Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward understanding natural language directions

Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. We use a probabilistic graphical model that factors into three key components. The first component grounds landmark phrases such as “the computers” in the perceptual frame of the robot by exploiting co-occurrence statistics from a database of tagged images such as Flickr. Second, a spatial reasoning component judges how well spatial relations such as “past the computers” describe a path. Finally, verb phrases such as “turn right” are modeled according to the amount of change in orientation in the path. Our system follows 60% of the directions in our corpus to within 15 meters of the true destination, significantly outperforming other approaches.
Non-stationary Bayesian modelling and enhancement of speech signals

This report applies time-varying AR (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. For the TVAR coefficients the standard parameterisation, i.e. the coefficients of the TVAR polynomial themselves, and one i.t. o. the characteristic roots of the TVAR polynomial (or system poles) are investigated. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The problem and estimation objectives are formulated within a Bayesian framework. Two efficient iterative algorithms are developed to achieve these objectives. The first is a Markov chain Monte Carlo (MCMC) algorithm which generates samples from the posterior distribution based on which the minimum mean square error (MMSE) estimates of the TVAR parameters and clean speech can be computed. The second is a stochastic optimisation algorithm which computes the marginal maximum a posteriori (MMAP) estimate of the TVAR parameters. The clean speech can then be obtained by running a fixed-interval Kalman smoother with this estimate of the TVAR parameters. Contrary to the EM-type algorithms, the estimation schemes work without introducing a set of " missing data " (the clean speech in this case). Nevertheless, at each iteration the computational complexity of the algorithms is still linear in the number of samples in the analysis window. Performance measures based on predictive distributions are used in simulation studies to compare the modelling and signal reconstruction performance of the proposed TVAR models to that of the standard fixed-parameter AR model on both synthetic and real speech data sets.
Optimizing Link Sleeping Reconfigurations in ISP Networks with Off-Peak Time Failure Protection

Energy consumption in ISP backbone networks has been rapidly increasing with the advent of increasingly bandwidth-hungry applications. Network resource optimization through sleeping reconfiguration and rate adaptation has been proposed for reducing energy consumption when the traffic demands are at their low levels. It has been observed that many operational backbone networks exhibit regular diurnal traffic patterns, which offers the opportunity to apply simple time-driven link sleeping reconfigurations for energy-saving purposes. In this work, an efficient optimization scheme called Time-driven Link Sleeping (TLS) is proposed for practical energy management which produces an optimized combination of the reduced network topology and its unified off-peak configuration duration in daily operations. Such a scheme significantly eases the operational complexity at the ISP side for energy saving, but without resorting to complicated online network adaptations. The GÉANT network and its real traffic matrices were used to evaluate the proposed TLS scheme. Simulation results show that up to 28.3% energy savings can be achieved during off-peak operation without network performance deterioration. In addition, considering the potential risk of traffic congestion caused by unexpected network failures based on the reduced topology during off-peak time, we further propose a robust TLS scheme with Single Link Failure Protection (TLS-SLFP) which aims to achieve an optimized trade-off between network robustness and energy efficiency performance.
Poloxamer synperonic F108 improves cellular transduction with lentiviral vectors

Although lentiviral transduction methods are widely used, their broader application is dependent upon the optimization of lentiviral transduction efficiency for a broad range of cell types. In the present study, we focus on the evaluation of two chemical classes with respect to their ability to increase lentiviral transduction without cytotoxicity.
Comparison of Routing Protocols in Wireless Sensor Networks for Monitoring Applications

recent years, evaluation and development of the routing protocols in wireless sensor networks (WSNs) are very important and attractive research topic especially for monitoring applications. Because of, the difficulties of studying WSNs routing protocols in real implementation which takes a lot of time and it can be very expensive, using a suitable simulator become a common trend in such evaluation. This paper presents a systematic performance study of three routing protocols, Ad hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) protocols for WSNs by proposing a simulation model that targeted to the sensor networks with mobile sensor nodes and single sink as it is often seen in many monitoring applications such as military, agriculture, medical, transport, industry, etc to monitor physical environments. The performance study of WSNs routing protocols is analyzed by comparing important metrics like the end-to-end delay, total packets dropped, load, routing overhead, route discovery time, and number of hops per route in the Network under the same random waypoint mobility model for the three protocols. These routing protocols are implemented and simulated using OPNET Modeler simulator. Theoretical analysis and simulation results show that both AODV and DSR protocols have identical on demand behavior but with performance differentials resulted from the differences in protocol mechanics. In addition to, they are suffering from higher end to end delay compared to the Optimized Link State Routing (OLSR) protocol. The results obtained may be useful for implementation of wireless sensor networks for many monitoring and control applications.
End-to-end text-dependent speaker verification

