Miracle-dz/UniFAR-FTUs huggingface dataset:
A Topology-Based Approach to Pattern Recognition on Graph-Structured Data

With the ever-increasing scale and complexity of data, the form of graphs will gradually become the mainstream of data storage and display. As a result, the techniques of classification and pattern recognition on graph-structured data have become more and more important and can be applied to a broader range of domains. Based on the recently introduced approach which learned the representations of graphs using convolutional networks, we proposed an improved model, which is called T-PSCN, to classify arbitrary graphs with attributed nodes and edges more effectively. In our model, we integrate the edge attributes as node attributes and exploit topological information at both node and graph level for each graph to enrich the graph representations during the training, which has a significant improvement in accuracy. Results demonstrated that T-PSCN is highly competitive with other graph classification approaches.
Victim Detection System Using Autonomous UAV Flight and Data Analysis based on Multiple Beacon Radio Sensing

In recent years, the number of people who get lost in a mountainous area due to natural disaster and bad weather is increasing. In order to quickly detect the location of the victims when the accident is occurred, a beacon device for climbers has been utilized so far. However, range of the radio wave transmitted from the beacon device is very short, hence dangerous situations might occur on the rescue team when they get in to the mountainous area in order to search for victims.Therefore, in this research, we develop a new system which can detect victim’s location and condition by utilizing UAV (Unmanned Aerial Vehicle). In the proposed system, after a rescuer starts to control the UAV by using a control application of a smartphone, the UAV automatically moves to a location where the accident is occurred, and searches the location of victim by moving to the direction where a radio wave from the beacon can be measured strongly. Furthermore, the UAV identifies a condition of the victim by measuring and analyzing the received signal strength of radio waves transmitted from the beacon device on multiple frequency bands which has different propagation characteristics. For example, a radio wave of the 429MHz frequency bands is easily absorbed by moisture compared with the 920MHz bands. These mean that the received signal strength of radio waves of the 429MHz band is lower than that of the 920MHz band when the beacon is covered by snow. By utilizing these propagation characteristics, we attempt to determine the condition of the victim.
Chinese Story Generation with FastText Transformer Network

The sequence transformer models are based on complex recurrent neural network or convolutional networks that include an encoder and a decoder. High-accuracy models are usually represented by used connect the encoder and decoder through an attention mechanism. Story generation is an important thing. If we can let computers learn the ability of story-telling, computers can help people do more things. Actually, the squence2squence model combine attention mechanism is being used to Chinese poetry generation. However, it difficult to apply in Chinese story generation, because there are some rules in Chinese poetry generation. Therefore, we trying to use 1372 human-labeled summarization of paragraphs from a classic novel named “Demi-Gods and Semi-Devils” (天龍八部) to train the transformer network. In our experiment, we use FastText to combine Demi-Gods and Semi-Devils Dataset and A Large Scale Chinese Short Text Summarization Dataset to be input data. In addition, we got a lower loss rate by using two layer of self-attention mechanism.
Emotion Expression in HRI – When and Why

In this paper, we draw attention to the social functions of emotional display in interaction. A review of HRI papers on emotion suggests that this perspective is rarely taken in the field, but that it is useful to account for the context- and culture-dependency of emotional expression. We show in two case studies that emotional display is expected to occur at very specific places in interaction and rather independently from general emotional states, and that different cultures have different conventions regarding emotional expression. Based on conversation analytic work and the results from our case studies, we present design recommendations which allow the implementation of specific emotional signals for different human-robot interaction situations.
Power of Using Regular Expression Patterns in Software Coding Standards Quality Control

The regular expression is a method to describe strings that conform to a pattern. The need to use regular expressions arise to deal with strings which have a specific pattern such as a user input string, formatted data, and computer programs code elements. In this paper, the authors intend to establish a framework that aims to improve the quality control process to prove that the source code meets standards using a regular expressions. The framework extracts the code from the files then determines if the written code is following the company's own coding standards. The proposed framework covers the naming convention standards, data types utilizations, undesired programming language commands and keywords, hardcoded literals usage, and as well as the business dictionary tags compliance. The short achieved results shows the effectiveness of regular expressions to figure out the compliance of the source code with the standards rules set.
ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views

