Miracle-dz/UniFAR-FTUs huggingface dataset:
NOAH: Creating Data Integration Pipelines over Continuously Extracted Web Data

We present Noah, an ongoing research project aiming at developing a system for semi-automatically creating end-to-end Web data processing pipelines. The pipelines continuously extract and integrate information from multiple sites by leveraging the redundancy of the data published on the Web. The system is based on a novel hybrid human-machine learning approach in which the same type of questions can be interchangeably posed both to human crowd workers and to automatic responders based on machine learning (ML) models. Since the early stages of pipelines, crowd workers are engaged to guarantee the output data quality, and to collect training data, that are then used to progressively train and evaluate automatic responders. The latter are fully deployed into the data processing pipelines to scale the approach and to contain the crowdsourcing costs later. The combination of guaranteed quality and progressive reductions of costs of the pipelines generated by our system can improve the investments and development processes of many applications that build on the availability of such data processing pipelines.
Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

In the Web of Things (WoT) context, an increasing number of stationary and mobile objects provide functions as RESTful services, also called resources, that can be combined with other existing Web resources, to create value-added processes. However, nowadays resource discovery and selection are challenging, due to (1) the growing number of resources providing similar functions, making Quality of Resource (QoR) essential to select appropriate resources, (2) the transient nature of resource availability due to sporadic connectivity, and (3) the location changes of mobile objects in time. In this paper, we first present a location-aware resource discovery that relies on a 3-dimensional indexing schema, which considers object location for resource identification. Then, we present a QoR-driven resource selection approach that uses a Selection Strategy Adaptor (SSA) to form i-compositions (with i ∈N*) offering different implementation alternatives. The defined SSA allows forming resource compositions while considering QoR constraints and Inputs/Outputs matching of related resources, as well as resource availability and users different needs (e.g., optimal and optimistic compositions obtained using a scoring system). Analyses are made to evaluate our service quality model against existing ones, and experiments are conducted in different environments setups to study the performance of our solution.
Suggesting Reasonable Phenotypes to Clinicians

We describe a method for aiding clinicians in the process of reporting clinical features (abnormalities and symptoms) in newborns with suspected rare genetic diseases. The Human Phenotype Ontology (HPO) is well suited for such characterization, but impractical to navigate in a clinical situation. We enable diﬀerent entry points depending on clinical specialization, detect certain types of errors that may occur, and suggest related phenotypes and candidate diseases to check for further clinical workup. Our main improvements are: (1) incompatible entries are highlighted and/or denied; (2) relevant related phenotypes are suggested; and (3) diseases are proposed in cases where the clinical report contains a complete proﬁle and includes highly speciﬁc phenotypes.
Speculative Execution of Similarity Queries: Real-Time Parameter Optimization through Visual Exploration

ABSTRACT The parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this issue, supporting the user in understanding the connection between abstract model parameters and model results. We present a visual analytics system enabling result understanding and model refinement on a ranking-based similarity search algorithm. Our system (1) visualizes the results in a projection view, mapping their pair-wise similarity to screen distance, (2) indicates the influence of model parameters on the results, and (3) implements speculative execution to enable real-time iterative refinement on the time-intensive offline similarity search algorithm.
An In-depth Investigation of Large-scale RDF Relational Schema Optimizations Using Spark-SQL

This paper discusses one of the most significant challenges of large-scale RDF data processing over Apache Spark, the relational schema optimization. The choice of RDF partitioning techniques and storage formats using SparkSQL significantly impacts query performance. The impact of the relational schemas and the underlying data storage formats is indisputable; they significantly affect the query performance. Nevertheless, the trade-offs in different configurations have not been a subject of intensive study in the literature. This paper presents an in-depth investigation for practitioners to understand such trade-offs and their best practices. It also reports on the pitfalls behind the implementation SPARQL optimizations over SparkSQL . Our experiments provide insights into these schemas’ relative strengths by comparing three different partitioning techniques and four other storage formats. Our results draw a better understanding of the current State-Of-The-Art (S.O.T.A) and pave the way for a wide range of best practices and systematically tuning the performance of distributed systems to handle vast RDF data.
A Blueprint for Semantically Lifting Field Trial Data: Enabling Exploration using Knowledge Graphs

