Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel knowledge-based system with relation detection and textual evidence for question answering research

With the development of large-scale knowledge bases (KBs), knowledge-based question answering (KBQA) has become an important research topic in recent years. The key task in KBQA is relation detection, which is the process of finding a compatible answer type for a natural language question and generating its corresponding structured query over a KB. However, existing systems often rely on shallow probabilistic methods, which are less expressive than deep semantic representation methods. In addition, since KBs are still far from complete, it is necessary to develop a new strategy that leverages unstructured resources outside of KBs. In this work, we propose a novel Question Answering method with Relation Detection and Textual Evidence (QARDTE). First, to address the semantic gap problem in relation detection, we use bidirectional long-short term memory networks with different levels of abstraction to better capture sentence structures. Our model achieves improved results with robustness against a wide diversity of expressions and questions with multiple relations. Moreover, to help compensate for the incompleteness of KBs, we utilize external unstructured text to extract additional supporting evidence and combine this evidence with relation information during the answer re-ranking process. In experiments on two well-known benchmarks, our system achieves F1 values of 0.558 (+2.8%) and 0.663 (+5.7%), which are state-of-the-art results that show significant improvement over existing KBQA systems.
Mutations in the NOD2 gene are associated with a specific phenotype and lower anti‐tumor necrosis factor trough levels in Crohn's disease

Nucleotide‐binding oligomerization domain‐containing protein 2 (NOD2) gene mutations are known to be an important risk factor in the pathogenesis of Crohn's disease (CD). Specific disease phenotypes are associated with the presence of NOD2 gene mutation. One treatment option is to use an anti‐tumor necrosis factor (TNF)‐α agent. Therapeutic drug monitoring (TDM) is usually performed in cases of a loss of response. Our aim was to explore whether NOD2 gene mutations have an effect on the disease phenotype, vitamin D levels, and on TDM in CD patients.
Exploration of Carbon Allotropes with Four‐membered Ring Structures on Quantum Chemical Potential Energy Surfaces

The existence of a new carbon allotrope family with four‐membered rings as a key unit has been recently predicted with quantum chemical calculations. This family includes carbon allotropes in prism‐, polymerized prism‐, sheet‐, tube‐, and wavy‐forms. An atypical bond property has been observed in this series of carbon structures, which differs from the typical sp3, sp2, and sp hybridizations. The lowest energy barrier from some of the equilibrium states of the carbon structures has been determined with the SHS‐ADDF (scaled‐hypersphere‐search combined with the anharmonic downward distortion following) method within the GRRM software program package. The height of the barriers indicates that the well is deep enough for the carbon structures to exist. This class of carbon allotropes is expected to be energy‐reservoirs with extra energy of 100–350 kJ mol−1 per one carbon atom. This article presents the structures, energies and reactivity of the carbon allotropes with four‐membered ring structures as well as the background of the findings in the context of the global exploration of potential energy surfaces. © 2018 Wiley Periodicals, Inc.
Legs of male fiddler crabs evolved to compensate for claw exaggeration and enhance claw functionality during waving displays

Many exaggerated morphological traits evolve under sexual selection. However, the optimal level of exaggeration is dictated by a trade‐off between natural and sexual selection, representing a balance between its benefits and associated costs. Male fiddler crabs wave an enlarged major claw during behavioural displays that eliminates the need for direct combat, and determines courtship outcomes. The outcomes of these displays often depend on claw size, exposing males to selection for larger claws to improve mating and combat success. Applying phylogenetic comparative methods to 27 fiddler crab species, we examined the evolution of major claw morphologies, leg morphologies, and waving displays to determine whether these traits coevolved to optimise functioning of the exaggerated claw, or to mitigate potential metabolic or locomotor costs. We found legs to be sexually dimorphic, with males having longer legs than females. Legs were also longer in species that waved laterally rather than vertically, in species with larger major claws, and in species whose major claws were relatively elongate. These results suggest that leg morphology has coevolved with claw enlargement to enhance functionality of the major claw during waving displays, in addition to compensating for any negative effects of claw size.
To Analysis the Effects of Compressive Sensing on Music Signal with variation in Basis & Sensing Matrix

Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signals using very minute observations. These minute observations are also called the number of measurement. The basic benefits of CS are that the number of measurements which are required for proper reconstruction of the compressed signal is very less than the conventional method. If we go through the literature then, we get that for proper reconstruction of signal a theory is given by Shannon. This theory states that the sampling frequency must be higher than twice the highest frequency component in that signal. So the limitation of the conventional method is that it requires so much storage to store and a large bandwidth to transmit the data. Both the things are so much scarce now days, as we know that if we have to required high resolution of signal then the storage which required to store this is also so much high. As there are various parameters in the theory of CS. But the two parameters are so much important than the others. These two parameters are basis and sensing matrices. Various types of other properties like RIP property and IID property also shows a big role in CS theory. By changing the sensing and measurement matrix the SNR value can also be enhanced. In this paper Gaussian matrix is taken as a sensing matrix & DST, DCT considered as the Basis matrices. The combination of basis and sensing matrix enhances the quality & level of compression. As the quality of compression enhanced it enhances the Signal to Noise ratio too. We cannot check the quality by using only one signal so comparison is made using Single Tone, Multi Tone and Vocal Song. 11minimization technique is used for reconstruction of compressed signal
Comparison of Healthy and Dandruff Scalp Microbiome Reveals the Role of Commensals in Scalp Health