In this paper, we introduce the ShopSign dataset, which is a newly developed natural scene text dataset of Chinese shop signs in street views. Although a few scene text datasets are already publicly available (e.g. ICDAR2015, COCO-Text), there are few images in these datasets that contain Chinese texts/characters. Hence, we collect and annotate the ShopSign dataset to advance research in Chinese scene text detection and recognition. The new dataset has three distinctive characteristics: (1) large-scale: it contains 25,362 Chinese shop sign images, with a total number of 196,010 text-lines. (2) diversity: the images in ShopSign were captured in different scenes, from downtown to developing regions, using more than 50 different mobile phones. (3) difficulty: the dataset is very sparse and imbalanced. It also includes five categories of hard images (mirror, wooden, deformed, exposed and obscure). To illustrate the challenges in ShopSign, we run baseline experiments using state-of-the-art scene text detection methods (including CTPN, TextBoxes++ and EAST), and cross-dataset validation to compare their corresponding performance on the related datasets such as CTW, RCTW and ICPR 2018 MTWI challenge dataset. The sample images and detailed descriptions of our ShopSign dataset are publicly available at: this https URL.
Genotypic variability and stability of poplars and willows grown onnitrate-contaminated soils

Abstract Phyto-recurrent selection is an established method for selecting tree genotypes for phytoremediation. To identify promising Populus (poplar) and Salix (willow) genotypes for phytotechnologies, our objectives were to (1) evaluate the genotypic variability in survival, height, and diameter of poplar and willow clones established on soils heavily contaminated with nitrates; and (2) assess the genotypic stability in survival and diameter of selected poplar clones after one and eleven growing seasons. We tested 27 poplar and 10 willow clones planted as unrooted cuttings, along with 15 poplar genotypes planted as rooted cuttings. The trees were tested at an agricultural production facility in the Midwestern, United States. After 11 growing seasons, using phyto-recurrent selection, we surveyed survival and measured the diameter of 27 poplar clones (14 unrooted, 13 rooted) that were selected based on superior survival and growth throughout plantation development. Overall, willow exhibited the greatest survival, while poplar had the greatest height and diameter. At 11 years after planting, superior clones were identified that exhibited above-average diameter growth at the establishment- and rotation-age, most of which had stable genotypic performance over time. Selection of specific clones was favorable to genomic groups, based on the geographic location and soil conditions of the site.
[Long-term individual cognitive stimulation program in patients with mild neurocognitive disorder: a pilot study].

INTRODUCTION There is evidence to suggest that cognitive stimulation produces cognitive benefits in people with mild neurocognitive disorder. However, the effect has been previously demonstrated to be minimal to moderate and the effect of long-term individual interventions, namely on specific cognitive domains, is unknown. AIM To assess the efficacy, feasibility and acceptability of a long-term individual cognitive stimulation intervention for patients with mild neurocognitive disorder. PATIENTS AND METHODS Patients (n = 30) with mild neurocognitive disorder were assigned to a cognitive stimulation intervention group (n = 15) or to a control group (n = 15). The intervention consisted of 88 individual sessions, approximately 45 minutes long, with two sessions per week. External evaluators assessed the level of alteration in cognitive performance, depressive symptoms and the level of independence in the performance of basic activities of daily living. RESULTS After the intervention, a significant improvement was found in the intervention group compared to the control group in overall cognitive performance (d = 0.83), specifically in the language domain (d until 1.50). There were also lower depressive symptoms in the intervention group compared to the control group (d = 0.93). Only 6.7% of the participants dropped out the study, with participants attending a mean of 83 ± 12.1 sessions. CONCLUSIONS The results support the efficacy, feasibility and acceptability of the intervention for mild neurocognitive disorder and justify a randomized controlled trial of the program with a larger sample.
Soft Decision-based Data Detection Scheme for NR Communications System

In this paper, the low-density parity-check code and 256 QAM of new radio (NR) technology were applied to meet the high reliability and high data-rate requirements of 5G communications systems. In addition, we propose a data detection scheme based on a soft decision to improve the reliability. The simulation results show that the proposed data detection scheme can improve the error rate and throughput of a conventional system.
Design of a low-profile wideband patch antenna with L-shaped feeding mechanism

This paper has presented the design of a novel low-profile wideband patch antenna. The antenna structure is formed of three layers of substrates with the radiating patches on the top, the grounded patches in the middle and the feeding portion at the bottom. The large operating frequency bandwidth has been obtained due to the L-shaped feeding technique. Moreover, the low cross-polarization level can also be achieved as the feeding structure can be regarded as the preferred differential feeding. The obtained results can confirm that the proposed antenna has achieved a wide impedance bandwidth (VSWR ≤ 2) of about 60% (3-5.5 GHz) and the whole height of the antenna is 6 mm (around 0.06λl at the lowest operating frequency). The obtained cross-polarization level is less than-30dB compared with co-polarization.
The Implementation of Gradient Descent Based Methods Using Parallel Computing in R for Regression Tasks

