Miracle-dz/UniFAR-FTUs huggingface dataset:
Wearable artificial skin layer for the reconstruction of touched geometry by morphological computation

ABSTRACT This paper proposes a wearable haptic sensor for the reconstruction of the surface geometry of an object to be touched. The proposed haptic sensor is a thin rubber artificial skin layer that is formed around the user's finger or other body part and contains a small embedded strain gauge for measuring large deformations. The sensor can be easily fabricated by rubber dipping. First, it was demonstrated that the proposed sensor is not only able to statically detect the curvature of the touched surface but can also measure deformations due to rapid light tapping with robustness against motion noise. As an illustrative demonstration of morphological computation in haptics, it was then demonstrated that it is possible to reconstruct the geometry of tiny undulations in the surface of the touched object from the information obtained by the proposed sensor. GRAPHICAL ABSTRACT
An Empirical Performance Analysis on Hadoop via Optimizing the Network Heartbeat Period

To support a large-scale Hadoop cluster, Hadoop heartbeat messages are designed to deliver the significant messages, including task scheduling and completion messages, via piggybacking to reduce the number of messages received by the NameNode. Although Hadoop is designed and optimized for high-throughput computing via batch processing, the real-time processing of large amounts of data in Hadoop is increasingly important. This paper evaluates Hadoop’s performance and costs when the heartbeat period is controlled to support latency sensitive applications. Through an empirical study based on Hadoop 2.0 (YARN) [1] architecture, we improve Hadoop’s I/O performance as well as application performance by up to 13 percent compared to the default configuration. We offer a guideline that predicts the performance, costs and limitations of the total system by controlling the heartbeat period using simple equations. We show that Hive performance can be improved by tuning Hadoop’s heartbeat periods through extensive experiments.
InferLine: ML Inference Pipeline Composition Framework

The dominant cost in production machine learning workloads is not training individual models but serving predictions from increasingly complex prediction pipelines spanning multiple models, machine learning frameworks, and parallel hardware accelerators. Due to the complex interaction between model configurations and parallel hardware, prediction pipelines are challenging to provision and costly to execute when serving interactive latency-sensitive applications. This challenge is exacerbated by the unpredictable dynamics of bursty workloads. In this paper we introduce InferLine, a system which efficiently provisions and executes ML inference pipelines subject to end-to-end latency constraints by proactively optimizing and reactively controlling per-model configuration in a fine-grained fashion. Unpredictable changes in the serving workload are dynamically and cost-optimally accommodated with minimal service level degradation. InferLine introduces (1) automated model profiling and pipeline lineage extraction, (2) a fine-grain, cost-minimizing pipeline configuration planner, and (3) a fine-grain reactive controller. InferLine is able to configure and deploy prediction pipelines across a wide range of workload patterns and latency goals. It outperforms coarse-grained configuration alternatives by up 7.6x in cost while achieving up to 32x lower SLO miss rate on real workloads and generalizes across state-of-the-art model serving frameworks.
E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

The massive spread of the smart devices demanding high data rate and delay‐sensitized applications has escalated energy consumption levels in the fifth‐generation (5G) wireless communication networks (WCNs). It thus becomes imperative for the mobile network operators to meet the tremendous subscriber demands using energy‐efficient techniques. The limited battery capacities of the mobile terminals (MTs) further make energy efficiency (EE) critical. Device‐to‐device (D2D) communication ensues to be a key enabler technology in the realization of an energy‐efficient 5G WCN. It also serves as a paramount technology for green communication. This paper studies a resource allocation scheme, which iteratively accentuates adaptive reuse of resource blocks (RBs) from the active cellular users to the D2D pairs in a tri‐sectored cell. The cellular users involved in RB allocation to pairs are termed as active. Required count of RBs for each D2D pair is chosen by employing the hidden Markov model. The RB reuse strategy favors enhancement in throughput. The proposed energy‐efficient adaptive RB allocation with low complexity (E2ARC) scheme aims to guarantee ample RBs to individual D2D pairs to optimize the power consumption levels. With advanced throughput and optimal power, augmentation in EE is achieved with a high degree of fairness. E2ARC also comprehends low complexity. The efficacy of the proposed scheme is demonstrated by the simulation results.
Popularity-Aware Item Weighting for Long-Tail Recommendation.

Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to improve long-tail recommendation. Our approach works as a simple yet powerful add-on to existing recommendation algorithms for making a tunable trade-off between accuracy and long-tail coverage.
The disproportionate importance of long‐unburned forests and woodlands for reptiles

Abstract Our understanding of the impacts of time since fire on reptiles remains limited, partly because there are relatively few locations where long‐term, spatially explicit fire histories are available. Such information is important given the large proportion of some landscapes that are managed with frequent prescribed fire to meet fuel management objectives. We conducted a space‐for‐time study across a landscape in southeastern Australia where the known fire history spanned 6 months to at least 96 years. Four methods were used to survey reptiles in 81 forest and woodland sites to investigate how time since fire (TSF), habitat, and environmental variables affect reptile richness, abundance, and composition. We used generalized linear models, generalized linear mixed‐effects models, PERMANOVA, and SIMPER to identify relationships between the reptile assemblage (richness, abundance, and composition, respectively) and TSF, habitat, and environmental variables. All three reptile metrics were associated with TSF. Reptile richness and abundance were significantly higher in sites >96 years postfire than younger fire ages (0.5–12 years). Reptile composition at long‐unburned sites was dissimilar to sites burned more recently but was similar between sites burned 0.5–2 and 6–12 years prior to sampling. Synthesis and applications. Long‐unburned forests and woodlands were disproportionately more important for reptile richness and abundance than areas burned 6 months to 12 years prior to sampling. This is important given that long‐unburned areas represent <8% of our study area. Our results therefore suggest that reptiles would benefit from protecting remaining long‐unburned areas from fire and transitioning a greater proportion of the study area to long‐unburned. However, some compositional differences between the long‐unburned sites and sites 0.5–12 years postfire indicate that maintaining a diversity in fire ages is important for conserving reptile diversity.
Stand dynamics and competition in a mixed forest at the northern distribution limit of evergreen hardwood species

Abstract Tree species of three growth forms (evergreen conifers, deciduous hardwoods, and evergreen hardwoods) codominate at the northern distribution limit of evergreen hardwoods in central Japan. This study examined the stand dynamics and competition during 13 years at a single plot to reveal how three growth forms codominate at the ecotone. Species were characterized as large DBH and low tree density for evergreen conifers, and conversely for evergreen hardwoods. Total basal area increased during the examined period, accompanied with the reduction in tree density (i.e., mortality exceeded the recruitment rate). Mortality increased with time especially for small trees of deciduous hardwoods. The effect of competition among the three growth forms on tree growth was not detected. Species were classified into two axes. Ingrowth and recruitment rates of large evergreen conifers were lower than those of small evergreen hardwoods. The population growth rate was lower in species with greater mortality within each growth form. Deciduous hardwoods showed the highest mortality and lowest population growth rates among the three growth forms. Although the tree‐ring analysis revealed that disturbances occurred to some extent, the current disturbance regime would not trigger the regeneration of deciduous hardwoods. This study suggests that negative relations of maximum DBH with ingrowth and recruitment rates contribute to codominance of evergreen conifers and evergreen hardwoods, and more frequent or larger disturbances than at present are necessary for regeneration of deciduous hardwoods.
Who are the right teachers for medical clinical students? Investigating stakeholders’ opinions using modified Delphi approach

Purpose The purpose of this study was to realize that learning in a clinical setting, the interactions of the students with teachers, learning materials, and learning environments are essential. In clinical education, different groups may play the role of the teacher for medical students. This study was designed to determine the optimal characteristics for medical clinical teachers, their selection criteria, and their responsibilities. Methods The modified Delphi technique was used in this study. Participants comprised vice-chancellors of education, deans of medical schools, and deputies of education in medical schools across Iran. This study was conducted in three rounds. In the first round, the participants were selected using purposive sampling, and the data were collected through focus group discussions and analyzed through content analysis. The data collection tool in the second and third rounds involved a questionnaire derived from the first round, and the consensus criterion to accept or reject the questionnaire items was frequency distribution. Results The final number of statements in the first round was 157. The second-round questionnaire was designed in the four sections of teaching team, selection criteria, task description of the teaching team (including faculties, specialist staffs, residents, general practitioners, and health and treatment staff), and incentives separately for the specialist staff, residents, general practitioners, and health and treatment staff. The third-round questionnaire included feedback and items that were not agreed upon in the second round. Conclusion The results of this study indicated the necessity of forming a teaching team, paying attention to the selection criteria, and planning requirements for assigning responsibilities to the teaching team in accordance with the objectives, programs, and requirements of medical schools, along with using strategies to attract participation and create motivation in the teaching team.
A two-stage forecasting approach for short-term intermodal freight prediction

