Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Considerations of Insight-Driven Dashboard Generation

—Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to conﬁgure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Speciﬁcally, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.
Orientation Model of Mobile Device for Indoor VLC and Millimetre Wave Systems

Visible light communications (VLC) and Millimetre wave (mmWave) systems are two emerging technologies for short-range mobile communications. One of the limiting factors of both VLC and mmWave systems is the random orientation of mobile devices that can significantly affect the channel gain in both systems. Since there is no proper model for device orientation, many studies have assumed that device orientation is fixed or modelled as a uniform distribution. To address this issue, an experimental study of mobile user behaviour is conducted and a statistical orientation model is proposed in this paper. The results show that the probability density function (PDF) of the elevation angle follows a Laplace distribution. Based on the statistical orientation model, Monte-Carlo simulations are carried out to analyse the system performance of VLC and mmWave systems. The statistics of signal-to-noise-ratio (SNR) are compared with the experiment-based simulation results in both VLC and mmWave systems.
Metric properties of Oppenheim continued fractions in the field of Laurent series

Abstract Let be the field of Laurent series over the finite field complete with respect to the degree valuation |·| and let I denote the ring . It is known that there is a probability measure P with respect to the Haar measure on normalized by P(I) = 1. The aim of this work is to study some metric properties involving the sets of digits appeared in the Oppenheim continued fraction expansions of the Laurent series, using the measure P.
Energy-Efficient Resource Allocation for Latency-Sensitive Mobile Edge Computing

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.
Weekly Fluctuations in Motivation in Introductory Programming

In this study, we look into fluctuations in types of motivation in an introductory programming course. Using weekly surveys to measure intrinsic motivation through interest and enjoyment, willingness to learn, interest in context, and time spent on the programming course, we study the evolution of students’ motivation throughout the course. We identify subgroups based on the collected data, and study to what extent the early course motivation and time spent on the course can be used to predict course retention. We observe substantial fluctuations in motivation and time spent over the course rounds, and further identify three subgroups of students in the data with considerable variation in motivation and time spent. At the same time, we observe that despite marked differences in motivation and the time that students spend on the course, these differences, early on in the course, do not seem to help in predicting course dropouts.
An Experimental Investigation of Text-based CAPTCHA Attacks and Their Robustness

Text-based CAPTCHA has become one of the most popular methods for preventing bot attacks. With the rapid development of deep learning techniques, many new methods to break text-based CAPTCHAs have been developed in recent years. However, a holistic and uniform investigation and comparison of these attacks’ effects is lacking due to inconsistent choices of model structures, training datasets, and evaluation metrics. In this article, we perform an experimental investigation on the effects of existing attacks on text-based CAPTCHA schemes. We first summarize existing text-based CAPTCHAs using a newly proposed taxonomy based on their resistance mechanisms and systematically review corresponding attacks in terms of methods and pros/cons. Then, we introduce a unified attack framework that contains a number of different attack modules and transfer learning strategies. Applying this framework, we extensively evaluate the performance of known attacks on 20 CAPTCHA schemes in terms of accuracy and efficiency; then, we investigate the robustness of these widely used schemes and discover the effects of previously unexplored attacks. Finally, we discuss future CAPTCHA designs based on our experimental results and findings. Our work also contributes to the CAPTCHA community by offering an open-access dataset that contains 22 different CAPTCHA sample sets.
Generating circuits with generators

The most widely used languages and methods used for designing digital hardware fall into two rough categories. One of them, register transfer level (RTL), requires specifying each and every component in the designed circuit. This gives the designer full control, but burdens the designer with many trivial details. The other, the high-level synthesis (HLS) method, allows the designer to abstract the details of hardware away and focus on the problem being solved. This method however cannot be used for a class of hardware design problems because the circuit's clock is also abstracted away. We present YieldFSM, a hardware description language that uses the generator abstraction to represent clock-level timing in a digital circuit. It represents a middle ground between the RTL and HLS approaches: the abstraction level is higher than in RTL, but thanks to explicit information about clock-level timing, it can be used in applications where RTL is traditionally used. We also present the YieldFSM compiler, which uses methods developed by the functional programming community -- including continuation-passsing style translation and defunctionalization -- to translate YieldFSM programs to Mealy machines. It is implemented using Template Haskell and the Clash functional hardware description language. We show that this approach leads to short and conceptually simple hardware descriptions.
Detecting Physiological Needs Using Deep Inverse Reinforcement Learning