In the previous research, it has been developed the R package gradDescent, which contains the gradient descent algorithm and more than 10 methods of its variations. However, all algorithms in the package can only run in the standalone/single core mode. On the other hand, the amount of data processed in the future tends to increase. Therefore, this research is aimed to develop the previous package by extending 4 algorithms (i.e., mini batch gradient descent (MBGD), stochastic gradient descent (SGD), stochastic average gradient descent (SAGD), and accelerated gradient descent (AGD)) with the R high-performance computing packages: foreach, doSNOW, and pbdMPI, in order to have the ability to process data in multicore / parallel computing. By utilizing the parallel-computing tools, the computation cost can be reduced. After designing and implementing the proposed model, some simulations were conducted by using the California Housing dataset. Additionally, some comparisons with other software libraries were presented. The results show that the implementation of the proposed model provided competitive root mean squared errors and smallest computation cost.
A portfolio of biologic self-injection devices in rheumatology: how patient involvement in device design can improve treatment experience

Abstract Biologic drugs (e.g. anti-tumor necrosis factors) are effective treatments for multiple chronic inflammatory diseases including rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis. Administration of biologic drugs is usually via subcutaneous self-injection, which provides many patient benefits compared to infusions including increased flexibility, reduced costs, and reduced caregiver burden. However, it is also associated with challenges such as needle phobia, patient treatment misconceptions and incorrect drug administration, and can be impacted by dexterity problems. Evidence suggests these problems, along with other drug administration challenges (e.g. patient forgetfulness, busy lifestyles, and polypharmacy), can reduce patient adherence to treatment. To combat these challenges, patient feedback has been used to develop a range of self-injection devices, including pre-filled syringes, pre-filled pens, and electronic injection devices. Providing different devices for drug administration gives patients the opportunity to choose a device that addresses the challenges they face as an individual. Research suggests involving patients in medical device development, providing patients with a choice of devices and enrolling individuals in patient support programs can empower patients to take control of their treatment journey. By providing a portfolio of self-injection devices, designed based on patient needs, patient experience will improve, potentially improving adherence and hence, long-term treatment outcomes.
A Comprehensive Review on Axial Flux Machines and Its Applications

This paper presents a thorough review concerning the design types of axial flux permanent magnet machines (AFPM) in terms of different features such as construction, design, materials, and manufacturing. Particular emphasis is given on the design and its effect on the performance AFPM machines. First of all, early and modern axial flux machines are mentioned. Secondly, rotor construction of different axial flux machines is described, then different stator constructions are mentioned depending upon the presence of slots and stator back iron. Then, according to the arrangement of the rotor stator structure, the machines are classified into single, double, and multi-stack arrangements. Advantages, disadvantages, and applications of each type of rotor and stator are pointed out. Finally, on the basis of the reviewed literature merits, demerits, features and application of different axial flux machines structures are explained and clarified. Thus, this paper provides connection between the machines that are currently being used in industry and the developments of AFPM throughout the years.
Comparing Graph Sampling Methods Based on the Number of Queries

Random walk-based graph sampling methods can effectively estimate feature values in large-scale social networks wherein the node IDs are unknown. Real social networks are sampled by repeatedly querying their APIs to acquire the lists of adjacent nodes. These queries can then become a bottleneck in the sampling process because nearly all social network services restrict the rate at which queries can be issued. However, most existing graph sampling studies have not focused on the number of queries but have instead compared methods based on sample size. Therefore, such graph sampling methods cannot be recommended for estimating feature values in actual social networks. This study presents an approach to assess graph sampling methods that focus on the number of queries. This describes the time taken by algorithms for typical random walk-based graph sampling methods, such as a simple random walk with re-weighting (SRW-rw), a non-backtracking random walk with re-weighting (NBRW-rw), and a Metropolis ? Hastings random walk (MHRW), which require queries. The graph sampling precision was then experimentally evaluated based on sample size and query number standards using actual social networks, and the types of changes that occur were observed.
Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

