Miracle-dz/UniFAR-FTUs huggingface dataset:
W-PathSim++: the novel approach of topic-driven similarity search in large-scaled heterogeneous network with the support of Spark-based DataLog

Similarity measurement on objects in a HIN is considered as a challenging problem of networked data mining. There are several proposed models that are used to compute the similarity score between objects in the context of multi-typed objects and links network, such as most well-known PathSim, HeteSim, etc. which are considered as the meta-path-based approaches. However, these meta-path-based models involve the information loss due to the shortage of evaluating the similarity in the text data of content-based objects such as papers. Moreover, these models also encounter the shortage of evaluating the common neighbor relevancy between two pairwise objects. Furthermore, the capability of handling big networked data is also common challenge for prior similarity search models which are designed to work on standalone environment. Therefore, in this paper, we present the W-PathSim++ model which are the extended version of our previous work for solving the problems related to content-based similarity between objects in HIN mining. Moreover, our works in this paper also focus on improving the accuracy of similarity measurement between two objects via thoroughly evaluating their common neighborhood objects. The proposed model is also designed for taking the advantages of DataLog in network representation and meta-path querying as well as implementing within the Spark-based distributed environment in order to enable for handling large-scaled HINs. We test the W-PathSim++ model in compare with previous similarity measure approaches within DBLP dataset in order to demonstrate the effectiveness of our proposed model.
Effects of 2 Types of Resistance Training Models on Obese Adolescents' Body Composition, Cardiometabolic Risk, and Physical Fitness.

Magnani Branco, BH, Carvalho, IZ, Garcia de Oliveira, H, Fanhani, AP, Machado dos Santos, MC, Pestillo de Oliveira, L, Macente, SB, and Nelson, NJ. Effects of 2 types of resistance training models on obese adolescents' body composition, cardiometabolic risk, and physical fitness. J Strength Cond Res XX(X): 000-000, 2018-The main objective of this study was to investigate the effects of 2 types of resistance training (RT) models in conjunction with interdisciplinary interventions by other health professionals to reduce the body fat and cardiometabolic risk of obese adolescents while improving their general health-related physical fitness. The 12-week analyses involved 18 male adolescents who were split into 2 groups (weightlifting: n = 9 and functional: n = 9), with equalization according to the primary muscle group (whenever possible), the effort:pause ratio, and intensity. The results showed reductions in fat mass and body fat, as well as in waist and hip circumferences (p < 0.05) after the intervention period. However, no significant differences were observed in terms of the body mass, body mass index, neck circumference, systolic and diastolic blood pressures, and for lean mass (p > 0.05) after the respective period. Maximal isometric strength, abdominal strength resistance, flexibility, and maximal oxygen consumption all produced significant increases after the interventions (p < 0.05). There were reductions in low-density lipoproteins and triglyceride levels after the intervention period (p < 0.05). For fasting glycemia, high-density lipoproteins, and alanine aminotransferase, no differences were observed (p > 0.05). In addition, no differences were observed in rating of perceived recovery, internal training load, or caloric intake (p > 0.05). With the results presented, it is concluded that both RT methods were effective at reducing both fat mass and body fat, thus improving health-related physical fitness components and decreasing cardiometabolic risk.
Combining Apache Spark & OrientDb to Find the Influence of a Scientific Paper in a Citation Network

The influence of a scientific paper in a citation network can be used as a basis for assessing the work and research results of a scientist. At present, there are lots of research determining the influence of a scientific paper in a citation network. However, combining Big Data processing technologies (such as Apache Spark) and Graph Databases (such as OrientDb) to address this problem is still challenging. In this paper, we present our research results in combining Apache Spark and OrientDb to find the influence of a scientific paper in a citation network and solve some related problems. In addition, we also present the test results of the processing time of some important functions in our experimental system. Finally, that is the conclusions and future works.
Where are the breaks in translation from theory to clinical practice (and back) in addressing depression? An empirical graph-theoretic approach

Abstract Background Research in depression has progressed rapidly over the past four decades. Yet depression rates are not subsiding and treatment success is not improving. We examine the extent to which the gap between science and practice is associated with the level of integration in how depression is considered in research and stakeholder-relevant documents. Methods We used a network-science perspective to analyze similar uses of depression relevant terms in the Google News corpus (approximately 1 billion words) and the Web of Science database (120 000 documents). Results These analyses yielded consistent pictures of insular modules associated with: (1) patient/providers, (2) academics, and (3) industry. Within academia insular modules associated with psychology, general medical, and psychiatry/neuroscience/biology were also detected. Conclusions These analyses suggest that the domain of depression is fragmented, and that advancements of relevance to one stakeholder group (academics, industry, or patients) may not translate to the others. We consider potential causes and associated responses to this fragmentation that could help to unify and advance translation from research on depression to the clinic, largely involving harmonizing employed language, bridging conceptual domains, and increasing communication across stakeholder groups.
Generating Pictorial Maps for Tourists using Flickr Photo Data

