Miracle-dz/UniFAR-FTUs huggingface dataset:
Assemble new object detector with transferred auxiliary filters and distinct target filters

By transferring of prior knowledge from source domains and synthesizing the new knowledge extracted from the target domain, the performance of learning can be improved when there are insufficient training data in the target domain. In this paper we propose a new method to transfer a deformable part model (DPM) for object detection, using sharable filters from offline-trained auxiliary DPMs of similar categories and new filters learnt from the target training samples to improve the performance of the target object detector. A DPM consists of a collection of root and part filters. The filters of the auxiliary detectors capture the sharable appearance features and can be used as prior knowledge. The sharable filters are employed by the new detector with a coefficient reweighting algorithm to fit the target object much better. Meanwhile the target object still has some distinct local appearance features that the part filters in the auxiliary filter pool can not represent. Hence, new part filters will be learnt with the training samples of the target object and added to the filter pool as complementary. The final learnt model will be an assembly of transferred auxiliary filters and additional target filters. With a latent transfer learning algorithm, appropriate local features are extracted for the transfer of the auxiliary filters and the description of the distinct target filters. Our experiments demonstrate that the proposed strategy precedes some state-of-the-art methods.
Observation of executive processes and associated dysfunction in aging and dementia for cognitive assistance systems

Aging and dementia of the Alzheimer’s type lead to the progressive deterioration of cognitive and executive functions. In the context of smart homes and gerontechnology, an essential step to assist people suffering from these disorders is to identify their difficulties in daily living. A better utilization of assistive devices relies on a better understanding of cognitive handicaps. This paper presents an evaluation of executive processes impairment in daily living according to cognitive impairment. The observation of the performance of an activity of daily living by different groups of subjects (young, elderly, and MCI-AD people) and the specification of a precise scoring system show a deterioration of executive processes and a loss of autonomy during the task performance.
Trapezoidal companding scheme for peak-to-average power ratio reduction of OFDM signals

A novel nonlinear companding scheme is proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. This scheme can transform the original Gaussian-distributed OFDM signals into trapezoidal-distributed. Moreover, by properly choosing variable parameters of the proposed scheme, it can achieve an effective trade-off between PAPR reduction and bit error rate performance, which gives the OFDM system more design flexibility.
Pi-shaped quarter wavelength structure for multiband applications

In this paper, for the first time, we present a novel Pi-shaped structure using resonators for multiband applications. The multiband Pi-shaped structure with LC resonators is analyzed. In order to demonstrate the proposed multiband Pi-shaped structure, one tri-band Wilkinson power divider and one tri-band rat-race coupler are designed, fabricated, and tested. The compactness of the two demonstrated components is well kept by putting all stubs with resonators inside the components themselves. Measured results are in good agreement with the simulated results.
Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.
In-line optical phase-sensitive amplifier employing pump laser injection-locked to input signal light

An in-line optical phase-sensitive amplifier with a phase-locked pump source is demonstrated. The pump source, which consists of a modelocked semiconductor ring laser, is phase-locked to the input signal light by optical injection of the signal. The randomly modulated signal pulses are successfully amplified in a Sagnac interferometer-type parametric amplifier pumped by the injection-locked laser.
Real-time scheduling of electric vehicles charging in low-voltage residential distribution systems to minimise power losses and improve voltage profile

Unscheduled charging of plenty of electric vehicles (EVs) might exert an adverse effect on the existing power grid, especially when the charging coincides with daily peak load at distribution level. In this study, a scalable real-time scheduling scheme for EV charging in low-voltage residential distribution system is proposed. Since most often, voltage drop would become a binding constraint when a distribution feeder is subject to a high EV penetration level, a scheduling method is first developed to increase the voltage safety margin. Then, a novel factor is derived to allow the scheduling to shift from voltage-safety-oriented to loss-minimisation-oriented, or vice versa, on demand of the EV penetration level. A number of charging scenarios were simulated to evaluate the proposed scheduling scheme. Simulation results verified that the proposed scheduling scheme is fast and efficient with circuit losses close to optimal at a low EV penetration level and voltage drops maintained within the tolerable limit at a high EV penetration level. The high scalability and effectiveness of the proposed scheme has made it suitable for coordinating large number of EV charging activities in real-time.
Identification and Equalization of Nonlinear Channel Based on ANFIS

