Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between Serological Biomarkers and Disease Activity in Patients with Inflammatory Bowel Disease

Background Current biomarkers have been routinely used noninvasive methods for assessing disease activity of inflammatory bowel disease (IBD), but none of them are specific. This study was aimed to determine the performance of the serological biomarkers for detecting disease activity in patients with IBD. Methods A prospective study that included 73 ulcerative disease (UC) subjects, 141 Crohn's disease (CD) subjects, and 30 of them complicated with C. difficile infection (CDI) were diagnosed at a single-institution IBD center. Disease activity was assessed using by Truelove and Witts criteria for UC and Harvey Bradshaw Simple Index for CD. Serological inflammatory biomarkers were compared in different severity groups. Receiver operator curve analyses assessed the performance of each biomarker in discriminating disease states. Results For UC patients, elevated monocyte counts, C-reactive protein (CRP), and decreased lymphocyte counts and lymphocyte/monocyte ratio (LMR) significantly differed between subjects with active and inactive UC. LMR of 3.1 was 76% sensitive and had a specificity of 67% for active UC. For CD patients, higher values of neutrophils, monocytes, neutrophil/lymphocyte ratio, CRP, fibrinogen, and lower values of LMR and hemoglobin were significantly different between subjects with active and inactive CD. None of the biomarkers included had a good correlation with disease activity (area under the ROC Curve < 0.70). Conclusions A low LMR represents an inexpensive, readily available test with a promising value to identify disease activity in UC patients, whereas none of the inflammatory biomarkers showed a discriminative value in disease activity of CD.
Argumentation Schemes for Clinical Interventions Towards an Evidence-Aggregation System for Medical Recommendations

The paradigm of evidence-based medicine requires that medical decisions are taken based on available, verified and high quality evidence. Such evidence has to be obtained from multiple relevant studies, considering their potential biases and shortcomings. Rationalizing and aggregating evidence from multiple studies is key to evidence-based decision making. Towards a system that is able to aggregate and summarize the evidence available in multiple studies, we have defined two argument schemes, which respectively provide reasons as to why a certain therapy may be regarded superior to another in terms of efficacy and safety. The argument schemes can be automatically instantiated via the semantic query language SPARQL from a knowledge base in which clinical studies have been formalized according to our own clinical trial ontology. The argument schemes are meant to be part of the framework of a configurable system that generates clinical recommendations by aggregating and summarizing the evidence from different clinical studies. We demonstrate the instantiation of the argument schemes in a study case on glaucoma and show that they are able to capture the reasoning behind determining such intervention superiority. Keywords–Argumentation in Medicine; Argumentation Schemes; Evidence-based Medicine; Summarization of Clinical Evidence
A multifunctional method (ERP and fMRI) of analysis on facial expression. Three pilot studies

As social primates, one of the most important cognitive tasks we conduct, dozens of times a day, is to look at a face and extract the person's identity. During the last decade, the neural basis of face processing has been extensively investigated in humans with event-related potential (ERP) and functional MRI (fMRI). These two methods provide complementary information about the temporal and spatial aspects of the neural response, with ERPs allowing high temporal resolution of milliseconds but low spatial resolution of the neural generator and fMRI displaying a slow hemodynamic response but better spatial localization of the activated regions. Despite the extensive fMRI and ERP research of faces, only a few studies have assessed the relationship between the two methods and no study to date have collected simultaneous ERP and fMRI responses to face stimuli. In the current paper we will try to assess the spatial and temporal aspects of the neural response to faces by simultaneously collecting functional MRI and event-related potentials (ERP) to face stimuli. Our goals are twofold: 1) ERP and fMRI show a robust selective response to faces. In particular, two well-established face-specific phenomena, the RH superiority and the inversion effect are robustly found with both ERP and fMRI. Despite the extensive research of these effects with ERP and fMRI, it is still unknown to what extent their spatial (fMRI) and temporal (ERP) aspects are associated. In Study 1 we will employ an individual differences approach, to assess the relationship between these ERP and fMRI face-specific responses. 2) Face processing involves several stages starting from structural encoding of the face image through identity processing to storage for later retrieval. This representation undergoes several manipulations that take place at different time points and in different brain regions before the final percept is generated. By simultaneously
Fit for Leverage-Modelling of Asian Hedge Fund Returns