The forecasting of the freight transportation, especially the short-term case, is an important topic in the daily supply chain management. Intermodal freight transportation is subject to multiple complex calendar effects arising in the port environment. The use of prediction methods provides information that may be helpful as a decision-making tool in the management and planning of operations processes in ports. This work addresses the forecasting problem on a daily basis by a novel two-stage scheme combination to offer reliable predictions of fresh freight weight on Ro-Ro (roll-on/roll-off) transport for 7 and 14 days ahead. The study compares daily forecasting with a weekly forecasting approach. The applies database preprocessing and Bayesian regularization neural networks (BRNN) in Stage I. In Stage II, an ensemble framework of the best BRNN models is used to enhance the Stage I forecasting. The results show that the models assessed are a promising tool to predict freight time series for Ro-Ro transport.
Photo-Realistic Blocksworld Dataset

In this report, we introduce an artificial dataset generator for Photo-realistic Blocksworld domain. Blocksworld is one of the oldest high-level task planning domain that is well defined but contains sufficient complexity, e.g., the conflicting subgoals and the decomposability into subproblems. We aim to make this dataset a benchmark for Neural-Symbolic integrated systems and accelerate the research in this area. The key advantage of such systems is the ability to obtain a symbolic model from the real-world input and perform a fast, systematic, complete algorithm for symbolic reasoning, without any supervision and the reward signal from the environment.
3-Step Speaker Identification Approach in Degraded Conditions

The human voice is a perfect source of data for person identification in many applications. With a growing need for security in various public places, voice biometrics seems to be a good solution, since voice records can be easily taken. This paper provides a brief overview of approaches used in speaker recognition. It also presents a novel approach for speaker recognition in degraded conditions in the context of smart homes. The proposed method includes a pre-processing phase, feature extraction phase and classification phase. The second phase is based on the extraction of the sound spectrum energy maxima of the speech sound called formant. The next step is the use of a well-known technique called Dynamic Time Warping to find an optimal alignment between two given (time-dependent) sequences under certain restrictions. Complimentary steps are needed to complete the processing. The experiments are performed on a 1248-sample database to validate the proposed approach. The novel approach leads to better results regarding the state-of-the-art with an accuracy of 94.5%.
Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes

Background: Exposure to ambient air pollution is strongly associated with increased cardiovascular morbidity and mortality. Little is known about the influence of air pollutants on cardiac structure and function. We aim to investigate the relationship between chronic past exposure to traffic-related pollutants and the cardiac chamber volume, ejection fraction, and left ventricular remodeling patterns after accounting for potential confounders. Methods: Exposure to ambient air pollutants including particulate matter and nitrogen dioxide was estimated from the Land Use Regression models for the years between 2005 and 2010. Cardiac parameters were measured from cardiovascular magnetic resonance imaging studies of 3920 individuals free from pre-existing cardiovascular disease in the UK Biobank population study. The median (interquartile range) duration between the year of exposure estimate and the imaging visit was 5.2 (0.6) years. We fitted multivariable linear regression models to investigate the relationship between cardiac parameters and traffic-related pollutants after adjusting for various confounders. Results: The studied cohort was 62±7 years old, and 46% were men. In fully adjusted models, particulate matter with an aerodynamic diameter <2.5 µm concentration was significantly associated with larger left ventricular end-diastolic volume and end-systolic volume (effect size = 0.82%, 95% CI, 0.09–1.55%, P=0.027; and effect size = 1.28%, 95% CI, 0.15–2.43%, P=0.027, respectively, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 µm) and right ventricular end-diastolic volume (effect size = 0.85%, 95% CI, 0.12–1.58%, P=0.023, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 µm). Likewise, higher nitrogen dioxide concentration was associated with larger biventricular volume. Distance from the major roads was the only metric associated with lower left ventricular mass (effect size = −0.74%, 95% CI, −1.3% to −0.18%, P=0.01, per interquartile range increment). Neither left and right atrial phenotypes nor left ventricular geometric remodeling patterns were influenced by the ambient pollutants. Conclusions: In a large asymptomatic population with no prevalent cardiovascular disease, higher past exposure to particulate matter with an aerodynamic diameter <2.5 µm and nitrogen dioxide was associated with cardiac ventricular dilatation, a marker of adverse remodeling that often precedes heart failure development.
Correlation Filter of Deep Features for Robust Face Recognition