Flickr is frequently used to share photos at popular tourist spots. In particular, the number of views and the number of comments of Flickr photos often reflect user interests and attention. Therefore, in this work, we propose novel pictorial maps for tourists by calculating degrees of popularity and interest of spots using various metadata (e.g., the number of views, location information, posting times) of Flickr photos. We defined a pictorial map which is a map intuitively represents characteristics of a region by using pictures and photos. Through this, users will be able to deepen their interests and attention in various spots in a certain region, and it can be expected to promote tourism development and local economic growth.
An Improved Laplacian Semi-Supervised Regression

In this paper, we present an improved approach for semi-supervised regression problems. Our proposal is based on both, the use of the top eigen functions of integral operator derived from both labeled and unlabeled examples as the basis functions; and the learning of the prediction function by a Laplacian regularized regression. We compare our method with some representative ones dealing with semi-supervised regression. This comparison is done over several public data sets. We also verify the effectiveness of the proposed algorithm to reconstitute the installation date of the pipes of the Lyon Metropolis sewer network.
Deep Learning Based Method for Output Regularization of the Seizure Prediction Classifier

Seizure prediction has been a great challenge for neuroscientists in the last decade. Forecasting an epileptic episode has a substantial role in preventing or mitigating the harm that comes along, especially in some medically intractable cases. Despite the remarkable breakthrough in Artificial Intelligence and automated reasoning, the prediction of the epileptic seizure is still challenging due to the lack of dataset. Additionally, Electroencephalogram (EEG) signals are patient-specific, thus making it difficult to benchmark findings. In this study, instead of classifying short EEG segment, we detected the preictal phase as one long sequence of a latent space representation. We have compared different deep learning architectures to elect the best model. Using Long Short-Term Memory (LSTM) neural network or Gated Recurrent Units (GRUs) helps to obtain promising results. In fact, we proved that LSTM/GRUs could be applied successfully as a post-processing regularizer for the classifier output.
POStCODE Middleware for Post-Market Surveillance of Medical Devices for Cyber Security in Medical and Healthcare Sector in Australia

Postmarket surveillance for cyber security of medical devices is an area within the critical infrastructure of health care and public health that has been largely neglected. In developed countries post market quality assurance is passive following complaints from the health care institutions to the manufacturers of the medical devices. Recently, the individual devices can be made traceable allowing any malfunctions to be uniquely identified in each device. There is a lack of clarity on post-sale ownership and management of devices and the updates to the device software. These devices, once plugged into Healthcare Information Systems (HIS) act as FDA approved black boxes that cannot be patched, updated, or secured by anyone other than the manufacturer. Moreover, these unpatched devices provide back doors to cyber criminals to invade the HIS. These devices are soft targets for cyber criminals. So far, we have not come across any mechanisms that address the surveillance of these devices for cyber security. In this paper, we analyzed the post-sale surveillance regulations in Australia. Based on our findings, we present fog-based POSTmarket SurveillanCe Of DEvices (POStCODE) middleware that provides the operational details (excluding the private data of patient) of the devices directly to the manufacturers. The introduction of the POStCODE will give device manufacturers the means to closely monitor the functioning of their devices. Manufacturers will be able to upgrade devices, patch security vulnerabilities and monitor device performance thereby enhancing health care outcomes. The POStCODE middleware enhances device security whilst building partnerships between the health care facilitators and the device manufacturers.
RNDM: A Random Walk Method for Music Recommendation by Considering Novelty, Diversity, and Mainstream

Music recommendation systems automatically suggest a track to a user according to the user's preference. Thus, effectiveness information mining approach is necessarily required to analyze and measure the multiple attributes which could affect the users' preference such that more personalized recommendation can be achieved. However, traditional music recommendation systems rarely consider the multiple attributes of users' preference, leading to a misguided recommendation. In this paper, we propose a novel music recommendation approach, named RNDM. RNDM which employs three computational attributes to describe the user's music taste: novelty, diversity and mainstream. Based on these three attributes, a virtual friendship of each user is constructed by using a modified random walk algorithm. Finally, more personalized recommendations can be achieved by exploiting the music preferences of both the target user and his virtual friends. Compared with the state-of-the art music recommendation approaches, the experimental results show that RNDM significantly improves the accuracy of recommendation and provides more personalized music recommendation for users.
An Angry Birds-like Game System for Promoting Players’ Emotion

