Miracle-dz/UniFAR-FTUs huggingface dataset:
An algorithmic framework for robust access control in wireless sensor networks

If the data collected within a sensor network is valuable or should be kept confidential then security measures should protect the access to this data. We first determine security issues in the context of access control in sensor networks especially focusing on the problem of node capture, i.e., the possibility that an attacker can completely take over some of the sensor nodes. We then introduce the notion of t-robust sensor networks which can withstand capture of up to t nodes and consider three basic security concepts for such networks: (1) t-robust storage, a mechanism to securely store data within a set of sensors such that capture of any t sensors does not reveal that data to the adversary; (2) n-authentication which ensures that authentication is achieved with every uncompromised sensor in the broadcast range of a client (n denotes the number of nodes in that broadcast range); and (3) n-authorization, an authorization primitive with similar properties like n-authentication. We present a generic t-robust protocol for implementing access control using these primitives.
High-temperature differential emission measure and altitude variations in the temperature and density of solar flare coronal X-ray sources

The detailed knowledge of plasma heating and acceleration region properties presents a major observational challenge in solar flare physics. Using the Ramaty High Energy Solar Spectroscopic Imager (RHESSI), the high temperature differential emission measure, DEM(T), and the energy-dependent spatial structure of solar flare coronal sources are studied quantitatively. The altitude of the coronal X-ray source is observed to increase with energy by ~+0.2 arcsec/keV between 10 and 25 keV. Although an isothermal model can fit the thermal X-ray spectrum observed by RHESSI, such a model cannot account for the changes in altitude, and multi-thermal coronal sources are required where the temperature increases with altitude. For the first time, we show how RHESSI imaging information can be used to constrain the DEM(T) of a flaring plasma. We develop a thermal bremsstrahlung X-ray emission model with inhomogeneous temperature and density distributions to simultaneously reproduce: i) DEM(T), ii) altitude as a function of energy, and iii) vertical extent of the flaring coronal source versus energy. We find that the temperature-altitude gradient in the region is ~+0.08 keV/arcsec (~1.3 MK/Mm). Similar altitude-energy trends in other flares suggest that the majority of coronal X-ray sources are multi-thermal and have strong vertical temperature and density gradients with a broad DEM(T).
Quantitative MRI criteria for optic pathway enlargement in neurofibromatosis type 1

Objective: To determine quantitative size thresholds for enlargement of the optic nerve, chiasm, and tract in children with neurofibromatosis type 1 (NF1). Methods: Children 0.5–18.6 years of age who underwent high-resolution T1-weighted MRI were eligible for inclusion. This consisted of children with NF1 with or without optic pathway gliomas (OPGs) and a control group who did not have other acquired, systemic, or genetic conditions that could alter their anterior visual pathway (AVP). Maximum and average diameter and volume of AVP structures were calculated from reconstructed MRI images. Values above the 95th percentile from the controls were considered the threshold for defining an abnormally large AVP measure. Results: A total of 186 children (controls = 82; NF1noOPG = 54; NF1+OPG = 50) met inclusion criteria. NF1noOPG and NF1+OPG participants demonstrated greater maximum optic nerve diameter and volume, optic chiasm volume, and total brain volume compared to controls (p < 0.05, all comparisons). Total brain volume, rather than age, predicted optic nerve and chiasm volume in controls (p < 0.05). Applying the 95th percentile threshold to all NF1 participants, the maximum optic nerve diameter (3.9 mm) and AVP volumes resulted in few false-positive errors (specificity >80%, all comparisons). Conclusions: Quantitative reference values for AVP enlargement will enhance the development of objective diagnostic criteria for OPGs secondary to NF1.
Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text

We describe an efficient neural network method to automatically learn sentiment lexicons without relying on any manual resources. The method takes inspiration from the NRC method, which gives the best results in SemEval13 by leveraging emoticons in large tweets, using the PMI between words and tweet sentiments to define the sentiment attributes of words. We show that better lexicons can be learned by using them to predict the tweet sentiment labels. By using a very simple neural network, our method is fast and can take advantage of the same data volume as the NRC method. Experiments show that our lexicons give significantly better accuracies on multiple languages compared to the current best methods.
Short-Term Red Wine Consumption Promotes Differential Effects on Plasma Levels of High-Density Lipoprotein Cholesterol, Sympathetic Activity, and Endothelial Function in Hypercholesterolemic, Hypertensive, and Healthy Subjects