In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time. Such an approach will result in simple and efficient systems, requiring little domain-specific knowledge and making few model assumptions. We implement the idea by formulating the problem as a single neural network architecture, including the estimation of a speaker model on only a few utterances, and evaluate it on our internal "Ok Google" benchmark for text-dependent speaker verification. The proposed approach appears to be very effective for big data applications Like ours that require highly accurate, easy-to-maintain systems with a small footprint.
QoS-Aware Energy-Efficient Radio Resource Scheduling in Multi-User OFDMA Systems

In this letter, the energy-efficient radio resource scheduling is studied with quality of service (QoS) guarantees in a multi-user OFDMA system. In contrast to existing works on QoS-aware scheduling, which mostly take sum throughput or total transmission power as optimization objectives, we formulate an optimization problem to maximize the energy efficiency subject to users' QoS requirements. Based on Lagrangian duality then, an effective algorithm named QoS-aware energy-efficient resource scheduling is proposed to address the problem. Simulation results show that our proposed algorithm can effectively achieve the optimal solution and bring remarkable improvements on energy efficiency with QoS guarantees.
Achieving secure and accurate friend discovery based on friend-of-friend's recommendations

Friend discovery has been one of the hot topics in our social activities over the past decade. Mobile users have more opportunities to discover and make new social interactions with others in vicinity to build and extend their social communities. However, the inevitable information releasing conflicts with the increasing privacy concerns. In this paper, we employ the concept of friend-of-friend and design a secure and accurate friend discovery for privacy-aware mobile users in Proximity-based Mobile Social Networks (PMSNs). We first construct a novel similarity function with fully considering the number of common attributes, the corresponding priorities and the ratio of matched attributes over all the inputs. Then we develop a secure friend recommendation phase based on a carefully combination of the commutative encryption function and the bilinear pairings. The security and performance are thoroughly analyzed and evaluated via detailed simulations.
Therapeutic effect of follow-up assessments on antidepressant and placebo response rates in antidepressant efficacy trials

Background It remains unclear how much various factors contribute to the placebo response. Aims To estimate the therapeutic impact of follow-up assessments on placebo response in antidepressant trials. Method Double-blind, placebo-controlled antidepressant trials that reported weekly changes in Hamilton Rating Scale for Depression (HRSD) scores over 6 weeks were selected. Included studies (n=41) were divided into those that conducted four, five or six follow-up assessments. Reductions in HRSD scores as a function of the different follow-up schedules were compared. Results An extra follow-up visit at week 3 was associated with a 0.86 further reduction in HRSD score; an extra visit at week 5 was associated with a 0.67 further reduction. These effects represented approximately 34–44% of the placebo response that occurred over these time frames. Two additional visits were associated with twice the reduction in HRSD score than one, suggesting that the therapeutic impact of assessment visits is cumulative and proportional. A comparable therapeutic effect was also found in participants receiving active medication. Conclusions Follow-up assessments in antidepressant treatment trials incur a significant therapeutic effect for participants on placebo, and this represents about 40% of the placebo response.
Repeatability of high-resolution 3D seismic data

High-resolution 4D (HR4D) seismic data have the potential for improving the current state-of-the-art in detecting shallow ([Formula: see text] below seafloor) subsurface changes on a very fine scale (approximately 3–6 m). Time-lapse seismic investigations commonly use conventional broadband seismic data, considered low to moderate resolution in our context. We have developed the first comprehensive time -lapse analysis of high-resolution seismic data by assessing the repeatability of P-cable 3D seismic data (approximately 30–350 Hz) with short offsets and a high density of receivers. P-cable 3D seismic data sets have for decades been used to investigate shallow fluid flow and gas-hydrate systems. We analyze P-cable high-resolution 4D (HR4D) seismic data from three different geologic settings in the Arctic Circle. The first two are test sites with no evidence of shallow subsurface fluid flow, and the third is an active seepage site. Using these sites, we evaluate the reliability of the P-cable 3D seismic technology as a time-lapse tool and establish a 4D acquisition and processing workflow. Weather, waves, tide, and acquisition-parameters such as residual shot noise are factors affecting seismic repeatability. We achieve reasonable quantitative repeatability measures in stratified marine sediments at two test locations. However, repeatability is limited in areas that have poor penetration of seismic energy through the seafloor, such as glacial moraines or rough surface topography. The 4D anomalies in the active seepage site are spatially restricted to areas of focused fluid flow and might likely indicate changes in fluid flow. This approach can thus be applied to detect migration of fluids in active leakage structures, such as gas chimneys.
Fillet-Based Batch Informed Trees (FB-BIT*): Rapid Convergence Path Planning for Curvature Constrained Vehicles