The demand of high-speed communication leads that the application for resource of channel exceeds the range of linear model. The channel should be described as a nonlinear model. So, the paper uses the adaptive neuron-fuzzy inference system (ANFIS) to identify and equalize the nonlinear channel of the high-speed communication system. Meanwhile, the subtract cluster is applied to identify the construction of the ANFIS and the hybrid learning algorithm based on the least square and back-propagation is used to train network. The simulation results show that the convergence rate and identification accuracy of the ANFIS are better than BP network and the efficiency of the ANFIS based on subtract cluster partition algorithm is higher than that of the ANFIS based on the grid partition algorithm.
Blind Source Separation of a Single Channel Based on Repeated Independent Component Analysis

Independent component analysis (ICA) is a kind of steady algorithm for the separation of blind source (BSS). However, there are three assumptions for the ICA application, one of which is that the number of test channels must be more than that of the signal sources. The limitation brings much inconvenience to practical signal acquisition and processing. In this paper, we propose a new method of repeated independent component analysis (Re-ICA) to realize the separation of blind sources. Under a single test channel, we can increase test channels by means of constructing virtual signal channels and use ICA repeatedly to separate every source signal in turn. Numerical simulation and signal processing of practical data acquisition by one single test-channel show that the proposed method is simple and feasible for operation, and is of great potential in engineering application.Copyright © 2011 by ASME
The Transition in Household Energy Use for Cooking in India: Evidence from a Longitudinal Survey

This article seeks to assess the transition in household energy use for cooking in India based on data from two rounds of the India Human Development Survey in 2004–2005 and 2011–2012. In this study, we have used the multinomial logistic regression and Multiple Classification Analysis conversion model to assess the transition in household energy use according to the socio-economic characteristics of households. Our findings suggest that although the transition from solid fuel to clean fuel is universal across households, it is greater among the socio-economically better-off households than their poorer counterparts. The use of solid fuel for cooking was more prevalent among the socio-economically disadvantaged households than among their socio-economically better-off counterparts in both 2004–2005 and 2011–2012. Convergence in clean cooking fuel use across the households can be possible only when socio-economically disadvantaged households progress faster than their already better-off counterparts. JEL Codes: B5, C23, D31, I3, Q5
Environmental Kuznets Curve for Carbon Dioxide Emissions and Economic Growth in Algeria

This article examines how economic growth affects the environment through the lens of carbon dioxide (CO2) emissions, by testing the validity of the environmental Kuznets curve (EKC) for the case of Algeria during the period 1973–2016. For this, the auto regressive distributed lag (ARDL) method is used. The results of the econometric analysis confirm the existence of a positive long-term relationship between CO2 emissions and real GDP and show that the direction of this relationship goes from economic growth to CO2 emissions according to Granger causality tests. Specifically, for a developing country like Algeria, economic growth determines the level of emissions. This implies that an energy policy in favour of the environment can be put in place without risking negative repercussions on economic growth. In addition, the results of the ARDL regression validate EKC’s hypothesis: in the first phase, economic growth leads to a higher level of CO2 emissions, however, when a given threshold (inflection point) is reached, these emissions decrease. JEL Codes: O10, O47, Q50, Q53
Effects of Weather on Foraging Success and Hunting Frequency in Winter-Irruptive Snowy Owls (Bubo scandiacus) in Upstate New York