ABSTRACT Smart health-care assistants are designed to improve the comfort of the patient where smart refers to the ability to imitate the human intelligence to facilitate his life without, or with limited, human intervention. As a part of this, we are proposing a new Intelligent Communication Assistant capable of detecting physiological needs by following a new efficient Inverse Reinforcement learning algorithm designed to be able to deal with new time-recorded states. The latter processes the patient’s environment data, learns from the patient previous choices and becomes capable of suggesting the right action at the right time. In this paper, we took the case study of Locked-in Syndrome patients, studied their actual communication methods and tried to enhance the existing solutions by adding an intelligent layer. We showed that by using Deep Inverse Reinforcement Learning using Maximum Entropy, we can learn how to regress the reward amount of new states from the ambient environment recorded states. After that, we can suggest the highly rewarded need to the target patient. Also, we proposed a full architecture of the system by describing the pipeline of the information from the ambient environment to the different actors.
Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network architectures as well as on recent alternatives like the Legendre memory unit, we show how spiking neural networks can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things applications.
Channel Estimation for MU-MIMO Systems with Some Inactive Users

Almost all existing channel estimation schemes, for multi-user multiple-input multiple-output (MU-MIMO) systems, were designed based on a fixed and known number of active users. However, in random access MU-MIMO systems, some users may be inactive and the estimation techniques designed with the assumption that all users are available may lead to performance losses. In this paper, we consider the popular least squares (LS) and linear minimum mean square error (LMMSE) estimations approaches. For such techniques, when the number of users varies, the channel estimation filter coefficients must be re-derived. The fundamental problem of interest here is to reduce the computational complexity of the LS and LMMSE channel estimation methods when some users are inactive. For each techniques, we propose an estimation approach with a low complexity and without performance loss. The proposed algorithms avoid the direct computation of matrix inverses required by the LS and LMMSE methods. Moreover, the mean square error (MSE) losses, due to the use of the LS and LMMSE estimator intended for the scenario without inactive users, are evaluated and compared with Monte-Carlo simulations results.
Negative Differential Conductivity in Carbon Nanotubes in the Presence of an External Electric Field

We study theoretically the electron transport properties in carbon nanotubes under the influence of an external electric field E(t) using Boltzmann's equation. The current-density equation is derived. Negative differential conductivity is predicted when {\omega}{\tau}<<1 (quasi-static case). We observed this in the neighbourhood where the constant electric field E_o is equal to the amplitude of the AC electric field E_1 and the peak decreases with increasing E_1. This phenomenon can also be used for the generation of terahertz radiation without electric current instability.
Approximate Membership for Words and Trees

Inspired by property testing, our objective is to obtain sublinear algorithms for deciding properties of XML databases approximatively. More precisely, we investigate the properties of whether an unranked tree is valid for a DTD, or more generally, whether it is recognized by a tree automaton.We start our studies by the simpler case of words and we considered the approximate membership problem for word non-deterministic automata. For this problem, we provide an eﬃcient tester that runs in polynomial time in the size of the input automata and the error precision. We also improve the previous [Alon, Krivelevich, Newman, and Szegedy, 2000b] approximate membership tester for regular languages modulo the Hamming distance, so that it runs in polynomial time in the size of the input automata.Secondly, we study approximate membership testing for tree automata modulo the standard edit distance, and obtain a tester with run time exponential in the input tree depth. Next we consider approximate DTD validity modulo the strong edit distance. We then provide a tester that depends polynomially on the height of the tree. Finally, modulo the strong edit distance, we prove a linear lower bound on the depth of the input tree.
Uncertainties in the Rain Profiling Algorithm for the TRMM Precipitation Radar(1. Precipitation Radar (PR), Precipitation Measurements from Space)

