Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology Extraction from Compound Sentences in Hungarian Language

The implementation of knowledge extraction from text is a complex task involving many processing layers. For Hungarian language, the available semantic analyzers can provide only partial functionality. This paper focuses on the analysis of compound sentences in Hungarian language and a novel model is proposed to generate RDF description for the input sentences. The presented model generates a set of candidate interpretations, and a weighting method - that uses syntactic and semantic components - is applied to select the winner semantic graph. Based on the test experiences, the implemented framework provides an improved semantic model of the input compound sentences.
On Systems of Systems Engineering: A Requirements Engineering Perspective and Research Agenda

The emergence of Systems of Systems (SoSs) and Systems of Systems Engineering (SoSE) is largely driven by global societal needs including energy-water-food nexus, population demographics, global climate, integrated transport, security and social activity. However, due to their scale, structural and functional complexity and emergent properties, these global spanning Cyber-Physical Systems of Systems are becoming increasingly complex and more difficult for current requirements engineering (RE) practices to handle. In this paper, we firstly introduce SoSE as an emerging discipline and key characteristics of SoSs. We then highlight the challenges that the RE discipline must respond to. We discuss some weaknesses of current RE techniques and approaches to cope with the complexity of SoSs. We then argue that there is a need for the global RE community to evolve current RE approaches and to develop new ways of thinking, new RE capabilities and possibly a new RE science as a key mechanism for addressing requirements engineering complexities posed by Systems of Systems. We then outline a requirements engineering perspective and research agenda that identifies 'top-10' research themes informed by a cluster of four Systems of Systems Engineering projects funded by the European Commission's Horizon 2020 research programme.
Outlier Detection Based on the Data Structure

Outlier detection is one of the most frequently demanded task for optimizing results. Distance-based methods are a popular approach. They require no prior assumptions about the data generating distribution and are uncomplicated to implement. However, related methods have different parameters that are difficult to determine such that the identification results are generally unstable. Presenting related techniques without sacrificing stability is a challenging task. In this paper, we propose a new distance-based method that depends on the data structure to detect such points. In the proposed method, a global binary tree is constructed and the local distance score of a point is calculated to evaluate to what degree the observation is an outlier. The greater the value of the distance score, the more likely the point is an outlier point. Unlike typical distance-based methods, our algorithm has good scalability. Even when the dimension of the data points increases, the performance of our algorithm does not diminish. To reduce extra parameters, the top-p ranked points can be identified as outliers. Experimental results on synthetic and real-world datasets demonstrate the effectiveness and stability of our method.
Predictors of Acute Kidney Injury in Patients Undergoing Adult Cardiac Surgery

Background: Acute kidney injury (AKI) after cardiac surgery (CS) is not uncommon and has serious effects on mortality and morbidity. A majority of patients suffer mild forms of AKI. There is a paucity of Indian data regarding this important complication after CS. Aims and Objectives: The primary objective was to study the incidence of AKI associated with CS in an Indian study population. Secondary objectives were to describe the risk factors associated with AKI-CS in our population and to generate outcome data in patients who suffer this complication. Methods: Serial patients (n = 400) presenting for adult CS (emergency/elective) at a tertiary referral care hospital in South India from August 2016 to November 2017 were included as the study individuals. The incidence of AKI-CS AKI network (AKIN criteria), risk factors associated with this condition and the outcomes following AKI-CS are described. Results: Out of 400, 37 (9.25%) patients developed AKI after CS. AKI associated with CS was associated with a mortality of 13.5% (no AKI group mortality 2.8%, P = 0.001 [P < 0.05]). When AKI was severe enough to need renal replacement therapy, the mortality increased to 75%. Patients with AKI had a mean hospital stay 16.92 ± 12.75 days which was comparatively longer than patients without AKI (14 ± 7.98 days). Recent acute coronary syndrome, postoperative atrial fibrillation, and systemic hypertension significantly predicted the onset of AKI-CS in our population. Conclusions: The overall incidence of AKI-CS was 9.25%. The incidence of AKI-CS requiring dialysis (Stage 3 AKIN) AKI-CS was lower (2%). However, mortality risks were disproportionately high in patients with AKIN Stage 3 AKI-CS (75%). There is a need for quality improvement in the care of patients with AKI-CS in its most severe forms since mortality risks posed by the development of Stage 3 AKIN AKI is higher than reported in other index populations from high resource settings.
Cost-sensitive Hybrid Neural Networks for Heterogeneous and Imbalanced Data