In this paper, a robust face recognition method combined correlation filter with deep learning is proposed. The deep features is first extracted for all the face images of one subject, and then merged to a correlation vector through Minimum Average Correlation Energy (MACE) filter. During recognition, the probe image only needs to be compared with the correlation filter rather than the raw image features. The proposed algorithm combines the advantages of both deep learning and correlation filter. Experiments show that the recognition result of our method is more accurate and the computation time is shorter compared with other state-of-the-art algorithms.
High-Rate Spatially Modulated Space Time Block Code

A high-rate spatially modulated space-time block code (HRSM-STBC) based on golden code is presented in which the spatial constellation (SC) codewords and the core STBC code matrix are designed for two active antennas. In addition to the high rate of the core STBC, the number of SC codewords offered by the HRSM-STBC scheme is the largest as compared to existing schemes that have the same antenna configuration. Consequently, the proposed scheme can provide the highest spectral efficiency to date. To maximize the achievable coding gain and diversity, the optimization of the HRSM-STBC is carried out. Performance advantages of our proposed scheme over its existing counterparts such as STBC-SM, H-STBC-SM, STBC-CSM, and DT-SM are demonstrated.
Modeling Interactions of Multimodal Road Users in Shared Spaces

In shared spaces, motorized and non-motorized road users share the same space with equal priority. Their movements are not regulated by traffic rules, hence they interact more frequently to negotiate priority over the shared space. To estimate the safeness and efficiency of shared spaces, reproducing the traffic behavior in such traffic places is important. In this paper, we consider and combine different levels of interaction between pedestrians and cars in shared space environments. Our proposed model consists of three layers: a layer to plan trajectories of road users; a force-based modeling layer to reproduce free flow movement and simple interactions; and a game-theoretic decision layer to handle complex situations where road users need to make a decision over different alternatives. We validate our model by simulating scenarios involving various interactions between pedestrians and cars and also car-to-car interaction. The results indicate that simulated behaviors match observed behaviors well.
A Novel Compensation Current Control Method for Grid-Connected PV Inverter to Improve Power Quality in Micro-Grid

This paper presents a new control method for grid-connected pulse width modulation (PWM) inverters to reduce the output current harmonic distortion in a wide range of grid-connected distributed generation applications, including photovoltaic (PV) inverter. The proposed control method is designed to eliminate harmonics in micro-grid and to correct the system unbalance. The proposed technique is able to increase the inverter output current around 18 times of conventional one. Another advantage of the proposed control method is that it can be easily adopted into the distributed generation (DG) control system without the installation of extra hardware. The proposed control method comprises of the advance synchronous reference frame method (ASRF).
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher-dimensional problems. However, these methods guarantee optimality of the resulting policy only in a statistical sense, which impedes their usage in safety critical systems, such as autonomous vehicles. Thus, we propose the Experience-Based-Heuristic-Search algorithm, which overcomes the statistical failure rate of a Deep-reinforcement-Iearning-based planner and still benefits computationally from the pre-learned optimal policy. Specifically, we show how experiences in the form of a Deep Q-Network can be integrated as heuristic into a heuristic search algorithm. We benchmark our algorithm in the field of path planning in semi-structured valet parking scenarios. There, we analyze the accuracy of such estimates and demonstrate the computational advantages and robustness of our method. Our method may encourage further investigation of the applicability of reinforcement-learning-based planning in the field of self-driving vehicles.
Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

A major issue in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) is the intrinsic nonstationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The nonstationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shift. To adapt to the covariate shift, we had proposed an adaptive learning method in our previous work and tested it on offline standard datasets. This paper presents an online BCI system using previously developed covariate shift detection (CSD)-based adaptive classifier to discriminate between mental tasks and generate neurofeedback in the form of visual and exoskeleton motion. The CSD test helps prevent unnecessary retraining of the classifier. The feasibility of the developed online-BCI system was first tested on ten healthy individuals, and then on ten stroke patients having hand disability. A comparison of the proposed online CSD-based adaptive classifier with conventional nonadaptive classifier has shown a significantly ( ${p}$ < 0.01) higher classification accuracy in both the cases of healthy and patient groups. The results demonstrate that the online CSD-based adaptive BCI system is superior to the nonadaptive BCI system and it is feasible to be used for actuating hand exoskeleton for the stroke-rehabilitation applications.