioners to recognize time-varying confidence in parameters of interest and adjust algorithmic settings accordingly.
FlashNormalize: Programming by Examples for Text Normalization

Several applications including text-to-speech require some normalized format of non-standard words in various domains such as numbers, dates, and currencies and in various human languages. The traditional approach of manually constructing a program for such a normalization task requires expertise in both programming and target (human) language and further does not scale to a large number of domain, format, and target language combinations. We propose to learn programs for such normalization tasks through examples. We present a domainspecific programming language that offers appropriate abstractions for succinctly describing such normalization tasks, and then present a novel search algorithm that can effectively learn programs in this language from input-output examples. We also briefly describe domain-specific heuristics for guiding users of our system to provide representative examples for normalization tasks related to that domain. Our experiments show that we are able to effectively learn desired programs for a variety of normalization tasks.
dmapply: A functional primitive to express distributed machine learning algorithms in R

Due to R's popularity as a data-mining tool, many distributed systems expose an R-based API to users who need to build a distributed application in R. As a result, data scientists have to learn to use different interfaces such as RHadoop, SparkR, Revolution R's ScaleR, and HPE's Distributed R. Unfortunately, these interfaces are custom, non-standard, and difficult to learn. Not surprisingly, R applications written in one framework do not work in another, and each backend infrastructure has spent redundant effort in implementing distributed machine learning algorithms. Working with the members of R-core, we have created ddR (Distributed Data structures in R), a unified system that works across different distributed frameworks. In ddR, we introduce a novel programming primitive called dmapply that executes functions on distributed data structures. The dmapply primitive encapsulates different computation patterns: from function and data broadcast to pair-wise communication. We show that dmapply is powerful enough to express algorithms that fit the statistical query model, which includes many popular machine learning algorithms, as well as applications written in MapReduce. We have integrated ddR with many backends, such as R's single-node parallel framework, multi-node SNOW framework, Spark, and HPE Distributed R, with few or no modifications to any of these systems. We have also implemented multiple machine learning algorithms which are not only portable across different distributed systems, but also have performance comparable to the "native" implementations on the backends. We believe that ddR will standardize distributed computing in R, just like the SQL interface has standardized how relational data is manipulated.
Do conditionalities in social programs exclude the poorest? Demand-side schooling interventions in Latin America

A model is provided for the optimal allocation by parents of a child’s time between schooling and labor given access to publicly funded demand-side schooling interventions. If the budget of the Governments is constrained, and if policy makers cannot dierentiate program benefits among the poor, they will fund programs that will help the poor, but whose value may not be high enough for take-up by the poorest if conditionalities are imposed for receiving the program’s benefits. The model is tested and validated with household survey data from Colombia, Honduras, and Mexico. JEL classification: I28, H52, J22
Dynamic Network Probes: A Stepping Stone to Omni Network Visibility

Effective SDN control relies on the network data collecting capability as well as the quality and timeliness of the data. As open programmable data plane is becoming a reality, we further enhance it with the support of runtime interactive programming in order to cope with application dynamics, optimize data plane resource allocation, and reduce control-plane processing pressure. Based on the latest technologies, we propose the Dynamic Network Probes (DNP) as a means to support real-time and on-demand network visibility. DNPs serve as an important building block of an integrated networking data analytics platform which involves the network data plane as an active component for in-network computing. In this paper, we show the types of DNPs and their role in the big picture. We have implemented an NP-based hardware prototype to demonstrate the feasibility and efficiency of DNPs. We lay out the research challenges and our future work to realize the omni network visibility based on DNPs.
Despite the best intentions: how racial inequality thrives in good schools