Several scalp microbiome studies from different populations have revealed the association of dandruff with bacterial and fungal dysbiosis. However, the functional role of scalp microbiota in scalp disorders and health remains scarcely explored. Here, we examined the bacterial and fungal diversity of the scalp microbiome and their potential functional role in the healthy and dandruff scalp of 140 Indian women. Propionibacterium acnes and Staphylococcus epidermidis emerged as the core bacterial species, where the former was associated with a healthy scalp and the latter with dandruff scalp. Along with the commonly occurring Malassezia species (M. restricta and M. globosa) on the scalp, a strikingly high association of dandruff with yet uncharacterized Malassezia species was observed in the core mycobiome. Functional analysis showed that the fungal microbiome was enriched in pathways majorly implicated in cell-host adhesion in the dandruff scalp, while the bacterial microbiome showed a conspicuous enrichment of pathways related to the synthesis and metabolism of amino acids, biotin, and other B-vitamins, which are reported as essential nutrients for hair growth. A systematic measurement of scalp clinical and physiological parameters was also carried out, which showed significant correlations with the microbiome and their associated functional pathways. The results point toward a new potential role of bacterial commensals in maintaining the scalp nutrient homoeostasis and highlights an important and yet unknown role of the scalp microbiome, similar to the gut microbiome. This study, therefore, provides new perspectives on the better understanding of the pathophysiology of dandruff.
PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data

Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access. Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo. Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species. Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo. com.
Communication change in ALS: engaging people living with ALS and their partners in future research

Abstract Objective: To involve people living with amyotrophic lateral sclerosis (plwALS) in the scoping and identification of research priorities related to the changes in ALS communication including the use of augmentative and alternative communication (AAC). Methods: Two focus groups of plwALS at early and late stages of change to speech intelligibility were carried out as a patient and participant involvement exercise. The transcriptions of these groups were analyzed thematically and compared between groups. Results: Aspects of anticipation, preparation, adaptation and partnership were identified as key to dealing with communication change as a result of ALS. In particular the involvement of partners was clearly significant, as was the impact on them as well as the plwALS. PlwALS successfully contributed to focus groups and the process of group discussion about communication itself appeared constructive. Conclusions: Research and new interventions should focus further on how to support the partners of plwALS. Future research with plwALS should place AAC strategies and technology in the context of wider communication and investigate how best to support the anticipation, preparation and adaptation to communication change. Specific areas of research need relating to this support are suggested. IMPLICATIONS FOR REHABILITATION People living with ALS (plwALS) are rarely involved in setting the research agenda in the areas of speech and augmentative and alternative communication (AAC). Enabling plwALS to contribute to research, even those with no speech, is feasible with appropriate support. Feedback indicates that communication change and AAC are experienced in different ways by plwALS. Research and intervention should focus on how to support the anticipation, preparation and adaptation to communication change for plwALS.
Blockchain-base structures for a secure and operate network of semi-autonomous Unmanned Aerial Vehicles

Unmanned Aerial Vehicles — UAVs, or drones — are now being operated by several military forces and currently, to a more limited extent, by civilian organizations. These latter operations, however, may eventually expand to exceed, in number and diversity, those of the military. Further expected development in battery capacity, construction materials and software, especially regarding machine learning algorithms and drone integration, will definitely increase UAVs’ autonomous. Unique risks associated with UAVs like risk of hackers’ attacks to intercept the control are also increasing. More incidents likely will occur once regulations are finalized that encourage more use that is widespread. Such incidents could result in multi-million dollar claims against businesses, operators and manufacturers.Blockchain is the basis technology for cryptocurrencies. However, Blockchain can have far larger applications in the field of UAVs, because Blockchain is highly distributed and publically viewable system of sequentially linked cryptographically.This paper presents a concept of application, where each UAV in the UAVNet is a Blockchain node, has on-board functionality for creating and reading transactions from the block, as well as communication tools for exchanging transactions with other UAVs.
Anonymization and Analysis of Horizontally and Vertically Divided User Profile Databases with Multiple Sensitive Attributes