Abstract. The effects of weather on an individual can often alter the population dynamics of a species. Knowledge of how weather influences individual behavior is therefore essential in understanding its full impact in the context of population ecology. Snowy Owls (Bubo scandiacus) exhibit expensive long-distance migrations in winters following population irruptions. During irruptive movements, many owls migrate past the southernmost extent of their traditional wintering grounds, the mechanism for which is still debated. We propose and test the “milder climate” hypothesis; Snowy Owls wintering in lower latitudes are better able to meet their metabolic demands due to higher temperatures and lower snow cover. During the Snowy Owl irruption of 2014–2015, we examined this hypothesis by assessing the influence of local weather variables on foraging success, frequency of prey capture attempts, and overall activity budgets in a sample of wintering Snowy Owls in New York, USA. We used eBird, an online citizen science resource, to help locate Snowy Owls, which we observed from an automobile. We found that none of the weather variables tested affected foraging success. However, the lack of effect of snow depth on foraging success may suggest that hearing is more important for hunting in Snowy Owls than previously thought. Hunting frequency decreased with increasing temperatures, suggesting Snowy Owls were better able to meet their metabolic demands in higher temperatures. We thus offer support for the milder climate hypothesis; Snowy Owls wintering in lower latitudes may be able to offset the energetic expenses of long-distance movements.
Off-Grid Downhole Video Recording System to Monitor Burrowing Owls

Abstract. Studying species that nest underground, such as the Burrowing Owl (Athene cunicularia) is challenging. Ecological and behavioral information about Burrowing Owls is often limited to activities occurring aboveground or to insights gained from live viewing using downhole cameras and burrow scopes with limited recording capacity. Although automated monitoring systems may enhance underground observations, such systems need to be self-powered to support research conducted in remote areas. We describe a self-powered (off-grid) digital video recording system developed in 2014 to monitor nesting Burrowing Owls at Rio Bosque Wetlands Park in El Paso, Texas, USA. We used solar panels, infrared cameras, and digital video recorders to create a self-contained system for monitoring two artificial Burrowing Owl nest sites. From 2014 to 2018, we collected high-resolution video recordings of Burrowing Owl nesting ecology and behavior in their underground artificial burrows and nest boxes in remote areas. We provide detailed instructions and recommendations for the design, construction, installation, and maintenance of this system. Although our system required some initial material and labor costs, it required less on-site work than traditional behavioral observations, while also providing high-quality video images of an understudied aspect of Burrowing Owl ecology. Key ecological insight gained included nest initiation dates, clutch sizes, incubation dates, hatching sequences, prey items delivered to the nest, and predators of Burrowing Owls in the El Paso area. We encountered substantial technical problems but recommend the overall design of our system.
Can Image Style Transfer Save Automotive Radar?

Compared to RGB camera and Lidar, single chip automotive radar is a promising alternative sensor with robustness to adverse weathers. But the sparseness of radar output drastically hinders its usefulness for autonomous driving tasks. Up-sampling via image style transfer could be a cure for a sparse measurement. However, it remains unknown whether style transfer can be an effective solution to automotive radar which features different and unique sparse and noisy issues. In this paper, we evaluate a variety of predominant image style transfer methods for a typical ego-vehicle pose estimation task on the public nuScenes dataset, and find that though image style transfer methods can improve the visual quality of automotive radar measurements, they can hardly contribute to the utility of radar for downstream tasks.
Muscle-Mind: towards the Strength Training Monitoring via the Neuro-Muscular Connection Sensing

Strength training is essential for both physical and mental well-being. Muscular mass and strength gain can help with weight loss, balance improvement, and fall prevention. The neuromuscular connection, or mind-muscle connection, is critical for improving strength training performance. However, many fitness trackers and applications are missing a feature that allows users to track their neuromuscular workout performance. The goal is to immerse the user experience while keeping the cost and size of the healthcare device to a minimum. A wearable EEG hairband and EMG shirt are outfitted with dry and non-invasive bio-signal detecting that securely attaches to the body's surface during exercise. Participants in our study are exposed to five upper-limb free-weight exercises. The result shows that low-intensity exercise can increase upper-limp muscle contraction by over 30%, and individuals with mental effort have an average precision of 81%.
OneFi: One-Shot Recognition for Unseen Gesture via COTS WiFi