This paper describes the basic structure and flow of the rain profiling algorithm for the TRMM Precipitation Radar, and discusses the major assumptions and sources of error in the algorithm. In particular, it describes how the uncertainties in individual parameters affect the attenuation correction and rain estimates. Major parameters involved are the drop size distribution, the phase state of precipitating particles, their density and shape, inhomogeneity of precipitation distribution within the footprint, attenuation due to cloud liquid water and water vapor, freezing height, uncertainty of the surface scattering cross section, and fluctuation of the radar echo signal. Among these parameters that affect the rain estimates, the effect of inhomogeneity of rain distribution is summarized in detail. The paper also describes how these parameters are taken into account in different versions of the standard algorithm 2A25.
ASYMPTOTIC ESTIMATION THEORY FOR TIME SERIES REGRESSION MODELS WITH MULTIPLE CHANGE POINTS

This paper discusses the problem of estimating multiple change points in the trend function of a time series regression model where the residual process is a circular ARMA model, and the trend function satis es a sort of Grenander's conditions. First, the asymptotic representation of the likelihood ratio between contiguous hypothesis is given. Then the limiting distributions of the maximum likelihood estimator (MLE) and the Bayes estimator (BE) for the regression coeÆcients and change points are derived. It is seen that the BE is asymptotically eÆcient, and that the MLE is not so generally.
A SLNR-Based Precoding Algorithm for Max-Min Fairness Problem in Physical-Layer Multicasting

Considering the fairness between the users in physical-layer multigroup multicasting, the Max-Min Fairness (MMF) precoding problem is investigated under the per-antenna power constraints. A suboptimal precoding algorithm is proposed by decoupling the problem into a precoding direction vector design problem and a power allocation problem. Particularly, the SLNR (Signal to Leakage plus Noise Ratio) metric is considered in the precoding direction vector design to properly mitigate the inter-group interference. Moreover, aiming at the MMF criterion, the power allocation procedure can guarantee fairness between users. The simulation results show that the proposed algorithm has less complexity than the primary MMF algorithm at the cost of some performance loss, while has better performance than a low complex algorithm, and consequently provides a solution for the tradeoff between complexity and performance.
Statistical inference of phylogenies

A major objective of biological systematics is inferring phylogenies (evolutionary trees) from data on species. This involves inferring an unknown tree from data generated by a stochastic process operating along the tree. Statistical work on inferring phylogenies has concentrated on two cases: nucleic acid sequence data, modelled by a stochastic process with four discrete states, and gene frequency data, modelled by Brownian motion. A review of this work is presented. There are many unsolved problems, the most important of which is to persuade biologists to think of the problem of inferring phylogenies as being basically statistical, and to abandon deductive frameworks that are used as a justification for "parsimony" methods.
Cell biology of anaerobic ammonium-oxidizing bacteria

Anammox bacteria perform anaerobic ammonium oxidation to dinitrogen gas and belong to the phylum Planctomycetes. Whereas most Prokaryotes consist of one compartment, the cytoplasm bounded by the cytoplasmic membrane and cell wall, the species within this phylum are compartmentalized by intracellular membranes. In the anammox case, compartmentalization results in the cytoplasm being divided into three compartments surrounded by individual bilayer membranes. The anammox cell plan thus consists of (from out- to inside): the paryphoplasm, the riboplasm (containing the ribosomes and the nucleoid) and the anammoxosome. The innermost compartment, the anammoxosome, has been speculated to be the locus of anammox catabolism. This hypothesis is based upon the immunogold localization of one of the key enzymes of the anammox reaction, hydrazine/hydroxylamine oxidoreductase, to the anammoxosome. In the hypothesis, the anammoxosome membrane is energized by the translocation of protons from the riboplasm to the anammoxosome. This creates a proton motive force that could be used to drive ATP synthesis by anammoxosome membrane-bound ATPases. The research presented in this thesis uses a combination of genome analysis, molecular tools and transmission electron microscopy including electron tomography to investigate whether the anammoxosome is a true bacterial organelle: a separate compartment with a specific function inside the cell. The results obtained support both the hypothesis that the anammoxosome compartment is indeed the place where the anammox catabolism takes place and that there is no connection between the anammoxosome membrane and the intracytoplasmic membrane. This suggests that anammox bacteria have evolved a true bacterial organelle with a similar function as the eukaryotic mitochondrion: energy metabolism.
Multiple Lines of Evidences Reveal Mechanisms Underpinning Mercury Resistance and Volatilization by Stenotrophomonas sp. MA5 Isolated from the Savannah River Site (SRS), USA