In many organizations, relevant data is distributed across multiple primary data sources using different data models, schemas, and vocabularies which prevents effective knowledge management at an organizational level. As a result, it is very cumbersome to retrieve and integrate relevant data necessary to answer specific business questions. We propose a semantic lifting architecture that lifts primary data resources into a single corporate knowledge graph, implemented following FAIR and Linked Data principles, which assigns unique URIs to entities and maps the data using a uniform shared vocabulary. This structure supports integration of heterogeneous and distributed data sources to enable advanced search and analysis. In this paper we consider the case of field trial data collected and managed by Bayer, present an example of our architecture, and describe an application using this architecture that provides semantic search and visual exploration of field trial data to relevant stakeholders. As a main benefit, our architecture allows domain experts and analysts to retrieve timely and consistently labeled data originating from multiple primary sources while maintaining data context to enable cross-domain analytics. This provides the means to answer more complex questions with minimal data preparation, improving speed and precision by driving decisions with semantically lifted data.
Scale-independent Data Analysis with Database-backed Dataframes: a Case Study

Big Data analytics are being used in many businesses and organizations. However, the requirements that big data analytics introduce cannot be solved by a single system, and require distributed system knowledge and data management expertise from data analysts who should instead be focus on gleaning information from the data. AFrame is a data exploration library that provides a data scientist’s familiar interface, Pandas DataFrame, and scales its operations to large volumes of data through a big data management system to enable a seamless analysis experience moving from small to large datasets. In this paper, we present a detailed case study that uses AFrame to perform an end-to-end data analysis from data cleaning and modeling through to deployment.
Documentation Gap in Ontology Creation: Insights into the Reality of Knowledge Formalization in a Life Science Company

To achieve the goal of FAIR — findable, accessible, interoperable, and reusable — data, life science companies employ Semantic Web standards and Linked Data principles. In doing so, they create ontologies that formally represent knowledge. This paper presents the results of a survey among knowledge engineers and domain experts involved in ontology creation for a global life science company. The survey results indicate that the conceptualization phase of the ontology creation process, including knowledge acquisition, remains largely undocumented. The majority of knowledge engineers surveyed begin to document during or after the creation of the formal knowledge model. The authors discuss the risks that may arise from this documentation gap and recommend addressing them by means of joint, timely, and structured documentation.
A Network-Based Analysis of a Worksite Canteen Dataset

The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees’ health and disease prevention, productivity increase, economic convenience vs. eating satisfaction when using canteen services. Even if food habits have already been studied using traditional statistical approaches, here we adopt an approach based on Network Science that allows us to deeply study, for instance, the interconnections among people, company and meals and that can be easily used for further analysis. In particular, this work concerns a multi-company dataset of workers and dishes they chose at a canteen worksite. We study eating habits and health consequences, also considering the presence of different companies and the corresponding contact network among workers. The macro-nutrient content and caloric values assessment is carried out both for dishes and for employees, in order to establish when food is balanced and healthy. Moreover, network analysis lets us discover hidden correlations among people and the environment, as communities that cannot be usually inferred with traditional or methods since they are not known a priori. Finally, we represent the dataset as a tripartite network to investigate relationships between companies, people, and dishes. In particular, the so-called network projections can be extracted, each one being a network among specific kind of nodes; further community analysis tools will provide hidden information about people and their food habits. In summary, the contribution of the paper is twofold: it provides a study of a real dataset spanning over several years that gives a new interesting point of view on food habits and healthcare, and it also proposes a new approach based on Network Science. Results prove that this kind of analysis can provide significant information that complements other traditional methodologies.
The impact of rewriting on coverage constraint satisfaction

Due to the impact of analytical processes on our life, an increasing effort is being devoted to the design of technological solutions that help humans in measuring the bias introduced by such processes and understanding its causes. Existing solutions can refer to either back-end or front-end stages of the data processing pipeline and usually represent bias in terms of some given diversity or fairness constraint. In our previous work [1], we proposed an approach for rewriting filtering and merge operations in pre-processing pipelines into the “closest” operations so that protected groups are adequately represented (i.e., covered ) in the result. This is relevant because any under-represented category in an initial or intermediate dataset might lead to an under-representation of that category in any subsequent analytical process. Since many potential rewritings exist, the proposed approach is approximate and relies on a sample-based cardinality estimation, thus introducing a trade-off between the accuracy and the efficiency of the process. In this paper, we investigate this trade-off by first presenting various measures quantifying the error introduced by the rewriting, due to the applied approximation and the selected sample. Then, we (preliminarly) experimentally evaluate such measures on a real-world dataset.
Identifying and Ranking Influential Nodes in Complex Networks Based on Dynamic Node Strength

