Miracle-dz/UniFAR-FTUs huggingface dataset:
Br-MAP: Concept map system using e-book logs

This preliminary study developed the concept map tool “BR-Map” using learning logs on eBook viewer, and investigated the relationships between self-regulated learning (SRL) awareness, learning behaviors (usage of BR-Map, and one-minute paper and report submission), and learning performance. Psychometric data and learning logs were collected in the lecture course, and their relationships were analyzed using Spearman’s correlation analysis. The results indicated that awareness of intrinsic value, use of cognitive learning strategies, and self-regulation had significant correlations with the usage of BR-Map. The awareness of cognitive learning strategies had significant correlation with standard deviation of one-minute paper submission hours. With regard to relationships between the BR-Map usage and learning behaviors, the relationships between the usage of BR-Map and one-minute paper submissions, which was a regularly weekly assigned task, were found.
Recognition of human activity based on sparse data collected from smartphone sensors*

This paper proposes a method of human activity monitoring based on the regular use of sparse acceleration data and GPS positioning collected during smartphone daily utilization. The application addresses, in particular, the elderly population with regular activity patterns associated with daily routines. The approach is based on the clustering of acceleration and GPS data to characterize the user’s pattern activity and localization for a given period. The current activity pattern is compared to the one obtained by the learned data patterns, generating alarms of abnormal activity and unusual location. The obtained results allow to consider that the usage of the proposed method in real environments can be beneficial for activity monitoring without using complex sensor networks.
Experimental Realization of Deadbeat Control on a Hybrid Model of Legged Locomotion

Experimental Realization of Deadbeat Control on a Hybrid Model of Legged Locomotion Tianqi Li Chair of the Supervisory Committee: Assistant Professor Samuel A. Burden Department of Electrical Engineering In this thesis work, I validate the deadbeat controller on a one-legged hopper by experiment, which includes the construction of the test bed, controller build-up, adjustment of dynamics for the controller and experimental validation for the controller. The mechanical test bed is designed and built for the one dimensional hopping experiment with changing terrain to validate the deadbeat controller. The deadbeat controller is generated by parameter optimization on the Poincaré Map, which is provided by [9] based on the analysis of running animals whose dynamics can be partially or fully controlled by the encoded control laws in the feedforward trajectories of the appendages. The controller is able to achieve deadbeat stability of the apex by controlling the dynamic with only sensing of apex heights. Based on dynamics of the hopper, the method of decreasing energy dissipation is applied that is to attach springs on the leg. By exploring the hopper, the return map of terrain disturbance and control input is measured in physical experiment. Finally the controller is validated on the experiment of hopping on the changing terrain with small terrain feedback sensing.
Development of analysis method for airborne radiation monitoring using the inverse problem solutions

Following the accident at the Fukushima Daiichi Nuclear Power Plant, some kind of unmanned aerial vehicles (UAVs) have been used to collect environmental radiation data. The Japan Atomic Energy Agency has been developed methods for analyzing radiation measurements taken by a small multirotor UAV. In the conventional method, count rate was converted to the air dose rate using a simple parameter. Therefore, such a conversion of the air dose rate using the conventional method is difficult in an area where the distribution of air dose rate is heterogeneous, or the terrain is complex. The goal of this study was to apply the inverse radiation method, which is used in medical applications such as positron emission tomography, for environmental radiation measurement. Basic algorithm was established for conversion from count rate to air dose rate in considering of topographic data. The algorithm was applied to real radiation data obtained by a UAV, and its accuracy was evaluated by comparing that data with ground measurement data. As compared with conventional methods, our algorithm was demonstrated to achieve a more accurate estimation of the air dose rate on the ground.
A Survey on Machine Learning Algorithm in Emerging Technologies