This paper presents a system aimed at encouraging players to smile when playing an Angry Birds-like game by generating levels whose designs look like pixel images and explicitly requiring the players to smile to see such levels. Our system consists of two major parts: level generator and game system. The level generator generates a level according to a given file that defines the level appearance of a pixel image. The game system includes emotion recognition and fog control; to encourage the player to smile, fog is first generated that covers the game screen, but when the player smiles it will gradually disappear. It is expected that the proposed system is useful in promoting the affect of its players, i.e., increasing the positive affect and decreasing the negative one.
Smart Meter Data Characterization and Clustering for Peak Demand Targeting in Smart Grids

Ahstract- The increasing popularity of smart meters deployed at customer sites provides a vital opportunity for network operators to efficiently support and implement demand response (DR) solutions to consumers. Currently, one focus for DR research is to extract knowledge from the smart meters data using data analytics techniques. Defining correct attributes is vital to access and target customers for DR. In this paper, a novel characterization model is proposed for peak load targeting of consumers. This model specifically describes customers' demand variations over one day period with consumption levels ranging from 0 to 1. A k-medoid clustering algorithm and a dynamic time warping (DTW) distance measure are proposed to cluster the characterized smart meter data. The COP index cluster validation technique is used to derive the optimal number of clusters. The proposed model is applied on the publicly available Irish smart meter data, and results show a well-defined grouping of customers based on their variation and peak load contribution.
High-Order Moment Models of Landmark Distribution for Local Homing Navigation

For local homing navigation, a mobile robot is supposed to return home using snapshots of the surrounding environment. It basically follows the snapshot model, comparing the home snapshot and the current view to determine the homing direction. In this paper, we suggest a high-order moment potential to describe the landmark feature distribution for local homing navigation. The moment potential function calculates the sum of products of the feature and the distance of landmark particles as a holistic view, allowing a high order of the distance. It effectively combines the range sensor values of landmarks in the current view and the visual features. By analogy with the moment in physics, the center of the moment is estimated as the reference point, which is the unique convergence point in the convex moment potential from any view, and using the property, the gradient of moment potential at the current position and home location can derive the homing vector. We provide a proof of convergence for any moment potential with order greater than or equal to one. Also, we demonstrate homing performances with various moment models in real environments to validate our models. The suggested moment models combining both landmark distance and visual feature have better performances than the visual information alone, and high-order moment potentials can be searched to obtain a better description of landmark distribution for a given environment.
Sampler: PMU-Based Sampling to Detect Memory Errors Latent in Production Software

Deployed software is still faced with numerous in-production memory errors. They can significantly affect system reliability and security, causing application crashes, erratic execution behavior, or security attacks. Unfortunately, existing tools cannot be deployed in the production environment, since they either impose significant performance/memory overhead, or can only detect partial errors. This paper presents Sampler, a library that employs the combination of hardware-based SAMPLing and novel heap allocator design to efficiently identify a range of memory ERrors, including buffer overflows, use-after-frees, invalid frees, and double-frees. Due to the stringent Quality of Service (QoS) requirement of production services, Sampler proposes to trade detection effectiveness for performance on each execution. Rather than inspecting every memory access, Sampler proposes the use of the Performance Monitoring Unit (PMU) hardware to sample memory accesses, and only checks the validity of sampled accesses. At the same time, Sampler proposes a novel dynamic allocator supporting fast metadata lookup, and a solution to prevent false alarms potentially caused by sampling. The sampling-based approach, although it may lead to reduced effectiveness on each execution, is suitable for in-production software, since software is generally employed by a large number of individuals, and may be executed many times or over a long period of time. By randomizing the start of the sampling, different executions may sample different sequences of memory accesses, working together to enable effective detection. Experimental results demonstrate that Sampler detects all known memory bugs inside real applications, without any false positive. Sampler only imposes negligible performance overhead (2.4% on average). Sampler is the first work that simultaneously satisfies efficiency, preciseness, completeness, accuracy, and transparency, making it a practical tool for in-production deployment.
Ophthalmic Diagnosis and Deep Learning - A Survey