In recent years the problem of planning paths through complex obstacles while respecting kinematic constraints has seen increased attention. Many have found that respecting curvature constraints is particularly difficult without substan-tially slowing convergence speed. Batch Informed Trees (BIT*) is a path-planning algorithm that has been shown to converge rapidly in large environments without considering kinematic constraints. This work proposes an extension to BIT* that employs fillets as motion primitives, enabling the incorporation of curvature constraints into the planning process. Path-length heuristics for fillet-based planning are introduced to accelerate convergence. Comparisons to pre-existing approaches are made with an Unmanned Aerial Vehicle (UAV) simulation modeled off of Manhattan, New York.
On the Stupid Algorithm for Satissability

I introduce an extremely stupid algorithm for the satissability problem. Where random 3-SAT problems are generated randomly to agree with a predetermined truth assignment, I show that the algorithm almost certainly solves problems with N variables and O(N ln N) clauses very easily. This helps connrm two pieces of folklore. First, that random problems generated in this way are usuually easy. Second, that extrapolating theoretical results beyond their range of direct application must be done with some care. 1 On \On the greedy algorithm for satissability" In 1], Koutsoupias and Papadimitriou study the greedy algorithm for satissability (as they call it). Actually it's not an algorithm, but a heuristic. This is a very simple algorithm. Choose a truth assignment at random. Then choose a variable such that ipping it increases the number of satissed clauses: do not allow sideways moves. Repeat until no improvement is possible. If this is a solution, report success. Otherwise give up. The study then mathematically shows that for 3-SAT, this process will nd a solution foràlmost all' soluble 3-SAT problems. In the introduction, Koutsoupias and Papadimitriou state that (my emphasis) Naturally, there is nothing surprising or original about an NP-complete problem with a good average-case algorithm under the most natural probabilistic distribution. What is perhaps a little surprising is that the problem is so fundamental, the algorithm so simple, and the proof so easy. Here, I introduce a simpler algorithm, and show with an easier proof a stronger result on the same fundamental problem class. The proof will actually follow very closely parts of the proof given by K&P, but missing bits of their proof out that I don't need in my context. First I introduce the framework used by K&P. Given that the problem is soluble, there is some truth assignment ^ T which solves it. A crucial phrase is: \If all clauses satissed by ^ T are chosen independently at random with probability p, then. " In other words, the random model of clause generation is to generate clauses consistent with a given model. This is known to give very easy problems, though that is not the focus of this note. In fact it turns out that when they talk about \almost all problems" they actually mean the following. Choose p = 1=2: then all but an exponentially small fraction of problems are solved rst time all
Effect of Cognitive Behavioral Nursing Intervention Program on Anxiety among Patients with Cancer

Background: A cancer diagnosis can have a wide-ranging impact on mental health. Cancer patients usually experience high levels of psychological stress result in psychological distress symptoms such as anxiety. Cognitive behavioral therapy is one of the most evidence-based psychological interventions for the treatment of several psychiatric disorders such as anxiety. Aim: The purpose of this study is to explore the effect of cognitive behavioral nursing intervention program on anxiety among patients with cancer. Subjects and Method: Design: A quazi-experimental design (pre, post, and follow-up evaluation) was used. Setting: Oncology unit at Al Shifa Medical Complex in Port Said city. Subjects: The study subjects included 63 patients with cancer. Tools: One tool were utilized to collect the data, Beck Anxiety Inventory; in addition to personal and clinical data sheet. Results: The study revealed nearly one third (35%) of studied patient with cancer had mild level of anxiety at pre intervention, while more than half (53.3%) of them had mild level of anxiety at post intervention. Also, half (50%) of the studied patients with cancer had moderate level of anxiety at pre intervention, while more than one third (36.7%) of them had moderate level of anxiety at post intervention. Conclusion: It was verified that, there are highly statistically significant relationship between levels of anxiety among the studied patients with cancer at pre-post nursing intervention. Recommendations : Cognitive behavioral nursing interventions should be utilized in combination with standard care for cancer treatment.
Effects of Beer, Non-Alcoholic Beer and Water Consumption before Exercise on Fluid and Electrolyte Homeostasis in Athletes

