Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Web Objects enabled Data-Driven Microservices for E-Health Service Provision in IoT Environment

Internet of Things (IoT) has the most significance in the intelligent healthcare service provision because of the wealth of data from disparate sources. By using the applications and tools of the IoT and web of data we can collect large of data and provide better care and monitoring services to the patients while reducing the cost and time. Depressive disorder (DD) is a common disease worldwide and it is estimated by the World Health Organization that in 2020, depression will be the largest medical burden on the society and government expenditures. To reduce the depression period is the important aspect for the practitioner psychiatric by using cognitive behavior therapy (CBT). To reduce cost and time we proposed an IoT based CBT system based on Web of Objects (WoO) data-driven microservices. We used data-driven microservices for the depressive person data accumulation, representation and assistance services. We also used semantically interoperable data representation microservices for IoT based DD symptoms monitoring. Our proposed model and use case scenario significance show that this approach is useful for the DD assistance and care.
High Microbial Diversity Despite Extremely Low Biomass in a Deep Karst Aquifer

Despite the importance of karst aquifers as a source of drinking water, little is known about the role of microorganisms in maintaining the quality of this water. One of the limitations in exploring the microbiology of these environments is access, which is usually limited to wells and surface springs. In this study, we compared the microbiology of the Madison karst aquifer sampled via the potentiometric lakes of Wind Cave with surface sampling wells and a spring. Our data indicated that only the Streeter Well (STR), which is drilled into the same hydrogeologic domain as the Wind Cave Lakes (WCL), allowed access to water with the same low biomass (1.56–9.25 × 103 cells mL-1). Filtration of ∼300 L of water from both of these sites through a 0.2 μm filter allowed the collection of sufficient cells for DNA extraction, PCR amplification of 16S rRNA gene sequences, and identification through pyrosequencing. The results indicated that bacteria (with limited archaea and no detectable eukaryotic organisms) dominated both water samples; however, there were significant taxonomic differences in the bacterial populations of the samples. The STR sample was dominated by a single phylotype within the Gammaproteobacteria (Order Acidithiobacillales), which dramatically reduced the overall diversity and species richness of the population. In WCL, despite less organic carbon, the bacterial population was significantly more diverse, including significant contributions from the Gammaproteobacteria, Firmicutes, Chloroflexi, Actinobacteria, Planctomycetes, Fusobacter, and Omnitrophica phyla. Comparisons with similar oligotrophic environments suggest that karst aquifers have a greater species richness than comparable surface environs. These data also demonstrate that Wind Cave provides a unique opportunity to sample a deep, subterranean aquifer directly, and that the microbiology of such aquifers may be more complex than previously anticipated.
Beyond Attributes: Adversarial Erasing Embedding Network for Zero-shot Learning

In this paper, an adversarial erasing embedding network with the guidance of high-order attributes (AEEN-HOA) is proposed for going further to solve the challenging ZSL/GZSL task. AEEN-HOA consists of two branches, i.e., the upper stream is capable of erasing some initially discovered regions, then the high-order attribute supervision is incorporated to characterize the relationship between the class attributes. Meanwhile, the bottom stream is trained by taking the current background regions to train the same attribute. As far as we know, it is the first time of introducing the erasing operations into the ZSL task. In addition, we first propose a class attribute activation map for the visualization of ZSL output, which shows the relationship between class attribute feature and attention map. Experiments on four standard benchmark datasets demonstrate the superiority of AEEN-HOA framework.
Functionalized Keratin as Nanotechnology-Based Drug Delivery System for the Pharmacological Treatment of Osteosarcoma

Osteosarcoma therapy might be moving toward nanotechnology-based drug delivery systems to reduce the cytotoxicity of antineoplastic drugs and improve their pharmacokinetics. In this paper, we present, for the first time, an extensive chemical and in vitro characterization of dual-loaded photo- and chemo-active keratin nanoparticles as a novel drug delivery system to treat osteosarcoma. The nanoparticles are prepared from high molecular weight and hydrosoluble keratin, suitably functionalized with the photosensitizer Chlorin-e6 (Ce6) and then loaded with the chemotherapeutic drug Paclitaxel (PTX). This multi-modal PTX-Ce6@Ker nanoformulation is prepared by both drug-induced aggregation and desolvation methods, and a comprehensive physicochemical characterization is performed. PTX-Ce6@Ker efficacy is tested on osteosarcoma tumor cell lines, including chemo-resistant cells, using 2D and 3D model systems. The single and combined contributions of PTX and Ce6 is evaluated, and results show that PTX retains its activity while being vehiculated through keratin. Moreover, PTX and Ce6 act in an additive manner, demonstrating that the combination of the cytostatic blockage of PTX and the oxidative damage of ROS upon light irradiation have a far superior effect compared to singularly administered PTX or Ce6. Our findings provide the proof of principle for the development of a novel, nanotechnology-based drug delivery system for the treatment of osteosarcoma.
Objective Reduction on Many-Objective Traffic Lights Signaling Optimization