emonies. Chapter 7 reiterates the arguments and the unique significance of these ceremonies as sites that bring together multiple questions of citizenship, nation-state, and belonging in the contemporary context. Reading this book, I was especially impressed with the rich literature within which Byrne situates her findings, speaking to what falls between the cracks. The observations in the ceremonies reveal to the reader the complex emotional register that citizenship constitutes. Her reflexive discussion of the methodology and the ways in which the obstacles she faced as a researcher become yet another source of data, telling of both tacit and explicit surveillance that the transition to citizenship entails and the angsts that surround it. Bryne convincingly makes the case that we need to pay close attention to these ceremonies, because they lay bare the interactions between multiple forces. The ceremonies encapsulate simultaneously demands of loyalty and belonging, situated against the continued relevance of the nation-state, as well as the reality of migration; the shifts between discourses of multiculturalism and securitization; and the reconfiguration of citizenship as institution, practice, and experience at their intersection. The book raises a variety of questions as well. I am familiar with Byrne’s other work on classed, gendered, and racialized practices in which people engage in everyday life. Thus, I was curious as to how the author could further integrate these practices in explaining the divergent experiences and claims of new citizens. I was also struck by the ways in which the differences in interviewers’ experiences, dispositions, and emotions can be said to correspond to class inequalities in and outside the nation-state, in addition to inequalities stemming from countries of origin, race, and motivations for migration. Could there be a case made about the relevance of transnational class inequalities for citizenship acquisition? A final research direction that the book has inspired is the following: how can we pit these cases against other citizenship regimes located in the Global South? Is there anything to be learned by comparing ceremonies across unequal citizenship regimes? In any case, this book is a must for anyone interested in these highly relevant issues.
Efficient and side-channel-aware implementations of elliptic curve cryptosystems over prime fields

Elliptic curve cryptosystems (ECCs) are utilised as an alternative to traditional public-key cryptosystems, and are more suitable for resource-limited environments because of smaller parameter size. In this study, the authors carry out a thorough investigation of side-channel attack aware ECC implementations over finite fields of prime characteristic including the recently introduced Edwards formulation of elliptic curves. The Edwards formulation of elliptic curves is promising in performance with built-in resiliency against simple side-channel attacks. To our knowledge the authors present the first hardware implementation for the Edwards formulation of elliptic curves. The authors also propose a technique to apply non-adjacent form (NAF) scalar multiplication algorithm with side-channel security using the Edwards formulation. In addition, the authors implement Joye's highly regular add-always scalar multiplication algorithm both with the Weierstrass and Edwards formulation of elliptic curves. Our results show that the Edwards formulation allows increased area-time performance with projective coordinates. However, the Weierstrass formulation with affine coordinates results in the simplest architecture, and therefore has the best area-time performance as long as an efficient modular divider is available.
Relieving hot spots in collaborative intrusion detection systems during worm outbreaks

The increasing number of stealthy and coordinated attacks on the Internet pose a significant threat to network security. Collaborative intrusion detection systems (CIDSs) have therefore been proposed to address this coordinated defense challenge by correlating patterns of suspicious activity based on the source addresses of the suspicious incoming traffic. However, during worm outbreaks, there can be a rapid growth in suspicious evidence that is reported about individual sources of the worm outbreak. In CIDSs that correlate suspicious activity by source address, the evidence relating to these worm spread sources can cause a load "hot-spot", which severely degrades the overall performance of the detection system. In this paper, we propose a load balancing scheme for a CIDS to evenly distribute the workload to avoid hot-spots during worm outbreaks. Rather than correlating suspicious evidence based on source addresses, we distribute the load in the CIDS using a scheme that enables different possible patterns of suspicious evidence to be automatically mapped onto different processing nodes in the CIDS. Simulation results show that our scheme can achieve significant improvements in load balancing without sacrificing detection accuracy.
Developing Lean and Agile Supply Chains in the Uk Housebuilding Industry

The paper presents the concept of ‘leagility’. Building on lean and agile literature, the paper describes the similarities and differences between these concepts and the application of each paradigm within the context of the total supply chain. Particular attention is paid to the notion of the ‘decoupling point’, which determines the location within the supply chain at which a product is customised. Using examples from various industries, the potential for the application of ‘leagility’ in housebuilding is described, as well as the barriers to its introduction in the UK.
Grazer‐induced chain length plasticity reduces grazing risk in a marine diatom

We show that Skeletonema marinoi suppresses chain formation in response to copepod cues. The presence of three different copepod species (Acartia tonsa, Centropages hamatus, or Temora longicornis) significantly reduced chain length. Furthermore, chain length was significantly reduced when S. marinoi was exposed to chemical cues from caged A. tonsa without physical contact with the responding cells. The reductions in chain length significantly reduced copepod grazing; grazing rates on chains (four cells or more) were several times higher compared to that of single cells. This suggests that chain length plasticity is a means for S. marinoi to reduce copepod grazing. In contrast, chain length was not suppressed in cultures exposed to the microzooplankton grazer Gyrodinium dominans. Size‐selective predation may have played a key role in the evolution of chain formation and chain length plasticity in diatoms.