Analyzing accumulated data has recently attracted huge attention for its ability to generate values by identifying useful information and providing an edge in global business competition. However, heterogeneous data and imbalanced class distribution present two major challenges to machine learning with real-world business data. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. These algorithms narrow complex data into a homogeneous, a balanced data space an inefficient process that requires a significant amount of pre-processing. In this paper, we focus on an efficient solution to the challenges with heterogeneous and imbalanced data sets that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive hybrid neural network that learns real-world heterogeneous data via a parallel network architecture. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications. And the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. The results of comparative experiments on six real-world data sets reflecting actual business cases, including insurance fraud detection and mobile customer demographics, indicate that the proposed approach demonstrates superior performance over baseline procedures.
Research of Recharging Scheduling Scheme for Wireless Sensor Networks Based on Cuckoo Search

Wireless sensor networks (WSN) have been in widely use due to its prominent capability of data sensing and processing. However, the lifetime of WSN is constrained because of the limited battery energy in each sensor node. Fortunately, recent breakthrough in the area of wireless energy transfer technology has created a new dimension to prolonging the lifetime of WSN. In this paper, we firstly introduce the conception and requirements of cyclic energy conservation in conditions of rechargeable WSN via Wireless Charging Vehicle (WCV). Then, in order to enhance the utility of charging, optimization of the minimal occupied time of WCV achieved by Cuckoo Search is carried out, hence leading to the prior solution of TSP and essential properties of cyclic energy conservation. Furthermore, the optimal solution of cyclic energy conservation in WSN is presented and the simulation result of the proposed scheme is given. Lastly, a conclusion of our research is drawn and a potential outlook of research is given as well.
Scalable NoC-based Neuromorphic Hardware Learning and Inference

Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timingdependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and communicate through the NoC. This enables chip-level interconnection, scalability and reconfigurability necessary for deploying different applications. The hardware is applied to learn MNIST digits as an evaluation of its learning capability. We explore the design space to study the trade-offs between speed, area and energy. How to use this procedure to find optimal architecture configuration is also discussed.
Personalization of medicine requires better observational evidence

Evidence-based medicine has become associated with a preference for randomized trials. Randomization is a powerful tool against both known and unknown confounding. However, due to cost-induced constraints in size, randomized trials are seldom able to provide the subgroup analyses needed to gain much insight into effect modification. To apply results to an individual patient, effect modification needs to be considered. Results from randomized trials are therefore often difficult to apply in daily clinical practice. Confounding by indication, which randomization aims to prevent, is caused by more severely ill patients being less or more likely to be treated. Therefore, the prognostic indicators that physicians use to make treatment decisions become confounders. However, these same prognostic indicators are also effect modifiers. This is in fact exactly why they are relevant to decision-making. We use simple, fictive numerical examples to illustrate these concepts. Then we argue that if we would record all relevant variables, it would simultaneously solve the problem of confounding by indication and allow quantification of effect modification. It has previously been argued that it is practically more feasible to “simply” randomize treatment allocation, than to adequately correct for confounding by indication. We will argue that, in the current age of evidence-based medicine and highly regulated randomized trials, this balance has shifted. We therefore call for better observational clinical research. However, careless acceptance of results from poorly performed observational research can lead clinicians seriously astray. Therefore, a more interactive approach toward the medical literature might be needed, where more room is made for scientific discussion and interpretation of results, instead of one-way reporting.
Cluster Structure Preserving Based on Dictionary Pair for Unsupervised Feature Selection

For unsupervised feature selection(UFS), some rep- resentative techniques are commonly employed to convert un-supervised feature selection to supervised case by generating pseudo labels, such as spectral clustering, matrix factorization and dictionary learning. Most existing studies address these techniques separately. In this paper, we propose an algorithm combining spectral clustering with dictionary learning for un-supervised feature selection. In dictionary learning, we share an intrinsic feature space for features and pseudo labels to ensure the consistency of data distribution. The cluster structure is encoded into dictionary learning to follow the priors of data distribution. Then the projection matrix is accessed from data matrix to the intrinsic feature space, which indicates the discriminative capability of original feature space. The $l_{2,1}$-norm is imposed on the projection matrix to get ranked features. An alternating minimization algorithm is employed to optimize the proposed model. The experimental results reveal the effectiveness of the proposed method.
A Reinforcement Learning Method for Continuous Domains Using Artificial Hydrocarbon Networks