Hedge Funds typically reveal some statistical properties like serial correlation, nonnormality, volatility clustering or leverage which have to be considered when risk positions of Hedge Funds are computed. We describe a simulation model based on a Markov-Switching autoregressive process that captures the specific features of Hedge Fund returns and allows especially for leverage effects in the data. The model is tested using publicly available Asian Hedge Fund Index data. We compare two different variants of the model by means of risk and performance measures. Our case study implies that if the leverage effect appears in the data, it is worth to fit for leverage in the parameter estimation process. JEL classification: C22; G10; G15;
Improving the Capability of Reduced Difference Expansion based Digital Image Data Hiding

Difference Expansion along with its variants such as Reduced Difference Expansion (RDE) is a popular approach in the field of data hiding. Nevertheless, current works have not yet achieved the optimal performance, in terms of the capacity of the secret data and the quality of the stego media. In this paper, we propose a two layer-based algorithm in order to specifically improve the capability of RDE method regarding to the capacity and the quality of the stego image. This is done in the following manners. The first is enhancing the difference transformation scheme by taking the difference between the two closest pixels; and correspondingly, a new pixel construction scheme is also adjusted. The second is optimizing the size of the pixel blocks. The third stage is expanding the algorithm for both grayscale and RGB color images. Fourth, the proposed method improves the use of non-changeable blocks to hide the secret data. Finally, every block is processed once again to embed more secret bits. It occurs by replacing the Least Significant Bit (LSB) of each pixel. The experimental results show that this method has made a significant improvement in terms of the capacity since around 1.85 bits are carried by each pixel where the quality is slightly higher than that of the presented by previous research. Overall, by using the same block size, this proposed method delivers higher performance for both capacity and quality; furthermore, by using a bigger block size, this method is able to obtain a significant increase of the capacity with only slightly decrease of the quality.
A Reinforcement Learning Approach for Target Tracking using Neural Network as Function Approximation

Reinforcement learning is a form of machine learning in which the agent learns by interacting with the environment. By doing so, for each action taken, the agent receives a reward or penalty, which is used to determine positive or negative behaviour. Unlike other machine learning forms, such as supervised learning, the agent is not explicitly told what action to take in each state in order to learn that, the agent has to go through a series of trial and error by interacting with the environment and receiving the rewards. The goal of the agent is to maximise the total reward received during the interaction. This form of machine learning has applications in different areas, such as: game solving, with the most known game being AlphaGO; robotics, for design of hard-to engineer behaviours; traffic light control, personalized recommendations, etc. In this work, we consider the problem of tracking a moving target in a simulated multi agent environment. The environment consists of a rectangular space bounded by walls. The first agent, which is the target, moves randomly in the space avoiding the walls and emits some light that makes it recognizable. The second agent has the task of detecting the moving target by the light it emits, and following it, keeping as close as possible without crashing. The target is expected to accelerate or decelerate, as well as change direction. We will use reinforcement learning in order for the tracker to learn how to detect any change in direction and stay within a certain range from the target. In this problem, the task of learning deals with continuous state. Since the state is continuous, we approximate the value function using neural network. We will apply the Q-Learning algorithm with different reward functions and compare the results of each for learning the best policy.
Considerations on LCA approaches for the evaluation of final urban waste sludge disposal options, including energy and materials recovery

Thermal processing of sludge remains a convenient and efficient approach for the disposal of waste urban sludge without causing excess secondary pollution, which is used as much as possible in many countries The paper presents different LCA approaches to the evaluation of urban waste sludge disposal options that include resources and materials recovery. Main current options (incineration, pyrolysis, etc.) under different technologies (co-incineration, conventional pyrolysis, microwave pyrolysis, etc.) have been previously investigated and compared. Life-cycle assessment (LCA, also known as life-cycle analysis, ecobalance, and cradle-to-grave analysis) is a well-known methodology to assess environmental impacts associated with all the stages of a product's or technological process’ life from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling. LCA application to the evaluation of urban sludge disposal options can objectively highlight which are the best “performing” technologies in respect to their impact on the environment. Results from several LCA application are compared, and show that one of the best processes to achieve energy and material recovery from urban waste sludge is the pyrolytic process, due to the advantages achieved when using microwave radiation as process-driving energetic input. This scenario also shows that, from a CO2-savings point-of-view, the maximum advantage occurs when producing bio-oil and using sludge-derived biochar as agricultural soil ammendant.
Sets, rules and natural classes: [ ] vs. { }