OBJECTIVES: To compare the metabolic, hemodynamic, autonomic, and endothelial responses to short-term red wine consumption in subjects with hypercholesterolemia or arterial hypertension, and healthy controls. METHODS: Subjects with hypercholesterolemia (n=10) or arterial hypertension (n=9), or healthy controls (n=7) were given red wine (250 mL/night) for 15 days. Analyses were performed before and after red wine intake. RESULTS: Red wine significantly increased the plasma levels of HDL-cholesterol in the controls, but not in the other groups. The effects on hemodynamic measurements were mild, non-significantly more prominent in healthy subjects, and exhibited high interindividual variability. Across all participants, mean blood pressure decreased 7 mmHg (p <0.01) and systemic vascular resistance decreased 7% (p = 0.05). Heart rate and cardiac output did not significantly change in any group. Red wine enhanced muscle sympathetic fibular nerve activity in hypercholesterolemic and hypertensive patients, but not in controls. At baseline, brachial artery flow-mediated dilation was impaired in patients with hypercholesterolemia and arterial hypertension; red wine restored the dilation in the hypercholesterolemic group but not in the hypertensive group. CONCLUSIONS: Red wine elicits different metabolic, autonomic, and endothelial responses among individuals with hypercholesterolemia or arterial hypertension and healthy controls. Our findings highlight the need to consider patient characteristics when evaluating the response to red wine.
Service Differentiation in Sensor Networks

Abstract Recent technological advances have made it possible to havelarge wireless ad-hoc networks of small devices. The primepurpose of such sensor networks is to disseminate informationabout the environmentthey sense. We observethat informationprovidedmayhavedifferent levelsof importanceandarguethatthe sensor networks should be willing to spend more resourcesin disseminating packets carrying more important information.In this paper, weintroducethe conceptof servicedifferenti-ationasapplicabletosensornetworks,highlightitsfundamentaldifferences with normal data networks and deﬁne the relevantmetrics. Variousmechanismswhichmay provideservicescom-patible with the packet priority levels are proposed. We evalu-ate one of the proposedmethod , Adaptive Forwarding Scheme(AFS) and show that our scheme provides the desired levels ofservice. Keywords SensorNetworks,ServiceDifferentiation, AdaptiveForwardingScheme 1. Introduction Technologicaladvanceshavemade it feasible to havesmall de-vices with computation, communication and sensing capabili-ties [1, 2, 3]. These devices run on batteries and thus their ca-pabilities are limited by the available energy. A sensornetworkconsistingofalargenumberofsuchnodescanbeusefulinkeep-ing track of various activities in a givenregion. However, oper-ations performed in such a sensor network have to be energy-efﬁcient becauseof the energy constraints of its nodes.In a sensor network, sensors would sensesome data, pack-etize it and send the packet to a monitor node along a path con-sisting of multiple wireless hops. Researchers have come upwith schemes to provide the functionality required to performthese operations efﬁciently. For routing the data to the moni-tor node, schemeslike Directed Diffusion [4] and Power-awarerouting [5] have been proposed. Clustering schemes [6, 7, 8]group the nodesinto clusters for network management[7]. Lo-cation dissemination schemes [9, 10] provide the sensors withinformation about their locations. Simple and efﬁcient operat-ing system like tinyOS [3] have been designed for these smallnodes. All these works focus on cutting down on the energyrequirements of a sensor network, while providing the minimalfunctionality for the network to operate.We observe that in sensor networks different data packetsmight have different importance.For example, if a temperaturesensorinaforest,senses60Fonanormalspringday,thevalueis
Exergy analysis of reactive combustion processes in gas heaters

In this study, exergy analysis and second law efficiency of gas heaters with reactive combustion processes are considered. The reactive combustion of natural gas is modeled via a chain of eight chemical reactions along with first and second law of thermodynamics. The main constitutional elements react in combustion chamber is considered at constant pressure and temperature of the basic elements hydrogen, carbon, oxygen, and nitrogen from the fuel and air. Heat transfer is modeled assuming free convection and radiation mechanisms from gas heater outer surfaces. The governed equations were solved using Engineering Equation Solver (EES) software tool. Results of this study reveal that preheating of air will considerably increase the second law efficiency of combustion process. Excessive air is effective up to 40 percent to prevent incomplete combustion; however, excessive air more than 80 percent is not recommended from exergy stand point of view in gas heaters.
A fuzzy hybrid recommender system

