Miracle-dz/UniFAR-FTUs huggingface dataset:
Raising Awareness of Security Challenges for the Internet of Trillions of Things

Computer security problems have evolved over the last 50 years from a minor concern to major operational risks. Every day new devices are added to the Internet of Things (IoT). Conservative projections have 50 billion devices on the internet by 2020, but—with autonomous vehicles, smart phones, smart wearables, smart cities, numerous other smart applications, and nanotechnology—we foresee an “Internet of Trillions of Things (IoTT)” before long. If computer security problems are formidable now, consider when there is an IoTT! The proliferation of devices and applications will give rise to many new complications and research challenges, especially in cyberphysical system (CPS) security. Because the smart devices of IoTT systems will be so numerous and easily accessible, and will interact directly with the physical world (including humans), they will exhibit tremendously large attack surfaces with increasing types and numbers of vulnerabilities. Attacks on these systems may cause the inoperability of major infrastructures such as transportation or energy, great financial losses, and many other negative impacts, even death. CPS technology is required to build IoTT systems. It is therefore necessary to increase awareness of CPS security problems and to address them John A. Stankovic and Jack Davidson Raising Awareness of Security Challenges for the Internet of Trillions of Things
Affordability Linked with Subsidy: Impact of Fertilizers Subsidy on Household Welfare in Pakistan

Historically, the government of Pakistan has been providing subsidy on fertilizers, and the most recent subsidy program started during 2015–2016. The current study aims to estimate the impact of the fertilizers subsidy program on fertilizers application and its impact on the crops yield and household income levels using a comprehensive cross-sectional dataset collected from 850 farmers from all four major provinces of Pakistan. The fertilizer subsidy program enabled 80% of the farmers to apply recommended doses of fertilizers, and also incentivized the farmers to bring more area under cultivation. The empirical analysis has been carried out by employing the propensity score matching approach (PSM), and results indicated that fertilizer subsidy has a positive impact on crops yield and household income levels. As a result of subsidy, the wheat yields are higher in the range of 65–71 kg per hectare while the rice yields are higher in the range of 34–43 kg per hectare. The household income levels are higher in the range of 5671 to 6549 rupees. The fertilizer subsidy on a sustainable basis can encourage the resource-poor farmers to apply recommended doses of fertilizers and thus increase crop production, thereby contributing to the improvement in their livelihood.
The Externalities of Corruption: Evidence from Entrepreneurial Firms in China*

Exploiting China’s anti-corruption campaign, we show that following a decrease in corruption, firm performance improves. Small and young firms benefit more. We identify the channels through which corruption hampers firm performance. Following the anti-corruption campaign, the allocation of capital and labor becomes more efficient. Firms operating in ex ante more corrupt environments experience larger productivity gains, higher growth of sales, and lower cost of debt than other firms. Taken together, our results suggest that corruption is a bad equilibrium for an economy because it creates negative externalities
Exploring Domain Adaptability for Sentiment Classification Models

In this paper, we seek to explore the domain adaptability of machine learning models in a sentiment classification task. We train our model on reviews from one domain and perform a study on how that works on reviews from a different domain. Our goal is to achieve a generalized representation of the input text in a way that it captures the general sense of discriminative words or expressions for sentiments irrespective of domains. We experiment with baselines such as Logistic Regression, SVM as well as various deep neural models to achieve a better representation of the text which generalize well in the target domain. Our experiments on the Amazon review dataset show that Google’s BERT, a transformer-based pretrained encoder, achieves the best performance both in terms of accuracy and F1 score. Finally, we present extensive experimental results to explore the domain adaptation phenomena.
Privacy-preserving auditable token payments in a permissioned blockchain system

Token payment systems were the first application of blockchain technology and are still the most widely used one. Early implementations of such systems, like Bitcoin or Ethereum, provide virtually no privacy beyond basic pseudonymity: all transactions are written in plain to the blockchain, which makes them linkable and traceable. Several more recent blockchain systems, such as Monero or Zerocash, implement improved levels of privacy. Most of these systems target the permissionless setting, and as such are not suited for enterprise networks. These require token systems to be permissioned and to bind tokens to user identities instead of pseudonymous addresses. They also require auditing functionalities in order to satisfy regulations such as AML/KYC. We present a privacy-preserving token management system for permissioned blockchains that also supports fine-grained auditing. The scheme is secure under computational assumptions in bilinear groups, in the random-oracle model. We provide performance measurements for our prototype built on top of Hyperledger Fabric.
A Simulator for Peer-to-Peer Overlay Algorithms