Traffic Lights Signaling Optimization consists in optimizing traffic-light cycle using optimization algorithms. In this context, a traffic simulator can represent different road intersections and traffic lights and can calculate traffic quality measures. A solution to this problem can be encoded in a vector representing the time of each traffic light on the simulator and the objective functions are the traffic quality metrics. Since there are several metrics this problem can be defined as a many-objective optimization problem. In spite of the existence of several objective functions, the majority of the related work selects a small set, often two. This paper proposes a manyobjective optimization framework based on objective reduction to solve Traffic lights Signaling Optimization Problem. Here, twelve objective functions are used. To deal with the high number of metrics, objective reduction techniques are applied along a multi-objective evolutionary algorithm. An experimental set is conducted to analyze if it possible to reduce the number of objective functions on the Traffic lights Signaling Optimization Problem and if this reduction enhances the performance of the optimization algorithm.
Optical Flow Dataset and Benchmark for Visual Crowd Analysis

The performance of optical flow algorithms greatly depends on the specifics of the content and the application for which it is used. Existing and well established optical flow datasets are limited to rather particular contents from which none is close to crowd behavior analysis; whereas such applications heavily utilize optical flow. We introduce a new optical flow dataset exploiting the possibilities of a recent video engine to generate sequences with ground-truth optical flow for large crowds in different scenarios. We break with the development of the last decade of introducing ever increasing displacements to pose new difficulties. Instead we focus on real-world surveillance scenarios where numerous small, partly independent, non rigidly moving objects observed over a long temporal range pose a challenge. By evaluating different optical flow algorithms, we find that results of established datasets can not be transferred to these new challenges. In exhaustive experiments we are able to provide new insight into optical flow for crowd analysis. Finally, the results have been validated on the real-world UCF crowd tracking benchmark while achieving competitive results compared to more sophisticated state-of-the-art crowd tracking approaches.
Trimetazidine Protects Against Atherosclerosis by Changing Energy Charge and Oxidative Stress

Background This study investigated the effect and the possible mechanism of trimetazidine in atherosclerosis. Material/Methods We established an atherosclerotic rat model by high-fat diet and vitamin D injection. Rats were separated into 3 different groups: control, atherosclerosis, and trimetazidine (n=10). The aortic artery was isolated and its morphological features were examined by hematoxylin and eosin (HE) staining. Serum low-density lipoprotein cholesterol (LDL-c), total cholesterol (TC), and triglycerides (TG) were analyzed using an automatic biochemical analyzer. Human aortic smooth muscle cells (HASMCs) were cultured and divided into 5 groups: no treatment, H2O2 treatment only, trimetazidine preincubation before H2O2 treatment, oxidized low-density lipoprotein (oxLDL) treatment only, and trimetazidine preincubation before oxLDL treatment. HASMCs proliferation was tested using the Cell Counting Kit-8. Reactive oxygen species (ROS) and malondialdehyde (MDA) levels, superoxide dismutase (SOD) activity of the aortic artery, and HASMCs were measured using commercially available kits. Results HE staining assay showed that trimetazidine suppressed the progression of atherosclerosis and reduced foam cell formation in the aortic artery without affecting serum lipid levels. HASMCs proliferation assay revealed that trimetazidine alleviated the inhibitory effect of H2O2 on HASMCs proliferation and inhibited oxLDL-induced proliferation of HASMCs. Moreover, trimetazidine ameliorated ROS up-regulation elicited by H2O2 or oxLDL in HASMCs. Additionally, trimetazidine restored SOD activity and reduced MDA content of HASMCs. Conclusions Trimetazidine suppressed the progression of atherosclerosis by enhancing energy value, decreasing ROS level of aortic artery, modulating HASMCs proliferation, and reducing oxidative stress in HASMCs.
Serverless Data Analytics in the IBM Cloud

Unexpectedly, the rise of serverless computing has also collaterally started the "democratization" of massive-scale data parallelism. This new trend heralded by PyWren pursues to enable untrained users to execute single-machine code in the cloud at massive scale through platforms like AWS Lambda. Inspired by this vision, this industry paper presents IBM-PyWren, which continues the pioneering work begun by PyWren in this field. It must be noted that IBM-PyWren is not, however, just a mere reimplementation of PyWren's API atop IBM Cloud Functions. Rather, it is must be viewed as an advanced extension of PyWren to run broader MapReduce jobs. We describe the design, innovative features (API extensions, data discovering & partitioning, composability, etc.) and performance of IBM-PyWren, along with the challenges encountered during its implementation.
Spatio‐Temporal Variation of Alkaloids in Annona purpurea and the Associated Influence on Their Antifungal Activity