Reinforcement learning in continuous states and actions has been limitedly studied in ocassions given difficulties in the determination of the transition function, lack of performance in continuous-to-discrete relaxation problems, among others. For instance, real-world problems, e.g. robotics, require these methods for learning complex tasks. Thus, in this paper, we propose a method for reinforcement learning with continuous states and actions using a model-based approach learned with artificial hydrocarbon networks (AHN). The proposed method considers modeling the dynamics of the continuous task with the supervised AHN method. Initial random rollouts and posterior data collection from policy evaluation improve the training of the AHN-based dynamics model. Preliminary results over the well-known mountain car task showed that artificial hydrocarbon networks can contribute to model-based approaches in continuous RL problems in both estimation efficiency (0.0012 in root mean squared-error) and sub-optimal policy convergence (reached in 357 steps), in just 5 trials over a parameter space $\theta \in \mathbb {R}^{86}$. Data from experimental results are available at: http://sites.google. com/up.edu. mx/reinforcement-learning/http://sites.google. com/up.edu. mx/reinforcement-learning/.
Reflections on the Workshop

In this paper we shortly summarize the discussions and outcomes of the first workshop on Analytics for Everyday Learning co-located with the 13 European Conference on Technology Enhanced Learning in Leeds (UK) on the 4 September 2018. First we will briefly summarize the discussions for each of the five papers and finally synthetize the most important findings.
RobinHood: Tail Latency Aware Caching - Dynamic Reallocation from Cache-Rich to Cache-Poor

Tail latency is of great importance in user-facing web services. However, maintaining low tail latency is challenging, because a single request to a web application server results in multiple queries to complex, diverse backend services (databases, recommender systems, ad systems, etc.). A request is not complete until all of its queries have completed. We analyze a Microsoft production system and find that backend query latencies vary by more than two orders of magnitude across backends and over time, resulting in high request tail latencies. We propose a novel solution for maintaining low request tail latency: repurpose existing caches to mitigate the effects of backend latency variability, rather than just caching popular data. Our solution, RobinHood, dynamically reallocates cache resources from the cache-rich (backends which don't affect request tail latency) to the cache-poor (backends which affect request tail latency). We evaluate RobinHood with production traces on a 50- server cluster with 20 different backend systems. Surprisingly, we find that RobinHood can directly address tail latency even if working sets are much larger than the cache size. In the presence of load spikes, RobinHood meets a 150ms P99 goal 99.7% of the time, whereas the next best policy meets this goal only 70% of the time.
Snore Sounds Excitation Localization by Using Scattering Transform and Deep Neural Networks

In this paper, we propose an algorithm for snoring sounds classification based on Deep Scattering Spectrum (SCAT), Gaussian Mixture Models (GMM) Supervectors and Deep Neural Networks (DNN). The task consists in the identification of the type of snoring among four target classes representing the snore sounds' excitation location, which can be highly useful for a successful medical treatment of the habitual snorer or patient afflicted with Obstructive Sleep Apnea. The SCAT is computed from excerpt the acoustic signals, then a GMM Supervector is calculated by adapting the GMM model of the acoustic space with the Maximum a Posteriori (MAP) algorithm and concatenating the mean values of the Gaussians. Resulting supervectors are used to feed the multiclass DNN classifier. The performance of the algorithm has been assessed on the Munich-Passau Snore Sound Corpus (MPSSC), composed of recordings of Drug-Induced Sleep Endoscopy (DISE) examinations. The results are expressed in terms of Unweighted Average Recall (UAR) and a remarkable improvement with respect to the state-of-the-art performance has been registered, achieving a score up to 67.14% and 67.71% respectively on the devel and test datasets.
Automated Assessment of Movement Impairment in Huntington’s Disease

Quantitative assessment of movement impairment in Huntington’s disease (HD) is essential to monitoring of disease progression. This paper aimed to develop and validate a novel low cost, objective automated system for the evaluation of upper limb movement impairment in HD in order to eliminate the inconsistency of the assessor and offer a more sensitive, continuous assessment scale. Patients with genetically confirmed HD and healthy controls were recruited to this observational study. Demographic data, including age (years), gender, and unified HD rating scale total motor score (UHDRS-TMS), were recorded. For the purposes of this paper, a modified upper limb motor impairment score (mULMS) was generated from the UHDRS-TMS. All participants completed a brief, standardized clinical assessment of upper limb dexterity while wearing a tri-axial accelerometer on each wrist and on the sternum. The captured acceleration data were used to develop an automatic classification system for discriminating between healthy and HD participants and to automatically generate a continuous movement impairment score (MIS) that reflected the degree of the movement impairment. Data from 48 healthy and 44 HD participants was used to validate the developed system, which achieved 98.78% accuracy in discriminating between healthy and HD participants. The Pearson correlation coefficient between the automatic MIS and the clinician rated mULMS was 0.77 with a p-value < 0.01. The approach presented in this paper demonstrates the possibility of an automated objective, consistent, and sensitive assessment of the HD movement impairment.
Event-Triggered Fault Detection Filter Design for Nonlinear Networked Systems