By relying on small sized and massively distributed infrastructures, the Edge computing paradigm aims at supporting the low latency and high bandwidth requirements of the next generation services that will leverage IoT devices (e.g., video cameras, sensors). To favor the advent of this paradigm, management services, similar to the ones that made the success of Cloud computing platforms, should be proposed. However, they should be designed in order to cope with the limited capabilities of the resources that are located at the edge. In that sense, they should mitigate as much as possible their footprint. Among the different management services that need to be revisited, we investigate in this paper the monitoring one. Monitoring functions tend to become compute-, storage- and network-intensive, in particular because they will be used by a large part of applications that rely on real-time data. To reduce as much as possible the footprint of the whole monitoring service, we propose to mutualize identical processing functions among different tenants while ensuring their quality-of-service (QoS) expectations. We formalize our approach as a constraint satisfaction problem and show through micro-benchmarks its relevance to mitigate compute and network footprints.
A New Quantitative Approach for Measuring Changes of 3D Structures in Trabecular Bone

A novel approach which is based on 3D complexity measures was developed in order to quantify the spatial geometrical properties of trabecular bone. These non-destructive measures are able to evaluate different aspects of the organization and complexity of the architecture of trabecular bone, such as complexity of its surface, node complexity, or trabecular bone surface curvature. Their application to 3D μCT images of human proximal tibiae of various osteoporotic stages illustrates the abilities of these measures. The outcome of the bone architecture evaluation by ∗marwan@agnld.uni-potsdam.de
Family medicine physician teachers and residents’ intentions to prescribe and interpret spirometry: a descriptive cross-sectional study

Abstract Background: Spirometry is the best test to demonstrate airway obstruction, but remains underused in primary care. Objectives: We assessed, among family medicine physician teachers and residents, their intention to prescribe spirometry in patients suspected of chronic obstructive pulmonary disease and their intention to interpret the results. This evaluation is based on the theoretical framework proposed by Godin et al. for the study of factors influencing healthcare professionals’ behavior. Methods: Participants of this descriptive cross-sectional study were recruited from eight Family medicine units (FMUs) of Laval University’s network. They completed a 23-item self-administered questionnaire measuring their intention to prescribe and to interpret spirometry as well as some determinants of this intention (beliefs about capabilities, beliefs about consequences, social influence and moral norm). Answers to each of the items were scored on a Likert scale (score 1 to 7) where a higher score indicated a greater agreement with the statement. Results: Of the 284 eligible physicians, 104 were included. The mean score ± standard deviation of physicians' intention to prescribe spirometry (6.6 ± 0.7) was higher than to interpret the results (5.8 ± 1.5). Mean scores for all determinants of intention measured were also higher for prescription than for interpretation of spirometry. Conclusion: The results suggest that participants have a very strong intention to prescribe spirometry. Although the intention to interpret the results is positive, it is weaker than for the prescription of the test. Further studies will be needed to assess the barriers to spirometry interpretation.
Long-term Health Outcomes and Health System Costs Associated With Surgical Site Infections

Supplemental Digital Content is available in the text Objectives: To examine the association between surgical site infections (SSIs) and hospital readmissions and all-cause mortality, and to estimate the attributable health care costs of SSIs 1 year following surgery. Background: SSIs are a common postoperative complication; the long-term impact of SSI on health outcomes and costs has not been formally evaluated. Methods: This retrospective cohort study included all adult patients who underwent surgery at the 1202-bed teaching hospital in Ottawa, Ontario, Canada, and were included in the National Surgical Quality Improvement Program database between 2010 and 2015. The study exposure was postoperative SSI. The study outcomes included hospital readmission, all-cause mortality, and health care costs at 1 year (primary) and at 30 days and 90 days (secondary) following surgery. Results: We identified 14,351 patients, including 795 patients with SSIs. Our multivariable analyses that accounted for competing risks demonstrated that at 1-year following the index date, superficial and deep/organ space SSIs were significantly associated with an increase in hospital readmission [hazard ratio (HR) = 1.63, 95% confidence interval (95% CI) 1.39–1.92 and HR = 3.49, (95% CI 2.76–4.17, respectively) and all-cause mortality (HR = 1.35, 95% CI 1.10–1.98 and HR = 2.21, 95% CI 1.44–2.78, respectively]. At 1 year after surgery, patients with superficial and deep/organ space SSIs incurred higher health care costs C$20,648 (95% CI) C$16,980- C$24,112and C$53,075 (95% CI) C$44,628- C$60,936), than non-SSI patients. Conclusion: SSIs, especially deep/organ space SSI, contribute to adverse health outcomes and health care costs across the entire year after surgery. Our findings highlight the importance of effective prevention/monitoring strategies targeting both short- and long-term consequences of SSI.