Annona purpurea grows in the areas of low elevation in deciduous forests of Mexico, those areas have marked rainy and dry seasons. This species produces more than 30 bioactive alkaloids that could have potential in the control of phytopathogens. This research provides data on the variation of the content and number of alkaloids during an annual cycle and the associated inhibitory potential of the compounds against three phytopathogenic fungi. For one year, alkaloidal extracts of stems and leaves were obtained every two months. The extract profiles were determined by gas chromatography with tandem mass spectrometry and their antifungal activity was examined in vitro. The alkaloids, annomontine and oxopurpureine, obtained from the roots and leaves, respectively, were also evaluated individually. The yields, profiles and activities of the extracts, as well as the abundance of annomontine and oxopurpureine in the extracts, were contrasted with the seasonality and phenological phases of the plant. The data indicate that the alkaloid content was higher at the height of the dry season. High yields also occurred during flowering. The strongest inhibitory effect was obtained from the root extracts during the last month of dry season. This finding seems to be explained by the higher chemodiversity of alkaloids in extracts from this season. Annomontine and oxopurpureine inhibited all three phytopathogens; however, they were not solely responsible for the activity of A. purpurea.
A Comparative Analysis of Content-based Geolocation in Blogs and Tweets

The geolocation of online information is an essential component in any geospatial application. While most of the previous work on geolocation has focused on Twitter, in this paper we quantify and compare the performance of text-based geolocation methods on social media data drawn from both Blogger and Twitter. We introduce a novel set of location specific features that are both highly informative and easily interpretable, and show that we can achieve error rate reductions of up to 12.5% with respect to the best previously proposed geolocation features. We also show that despite posting longer text, Blogger users are significantly harder to geolocate than Twitter users. Additionally, we investigate the effect of training and testing on different media (cross-media predictions), or combining multiple social media sources (multi-media predictions). Finally, we explore the geolocability of social media in relation to three user dimensions: state, gender, and industry.
Method of Kidney Image Segmentation Based on Improved C-V Model

Kidney medical image segmentation is the key step of medical image analysis and non-invasive computer aided diagnosis system in related kidney diseases. Based on the traditional Chan-Vese model, according to the continuity and redundancy of the kidney tissues between slices in the CT sequence images, combined with local statistical information for improving the curve evolution, combined with the initial contour based on a narrowband evolution curve and the termination conditions by using the biological continuity of adjacent slices, a kidney tissue segmentation model based on energy minimization was proposed. The model was used to process the 24 sets of standard segmentation test data sets. The segmentation results showed that the average PRA and DSC indices have improved over traditional models, reached 0.961 and 94.68%, respectively, the kidney tissue could be located and segmented efficiently and accurately.
Proposal of a consensus builder for environmental condition setting in spaces where people with various preferences coexist

Some services for controlling devices in homes and offices through networks have been proposed. Such services are required to achieve user satisfaction. However, it is difficult to set actuator parameters that build consensus among all users in an environment including users with various preferences. In this study, we propose a device control system for consensus building in such an environment. The proposed system constructs a user stress detection model through parameters for devices, sensor values, and biodata. A user stress detection model is constructed in an office testbed. This study focuses on the influence of temperature and light color on user stress, so we vary the parameters for air conditioning and light color when collecting experimental data. We use the LF/HF ratio used for general stress detection. Experimental results show user stress value related to environmental temperature for each user. We also show that user stress is reduced by changing light color. In addition, we construct a user stress detection model using experiment data, and propose a control algorithm for consensus building.
Realization of flat band with possible nontrivial topology in electronic Kagome lattice

We observed electronic Kagome lattice and possible nontrivial electronic flat band in twisted multilayer silicene at 77 K. The energy dispersion of fermions or bosons vanishes in momentum space if destructive quantum interference occurs in a frustrated Kagome lattice with only nearest-neighbor hopping. A discrete flat band (FB) without any dispersion is consequently formed, promising the emergence of fractional quantum Hall states at high temperatures. Here, we report the experimental realization of an FB with possible nontrivial topology in an electronic Kagome lattice on twisted multilayer silicene. Because of the unique low-buckled two-dimensional structure of silicene, a robust electronic Kagome lattice has been successfully induced by moiré patterns after twisting the silicene multilayers. The electrons are localized in the Kagome lattice because of quantum destructive interference, and thus, their kinetic energy is quenched, which gives rise to an FB peak in the density of states. A robust and pronounced one-dimensional edge state has been revealed at the Kagome edge, which resides at higher energy than the FB. Our observations of the FB and the exotic edge state in electronic Kagome lattice open up the possibility that fractional Chern insulators could be realized in two-dimensional materials.
Tear Film Classification Using Phylogenetic Diversity Indexes as Texture Descriptor