Recommender Systems (RSs) are largely used nowadays to generate interest items or products for web users. This paper proposed a novel recommendation technique based on fuzzy logic that combines a collaborative filtering and taxonomic based filtering together to make better quality recommendations as well as alleviate Stability/ Plasticity problem in RSs. Empirical evaluations are conducted, results are promising and they shown that the proposed technique is feasible and effective.
. Optimum bit allocation and accurate rate control for video coding via ρ-domain source modeling

We present a new framework for rate-distortion (R-D) analysis, where the coding rate R and distortion D are considered as functions of /spl rho/ which is the percentage of zeros among the quantized transform coefficients. Previously (see He, Z. et al., Int. Conf. Acoustics, Speech and Sig. Proc., 2001), we observed that, in transform coding of images and videos, the rate function R(/spl rho/) is approximately linear. Based on this linear rate model, a simple and unified rate control algorithm was proposed for all standard video coding systems, such as MPEG-2, H.263, and MPEG-4. We further develop a distortion model and an optimum bit allocation scheme in the /spl rho/ domain. This bit allocation scheme is applied to MPEG-4 video coding to allocate the available bits among different video objects. The bits target of each object is then achieved by our /spl rho/-domain rate control algorithm. When coupled with a macroblock classification scheme, the above bit allocation and rate control scheme can also be applied to other video coding systems, such as H.263, at the macroblock level. Our extensive experimental results show that the proposed algorithm controls the encoder bit rate very accurately and improves the video quality significantly (by up to 1.5 dB).
Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

One of the important issues in wireless sensor network (WSN) is to k-cover the target sensing field and to extend its lifetime. We propose a method to k-cover the field and maximize the WSN lifetime by moving mobile sensor nodes to appropriate positions for a WSN consisting of both static and mobile sensor nodes which periodically collect environmental information. Our target problem is NP-hard. So, we propose a genetic algorithm (GA) based scheme to find a near optimal solution in practical time. In order to speed up the calculation, we devised a method to check a sufficient condition of k-coverage of the field. For the problem that nodes near the sink node have to forward the data from farther nodes, we make a tree where the amount of communication traffic is balanced among all nodes, and add this tree to the initial candidate solutions of our GAbased algorithm. Through computer simulations, we confirmed that our method achieves much longer k-coverage lifetime than conventional methods for 100 to 300 node WSNs.
A simple radar simulation tool for 3D objects based on blender

Simulation is a powerful tool for radar modulation and array design, the development of radar signal processing algorithms and the evaluation of defined scenarios. With the evolution of radar hardware, the range resolution of radar is often much smaller than the size of the radar targets which cannot be treated as point targets any more. In the literature, various electromagnetic field simulations or ray tracing approaches exist. However, these approaches are often computationally complex and a sound understanding of the algorithms is required. In this paper, we present a simple radar simulation framework for the simulation of 3D objects larger than the radar range resolution. For this purpose, we use the existing ray tracer of the graphic simulation program Blender and process the rendering results of Blender to simulate radar raw signals.
A consensus review on the development of palliative care for patients with chronic and progressive neurological disease

The European Association of Palliative Care Taskforce, in collaboration with the Scientific Panel on Palliative Care in Neurology of the European Federation of Neurological Societies (now the European Academy of Neurology), aimed to undertake a review of the literature to establish an evidence‐based consensus for palliative and end of life care for patients with progressive neurological disease, and their families.
Motor Skill Learning, Retention, and Control Deficits in Parkinson's Disease

Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance.
A Biomechanical Comparison of Proportional Electromyography Control to Biological Torque Control Using a Powered Hip Exoskeleton

Background Despite a large increase in robotic exoskeleton research, there are few studies that have examined human performance with different control strategies on the same exoskeleton device. Direct comparison studies are needed to determine how users respond to different types of control. The purpose of this study was to compare user performance using a robotic hip exoskeleton with two different controllers: a controller that targeted a biological hip torque profile and a proportional myoelectric controller. Methods We tested both control approaches on 10 able-bodied subjects using a pneumatically powered hip exoskeleton. The state machine controller targeted a biological hip torque profile. The myoelectric controller used electromyography (EMG) of lower limb muscles to produce a proportional control signal for the hip exoskeleton. Each subject performed two 30-min exoskeleton walking trials (1.0 m/s) using each controller and a 10-min trial with the exoskeleton unpowered. During each trial, we measured subjects’ metabolic cost of walking, lower limb EMG profiles, and joint kinematics and kinetics (torques and powers) using a force treadmill and motion capture. Results Compared to unassisted walking in the exoskeleton, myoelectric control significantly reduced metabolic cost by 13% (p = 0.005) and biological hip torque control reduced metabolic cost by 7% (p = 0.261). Subjects reduced muscle activity relative to the unpowered condition for a greater number of lower limb muscles using myoelectric control compared to the biological hip torque control. More subjects subjectively preferred the myoelectric controller to the biological hip torque control. Conclusion Myoelectric control had more advantages (metabolic cost and muscle activity reduction) compared to a controller that targeted a biological torque profile for walking with a robotic hip exoskeleton. However, these results were obtained with a single exoskeleton device with specific control configurations while level walking at a single speed. Further testing on different exoskeleton hardware and with more varied experimental protocols, such as testing over multiple types of terrain, is needed to fully elucidate the potential benefits of myoelectric control for exoskeleton technology.
A fully integrated multi-channel impedance extraction circuit for biosensor arrays