The Peer-to-Peer (P2P) approach is not the first attempt to achieve large scale Internet multimedia streaming. P2P multimedia streaming is born out of the frustration that IP multicasting has not materialized even a decade after its initial specification. In P2P streaming overlays, peers are connected into a logical overlay structure emulating a multicast network. Since data is replicated at the application layer through a set of unicast connections formed at each peer, such systems are undoubtedly less efficient than IP multicast. Although recently there have been a multitude of P2P overlay streaming proposals, it is yet unclear which ones present the best solutions. The major obstacle in reaching such a conclusion involves the difficulties of evaluating large-scale overlay algorithms. Having a continuing trend where each research team develops its own ad-hoc overlay simulator would undoubtedly jeopardize the progress of this interesting field of research. There is a pressing need for a unified and unbiased simulation framework that enables a strong push forward for P2P overlay media streaming. It is our opinion that the simulator we have developed is an important step in the right direction. As far as we know we are the first to provide a unified and sophisticated P2P media streaming overlay simulator. We have demonstrated our simulator’s extensibility and adaptability by implementing four leading media streaming overlay algorithms. Our simulator comes with a rich set of join, duration, and bandwidth distributions as well as overlay metrics. Furthermore, the size of the synthetic network, as well as the number of peers in the overlay in our simulations, is two orders of magnitude larger than ever considered in simulations of P2P tree-based overlay algorithms.
Path Planning Using Modified Gradient Method for Small-Sized Robots

This paper deals with the path planning method for small-sized robots in an indoor environment where the activity is limited. In mobile robotics, the gradient method has been the most popular path planning method. The purpose of the gradient method is to generate an optimal path which has no collision with obstacles. However, in case of the small-sized robots, the robot might pass through the limited areas under the tables and chairs. The conventional gradient method assigned the costs around the obstacles in order to calculate the intrinsic cost to make collision-free paths. This method provides an optimal path for the robots in wide areas. On the other hand, the robot in the narrow areas cannot figure out an optimal path because the high intrinsic costs hinder from generating gradient paths. This paper proposes a novel scheme how the robots figure out a path in narrow areas. It assigns the intrinsic costs simultaneously during the adjacency cost assignment step. Then the robot can figure out a safe path in narrow areas. The proposed method reduces the computational time significantly by assigning the costs to the minimum area. The intrinsic cost is also used to control the robot’s velocity. The results show that the proposed method successfully works in various environments.
Three-dimensional structure of the basketweave Z-band in midshipman fish sonic muscle : implications for α-actinin and actin binding

1 Molecular Medicine Section, NHLI, Imperial College London, London SW7 2AZ, UK 2 Boulder Laboratory for 3D Electron Microscopy of Cells, Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309-0347, United States. 3 Institute of Cancer Research, London SW7 3RP, UK. 4 School of Optometry and Vision Sciences, Cardiff University, Cardiff, CF10 3AT, UK. 5 Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306-4380 6 Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, United States. 7 Laboratory of Muscle Biology, NIAMS, NIH, Bethesda, MD, United States and College of Biomedical Engineering, Taipei Medical University, 250 Wuxing Street, Taipei 11031, Taiwan. 8 Current Address: UCL institute of Ophthalmology, London, EC1V 9EL. 9 Current Address: Dept. of Microbial Pathogenesis, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510
Cell adhesion and culture medium dependent changes in the high frequency mechanical vibration induced proliferation , osteogenesis , and intracellular organization of human adipose stem cells

High frequency (HF) mechanical vibration appears beneficial for in vitro osteogenesis of mesenchymal stem cells (MSCs). However, the current mechanobiological understanding of the method remains insufficient. We designed high-throughput stimulators to apply horizontal or vertical high magnitude HF (HMHF; 2.5 Gpeak, 100 Hz) vibration on human adipose stem cells (hASCs). We analyzed proliferation, alkaline phosphatase (ALP) activity, mineralization, and effects on the actin cytoskeleton and nuclei using immunocytochemical stainings. Proliferation was studied on a standard tissue culture plastic (sTCP) surface and on an adhesion supporting tissue culture plastic (asTCP) surface in basal (BM) and osteogenic (OM) culture medium conditions. We discovered that the improved cell adhesion was a prerequisite for vibration induced changes in the proliferation of hASCs. Similarly, the adhesion supporting surface enabled us to observe vibration initiated ALP activity and mineralization changes in OM condition. The horizontal vibration increased ALP activity, while vertical stimulation reduced ALP activity. However, mineralization was not enhanced by the HMHF vibration. We performed imagebased analysis of actin and nuclei to obtain novel data of the intracellular-level responses to HF vibration in BM and OM conditions. Our quantitative results suggest that actin organizations were culture medium and stimulation direction dependent. Both stimulation directions decreased OM induced changes in nuclear size and elongation. Consequently, our findings of the nuclear deformations provide supportive evidence for the involvement of the nuclei in the mechanocoupling of HF vibration. Taken together, the results of this study enhanced the knowledge of the intracellular mechanisms of HF vibration induced osteogenesis of MSCs.
Antecedents of youth ' s intentions in seeking microcredit : Evidence from Jaffna district , Sri Lanka