Identifying and ranking the node influence in complex networks is an important issue. It helps to understand the dynamics of spreading process for designing efficient strategies to hinder or accelerate information spreading. The idea of decomposing network to rank node influence is adopted widely because of low computational complexity. Of this type, decomposition is a dynamic process, and each iteration could be regarded as an inverse process of spreading. In this paper, we propose a new ranking method, Dynamic Node Strength Decomposition, based on decomposing network. The spreading paths are distinguished by weighting the edges according to the nodes at both ends. The change of local structure in the process of decomposition is considered. Our experimental results on four real networks with different sizes show that the proposed method can generate a more monotonic ranking list and identify node influence more effectively.
Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic study of atherosclerosis

Background Pulse arrival time (PAT) is commonly used to estimate blood pressure response. We hypothesised that PAT response to obstructive respiratory events would be associated with increased cardiovascular risk in people with obstructive sleep apnoea. Methods PAT, defined as the time interval between electrocardiography R wave and pulse arrival by photoplethysmography, was measured in the Multi-Ethnic Study of Atherosclerosis Sleep study participants. The PAT response to apnoeas/hypopnoeas was defined as the area under the PAT waveform following respiratory events. Cardiovascular outcomes included markers of subclinical cardiovascular disease (CVD): left ventricular mass, carotid plaque burden score and coronary artery calcification (CAC) (cross-sectional) and incident composite CVD events (prospective). Multivariable logistic and Cox proportional hazard regressions were performed. Results A total of 1407 participants (mean age 68.4 years, female 47.5%) were included. Higher PAT response (per 1 SD increase) was associated with higher left ventricular mass (5.7 g/m2 higher in fourth vs first quartile, p<0.007), higher carotid plaque burden score (0.37 higher in fourth vs first quartile, p=0.02) and trended to greater odds of CAC (1.44, 95% CI 0.98 to 2.15, p=0.06). A total of 65 incident CVD events were observed over the mean of 4.1 (2.6) years follow-up period. Higher PAT response was associated with increased future CVD events (HR: 1.20, 95% CI 1.02 to 1.42, p=0.03). Conclusion PAT is independently associated with markers of subclinical CVD and incident CVD events. Respiratory-related PAT response is a novel and promising polysomnography metric with cardiovascular implications.
High Accuracy of Hybrid IDS System using Evidence Theory and SVM ML Technique

These days, intrusion detection system (IDS) is the most arising pattern in our general public. This basically screen network traffic and will alarm the organization chairman of any unordinary action. IDS System work by one or the other searching for marks of known assaults or deviations of typical movement. While there are a few detriments of IDS, for example, low recognition rate and high bogus caution rate. In this paper a mixture IDS (HIDS) strategy dependent on support vector machine (SVM) and evidence theory (ET) has been proposed too different assault recognition method to limit the low bogus alert rate and improve exactness.
A Study on Comprehensive Risk Level Analysis of IoT Attacks

IoT retains its popularity, and the application of IoT is widened to many aspects such as the smart home application, smart city application, health care, and many more. As the application gets widen, the issues and challenges of the IoT in regards to the security and privacy perspective are also gaining a big concern. To solve the issues, it is important to know which layers the attacks occur and their impact. Thus, describing the architecture of the Internet of Things(IoT), the comprehensive study of identified attacks and their approaches suggested on collected research, and analyzing the results of the IoT attack with the correlation of the architecture level and the power of the attack toward the IoT devices.
Electronic Solution to Address Traffic Issues Near Bus Stops

The common scene of metrocity is traffic congestion observed all over the world. The common reason is the public waiting for bus. Buses/lorries are the common vehicle use in transportation for short distances in cities. People usually have tendency to stand outside the bus stop on the road. This blocks the way for the bus to stop in front of the bus stop and so it has to stop in the middle of the road, blocking the traffic. Presented research work is focused to inform and aware people to stand inside the bus stop. For that three different methods are implemented. First one is by using image processing. This is working in real time. It detects the face of people standing on road and raise pre-recorded voice when observed for five second continuously. The second is by using ultrasonic sensors. This will send ultrasonic wave using transmitter and receive the wave from receiver. The time elapsed between the transmission of wave and receiving of wave is entered as an input. It is then taken is processed to calculate the distance of people from sensor and if it is greater than the pre-defined value then a prerecorded voice is raised to appeal people to move inside bus stop.
TMIR: Transient Length Extraction Strategy for ANN-inspired Musical Instrument Recognition

