Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Visualization at Home: A literature survey of consumption feedback design

Designing consumption feedback to support sustainable behavior is an active research topic. In recent years, relevant work has suggested a variety of possible design strategies. Addressing the more recent developments in this field, this paper presents a structured literature review, providing an overview of current information design approaches and highlighting open research questions. We suggest a literature-based taxonomy of used strategies, data source and output media with a special focus on design. In particular, we analyze which visual forms are used in current research to reach the identified strategy goals. Our survey reveals that the trend is towards more complex and contextualized feedback and almost every design within sustainable HCI adopts common visualization forms. Furthermore, adopting more advanced visual forms and techniques from information visualization research is helpful when dealing with ever-increasing data sources at home. Yet so far, this combination has often been neglected in feedback design.
Waste, so What? A reflection on waste and the role of designers in a circular economy

This paper discusses research currently being undertaken which addresses the interrelated volume, value and cost of waste and the responsibility designers have in its creation. The paper begins by outlining the contemporary waste problem (in the fashion industry). Then utilising observations made during recent field tests – where waste reduction and elimination strategies were applied to existing designs – the impact that explicit and implicit design hierarchies and complexity have on waste minimisation attempts are discussed. Questions such as: is waste a problem in the context of proposed Circular Economy models? After all, if we have a Circular Economy, then any waste we make can be put back into the cycle. So, will the CE let designers (and industry) off the hook? Lastly, I speculate as to what a fashion industry without waste might look like, discussing my design response to the issues raised.
New Approach for DOA Estimation in MIMO Radar With Nonorthogonal Waveforms

It is desirable to emit orthogonal waveforms in multiple-input multiple-output (MIMO) radar systems. Unfortunately, the waveform orthogonality may not be always guaranteed in practice. Nonorthogonal transmit waveforms will incur two challenges i.e., spatially colored noise and nonideal virtual direction matrix, for direction-of-arrival (DOA) estimation in colocated MIMO radar. In this article, a temporal cross-correlation matrix-based algorithm is proposed to overcome the above drawbacks. Also, the stochastic Cramér-Rao bound (CRB) is derived. The proposed algorithm does not require the prior knowledge of the waveform correlation matrix. Moreover, it is attractive from the perspective of efficiency as well as accuracy. Numerical experiments demonstrate the effectiveness of our algorithm.
Breakdown Characteristics of $\beta$ -(Al0.22Ga0.78)2O3/Ga2O3 Field-Plated Modulation-Doped Field-Effect Transistors

We investigate voltage breakdown of a <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-(Al<sub>0.22</sub>Ga<sub>0.78</sub>)<sub>2</sub>O<sub>3</sub>/Ga<sub>2</sub>O<sub>3</sub> modulation-doped field-effect transistor with a gate-connected field-plate and silicon nitride (SiN<sub>x</sub>) as the passivation layer. Breakdown characteristics were found to be limited by the Schottky gate. We demonstrate a high breakdown voltage of 1.37 kV for a gate-to-drain separation (<inline-formula> <tex-math notation="LaTeX">${L} _{\text {GD}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$16~\mu \text{m}$ </tex-math></inline-formula> with a specific ON-resistance <inline-formula> <tex-math notation="LaTeX">$\vphantom {\int ^{AAAAA^{AAA}}}$ </tex-math></inline-formula> of 120.1 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \text {cm}^{{2}}$ </tex-math></inline-formula>. A high average electric field of 3.9 MV/cm is extracted for <inline-formula> <tex-math notation="LaTeX">${L} _{\text {GD}}={320}$ </tex-math></inline-formula> nm. The reported results suggest SiN<sub>x</sub> as a potential passivation dielectric for Ga<sub>2</sub>O<sub>3</sub>.
A novel active balance assistive control strategy based on virtual stiffness model of XCoM