A largely understudied microbially mediated mercury (Hg) bioremediative pathway includes the volatilization of Hg2+ to Hg0. Therefore, studies on Hg resistant bacteria (HgR), isolated from historically long-term contaminated environments, can serve as models to understand mechanisms underpinning Hg cycling. Towards this end, a mercury resistant bacterial strain, identified as Stenotrophomonas sp. , strain MA5, was isolated from Mill Branch on the Savannah River Site (SRS); an Hg-impacted ecosystem. Minimum inhibitory concentration (MIC) analysis showed Hg resistance of up to 20 µg/mL by MA5 with 95% of cells retaining viability. Microcosm studies showed that the strain depleted more than 90% of spiked Hg2+ within the first 24 h of growth and the detection of volatilized mercury indicated that the strain was able to reduce Hg2+ to Hg0. To understand molecular mechanisms of Hg volatilization, a draft whole genome sequence was obtained, annotated and analyzed, which revealed the presence of a transposon-derived mer operon (merRTPADE) in MA5, known to transport and reduce Hg2+ into Hg0. Based on the whole genome sequence of strain MA5, qRT-PCR assays were designed on merRTPADE, we found a ~40-fold higher transcription of mer T, P, A, D and E when cells were exposed to 5 µg/mL Hg2+. Interestingly, strain MA5 increased cellular size as a function of increasing Hg concentrations, which is likely an evolutionary response mechanism to cope with Hg stress. Moreover, metal contaminated environments are shown to co-select for antibiotic resistance. When MA5 was screened for antibiotic resistance, broad resistance against penicillin, streptomycin, tetracycline, ampicillin, rifampicin, and erythromycin was found; this correlated with the presence of multiple gene determinants for antibiotic resistance within the whole genome sequence of MA5. Overall, this study provides an in-depth understanding of the underpinnings of Stenotrophomonas-mercury interactions that facilitate cellular survival in a contaminated soil habitat.
Conformal radiofrequency ablation to validate ultrasound thermometry

Thermal ablation is a clinical procedure that aims at destroying pathological tissue minimally invasively through temperature changes. Temperature monitoring during the treatment is instrumental to achieve a precise and successful ablation procedure: ensuring a complete target ablation while preserving as much healthy tissue as possible. Ultrasound (US) is a promising low cost and portable modality, that could provide real-time temperature monitoring. However, the validation of such a technique is challenging. It is usually done with thermometers. They provide temperature measurements with good temporal resolution but only at a few local points. Magnetic Resonance Imaging (MRI) is the gold standard in term of temperature monitoring nowadays. It could also be used for validation of other thermometry techniques with a more accurate spatial resolution, but it requires MR-compatible devices. In this paper, we propose to leverage the use of a novel bipolar radiofrequency (RF) ablation device that provides 10 different ablation shapes to validate an ultrasound-based temperature monitoring method. The monitoring method relies on an external ultrasound element integrated with the bipolar RF ablation probe. This element send through the ablated tissues ultrasound waves that carry time-of-flight information. The ultrasound waves are collected by a clinical diagnostic ultrasound probe and can be related to the changes in temperature due to the ablation since ultrasound propagation velocity in biological tissue changes as temperature increases. We use this ultrasound-based method to monitor temperature during RF ablation. First on simulation data and then on two ex-vivo porcine liver experiments. Those dataset are used to show that we can validate the proposed temperature reconstruction method using the novel conformal radiofrequency ablation device by generating different ablation shapes.