Miracle-dz/UniFAR-FTUs huggingface dataset:
Heart sound classification from wavelet decomposed signal using morphological and statistical features

Automatic classification of heart sound recordings is one of the widely known challenges for over 50 years. The fundamental objective of this study is to evaluate a large database of heart sounds collected from a variety of clinical and non-clinical surroundings and classify them into normal and abnormal categories. Daubechis-2 wavelet transform was applied to each phonocardiogram (PCG) recording after segmenting each cardiac cycle into four windows containing first heart sound S1-Systole-Second heart sound (S2)-Diastole states of a heart cycle. Morphological, statistical and time features were extracted from each cardiac states window. Heart sound classification into normal and abnormal was based on the SVM with Gaussian kernel function. The algorithm was trained by the recordings from all available training data sets (training set A to F). The performance of the proposed prototype was evaluated by five-fold cross-validation on the available training dataset as well as on the hidden test set by PhysioNet. Overall classification accuracies of 82% during Phase I submissions and 77% during Phase II submissions were achieved of the challenge. The final score on the blind test set was 74.65%. Based on the current result, the proposed prototype could be a potential solution for a robust and automatic classification technique of normal and abnormal heart sound recordings.
Inducible spy Transcription Acts as a Sensor for Envelope Stress of Salmonella typhimurium

Salmonella enterica infects a broad range of host animals, and zoonostic infection threatens both public health and the livestock and meat processing industries. Many antimicrobials have been developed to target Salmonella envelope that performs essential bacterial functions; however, there are very few analytical methods that can be used to validate the efficacy of these antimicrobials. In this study, to develop a potential biosensor for Salmonella envelope stress, we examined the transcription of the S. enterica serovar typhimurium spy gene, the ortholog of which in Escherichia coli encodes Spy (spheroplast protein y). Spy is a chaperone protein expressed and localized in the periplasm of E. coli during spheroplast formation, or by exposure to protein denaturing conditions. spy expression in S. typhimurium was examined by constructing a spy-gfp transcriptional fusion. S. typhimurium spy transcription was strongly induced during spheroplast formation, and also when exposed to membrane-disrupting agents, including ethanol and the antimicrobial peptide polymyxin B. Moreover, spy induction required the activity of regulator proteins BaeR and CpxR, which are part of the major envelope stress response systems BaeS/BaeR and CpxA/CpxR, respectively. Results suggest that monitoring spy transcription may be useful to determine whether a molecule particularly cause envelope stress in Salmonella.
Differential space time block codes using nonconstant modulus constellations

We propose differential space time block codes (STBC) using nonconstant modulus constellations, e.g., quadrature amplitude modulation (QAM), which cannot be utilized in the conventional differential STBC. Since QAM constellations have a larger minimum distance compared with the phase shift keying (PSK), the proposed method has the advantage of signal-to-noise ratio (SNR) gain compared with conventional differential STBC. The QAM signals are encoded in a manner similar to that of the conventional differential STBC. To decode nonconstant modulus signals, the received signals are normalized by the channel power estimated forgoing training symbols and then decoded with a conventional QAM decoder. Assuming the knowledge of the channel power at the receiver, the symbol error rate (SER) bound of the proposed method under independent Rayleigh fading assumption is derived, which shows better SER performance than the conventional differential STBC. When the transmission rate is more than 3 bits/channel use in time-varying channels, the simulation results demonstrate that the proposed method with the channel power estimation outperforms the conventional differential STBC. Specifically, the posed method using the channel power estimation obtains a 7.3 dB SNR gain at a transmission rate of 6 bits/channel use in slow fading channels. Although the performance gap between the proposed method and the conventional one decreases as the Doppler frequency increases, the proposed method still exhibits lower SER than the conventional one, provided the estimation interval L is chosen carefully.
Ear recognition based on Multi-bags-of-features histogram

This paper proposes a novel image feature representation method, called multi-BOF histogram, for ear recognition. Given an ear image, we at first convolve it with J Gabor filters sharing the same parameters except the parameter of orientation. Then they obtained responses of each pixel at each scale and orientation can get J features. Then, each pixel can be assigned a unique features vector, namely “multi scale Gabor features vector)”. The classification is based on the image's histogram. Extensive experiments conducted on the Delhi-I database demonstrate the overall superiority of Multi-bags-of-words histogram representation (M-BoF) over the other state-of-the-art ear representation methods evaluated.
Analysis of Random Regular LDPC Codes on Rayleigh Fading Channels