Machine Learning (ML) is the union of various teaches in science and technology. While it is imagined, ML is a piece of software engineering be that as it may, in its embodiment, it gets or uses techniques from other great teaches and develop computing hypotheses and technologies, for example, measurements, computational algorithms, advancement, and information mining. In this paper, we investigate how these orders and technologies function as an inseparable unit to set up an energetic scientist picks up a far reaching point of view for being a ML master. We have proposed a guide to indicate how unique orders and technologies add to the ML establishment and we talk about each piece of the guide independently. Besides, to apply the proposed guide in pragmatic terms, we additionally display how to utilize the proposed guide with regards to IoT and Fog Computing. The primary commitment of this paper is to give a rule by building up a guide for foundational necessities of being a Machine Learning topic master for the analysts or industry specialists.
Design of Convolutional Neural Network Based on FPGA

Recently, FPGAs have been widely used in the implementation of hardware accelerators for CNN, especially on mobile and embedded devices. This paper proposes an FPGA-based CNN accelerator. The highly reusable accelerator function is designed to construct the optimized convolutional neural network and memory optimization with a lower hardware resource consumption. The results show the advantage of performance and power when compared with Core i5 CPU and GTX 960 GPU.
Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization

We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user’s intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users.
Air Quality Forecast through Integrated Data Assimilation and Machine Learning

Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from desert and anthropogenic emissions. An accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on real-time observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably.
Bis{2-[3-(dimethylamino)propyliminomethyl]-6-methoxyphenolato}-κ3 N,N′,O 1;κ2 N,O 1-zinc(II) dihydrate

In the title mononuclear ZnII complex, [Zn(C13H19N2O2)2]·2H2O, the ZnII atom is coordinated by two O atoms and three N atoms from two crystallographically different Schiff base ligands in a distorted trigonal–bipyramidal environment. One O and two N atoms constitute the base of the pyramid, and one O and one N atoms occupy the apical positions. Intermolecular O—H⋯O and O—H⋯N hydrogen bonds between the lattice water molecules and N/O atoms of the Schiff base ligands stabilize the conformation, whereas intermolecular O—H⋯O hydrogen bonds between the two lattice water molecules lead to a chain structure in [001].
Bird Species Recognition Using Unsupervised Modeling of Individual Vocalization Elements

This paper investigates acoustic modeling for recognition of bird species from audio field recordings. First, the acoustic scene is decomposed into isolated segments, corresponding to detected sinusoids. Each segment is represented by a sequence of the frequency and normalized magnitude values of the sinusoid. The temporal evolution of these features is modeled using hidden Markov models (HMMs). A novel method for an unsupervised modeling of individual bird vocalization elements is proposed. The element models are initialized using HMM-based clustering and then further trained using an iterative maximum likelihood label re-assignment procedure. State duration modeling, performed in a post-recognition stage, is explored. Finally, we developed a hybrid deep neural network—hidden Markov model. The developed acoustic models are employed for bird species identification, detection of specific species, and recognition of multiple bird species vocalizing in a given recording. The detection system employs score normalization. Recognition of multiple bird species is performed based on maximizing the likelihood of a set of segments on a subset of bird species models, with penalization based on Bayesian information criterion applied. Experimental evaluations are performed on more than 37 h of sound field recordings, containing vocalizations of 48 bird species, plus more than 16 h of non-bird sound recordings. Using 3 s of the detected signal, the best system achieved: identification accuracy of 98.7%, detection with the equal error rate of 2.7%, and recognition accuracy of 97.3% and 95.4% when vocalizations of multiple bird species are present, with the number of bird species known and estimated, respectively.
Predictive Modeling of Pavement Damage Using Machine Learning and Big Data Processing

Potholes, soil settlement, and road subsidence have become major road safety hazards in South Korea. Such problems not only impede driver and pedestrian safety but also cause secondary accidents, economic losses, and damage the nation's image. To this end, we developed local predictive models that can be extrapolated to national estimation models. These models were developed from a specific area (Seoul Metropolitan City) that has the highest occurrences of potholes and road subsidence. This research utilized big data and artificial intelligence techniques to develop these models. The first step involved the dimensional reduction of independent variables using a mechanical-statistical approach. A data standardization process was then used for reducing the uncertainty of these variables. A total of 19 machine learning optimization methods were used to train the standardized variables. The optimized models were finally determined by an error comparison. As a result, the optimized prediction models for potholes, soil settlement, and road subsidence were found to be multiple regression analysis that showed an accuracy of 70% and robust regression analysis that showed an accuracy of 73%.
Demonstration of Multivariate Photonics: Blind Dimensionality Reduction With Integrated Photonics