Entrepreneurship plays a vital role in the economic development of a country. Economic growth is positively impacted by the emergence of a new business creation. In this context, this research focuses on the intention of youth in seeking microcredit based on the Theory of planned Behavior . The problem for this study is a general observation that the youths in the northern part of Sri Lanka, especially Jaffna based, are showing very much reluctance in applying for microcredit to create enterprises. This got proved when we had initial talks with microcredit lending institutions. Therefore, the researchers intended to find out the reasons/determinants of youth's intentions in seeking microcredit. The objective of the study is to find out the factors which determine youth's intention in seeking microcredit. Respondents were randomly selected youths in between 18 25 years of age from the University, Advanced Technical Institute and University College. Results revealed that, Intention to seek the micro credit is significantly influenced by subjective norms, knowledge of microcredit, perceived government support, tolerance for risk and entrepreneurial desire. In contrast, Intention to seek the micro credit is not significantly influenced by positive effect, perceived benefits, perceived deterrents and perceived behavioral control. Additionally, this research contributes a new body of knowledge in the field of microfinance and entrepreneurship. Beneficiaries of this study include various stakeholders, like youths in Jaffna region, the government institution, government policy makers and other commercial µ financial institutions.
Interpreting flux-based sediment budgets in a habitat context : Linking precise temporal-resolution measurements of sediment flux to spatially robust characterization of channel change

Continuous measurements of sediment transport at reach-bracketing gaging stations allow for the construction of continuous mass-balance sediment budgets for the intervening reach. Although these budgets identify periods of sediment surplus (net deposition) or sediment deficit (net erosion), such analyses cannot identify the locations within the reach where channel change occurs. Because channel change and associated changes in habitat are of greater interest to river managers than the precise value of reach-scale loss or accumulation of sediment, it is important to explicitly link reach-scale changes in sediment mass balance to field measurements of channel change. In this study we evaluated the relationship between the magnitude of the sediment mass imbalance measured by acoustic-Doppler profilers and the resulting channel change on the Yampa River in Dinosaur National Monument.
An Econometric Approach to the Extrapolation of PPPs to Non-Benchmark Years

The paper presents the development and implementation of a coherent econometric framework to extrapolate PPPs using information on PPPs from the benchmark data collected by the International Comparison Program as well as the information on price deflators from national sources. The paper describes the complete version of a method originally presented in Rao et al. [2010], referred to as RRD from here on. The basic framework of the method including a discussion of the stochastic assumptions are presented as a preamble to the analytical properties of the methodology with proofs of the main propositions provided in the Appendix. In particular, we are able to show that the methodology is invariant to the choice of the reference country and that the model is flexible enough to either track the PPPs in different benchmarks or to track the national price movements. A snapshot of the constructed panels resulting from the methodology
Transportation Detection Using Accelerometer on Mobile Phone

Activity recognition is a dominant research area and several research systems have been constructed and tested in the user study. Most have focused on sophisticated and high energy-consuming sensors and custom hardware only available in the laboratory. We instead seek to use commodity device to lower the barrier to creating transportation-informed mobile applications. This paper explores how accelerometer data from mobile phones can be used to recognize high-level properties of user transportation. We demonstrate that even without knowledge of transportation routine and mobile phone orientation, we can recognize transportation modes that are potential useful for real scenario. Our transportation detection system was evaluated with user-labeled trails from the everyday lives of 8 data collectors over a period of one month, yielding an overall average accuracy of 94% for offline analysis and 86% for online analysis. We believe our approach can be used in real daily life.
Working Paper 2 . 4 Econometric Models

In the context of the SustainCity project (www.sustaincity.eu), three European cities (Brussels, Paris and Zurich) will be modelled using the land use microsimulation platform UrbanSim. This platform relies on various models interacting with each other, to predict long term urban development. The aim of this paper is to present the different econometric tools used to estimate these models. The models analyzed here are: Real estate prices, Household location choice, Employment location choice and Real estate development. The associated econometric models are mainly linear regression, binary logit and multinomial (or nested) logit. Employment location choice models receive a special attention in Work Package 2.3 (firmographics). We attach here a particular attention on describing and comparing Real estate price models to Household location choice models (which are described in detail in Work Package 2.2). We start from hedonic approach, especially the seminal work of Rosen (1974), and compare it with discrete choice models, discussing each method’s advantages and disadvantages. At the end, some examples of applications of the hedonic models to real estate markets are presented.
REGULATORY MECHANISMS INVOLVED IN TH2 CELL DIFFERENTIATION