PurposeThis paper aims to propose a novel balance-assistive control strategy for hip exoskeleton robot.Design/methodology/approachA hierarchical balance assistive controller based on the virtual stiffness model of extrapolated center of mass (XCoM) is proposed and tested by exoskeleton balance assistive control experiments.FindingsExperiment results show that the proposed controller can accelerate the swing foot chasing XCoM and enlarge the margin of stability.Originality/valueAs a proof of concept, this paper shows the potential for exoskeleton to actively assist human regain balance in sagittal plane when human suffers from a forward or backward disturbing force.
Nurse Anesthesia Program Administrator's Decision-Making in Determining Interventions for a Student Exhibiting Unsatisfactory Clinical Performance

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Learning mixed membership models with a separable latent structure: Theory, provably efficient algorithms, and applications

In a wide spectrum of problems in science and engineering that includes hyperspectral imaging, gene expression analysis, and machine learning tasks such as topic modeling, the observed data is high-dimensional and can be modeled as arising from a dataspecific probabilistic mixture of a small collection of latent factors. Being able to successfully learn the latent factors from the observed data is important for efficient data representation, inference, and prediction. Popular approaches such as variational Bayesian and MCMC methods exhibit good empirical performance on some realworld datasets, but make heavy use of approximations and heuristics for dealing with the highly non-convex and computationally intractable optimization objectives that
Advanced Design of DSP-based High Precision Event Timer

Techniques for considerable advancing the precision and operational speed of event timers, based on the Enhanced Event Timing (EET) method, are considered. The method employs digital processing of analog signals that reflect the event positions. Emphasis is made on improving the generation of such signals, which, along with other technical enhancements of design, resulted in RMS resolution 3.5 ps and “dead time” 50 ns. In comparison with the previous event timer model based on the same method, the performance improvement is 2.5 times in terms of productivity factor characterizing the precision/speed ratio in a generalized form. Ill. 5, bibl. 3 (in English; summaries in English, Russian and Lithuanian).
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network

Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross-ensemble network. Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross-negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods.
Preparation and Characterization of a Dry Powder Inhaler Composed of PLGA Large Porous Particles Encapsulating Gentamicin Sulfate

Purpose: Direct delivery of aminoglycosides to the lungs was under extensive evaluations during the last decades. Because of large particle size, low density and porous structure, large porous particles (LPPs) are versatile carriers for this purpose. In this study, poly (lactic-co-glycolic acid) (PLGA) LPPs encapsulating gentamicin sulfate were prepared and in vitro characteristics of their freeze-dried powder as a dry powder inhaler (DPI) were evaluated. Methods: To prepare PLGA LPPs, a double emulsification-solvent evaporation method was optimized and gentamicin sulfate was post-loaded in the LPPs. in vitro characteristics including morphological features, thermal behavior, aerodynamic profile and cumulative drug release were evaluated by the scanning electron microscope (SEM), differential scanning calorimetry (DSC), next-generation cascade impactor (NGI) and Franz diffusion cell respectively. Results: The obtained results revealed that the preparation method was capable to produce spherical large homogenous highly porous particles. 94% of gentamicin sulfate released from LPPs up to 30 minutes. Mass median aerodynamic diameter (MMAD) and fine particle fraction (FPF) were 4.9 µm and 39% respectively. Conclusion: In this study, dry powder formulation composed of PLGA LPPs encapsulating gentamicin sulfate showed a promising in vitro behavior as a pulmonary delivery carrier. Improvements on the aerodynamic behavior and in vivo evaluations recommended for further developments.
Radar and vision fusion for the real-time obstacle detection and identification