Multi-antenna radio front-ends generate a multi-dimensional flood of information, most of which is partially redundant. Redundancy is eliminated by dimensionality reduction. This class of operations presents harsh fundamental tradeoffs to contemporary digital processing techniques. These tradeoffs can be broken in the analog domain, in which the performance of optical technologies greatly exceeds that of electronic counterparts. Here, we present concepts, methods, and a first demonstration of multivariate photonics: a combination of integrated photonic hardware, analog dimensionality reduction, and blind algorithmic techniques. We experimentally demonstrate 2-channel, 1.0 GHz principal component analysis in a photonic weight bank using recently proposed algorithms for synthesizing the multivariate properties of signals to which the receiver is blind. Novel methods are introduced for controlling blindness conditions in a laboratory context. This work provides a foundation for further research in multivariate photonic information processing, which is poised to play a role in future generations of wireless technology.
Hierarchical Reinforcement Learning Introducing Genetic Algorithm for POMDPs Environments

Perceptual aliasing is one of the major problems in applying reinforcement learning to the real world. Perceptual aliasing occurs in the POMDPs environment, where agents cannot observe states correctly, which makes reinforcement learning unsuccessful. HQ-learning is cited as a solution to perceptual aliasing. HQ-learning solves perceptual aliasing by using subgoals and subagent. However, subagents learn independently and have to relearn each time when subgoals change. In addition, the number of subgoals is fixed, and the number of episodes in reinforcement learning increases unless the number of subgoals is appropriate. In this paper, we propose the reinforcement learning method that generates subgoals using genetic algorithm. We also report the effectiveness of our method by some experiments with partially observable mazes.
Storage and Ingestion Systems in Support of Stream Processing: A Survey

Under the pressure of massive, exponentially increasing amounts of heterogeneous data that are generated faster and faster, Big Data analytics applications have seen a shift from batch processing to stream processing, which can reduce the time needed to obtain meaningful insight dramatically. Stream processing is particularly well suited to address the challenges of fog/edge computing: much of this massive data comes from Internet of Things (IoT) devices and needs to be continuously funneled through an edge infrastructure towards centralized clouds. Thus, it is only natural to process data on their way as much as possible rather than wait for streams to accumulate on the cloud. Unfortunately, state-of-the-art stream processing systems are not well suited for this role: the data are accumulated (ingested), processed and persisted (stored) separately, often using different services hosted on different physical machines/clusters. Furthermore, there is only limited support for advanced data manipulations, which often forces application developers to introduce custom solutions and workarounds. In this survey article, we characterize the main state-of-the-art stream storage and ingestion systems. We identify the key aspects and discuss limitations and missing features in the context of stream processing for fog/edge and cloud computing. The goal is to help practitioners understand and prepare for potential bottlenecks when using such state-of-the-art systems. In particular, we discuss both functional (partitioning, metadata, search support, message routing, backpressure support) and non-functional aspects (high availability, durability, scalability, latency vs. throughput). As a conclusion of our study, we advocate for a unified stream storage and ingestion system to speed-up data management and reduce I/O redundancy (both in terms of storage space and network utilization).
On Mining Conditions using Encoder-decoder Networks

A condition is a constraint that determines when something holds. Mining them is paramount to understanding many sentences properly. There are a few pattern-based approaches that fall short because the patterns must be handcrafted and it is not easy to characterise unusual ways to express conditions; there is one machine-learning approach that requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated on a small dataset with Japanese sentences on hotels. In this paper, we present an encoder-decoder model to mine conditions that does not have any of the previous drawbacks and outperforms the state of the art in terms of effectiveness.
Untangling Layered Garments: An Implicit Approach