Fluid and electrolyte status have a significant impact on physical performance and health. Pre-exercise recommendations cite the possibility of consuming beverages with high amounts of sodium. In this sense, non-alcoholic beer can be considered an effective pre-exercise hydration beverage. This double-blind, randomized study aimed to compare the effect of beer, non-alcoholic beer and water consumption before exercise on fluid and electrolyte homeostasis. Seven male soccer players performed 45 min of treadmill running at 65% of the maximal heart rate, 45 min after ingesting 0.7 L of water (W), beer (AB) or non-alcoholic beer (NAB). Body mass, plasma Na+ and K+ concentrations and urine specific gravity (USG) were assessed before fluid consumption and after exercise. After exercise, body mass decreased (p < 0.05) in W (−1.1%), AB (−1.0%) and NAB (−1.0%). In the last minutes of exercise, plasma Na+ was reduced (p < 0.05) in W (−3.9%) and AB (−3.7%), plasma K+ was increased (p < 0.05) in AB (8.5%), and USG was reduced in W (−0.9%) and NAB (−1.0%). Collectively, these results suggest that non-alcoholic beer before exercise could help maintain electrolyte homeostasis during exercise. Alcoholic beer intake reduced plasma Na+ and increased plasma K+ during exercise, which may negatively affect health and physical performance, and finally, the consumption of water before exercise could induce decreases of Na+ in plasma during exercise.
A 12-B 10-msamples/s CMOS switched-current delta-sigma modulator

This paper presents the design of fully differential second-order delta-sigma modulator. A current feedback technique is used in the proposed switched-current feedback memory cell (FMC) to decrease the input impedance and to improve the transmission error in the memory cell. Furthermore, the entire memory cell is designed in a coupled differential replicate (CDR) form to eliminate the clock feedthrough (CFT) error. In this paper, the SDM is simulated with TSMC 0.35 micrometre CMOS process technology. The simulation results reveal that the peak signal to noise plus distortion ratio (SNDR) is 75 dB at 10.24 MHz sampling rate with 40 kHz bandwidth, and the power dissipation is 16 mW.
Study On Performance Simulation of 100kW Proton Exchange Membrane Fuel Cell For Heavy Commercial Vehicle Applications

The integration of fuel cell technology utilizing hydrogen as a fuel holds immense promise for the advancement of heavy-duty commercial vehicle solutions. Presently, a plethora of fuel cell variants cater to diverse vehicle applications. Within this array, the proton exchange membrane fuel cell (PEMFC) demonstrates remarkable potential for automotive use owing to its attributes of low operating temperature and high power density. However, the direct global implementation of fuel cell technology concepts into the Indian commercial vehicle (CV) market is not feasible. Further, the functional requirements of the fuel cell (FC) system, including the balance of plant (BoP), should be meticulously chosen to ensure optimal FC vehicle performance without performance compromise. Inaddition, PEMFC power supplies are available in various power ratings however, the selection of an optimized fuel cell power configuration for heavy-duty applications proves to be a daunting task due to cost sensitivity and market competitiveness in India. To address this challenges, comprehensive simulation studies are imperative to unravel the performance dynamics of PEMFC under varying vehicle operating conditions. In this study, a simulation model of a Fuel Cell Electric Vehicle (FCEV) truck with a load range exceeding 20–40 tons is developed utilizing GT-SUITE software. Based on a thorough investigation of steady state and transient simulation performances, a 100-kW FC system is identified as the optimal power configuration for the specified vehicle tonnage.
Pemanfaatan Aplikasi X-Recorder dan Microsoft Sway Pada Pembelajaran IPS di SMP IT Darul Fikri Tanjungbalai

The purpose of this study is to describe the social studies learning process at SMP IT Darul Fikri Tanjungbalai and the creation of e-learning using the X-recorder and Microsoft Sway. This study uses descriptive qualitative research as a research method. Teachers and students at SMP IT Darul Fikri Tanjungbalai were used as research subjects and informants. In this study, interviews, observations, and documentation were used to collect data. This study also uses data analysis methods which include data reduction, data presentation, drawing conclusions, and clarification. While the use of the data triangulation method is carried out to verify the validity of the data. The use of source triangulation and technique triangulation was used in this research triangulation technique. The results of the study show that the use of x-recorders and Sway in social studies learning can be interactive learning media, both in the form of quizzes, audio-visual presentations, and visually attractive product displays, so students can understand material effectively and increase interactivity between educators and students in social studies learning at school.
EJS-Based Laboratory for Learning the Function of the Cardiovascular System

Abstract One of the career areas included in the field of Biomedical Engineering is the application of engineering system analysis: physiological modelling, simulation and control. This paper describes a Virtual Laboratory for the analysis and the study of Human circulatory system. The Virtual Laboratory is based on the compilation of several mathematical models described in the literature. Presented application has been build using MATLAB/Simulink and EJS, so it combines good computation capabilities and it is completely interactive. The Virtual Laboratory is designed in order to understand the operation of the circulatory system under normal conditions, and to predict circulatory variables at different levels of stimuli and conditions.