Purpose The purpose of this paper is the presentation and research of a novel sensor fusion-based system for obstacle detection and identification, which uses the millimeter-wave radar to detect the position and velocity of the obstacle. Afterwards, the image processing module uses the bounding box regression algorithm in deep learning to precisely locate and identify the obstacles. Design/methodology/approach Unlike the traditional algorithms that use radar and vision to detect obstacles separately, the purposed method of this paper uses radar to determine the approximate location of obstacles and then uses bounding box regression to achieve accurate positioning and recognition. First, the information of the obstacles can be acquired by the millimeter-wave radar, and the effective target is extracted by filtering the data. Then, use coordinate system conversion and camera parameter calibration to project the effective target to the image plane, and generate the region of interest (ROI). Finally, based on image processing and machine learning techniques, the vehicle targets in the ROI are detected and tracked. Findings The millimeter wave is used to determine the presence of an obstacle, and the deep learning algorithm of the image is combined to determine the shape and the class of the obstacle. The experimental results indicate that the detection rate of this method is up to 91.6 per cent, which can better implement the perception of the environment in front of the vehicle. Originality/value The originality is based on the combination of millimeter-wave sensors and deep learning. Using the bounding box regression algorithm in RCNN, the ROI detected by radar is analyzed to realize real-time obstacle detection and recognition. This method does not require processing the entire image, greatly reducing the amount of data processing and improving the efficiency of the algorithm.
A INFLUÊNCIA DA CONSCIÊNCIA FONOLÓGICA EM CRIANÇAS ALFABETIZADAS PELOS MÉTODOS FÔNICO E SILÁBICO The Infl uence of Phonological Awareness in Children Taught to Read and Write with Phonic and Syllabic Methods

ABSTRACTPurpose: to compare the level of phonological awareness in classes taught with phonic and syllabic methods, through the investigation of the inﬂ uence of teaching methods on the development of phonological awareness. Methods: a sample of twelve children aged seven in average, students of two private schools in Brasilia, took part in the experiment. One school teaches through phonic method and the other one teaches through syllabic method. Six of the children were taught with phonic method while the other six were taught with syllabic method. All the children were submitted to the “Phonological Ability Proﬁ le” test. Results: the students of both schools achieved similar scores on the test in general and on most of the sub-tests. On the sub-tests involving phoneme sums and syllable and phoneme subtractions the children taught with phonic method scored higher. On the test of phrasal segmentation, the female participants had higher scores. Conclusion: the participants of the two schools had similar results on the general ﬁ nal score of the phonological awareness test.
Recording methods in applied environments

Recording methods in applied environments make incrasing use of new techniques for the acquisition of physiological and psychological data. Portable multichannel recorder/analyser systems and handheld computers provide new means of ambulatory assessment and foster a more ecologically valid approach to many issues in the applied fields. In this chapter, such developments are reviewed with particular emphasis on ambulatory techniques.
The Application of Image Compression Technology in Library

With the rapid development of information technology, more and more digital and electronic literature resources, including image compression technology, are widely used. This paper compares several kinds of image compression technology commonly used in library and finds that JPEG2000 as digital image storage format is more suitable for library image processing and storage. Keywords—Image compression technology; Library
POINT CLOUD CLASSIFICATION BY FUSING SUPERVOXEL SEGMENTATION WITH MULTI-SCALE FEATURES

Abstract. Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.
A Heuristic Algorithm of Cooperative Agents Communication for Enhanced GAF Routing Protocol in WSNs

Rapid progress in technologies has led to the development of small sensor nodes. A wireless sensor network (WSN) is an interconnected collection of a large number of these small sensor nodes that is used to monitor and record the physical environment. WSNs have applications in diverse scenarios. They play an important role in tracking and monitoring in different domains, such as environmental research, military, and health care. In most of these applications, the WSN is composed of a large number of nodes deployed in an area of interest, and not all nodes are directly connected to the base station (BS). In some cases, batteries of nodes cannot be recharged or changed. For that, the most solution required to overcome these problems is to optimize energy consumed during communication. Data transmission in networks is maintained by routing protocols, which are responsible for discovering the required paths. This paper presents an improvement of the Geographic Adaptive Fidelity (GAF) routing protocol created on a smart actives node selection. The routing process works on cooperative agents communication where another node is activated in the same grid if the data collected are considered as important data, and a heuristic method is used to find an optimal path in terms of energy to transmit data collected until reaching the BS. Simulation results prove that the cooperative agents GAF (CAGAF) routing protocol proposed is more efficient compared to the basic version in terms of considering important data, energy consumed, and dead nodes.
Using SPARQL/UPDATE to Extend RDB-to-RDF: A Mapping Approach