The efficient animation of layers of garments is a challenging task, as it requires handling collisions and contacts between multiple thin surfaces, which may be difficult to untangle once inter-penetrations have occurred. We propose a novel geometric approach, based on implicit surfaces, to robustly handle such situations. At each animation step, our method converts the possibly intersecting garment surfaces to an implicit representation. They are then combined using a new binary operator that guarantees, as output, collision free states of the surfaces. In addition to a precise modeling of contact situations, our method enables to model the relative influence of each cloth layer, based on their relative stiffnesses and thicknesses.
A Node Importance Analytical Approach for Multiple Relationships Online Social Network

Multiple relationships online social network node importance analysis was proposed based on signaling process, and considering the characteristics of multiple relationships which would interrelate with each other. The novel approach has been approved to be efficient by experiments described in this paper, and can improve efficiency of the analysis of public opinions, community structure detection and information propagation.
Gamified Approaches for Water Management Systems: An Overview

Water demand is growing worldwide, especially in densely populated areas, as a consequence of population growth [1] and urbanization [2]. The spatial concentration of water demand in urban areas is impacting demand magnitude, peak intensity, share between use sectors, and indoor and outdoor usage [3]. This, coupled with climate change and land use change, is intensifying the stress on finite water resources, creating both operational and environmental challenges to water supply. To cope with this evolving context, traditional supply and management schemes need to be adapted to meet future demand whilst preventing unsustainable resources exploitation. In this context, keeping into account geographical constraints and increasing marginal cost often limiting capacity expansion through infrastructural interventions [4], demand-side management strategies are key to complement supply-side interventions for securing reliable water supply as well as reducing shortages and overall utilities’ costs [5]. The potential of demand-side management interventions has been demonstrated in a number of works (see, for a review, [6] and references therein), especially in the last two decades, fostered by the promotion of several water saving programs worldwide, particularly in areas affected by prolonged droughts (e.g., California [7]) or low-recharge periods (e.g., Australia [5]).
Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments

Background: Determining the activated brain areas due to diﬀerent activities is one of the most common targets in functional magneticresonanceimaging(fMRI)dataanalysis,whichcouldbecarriedoutbyhemodynamicresponsefunction(HRF)evaluation. The HR functions reﬂect changes of cerebral blood ﬂow (CBF) in response to neural activity. Objectives: In this study, ﬁve models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data. Methods: The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO 2 gas was administered using a face-maskunderphysiologicalmonitoring. Threepatientswithbraintumorswerescanned. ThefMRIdataanalysiswasperformedusing theSPM12MATLABtoolbox. Akaike’sinformationcriterion(AIC),Schwarz’ Bayesian(SBC),andmeansquareerror(MSE)criteriawere used to select the best HRF estimation model. Results: In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), ﬁnite impulse response (FIR),andinverselogistic(IL)modelswerealmostequaltothestandardHRF.Meansquareerror,AIC,andSBCindiceswereignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies. Conclusions: The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood ﬂow.
ECG analysis for human recognition using non-fiducial methods

The electrocardiogram (ECG) has emerged as a new biometric for human recognition due to its robustness against fraudulent attacks. This article presents a novel method of ECG biometric for human recognition using autocorrelation (AC) followed by one of the three transformation techniques, i.e. discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT). The effectiveness of these transformations is evaluated on the dimensionality reduction techniques i.e. principal component analysis and linear discriminant analysis (LDA). Thus, the systems prepared by different combinations of transformations and dimensionality reduction techniques are evaluated on publically available databases of Physionet. The authentication and identification accuracies achieved by these systems are found the best on DFT and LDA combination. The authentication performance is reported to 99.98% (99.83%), whereas the average rank classification accuracy is reported to 100% (97%) on QT database (MIT-BIH arrhythmia database) of Physionet.