This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems. In the system model, the parameter uncertainty is captured effectively by the membership functions (MFs) with upper and lower bounds. For reducing the utilization of limited communication bandwidth, an event-triggered communication mechanism is applied. A novel FD filter subject to event-triggered communication mechanism, data quantization, and communication delay is designed to generate a residual signal and detect system faults, where the premise variables are different from those of the system model. Consequently, the augmented FD system is with imperfectly matched MFs, which hampers the stability analysis and FD. To relax the stability analysis and achieve a better FD performance, the information of MFs and slack matrices are utilized in the stability analysis. Finally, two examples are employed to demonstrate the effectiveness of the proposed scheme.
Enhancement of Deep Architecture using Dropout/ DropConnect Techniques Applied for AHR System

Remarkable performance on computer vision, and especially on pattern recognition field has been known for a long time to be produced by Deep learning algorithms. It is clear that amongst the successful applications in the pattern recognition domain, Arabic handwriting recognition (AHR) is a must. In this survey, we use two deep networks: Deep Belief Network (DBN) and Convolutional Neural Networks (CNN), for Arabic handwritten script (AHS) recognition. Despite the triumph of DBN and CNN methods, over-fitting is able to take place on these networks thanks to the massive number of parameters. In order to fight over-fitting, we have deeply inquired two regularization techniques called Dropout and DropConnect. While training with the two regularization methods, a randomly chosen subsets of activations/weights are dropped. Consequently, the assessment on the HACDB database to treat character level proves shows an improvement of classification error rate once adding Dropout and DropConnect techniques.
Privacy Architectural Strategies: An Approach for Achieving Various Levels of Privacy Protection

A wide array of Privacy-Enhancing Technologies (PETs) have been proposed as technical measures to provide various levels of privacy protection. Each technical measure is a building block that addresses specific privacy issues and is applicable to specific contexts. Existing approaches, however, do not provide step-by-step guidance to illustrate how these PETs can be appropriately adopted in a contextual and structured manner. From an engineering perspective, it is important to illustrate precisely how to design and implement privacy requirements and incorporate them into software architectures, as well as to choose between alternative PETs. We present an engineering approach to Privacy by Design (PbD) that uses the concept of architectural strategies to support the adoption of PETs in the early stages of the design process to achieve various levels of privacy protection. These strategies are collections of architectural tactics, which are described through design patterns and realised by PETs. We illustrate the approach's use in the context of eToll pricing systems and argue that this contribution lays the foundation for developing appropriate privacy engineering methodologies.
Building long-term empathy: A large-scale comparison of traditional and virtual reality perspective-taking

Virtual Reality (VR) has been increasingly referred to as the “ultimate empathy machine” since it allows users to experience any situation from any point of view. However, empirical evidence supporting the claim that VR is a more effective method of eliciting empathy than traditional perspective-taking is limited. Two experiments were conducted in order to compare the short and long-term effects of a traditional perspective-taking task and a VR perspective-taking task (Study 1), and to explore the role of technological immersion when it comes to different types of mediated perspective-taking tasks (Study 2). Results of Study 1 show that over the course of eight weeks participants in both conditions reported feeling empathetic and connected to the homeless at similar rates, however, participants who became homeless in VR had more positive, longer-lasting attitudes toward the homeless and signed a petition supporting the homeless at a significantly higher rate than participants who performed a traditional perspective-taking task. Study 2 compared three different types of perspective-taking tasks with different levels of immersion (traditional vs. desktop computer vs. VR) and a control condition (where participants received fact-driven information about the homeless). Results show that participants who performed any type of perspective-taking task reported feeling more empathetic and connected to the homeless than the participants who only received information. Replicating the results from Study 1, there was no difference in self-report measures for any of the perspective-taking conditions, however, a significantly higher number of participants in the VR condition signed a petition supporting affordable housing for the homeless compared to the traditional and less immersive conditions. We discuss the theoretical and practical implications of these findings.