WiFi-based Human Gesture Recognition (HGR) becomes increasingly promising for device-free human-computer interaction. However, existing WiFi-based approaches have not been ready for real-world deployment due to the limited scalability, especially for unseen gestures. The reason behind is that when introducing unseen gestures, prior works have to collect a large number of samples and re-train the model. While the recent advance of few-shot learning has brought new opportunities to solve this problem, the overhead has not been effectively reduced. This is because these methods still require enormous data to learn adequate prior knowledge, and their complicated training process intensifies the regular training cost. In this paper, we propose a WiFi-based HGR system, namely OneFi, which can recognize unseen gestures with only one (or few) labeled samples. OneFi fundamentally addresses the challenge of high overhead. On the one hand, OneFi utilizes a virtual gesture generation mechanism such that the massive efforts in prior works can be significantly alleviated in the data collection process. On the other hand, OneFi employs a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training. We additionally design a self-attention based backbone, termed as WiFi Transformer, to minimize the training cost of the proposed framework. We establish a real-world testbed using commodity WiFi devices and perform extensive experiments over it. The evaluation results show that OneFi can recognize unseen gestures with the accuracy of 84.2, 94.2, 95.8, and 98.8% when 1, 3, 5, 7 labeled samples are available, respectively, while the overall training process takes less than two minutes.
An Analysis of the Formal Properties of Bloom's Taxonomy and Its Implications for Computing Education

Educational taxonomies provide conceptual frameworks to deal with different educational issues. In particular, Bloom's taxonomy is popular in CSE, especially in programming courses for assessment purposes. However, different authors have reported difficulties of use, and have even suggested as hypothetical cause an inherent deficiency of the taxonomy. In this paper, we present an analysis of the formal properties of the two versions of Bloom's taxonomy. According to their authors’ intents, the original taxonomy should be considered a comparative system, while the revised taxonomy would be a classification system derived from the product of two simpler classifications. However, our analysis shows that, among other deficiencies, the taxonomies are ill-defined as classification systems. Two major implications of the analysis are: (a) that instructors’ difficulties are primarily due to the taxonomies themselves, not to instructors, and (b) an identification of those features of the taxonomies that are deficient, and that therefore allow explaining the difficulties reported. Finally, we discuss some implications of these findings, both in general and for CSE, as well as possible future actions.
LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors

Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate.
Elements of AI: Busting AI Myths on a Global Scale

Considering the quick technological development in our society, as well as the continuous demand for workforce equipped with sufficient technology skills, self-paced online learning materials on hot topics like artificial intelligence (AI) play an important role in filling knowledge gaps and supporting life-long learning. In addition to technology professionals, citizens with all kinds of backgrounds have the right to gain a basic understanding in ground-breaking technologies, in order to have an equal and balanced say in what kind of technological solutions we should have in the future. Furthermore, the need for quality distance learning has been accelerated by the COVID-19 pandemic, and massive, open online courses (MOOCs) have the capability to cater for both academic students and the general public searching for reskilling and upskilling opportunities.
Motion Tracklet Oriented 6-DoF Inertial Tracking Using Commodity Smartphones

Motion tracklets are the basic fragments of the track followed by a moving object and constitute various everyday motion behavior. An accurate estimation of motion tracklets in 3-D space can enable a wide range of applications, ranging from human computer interaction to medical rehabilitation. This paper presents a novel dataset for accurate 6-DoF motion tracklet estimation with the inertial sensors on commodity smartphones. The dataset consists of around 100 minutes of handheld motion with 3 predominant types of motion track-lets and accurate ground truth using the Vicon systems. With the presented dataset, we further benchmarked the trajectory estimation using a lightweight neural odometry model, showcasing how the dataset can be used while providing quantitative performance for downstream tasks. Our dataset, toolkit and source code available at https://github.com/MAPS-Lab/smartphone-tracking-dataset.