Asthma and allergies are prevalent and severe diseases that affect millions of individuals worldwide. In vivo animal studies indicate that interleukin-4 (IL-4) is important for the development and perpetuation of allergic asthma and allergy. It is needed for the proper development of Th2 type effector cells that have a key role in the onset of these diseases. IL-4 is also produced by Th2 cells and it promotes the subsequent stages of these diseases. The signal transducer and activator of transcription 6 (STAT6), which is activated by IL-4 stimulation, is especially needed to acquire and maintain Th2 type responses in antigen induced asthmatic airway inflammation. The general aim of this doctoral thesis was to identify novel proteins important for Th2 cell differentiation using two-dimensional electrophoresis (2-DE) based proteomics and mass spectrometry. Undifferentiated T helper cells were isolated from human umbilical cord blood or mouse spleen. Cells were activated through T cell and costimulatory receptors and differentiated in the Th1 or Th2 direction with the polarizing cytokines IL-12 and IL-4, respectively. In the first study, the proteomes of in vitro differentiated human Th1 and Th2 cells were compared to identify differences in the protein expression levels or in the protein modifications. The subsequent studies focused on IL-4 induced protein regulation within 24 hours after T cell activation. In the first of these, IL-4 induced differences were identified from activated human T helper cells. IL-4 was found to regulate several proteins in caspase mediated pathways and to enhance T cell survival and activation. In the other, the proteomes of STAT6 deficient mouse lymphocytes were compared to the wild-type control cells after T cell activation and IL-4 stimulation. In these studies overall, we have characterized several new IL-4 and STAT6 target proteins and revealed new regulatory networks, which has provided new hypothesis on the mechanisms of Th2 cell differentiation.
Routing-State Abstraction Based on Declarative Equivalence

Providing abstract views on top of raw network state can provide substantial benefits to both the network OS, who manages the network state, and the network control applications, who consume the network state. In this paper, we conduct the first study to provide control applications with access to the routing state, which is a key component of the network state. We design a simple, efficient algorithm to look up a route query in a flow rule manager (FRM), which is a common data structure storing a network’s routing state. More importantly, we design a simple, novel interface based on a principle called declarative equivalence for network control applications to query the routing state, and the network OS uses redundancy elimination to compute equivalent, but minimal routing state, providing the first, novel, systematic algorithm to compute abstract, compressed routing state. We implement our design in OpenDaylight and show substantial performance benefits.
“Tell Me When ‘Normal’ Stops”: How Parents Recognized Their Child’s Mental Illness

Many parents do not recognize psychological disorder, and current mental health service delivery programs are not sufficiently responsive to the early help-seeking dynamics of families. This mixed-methods study explored Colorado parents’ experiences of recognizing their child’s mental illness as a precursor to seeking treatment, revealing that the phenomenon of parental recognition was a process of “waiting to hear that ‘normal’ had stopped,” wherein parents miscategorized symptoms as typical behaviors in a passing developmental phase. Prior experience with mental illness appeared to significantly decrease both the length of time and the level of distress necessary for recognition. Ultimately, recognition did not occur until someone in the parents’ social network explicitly validated their concerns, which galvanized them to seek treatment. The results of this study can have wide applications for positive social change, because many chronic mental disorders manifest in childhood and benefit from early and sustained treatment. Further, mental health underscores many societal issues such as homelessness, school dropout rates, child abuse and neglect, foster care, and prison overpopulation. Positive social change and parental recognition can be promoted through public policies and programs such as universal mental health screening, mental health literacy, and school and health policies that are more supportive and responsive to the early help-seeking needs of children and families
C 3 IT-2012 Non-Additive Random Data Perturbation for Real World Data

Privacy preservation is the major concern while real datasets are handled. A specific topicprivacy preserving data mining (PPDM), completely deals with data modification but also limits rule loss. Data perturbation is one of the PPDM techniques, which mostly deals with numerical data and concentrates on the statistical analysis of the data. Perturbation is of two types, additive perturbation and multiplicative perturbation, where generated random data is either added or multiplied with the data, which results in a random modified data. In this paper we have proposed a model in which the perturbation is done by randomization, where the data is generated in intervals based on the level of privacy specified by each customer. Our model is proved by applying classification algorithm on the perturbed data set and the accuracy is still maintained the same. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of C3IT