Impedance spectroscopy (IS) is a powerful tool for characterizing materials that exhibit a frequency dependent behavior to an applied electric field. This paper introduces a fully integrated multi-channel impedance extraction circuit that can both generate AC stimulus signals over a broad frequency range and also measure and digitize the real and imaginary components of the impedance response. The circuit was fabricated in 0. 5μm CMOS and consumes 355μW at 3.3V. Tailored for protein and lipid bilayer characterization, the signal generator produces sinusoidal waves from 1Hz to 10kHz. To suit a variety of applications, the impedance extraction circuit provides a programmable current measurement range from~100pA to 100nA with a measured resolution of ~100fA. Occupying only 0.045mm2 per measurement channel, the circuit is compact enough to include nearly 100 channels and the signal generator on 3 × 3mm die.
Characterization of Mammalian Selenoproteomes

In the genetic code, UGA serves as a stop signal and a selenocysteine codon, but no computational methods for identifying its coding function are available. Consequently, most selenoprotein genes are misannotated. We identified selenoprotein genes in sequenced mammalian genomes by methods that rely on identification of selenocysteine insertion RNA structures, the coding potential of UGA codons, and the presence of cysteine-containing homologs. The human selenoproteome consists of 25 selenoproteins.
FASiRec: A Fast Session Recovery Scheme for Large-scale VPNs Using IPSec

As a de facto standard protocol, Internet protocol security (IPSec) provides secure communication between networked systems. One of the most common issues today in large-scale virtual private networks using IPSec is the problem of a stale security association, which occurs when the device at one end of the tunnel maintains the tunnel state but the other end does not. Internet key exchange (IKE) keepalives resolve this by removing the state of the old tunnel and setting up a new tunnel. In particular, the loss of state by a security gateway requires multiple hosts to reestablish tunnels, resulting in high network costs in time, bandwidth usage, etc. The resulting time delay can lead to poor performance, which in turn increases the packet drop rate. To solve this problem, we propose the Fast Session Recovery scheme for IPSec, which reestablishes tunnels, not by renegotiating the session key using IKE, but by backing up the session key. We tested the proposed scheme with an experiment which showed that it can significantly speed up the session reestablishment while increasing total throughput in the network.
Middle Managers: New Purpose, New Directions

Examines the changes facing middle managers as organizations move to leaner, empowered organization focused on delivering service to customers. The model of the inverted triangle leaves middle managers still in the middle, but now confused as to their new role – their world is literally topsy‐turvy. The reasons for these changes are the revolution of information technology, the absence of a hierarchically‐based career path, the need for greater creativity in organizations and the increasing focus on values. Although much has changed, much remains the same. Managers today still have many of the same needs they had in 1967. Identifies five factors of a high‐performing manager.
HaTen2: Billion-scale tensor decompositions

How can we find useful patterns and anomalies in large scale real-world data with multiple attributes? For example, network intrusion logs, with (source-ip, target-ip, port-number, timestamp)? Tensors are suitable for modeling these multi-dimensional data, and widely used for the analysis of social networks, web data, network traffic, and in many other settings. However, current tensor decomposition methods do not scale for tensors with millions and billions of rows, columns and `fibers', that often appear in real datasets. In this paper, we propose HaTen2, a scalable distributed suite of tensor decomposition algorithms running on the MapReduce platform. By carefully reordering the operations, and exploiting the sparsity of real world tensors, HaTen2 dramatically reduces the intermediate data, and the number of jobs. As a result, using HaTen2, we analyze big real-world tensors that can not be handled by the current state of the art, and discover hidden concepts.