Dry eye is a common disease that affects the tear film and the ocular surface causing a great variety of symptoms impairing the patient daily activities. The diagnosis of this disease requires a number of tests to evaluate different physiological characteristics. One of the tests captures the appearance of the tear film using the Doane interferometer, which generates images that can be categorized into five groups: strong fringes, coalescing strong fringes, line fringes, coalescing line fringes, and debris. The images can be manually classified by specialists into one of these five groups. The use of automatic systems for the diagnosis of dry eye can assist experts in the classification of these images, contributing to more accurate exams. Therefore, this work presents a method for automatic classification of images from the the lipid layer of the tear film using phylogenetic diversity indexes to extract texture features of the images. The proposed method presents promising results, reaching accuracy rates higher than 96%.
Interference Analysis of Joint-Spatial-Division and Reuse method for Next-Generation WLAN System

To improve spectral efficiency in Next-generation WLAN environment, a Joint-Spatial-Division and Reuse method is proposed. With the hope to use this high spectral efficiency user selection and scheduling method, we calculate the interference distribution situation at both Tx position and Rx position when using JSDR method. We provide a statistical interference distribution analysis to prove the proposed JSDR method can be realized in realistic environment.
Does Customer-Salesperson Guanxi or Salesperson's Influence Tactic Matter in social media context?: Empirical Evidence from Vietnam

This study aims to examine the effects of customer-salesperson guanxi, and purchase-decision involvement on the use of influence tactics and effectiveness. We administered an online survey in Vietnam. Among the 250 questionnaires issued, 200 (80%) valid responses were obtained. Multi-variate regression analysis was applied to explore effects of salesperson's influence tactics and customer-salesperson guanxi on influence effectiveness. The results show: (a) the greater the customer-salesperson guanxi, the more a salesperson uses give-oriented approaches over require-oriented approaches; (b) the higher the purchase-decision involvement, the greater the extent of the use of inspiration tactics. (c) the threat tactic generates the highest influence effectiveness, following by inspiration and promise tactics. Our research confirms the significant effect of customer-salesperson guanxi and addresses the positive role of purchase-decision involvement in the use and effectiveness of salespeople's influence tactics. Our results also demonstrate the crucial role of inspiration tactics among customers with higher purchase-decision involvement. We suggest several guidelines for adopting the require-oriented and give-oriented approach according to the level of customer-salesperson guanxi and purchase-decision involvement. This study provides the appropriate influence tactics in conjunction with customer-salesperson guanxi and purchase-decision involvement, especially in the social media context.
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations

To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence. Many research efforts have been dedicated to building such dialogue systems, yet few shed light on modeling the conversation flow in an ongoing dialogue. Besides, it is common for people to talk about highly relevant aspects during a conversation. And the topics are coherent and drift naturally, which demonstrates the necessity of dialogue flow modeling. To this end, we present the multi-turn cue-words driven conversation system with reinforcement learning method (RLCw), which strives to select an adaptive cue word with the greatest future credit, and therefore improve the quality of generated responses. We introduce a new reward to measure the quality of cue words in terms of effectiveness and relevance. To further optimize the model for long-term conversations, a reinforcement approach is adopted in this paper. Experiments on real-life dataset demonstrate that our model consistently outperforms a set of competitive baselines in terms of simulated turns, diversity and human evaluation.
Deep Inverse Tone Mapping Optimized for High Dynamic Range Display

The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.
A Hierarchical Neural Network for Sequence-to-Sequences Learning

In recent years, the sequence-to-sequence learning neural networks with attention mechanism have achieved great progress. However, there are still challenges, especially for Neural Machine Translation (NMT), such as lower translation quality on long sentences. In this paper, we present a hierarchical deep neural network architecture to improve the quality of long sentences translation. The proposed network embeds sequence-to-sequence neural networks into a two-level category hierarchy by following the coarse-to-fine paradigm. Long sentences are input by splitting them into shorter sequences, which can be well processed by the coarse category network as the long distance dependencies for short sentences is able to be handled by network based on sequence-to-sequence neural network. Then they are concatenated and corrected by the fine category network. The experiments shows that our method can achieve superior results with higher BLEU(Bilingual Evaluation Understudy) scores, lower perplexity and better performance in imitating expression style and words usage than the traditional networks.