This review presents an overview of the applications of deep learning inophthalmic diagnosis using retinal images and a review of the available retinalimage datasets. Applications of deep learning for segmentation of optic disk,blood vessels and retinal layer as well as detection of red lesions are reviewed.Recent deep learning models for classification of retinal disease includingage-related macular degeneration, glaucoma, diabetic macular edema anddiabetic retinopathy are also reported
How Many Doses Make a Difference? An Analysis of Secondary Prevention of Rheumatic Fever and Rheumatic Heart Disease

Background Acute rheumatic fever (ARF) and rheumatic heart disease cause substantial burdens worldwide. Long‐term antibiotic secondary prophylaxis is used to prevent disease progression, but evidence for benefits of different adherence levels is limited. Using data from northern Australia, we identified factors associated with adherence, and the association between adherence and ARF recurrence, progression to rheumatic heart disease, worsening or improvement of rheumatic heart disease, and mortality. Methods and Results Factors associated with adherence (percent of doses administered) were analyzed using logistic regression. Nested case–control and case–crossover designs were used to investigate associations with clinical outcomes; conditional logistic regression was used to estimate odds ratios (OR) with 95% CIs Adherence estimates (7728) were analyzed. Being female, younger, having more‐severe disease, and living remotely were associated with higher adherence. Alcohol misuse was associated with lower adherence. The risk of ARF recurrence did not decrease until ≈40% of doses had been administered. Receiving <80% was associated with a 4‐fold increase in the odds of ARF recurrence (case–control OR: 4.00 [95% CI: 1.7–9.29], case–crossover OR: 3.31 [95% CI: 1.09–10.07]) and appeared to be associated with increased all‐cause mortality (case–control OR: 1.90 [95% CI: 0.89–4.06]; case–crossover OR 1.91 [95% CI: 0.51–7.12]). Conclusions We show for the first time that increased adherence to penicillin prophylaxis is associated with reduced ARF recurrence, and a likely reduction in mortality, in our setting. These findings can motivate patients to receive doses since even relatively low adherence can be beneficial, and additional doses further reduce adverse clinical outcomes.
CounterMiner: Mining Big Performance Data from Hardware Counters

Modern processors typically provide a small number of hardware performance counters to capture a large number of microarchitecture events. These counters can easily generate a huge amount (e.g., GB or TB per day) of data, which we call big performance data in cloud computing platforms with more than thousands of servers and millions of complex workloads running in a "24/7/365" manner. The big performance data provides a precious foundation for root cause analysis of performance bottlenecks, architecture and compiler optimization, and many more. However, it is challenging to extract value from the big performance data due to: 1) the many unperceivable errors (e.g., outliers and missing values); and 2) the difficulty of obtaining insights, e.g., relating events to performance. In this paper, we propose CounterMiner, a rigorous methodology that enables the measurement and understanding of big performance data by using data mining and machine learning techniques. It includes three novel components: 1) using data cleaning to improve data quality by replacing outliers and filling in missing values; 2) iteratively quantifying, ranking, and pruning events based on their importance with respect to performance; 3) quantifying interaction intensity between two events by residual variance. We use sixteen benchmarks (eight from CloudSuite and eight from the Spark version of HiBench) to evaluate CounterMiner. The experimental results show that CounterMiner reduces the average error from 28.3% to 7.7% when multiplexing 10 events on 4 hardware counters. We also conduct a real-world case study, showing that identifying important configuration parameters of Spark programs by event importance is much faster than directly ranking the importance of these parameters.
A systematic review of patients’ drawing of illness: implications for research using the Common Sense Model

ABSTRACT Recent research has examined patients’ drawings of their illness as a means to identify patients’ illness representations. The aim of this systematic review was to examine which representations are evident in patients’ drawings, and whether drawing assessments are associated with patient outcomes. Ten electronic databases were searched for published journal papers in English up to 1 July 2017. Narrative synthesis summarised findings by participant characteristics, study design, illness representations, and associations with outcomes. There were 101 eligible studies, published across 29 different countries, with 27 different disease categories; 54 of the studies were with adults and 80 were cross-sectional. All core illness perception domains were evident; the most common being identity and related concepts (including symptoms, anatomy, pathophysiology), and emotional representations (including fear, denial, stigma). Perceptions of treatment and the clinical and social environment were evident. More organ damage drawn and larger drawing size were associated with worse perceptions and health outcomes, and drawings distinguished between patient groups. Limitations include the inability to conduct meta-analysis. In conclusion, patients’ drawings reveal additional domains of illness representations, specifically perceptions of pathophysiology, treatment and social environments, as well as illness pre-occupation. These findings expand theories of self-regulation and suggest image-based intervention strategies.