Converting a huge amount of Web data stored in databases to semantic data is a critical requirement for the development of current Web. The literal to URI and implicit information issues existing in some traditional approaches of mapping Relational Database (RDB) to RDF are studied in this paper. In order to solve the above problems, SPARQL/UPDATE is used to extend the traditional approaches. This extensible approach not only solves the literal to URI problem but also gives methods to process implicit information with rules in the original RDF document. Experimental results show that the new approach enable semantic query engines to find more exact results.
The Effect of Embryonic Arsenic Exposure on the Sensorimotor Behavior of Zebrafish (Danio Rerio)

The goal of this study is to determine the effect of arsenic exposure on vision in zebrafish (Danio rerio). The optic system of D. rerio is ideal for examining visual defects. Their eyes are similar to eyes of humans and can therefore be useful models in studies of human eye disease. Their optic system functions similarly to humans, so it is beneficial to observe how zebrafish are affected by contaminants in the environment. Arsenic is ubiquitous in groundwater, due to its natural presence in bed rock, but is elevated by human activities. In order to see any immediate effects on vision by arsenic, a behavioral assay was used. Due to accumulation in eye tissues, we predict that visual acuity will decrease with increased exposure to arsenic. Zebrafish embryos were exposed to 0, 10, 50, and 500 parts per billion of arsenic. At five days post hatch, a striped, rotating cylindrical drum created a pattern designed to elicit the optomotor response in zebrafish. Time spent following and going against the striped pattern was calculated to determine if the optomotor response was evoked in the zebrafish. A positive value would indicate an optomotor response, and a negative value would indicate no response. The control group did not behave as expected, as they showed a negative value in response to the cylindrical drum. Statistical analyses revealed a batch effect in this data set, and the total distance travelled showed a significant difference in activity level between batch one and batches two and three. Batch one showed an unexpected positive trend in optomotor response with increased arsenic exposure, with 50 ppb arsenic treatment group following the rotational pattern of the striped drum for a longer amount of time relative to the control. Batches two and three showed no significant differences between arsenic treatments and the control group. Changes in experimental design may result in the expected control behavior, and further replication would be necessary to determine any effects of arsenic on vision in zebrafish.
A Hybrid Model for Network Traffic Identification Based on Association Rules and Self-Organizing Maps (SOM)

Considerable effort has been made by researchers in the area of network traffic classification, since the Internet grows exponentially in both traffic volume and number of protocols and applications. The task of traffic identification is a complex task due to the constantly changing Internet and an increase in encrypted data. There are several methods for classifying network traffic such as known ports and Deep Packet Inspection (DPI), but they are not effective since many applications constantly randomize their ports and the payload could be encrypted. This paper proposes a hybrid model that makes use of a rule-based model along with a self-organizing map (SOM) model to tackle the problem of traffic classification without making use of the payload or ports. The proposed method also allows the generation of association rules for new unknown applications and further labeling by experts. The proposed hybrid model was superior to a rule-based model only and presented a precision of over 94% except for eMule application. The model was validated against a Measurement and Analysis on the WIDE Internet (MAWI) trace and presented true positive results above 99% and 0% false positives. It was also validated against another model based on computational intelligence, named Realtime, and the hybrid model proposed in this work presented better results when tested in real time network traffic.
Visual Analytics for Monitoring and Exploration of Blockchain Data With a Focus on the Bitcoin Blockchain

The goal of our work is to devise visual analytics techniques to deeply explore data stored on blockchains. Given the novel data storage nature of blockchains, they offer a unique opportunity to study the evolution of the stored data as well as data storage patterns. Opposed to most existing approaches that analyze the raw data we focus on aggregated measures. In particular, we analyze long-term activities and monitoring of activities that led to building the blockchain. We currently focus on the Bitcoin blockchain as the most popular blockchain with a turbulent history.
Role of Systemic Inflammation in Neurodegenerative Diseases

The role of systemic inflammation in neurodegeneration is complex. It involves interplay between several pro-inflammatory and anti-inflammatory factors, DNA damage and a cycle of neurotoxic inflammation and tissue degradation. Neuroinflammation can trigger neurodegeneration. Though suppressing system ic inflammation might be assumed to be neuroprotective against neurodegeneration, inhibiting certain features of the systemic immune response may cause damage.