We discuss a set-theoretic treatment of segments as sets of valued features and of natural classes as intensionally defined sets of sets of valued features. In this system, the empty set { } corresponds to a completely underspecified segment, and the natural class [ ] corresponds to the set of all segments, making a feature ± Segment unnecessary. We use unification, a partial operation on sets, to implement feature-filling processes, and we combine unification with set subtraction to implement feature-changing processes. We show how unification creates the illusion of targeting only underspecified segments, and we explore the possibility that only unification rules whose structural changes involve a single feature are UG-compatible. We show that no such Singleton Set Restriction can work with rules based on set subtraction. The system is illustrated using toy vowel harmony systems and a treatment of compensatory lengthening as total assimilation.
Mental Health, Quality of Life and Medication Use Among Care Home Residents and Community Dwelling Older People

In most countries, the prevalence of depression in care homes is substantially higher than among community-dwelling older people. Negative media reports and the stigma associated with long-term care perpetuates the belief that community care is associated with a better quality of life than nursing or residential home care. The aim of this paper is to investigate mental health, quality of life and medication use among a sample of care home residents and community dwelling older people. This cross-sectional study comprised of structured interviews with 75 care home residents and 852 community dwelling older adults in a large health and social care trust within the U.K. Data were collected using an abridged version of the World Mental Health-Composite International Diagnostic Interview (WMH-CIDI). Health-related quality of life was assessed using the SF-6D, derived from responses to the SF-12, which was embedded within the screening section of the WMH-CIDI. Results showed a significantly lower level of mental disorders among older people in the care home sample. Health-related quality of life results indicated that although care home participants were significantly more limited in their physical functioning, they reported better mental health and social functioning than older people living in the community. These results were statistically significant. Prescription rates for mental health medication were higher in the care home sample. This study provides tentative findings about mental health and quality of life in care homes, which should be further explored in future research. The study addresses the negative public perception of quality of life in care homes while contributing to the debate around the health care implications of ‘ageing in place’ for older people. There is a need to address the relationship between mental health, quality of life and medication use among older people across a range of care settings.
Accelerating Agricultural Productivity and Marketing for Rural Transformation in Nigeria

This study examined the essential factors required to drive agricultural productivity and marketing towards enhancing rural transformation. Nigeria data on Total Factor Productivity (TFP) generated using the Solow’s residual approach, quality of labour force captured by different educational levels and health status, availability of credit, and the role of female gender among others were used for the period between1981-2016. Autoregressive distributed lag method was employed as no existence of long-run association was found among variables confirmed by the bound testing result. Findings showed that the female labour force is positively significantly related to TFP. Educational levels of labour were positively but insignificantly related to TFP except the primary educational level. Life expectancy rate was significantly negatively related to TFP. We thus recommended among others that female access to education and credit be enhanced to improve the female labour agricultural productivity, enhances the rural secondary and tertiary educational levels as well as increase in investment in farmers’ health status.
LUMINANCE CALIBRATION OF THE NIKON COOLPIX 990 DIGITAL CAMERA. APPLICATION TO GLARE EVALUATION

In this paper, we present the luminance calibration of the Coolpix 990, a digital camera made by Nikon. We test the different settings of the camera and choose which ones will be adapted to the calibration procedure. We characterize the projection resulting from the fish-eye lens made by Nikon for this camera. After this, the procedure and the experimental set-up used to calibrate it are detailed. We show how we used both an artificial sky and a sky luminance scanner to allow the calibration on the all EV (Exposure Value) scale. We explain why the luminance is an exponential function of the lightness of the pixel, the lightness being the L* component of the pixel value expressed in the CIE Lab color space; the coefficients of this function depending on the speed and the aperture used to take the picture. We conclude with the different applications in which the Coolpix 990 could be used.
Deep Sea Research Part I: Oceanographic Research Papers

Classification of ocean data maps is important for analysis of ocean data. Here, we compare Self-Organizing Map (SOM) analysis with cluster methods such as the Ward method and K-means method. The HF (high-frequency) radar surface current data east of Okinawa Island, Japan were used for the comparison. There are two typical current patterns in the observation area: a strong southward current and a clockwise eddy-like current pattern. The classification results by the Ward method was similar to that by the SOM analysis. SOM analysis was insensitive to the cut-off Empirical Orthogonal Function (EOF) mode number for reducing the data dimensions and noise, while the K-means method was the most sensitive to the EOF mode number.
Equipping Companions with Theory of Mind