Low-density parity check (LDPC) codes of small, realizable size and regular form are analyzed in different multipath fading conditions. The multipath fading conditions vary from uncorrelated channels (with and without channel state information (CSI)) to a correlated land mobile channel at a frequency of 900 MHz. The effects of an external channel interleaver and varying mobile speed in a correlated fading channel are investigated. Obtained results show that the channel interleaver does not provide substantial improvement in the performance of the codes. However, the increasing speed of a mobile in a correlated channel can provide better bit error rate (BER) performance.
Reply to Siegel et al.: Alcohol Advertising in Magazines and Disproportionate Exposure

Nelson investigated advertising placements for a diverse sample of 28 magazines and concluded that targeting of underage youth by alcohol advertisers was not occurring. Siegel et al. claim that my results suffer from collinearity, but fail to present a comprehensive measure of multicollinearity. For my model, variance inflation factors are within acceptable limits and estimation using redefined variables does not alter my prior results or conclusion. Further, Siegel et al.’s empirical results are fragile and do not support a targeting outcome. I also discuss the limitations of estimates of magazine readerships and the shortcomings of the public health literature on advertising and youth alcohol behaviors. Neither the empirical results in Siegel et al. nor their literature citations support a public policy based on a simple rule of disproportionate exposure. (JEL L82, L66, M37)
The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.
Framing Smart Nation: A moderated mediation analysis of frame-focus effects

ABSTRACT The rise of smart cities has emerged as a key policy thread that unites various government agencies in pursuit of a common objective: to become a digital city. However, these smart initiatives generate tradeoffs between economic growth and privacy concerns, with emphasis placed on data sharing for innovation, and surveillance for cybersecurity purposes. This study examines media framing in Singapore’s Smart Nation initiatives. A between-subject experiment investigated the effect of news frames (gain versus loss) and regulatory focus (prevention versus promotion) on respondents’ (N = 157) opinions toward the Smart Nation. Gain frames were more effective when coupled with a promotion focus, and loss frames had a more impact when combined with a prevention focus. A moderated mediation analysis demonstrated support, attitudes, and perceived vulnerability mediated an interaction effect between gain/loss frames and regulatory focus, on the intention to participate in Smart Nation initiatives.
Clustering massive categorical data with class association rules

Clustering algorithms partition data sets into groups of objects such that the pairwise similarity between objects within the same cluster is higher than those assigned to different clusters. Defining a similarity measure becomes challenging in the presence of categorical data and affects the quality and meaningfulness of the clusters formed. Furthermore, the curse of dimensionality diminishes the robustness of such measures. This paper introduces SCAR (supervised clustering with association rules) a nontraditional algorithm for clustering massive high dimensional categorical data. SCAR is robust to the curse of dimensionality, it relies on association rules as an intuitive way to evaluate the similarity between objects and group them.
The RoadRunner framework for efficient and scalable processing of big data

In this paper, we present the overall architecture of RoadRunner, a Hadoop-based framework that enhances the efficiency of rank-aware query processing by introducing various optimizations to Hadoop, without changing its internal operation. RoadRunner focuses on a specific class of queries that involve ranking, such as top-k queries and top-k joins, as well as on preference-aware queries, such as skyline queries, which are tightly related. For this class of queries, we identify improvements on various stages of MapReduce processing, which result in improved performance without sacrificing scalability. We describe the RoadRunner framework, along with individual modules and their roles, and we demonstrate the merits of the proposed framework by means of showcase query examples.
Task-Orientated Biofeedback System for the Rehabilitation of the Upper Limb

Stroke forms one of the leading causes of disability in most industrialized countries. Robot mediated task-orientated physiotherapy is the recent answer to the shortage of staff and the cost associated with the treatment of strokes. The role of biofeedback as a rehabilitation tool has also being acknowledged recently. In this paper we present Rehab Lab, a multi-modal environment for implementing task-orientated therapy. The work focuses on how an arm exoskeleton operating in 3D space can be used in conjunction with rehabilitation software for training patients in relearning daily motor tasks as well as providing them with quality feedback. The Salford rehabilitation exoskeleton (SRE) is used as an assistive device which helps individuals retrain in performing motor tasks by assisting them to complete therapy regimes.
Rectenna measurement in a realistic environment