Technology has the profound impact in every sphere and the music industry has not been an exception to this. In this paper, we have illustrated the strategy for extracting a set of distinct features from audio samples based on the frequency domain analysis and shape analysis, which has been entitled as TMIR. From the specific range of frequency, the features have been retrieved that represents the distinct characteristics for individual musical instruments. We have considered the specific frequency range rather than the total frequency spectrum. The analyzed characteristics have been extracted by searching the onset position and maximum value of the frequency of transient length. We have trained Artificial Neural Network-based model using Normalized input data that have successfully been capable for identifying different types of musical instruments and has an accuracy rate of ~96.47%. To achieve higher performance, a new paradigm has additionally been implemented by modifying the position of onset of true fixed transient length and consequently the final accuracy rate of ~98.22% has been achieved.
Machine Learning-Based Classification of Dementia Types: MRI Study

We develop an automated machine learning classifier in differentiating the healthy controls, Parkinsonism dementia, Alzheimer’s disease and mild cognitive impairment using magnetic resonance imaging (MRI). T1-weighted structural MR images of 117 individuals were collected with age between 60 to 96 years, including 33 HC, 27 PD, 30 MCI and 27 AD individuals. We first perform 3D tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MR images after skull striping using FSL tool. We calculate the surface fractal dimension from segmented brain tissue using 3D box-counting method. Then we estimate the gray matter density (GMD) from GM tissue and local gyrification index (LGI) from WM tissue after performing the surface reconstruction based on anatomical lobar parcellation using Freesurfer. After estimation of all the indices, we trained these indices for two-third dataset and tested on remaining one-third dataset using various machine learning classification algorithms. We found that naïve Bayesian and support vector machines are the best classifiers with an accuracy of 78% when compared to other methods for 5-fold cross validation. We conclude that machine learning methods are potential in classifying the healthy and dementia individuals.
Dynamic bandwidth allocation for multi-QoS guarantee based on bee colony optimization

There is a growing need for efficient bandwidth allocation in broadband satellite communication system. Firstly, a cross-layer bandwidth framework is constructed by considering information exchange between application layer, medium access control (MAC) layer and physical layer. Then, in order to allocate bandwidth resources, a utility function is established with modified factor by optimized bee colony algorithm. In the bee colony optimization-based cross layer dynamic bandwidth allocation algorithm (BO-CL-DBA), important channel factors including modulation order, coding efficiency, transmission rate and quality of service (QoS) priority for different users are considered and analyzed. Furthermore, a simulation model is developed to evaluate performance such as utility value, user satisfaction and throughput. The result demonstrates high allocation efficiency which meets multi QoS requirements.
Nonlinear Backstepping Based Control of Single-phase Inverter in a Standalone Photovoltaic System

This research article presents a nonlinear robust nonlinear control for a single-phase PV inverter. Based on the Lyapunov stability theory, the proposed backstepping controller ensures the desired sinusoidal voltage at the output of the H-bridge inverter designed for a PV system standalone operation. The output voltage of the inverter is maintained even though significant variations are present at the input. The scheme aims to reduce the computational complexity of the system by tracking the desired voltage. The overall system is simulated in MATLAB/SIMULINK, in the presence of MPPT control law and single-phase H-bridge inverter. Various parameters are observed and compared with conventional PID control to endorse the effective performance of the control scheme.
IOT enabled Book Publishing Platform

India has the world's largest number of visually disabled individuals. The advancement in technology is bringing new assistive devices to support these people and help them in overcoming the obstacles in day-to-day life. In this paper an attempt has been made to design a cost efficient IOT enabled handheld, compact, pocket-sized reader device to annotate indexed text on written media. The pen can read written media aloud with a pre-recorded human voice from audiobook or a high-quality human-like digital voice. This paper presents a standard for a smart IoT enabled platform for book publishers to publish keeping in mind to make the same printed book accessible to visually impaired people as well. The same written media can be used by normal and visibility impaired people instead separately printing the media in braille. This paper presents our vision defining the new standards for printing written media and design of an IoT enabled device for reading the stealth code to be used for annotation in written media, such assistive devices can bring change in society at work, education and lifestyle.