Theory of Mind (ToM) is a fundamental mechanism of the human mind, allowing us to attribute mental states to other beings [Premack and Woodruff, 1978]. These mental states include beliefs, desires, intentions, and goals. Reasoning about the beliefs and desires of others is not only an important step towards building a conscious machine; several authors (e.g. [Krämer, 2008]) have also noted that it is a prerequisite for improving the communication with embodied agents.
INFSO-ICT-216284 SOCRATES D2.3 Assessment criteria for self-organising networks

The SOCRATES (Self-Optimisation and self-ConfiguRATion in wirEless networkS) project aims at the development of self-organisation methods for LTE radio networks. Self-organisation comprises selfoptimisation, self-configuration and self-healing. In this document criteria and methodologies to assess the solutions for self-organisation are described, together with the reference scenarios that will be used for assessing the developed solutions. Keyword list: Self-organisation, self-configuration, self-optimisation, self-healing, assessment criteria, metrics, benchmarking, reference scenarios, simulation models, drive tests
Static and Dynamic Analysis of WannaCry Ransomware

Nowadays ransomware presents a huge and the fastest growing problem for all types of users from small households to large corporations and government bodies. Modern day ransomware families implement sophisticated encryption and propagation schemes, thus limiting chances to recover the data almost to zero. In order to design and develop appropriate detection and mitigation mechanisms it is important to perform ransomware analysis and indemnify its features. In this work, we present our ransomware analysis results focusing on the infamous WannaCry ransomware. In particular, the presented research examines the WannaCry behaviour during its execution in a purpose-built virtual lab environment. We perform static and dynamic analysis using a wide range of malware analysis tools. The obtained results can be used for developing appropriate detection and mitigation mechanisms for WannaCry or other ransomware families that exhibit similar behaviour. Keywords—Malware analysis, ransomware, WannaCry
Making Regional Forecasts Add Up

Suppose we want to predict both the electricity consumption for K regions individually and the total consumption for all the regions together. For operational reasons, it is sometimes required that the prediction for the total consumption is equal to the sum of the predictions for the individual regions, but this constraint might prevent us from making the best possible predictions. In this work, we therefore introduce a new way to work around this restriction, for the case of quadratic loss. The idea is to adjust the regional predictions slightly so that their sum is closer to the prediction we would like to make for the total consumption. The amount of adjustment is determined in a gametheoretically optimal way.
LW-Sketch-Net on Mobile Phones

With the popularity of smart terminals, people tend to draw simple sketches to express emotions and ideas in communication, which means the era of reading pictures is coming. Therefore, in the field of sketch recognition, the application of deep network models in mobile devices is an irreversible trend. However, using deep learning method, most existing works with good performance has a large number of parameters. In order to further improve recognition speed and ensure the accuracy, we propose a lightweight neural network architecture to recognize sketch object. Specifically, we apply depthwise separable convolution into the network to reduce parameters and adjust the network effectively for the sparsity of sketch. Outperforming the state-of-the-art approaches, we achieves 85.8% and 84.1% on TU-Berlin and QuickDraw benchmarks respectively. Furthermore, the number of parameters is reduced to a large extent, which are 5% and 20% of the amount of Sketch-A-Net and MobileNets. We also develop a sketch recognition application on mobile phones to demonstrate the proposed scheme.
Face Recognition in Video Based on One Frontal View

Many face recognition techniques use image sequence as input for pose invariant face recognizer. But what if there is only one frontal image of person like the ones defined by biometric norm that can be seen on passports and other documents. This paper presents a new technique in face recognition in video (FRiV) using a single image per person as training data. Because of one example view technique was tested under same controlled lightning and with the same camera. The novelty of this paper is using Elliptical and Circular Gabor Filter to find accurate position of persons’ left and right eye on faces in video clips. Accurate eye location is essential parameter in creating normalized face images from video clip frames suitable for recognition process. In this paper recognition is realized using eigenfaces. Recognition results on video clip depend on how accurate the frames with face are normalized.
Continuum Concept of Motivation Factors and Discipline of Employees to Improve Quality of Service

To improve the motivation of employees to work, it needs to give the reward objectively, such as giving a reward for the best employees. This reward can be increasing the incentive, bonus, praise, or the best employee achievement. The leader needs to improve a personal approach to the employee to accommodate the aspirations’ or complaints’ of the employees related to their job. Giving employees attention is a kind of motivation for them. So they are motivated to work. To improve employee discipline, it needs to implement a fair punishment system for those who are undisciplined in the work through persuasive approach. It is hoped that it can raise their self-awareness to work with discipline. That mecanism concept can lead to improving the quality of service to society.