Rectennas have received considerable attention in the recent decades thanks to their unlimited applications. In this paper, rectenna measurements in a realistic environment will be investigated. The space diversity technique has been used to mitigate the multipath situation. Two configurations for evaluating the performance of the diversity technique applied to the rectenna(s) were carried out. Especially, two fully automatic measurement setups for RF level and DC level are presented. The measurement system is described with the sleeve dipole transmitting antenna, the movable rectenna receiving system and the configuration of the environment. The reference rectennas operating at 2.45 GHz are presented. The experimental results demonstrate that the output signals of rectennas in the realistic environment fluctuate randomly as a function of their positions, resulting in deep fade of signal quality. By using the space diversity technique, we overcome this phenomenon.
Political Opportunism, Position Taking, and Court-curbing Legislation

ABSTRACT Although there is extensive scholarship on court-curbing efforts directed at the U.S. Supreme Court, much less is known about bills targeting the lower federal courts. This article argues that members of Congress also engage in position taking with respect to the U.S. Courts of Appeals, by proposing legislation to divide up the Ninth Circuit. Over seven decades, no other circuit has attracted as much court-curbing legislation as the Ninth Circuit, and yet no bill has succeeded. What accounts for this persistent focus on one court? We argue that bill sponsors are motivated primarily by electoral considerations and capitalize on the perception of the Ninth Circuit as an extremely liberal court. Using an original dataset of all bills to split the Ninth Circuit, we find that sponsorship of splitting bills is linked to legislator party, ideology, and state public mood relative to California, and that only conservative, Republican members of Congress are responsive to performance-related indicators, such as caseload increases and Supreme Court oversight. The results suggest that our current understanding of court curbing as it applies to the Supreme Court can be extended to new judicial contexts.
A compact low-power underwater magneto-inductive modem

In this work, a magneto inductive (MI) link design is studied to achieve high speed transmission applied to a high density underwater network. For a small loop antenna, a design procedure is described to define the optimal operating frequency constrained on the system bandwidth and range. A coherent link is established between two nodes in a controlled underwater environment. For a small coil with radius of 5 cm, simulation results indicate that a range above 10 meters can be achieved in the low frequency spectrum spanning 10 kHz to 1 MHz. The design procedure is validated through measurements in seawater: a very high output SNR equal to 31.4 dB is realized at the output of the equalizer, and in these conditions a perfectly reliable 8-kbps link is demonstrated at a center frequency of 22.5 kHz.
Verification of Embedded Systems Using Modeling and Implementation Languages

We propose a verification flow which aims at functional verification of embedded systems. We assume that the system is formally specified in a modeling language and implemented in a high-level programming language. The verification flow proceeds in two stages: first, we check for behavioral properties which indicate bad states at the level of the specification. Second, we prove that the individual components of the system conform to their specification. The first stage allows for the detection of design flaws in the system without considering implementation details, whereas the second stage refines the verification task allowing for the detection of functional bugs in the implementation. In both stages, the description of the verification task is automatically generated from the modeling language and the implementation language, respectively.
Discrete-time current regulator design for AC machine drives

This paper analyzes the behavior of discrete-time current regulators for AC machines operating at high ratios of fundamental-to-sampling frequencies, a situation common for high speed automotive drives and large traction drives. At high ratios of fundamental-to-sampling frequencies, highly oscillatory, or unstable, response can occur if the current regulator design does not properly incorporate the effects of the discrete nature of the controller, including delays between the sampling of signals and the application of the voltage commands through pulse-width modulation (PWM). This paper investigates these issues for different design methods and current regulator topologies. As part of this investigation a simple discrete-time domain AC machine model is developed that includes the delays associated with PWM. This model is then used to design a discrete-time domain version of the complex vector PI current regulator that demonstrates improved response compared with the other regulators studied. Simulation and experimental results are provided to compare the performance, stability, and robustness of the current regulators analyzed.
Increase and Subjective Evaluation of Feedback Stability in Hearing Aids by a Binaural Coherence-Based Noise Reduction Scheme