Preventing the identification of individuals is important when data analyzers have to guarantee the safety of the data analysis they work with. A method proposed to solve this problem entails altering a part of the data value or deleting it. As to the processes, attributes of the individual data are divided into three groups: identifier (ID), quasi-identifier (QID), and sensitive attribute (SA). ID is the data that identify an individual directly, such as name. QID is the attributes that could identify an individual by combining them, such as age and birthplace. SA is very important information and should not be exposed when the data is identified to an individual. Utilizing these concepts, a safety metric for the data, such as l-diversity, is proposed so far. Under l-diversity, we use the assumption that the SA value is not known for anyone, and we process the data to prevent attackers from identifying. However, there are scenarios in which existing methods cannot protect the data against an invasion of privacy. In an analysis completed by multiple organizations, they integrated their data to carry out the effective data research. Although they can obtain profitable results, the integrated data could include information that attackers use to identify people. Specifically speaking, if the attacker is an institute providing data, they can use their own data’ SA value as a QID value. The assumption of l-diversity is violated, so the existing safety metric loses its effect on protecting data. In this paper, we propose a new anonymization method to conceal organizations’ important data by inserting dummy values, thereby enabling analysts to use the data safely. At the same time, we provide a calculating method to decrease the influence of the noise generated from the dummy insertion. We confirm these methods’ effectiveness by measuring accuracy in a data analysis experiments.
Swarm Rules for Autonomous Vehicles in a Parking Lot

In this research, we aim at acquiring the moving behavior of each vehicle in a parking lot only for entering vehicles. The behaviors of each vehicle are desired based on the neighborhood environment of the vehicle, and they are not desired based on the whole environment in the parking lot with depending on the other vehicles' decision. Therefore, this research is a kind of studies about swarm robots. In this paper, vehicles are modeled in the parking lot for simulation. We confirm that the vehicle can reach the exit in the parking lot in simulation.
Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: A comparison of two methods

Non‐linear exposure‐outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two‐stage methods for meta‐analysis of such relationships, where the confounder‐adjusted relationship is first estimated in a non‐linear regression model in each study, then combined across studies. The “metacurve” approach combines the estimated curves using multiple meta‐analyses of the relative effect between a given exposure level and a reference level. The “mvmeta” approach combines the estimated model parameters in a single multivariate meta‐analysis. Both methods allow the exposure‐outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis‐specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all‐cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study‐specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study‐specific powers does not. For all‐cause mortality, all methods identify a steep U‐shape. The metacurve and mvmeta methods perform well in combining complex exposure‐disease relationships across studies.
Real-time Pallet Localization with 3D Camera Technology for Forklifts in Logistic Environments

This paper presents a novel approach for detection and localization of standardized euro pallets, which are orientated up to 90° in relation to the sensor plane. There is no a priori information about the pallets pose needed. We use a time-of-flight camera. Our algorithm is based on finding surfaces in the point cloud, which represent the three wooden blocks of a euro pallet. Different kinds of geometrical checks set up our detection pipeline, where no artificial markers on the pallets are needed. Since we perform the detection while driving a forklift, the algorithm must process the point cloud within a set time limit. The detection and localization result in the pallets position and orientation in relation to the camera coordinate system. This information can be provided to higher-level systems, like advanced driver assistance systems. The results show that the localization of pallets is possible in the scenario considered.
Business Intelligence using Machine Learning and Data Mining techniques - An analysis

Today's social informatics is facing the challenge of exponentially increasing data present online. However this massive amount of data is available across several heterogeneous platform making it a challenging task especially to comprehend useful information and effectively use it for business intelligence. Achieving Business intelligence through machine learning is one of the significant issues in recent era. Previously, outliers were being considered as noisy data and disregarded leading to loss of relevant information. This paper highlights the major research challenges in this mining sub domain. It provides a comprehensive taxonomy extracted for Business Intelligence methodologies along with current application sectors. Future research directions and suggestions have been pointed to address this anomaly gap to achieve effective business strategies
A Review of Graph and Network Complexity from an Algorithmic Information Perspective

Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths and limitations of Shannon’s entropy, lossless compressibility and algorithmic complexity when used to identify aspects and properties of complex networks. We review the fragility of computable measures on the one hand and the invariant properties of algorithmic measures on the other demonstrating how current approaches to algorithmic complexity are misguided and suffer of similar limitations than traditional statistical approaches such as Shannon entropy. Finally, we review some current definitions of algorithmic complexity which are used in analyzing labelled and unlabelled graphs. This analysis opens up several new opportunities to advance beyond traditional measures.
Global Shuffle Grouping (GSG): A Load Balancing Strategy for Continuous Range Queries on Storm

Apache Storm is a distributed stream processing framework to support real-time processing of big data. Even if many stream grouping strategies have been implemented in Storm to partition stream data in order to maximize usability of resources, but they cannot efficiently support continuous range query. It is the basis of location based services, in which both queries and objects are moving. The reason is that the spatial semantics of the query (range and data distribution) cannot be expressed by those strategies, and this is easy to result in load imbalance. For this problem, we propose a load-balancing strategy called global shuffle grouping (GSG) to support efficient continuous range queries on Storm. There the cost of the query is estimated based on the range and density of moving objects. The continuous range queries are grouped according to their costs by the way of round-robin. For the queries belonging to the same group, they are distributed according to a counter array by another round-robin. Double round-robins ensure that the load distributions to multiple downstream bolts are balanced. We implemented continuous range query topology with GSG into Storm. Compared with the most practicable built-in grouping strategy shuffle grouping, our proposed grouping is able to reduce load imbalance degree and load standard deviation by 2–3 times and reduce load fluctuation by 1–2 times. The throughput can be improved up to nearly 20%.