The effect of a binaural coherence-based noise reduction scheme on the feedback stability margin and sound quality in hearing aids has been analyzed. For comparison, a conventional adaptive feedback canceler (AFC) and the combination of the adaptive filter with the binaural coherence filter have been tested. The observed quantities are feedback stability and target signal attenuation. An objective measure of feedback stability, i.e., the added stable gain (ASG) was obtained for a number of algorithmic settings and compared to a subjective measure of feedback stability, the added tolerable gain (ATG). In an attempt to eliminate the subjective bias in estimating the ATG, the ldquounbiased added gainrdquo (UAG) is introduced as a new method. Both, objective and subjective measures give similar results for feedback stability. This allows for a valid comparison across different feedback reduction schemes both in isolation and in combination: whereas the ASG of the coherence filter without combination with AFC is negligible, the results indicate that a robust feedback suppression in hearing aids can be achieved if the benefit of de-correlation and the head-shadow effect in binaural hearing aids is used in an advantageous way. The ASG reaches 23 dB for the best combination at the expense of an average target signal attenuation of 15 dB at critical frequencies. The attribute of the coherence filter is that it adaptively limits the maximum gain before feedback becomes audible. The UAG analysis revealed that subjects used a stable quality criterion across the conditions tested and that the group of subjects covered a large range of individual quality criteria.
Incremental Wi-Fi scanning for energy-efficient localization

Wi-Fi based localization has proven to be a compelling alternative to GPS for mobile devices. But Wi-Fi scanning consumes a large amount of energy on smartphones because they perform full scans, i.e. all the channels in their band(s) are visited. This inefficient behavior greatly reduces battery life, raising the threshold for user acceptance. We propose a novel, incremental approach that reduces the energy consumption of Wi-Fi localization by scanning just a few selected channels. We evaluate our incremental scanning approach on eight Android devices using traces from five test subjects. Our results show that, compared to full scans, incremental scanning can reduce the energy consumption between 20.64% and 57.79%. The modern smartphones included in our study all show an energy reduction of at least 40%.
TTEthernet: A Powerful Network Solution for All Purposes

Ethernet is still gaining ground although it has been around for more than 30 years. With Ethernet being used as a universal network solution in office and web applications, production facilities, safety-critical systems, airplanes and automobiles, it has a big potential for cost savings in all areas. Engineering, maintenance and training costs are considerably lower than those for many proprietary bus systems. Additionally, Ethernet offers far higher bandwidths. But when Ethernet was developed in the first place, tasks with time-critical, deterministic or safety-relevant conditions were not taken into account. TTEthernet expands classical Ethernet with powerful services to meet all new requirements.
Can the string scale be related to the cosmic baryon asymmetry?

In a previous work, a mechanism was presented by which baryon asymmetry can be generated during inflation from elliptically polarized gravitons. Nonetheless, the mechanism only generated a realistic baryon asymmetry under special circumstances, which requires an enhancement of the lepton number from an unspecified GUT. In this paper we provide a stringy embedding of this mechanism, demonstrating that if the model-independent axion is the source of the gravitational waves responsible for the lepton asymmetry, one can observationally constrain the string scale and coupling.
A simple and rapid method for detecting living microorganisms in food using laser speckle decorrelation

Measuring microorganisms in food products is a critical issue for food safety and human health. Although various approaches for detecting low-levels of microorganisms in food have been developed, they require high-cost, complex equipment, invasive procedures, and skilled technicians which limit their widespread use in the food industry. Here, we present a simple, non-destructive, non-contact, and rapid optical method for measuring living microorganisms in meat products using laser speckle decorrelation. By simply measuring dynamic speckle intensity patterns reflected from samples and analyzing the temporal correlation time, the presence of living microorganisms can be non-invasively detected with high sensitivity. We present proof-of-principle demonstrations for detecting E. coli and B. cereus in chicken breast tissues.
Time series classification: Decision forests and SVM on interval and DTW features

This paper describes the methods used for our submission to the KDD 2007 Challenge on Time Series Classification. For each dataset we selected from a pool of methods (individual classifiers or classifier ensembles) using cross validation (CV). Three types of classifiers were considered: nearest neighbour (using DTW), support vector machines (linear and perceptron kernel) and decision forests (boosting, random forest, rotation forest and random oracles). SVM and decision forests used extracted features of two types: similarity-based and interval-based. Two feature selection approaches were applied: FCBF or SVM-RFE. Where the minimum CV errors of several classifiers tied, the labels were assigned through majority vote. We report results with both the practice and the contest data.