Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient-Controlled Personal Health Record Enforcing Patient Privacy in Cloud based Healthcare System

Personal Health Record (PHR) through Cloud computing is a new technology reaching its fame off late. Cloud computing has done miracles in the world of computers and internet through the concept of virtualization. In the field of Personal Health Record (PHR), cloud server acts as a third party server to store the records. The major concerns involed here is with finegrained access, cryptography, key management and diversifying the availability of information during on-demand user request. This paper provides a secure sharing of patient health information through cloud computing. Cloud-based PHR system allows pateints to secure their sensitive data on a semi-trusted cloud based service provider with various other security protecting attributes. In Cloud computing the information is available to a large group of crowd and customizing them to specified users like doctors, nurses, friends, relatives or family members is highly possible. In our proposed paper the PHR owner has the rights to encrypt their data in public domain using AES Encryption Scheme and it is re encrypted using Elliptic Curve Cryptography algorithm to strengthen the security. We have also introduced Hybrid Crypto Secure Secret Sharing Algorithm when the owner sends the secret code to random shares to the requester who wants access to the PHR. The algorithm is designed to allow the user with the correct key to access the International Journal of Pure and Applied Mathematics Volume 119 No. 1
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms

Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.
Morphological Pairing based on the Network Model

The paper presents a method of semi-automatic construction of a lexicon enhanced with constructional informations. The construction is carried out in two steps: (i) automatic pairing of the constructed words with their base words, then ( ii) manual validation of these base words by human operators. The method is based on the Network Model proposed by J. Bybee, which gives a central role to analogy and lexical memory (i.e. the mental lexicon): the lexicon is viewed as a network of fully in ected forms connected to each other by relations set up according to shared semantic and phonological features. Connections are organized into paradigms corresponding to analogical schemata. The automatic pairing of the constructed words with their bases takes place in three steps: ( i) unsupervised learning of analogical schemata, ( ii) association of a set of candidate bases with each constructed word, and (iii ) ltering of these candidates by means of a measure (ex. type frequency). The method can be used for any language with concatenative morphology since no linguistic knowledge is used. Moreover, the only resource needed is a lexicon of in ected forms. An evaluation of the method is presented in  4.
Lessons Learned from 23 Years of Experience in Testing Visual Field of Neurologically Impaired Children

Background: Despite general difficulty to measure visual field (VF) in Neurologically Impaired (NI) children, the timely finding of a VF impairment may contribute to finding the right diagnosis and treatment as well as to explaining their visual behaviour to parents and caregivers. We describe the Standard Conventional Perimetry (SCP) and a potential gain in time to diagnosis of a visual field defect (VFD) when using a behavioural visual field (BVF) test. Methods: NI children who underwent SCP at the University Medical Center Utrecht were retrospectively analysed and scored with the examiner-based assessment of reliability (EBAR) scoring system. The difference in time to diagnosis between the BVF and SCP tests was calculated. Results: In this cohort of 115 NI children (69 boys and 46 girls), the majority suffered from neoplasms (36%) and stroke/haemorrhage (34%). The mean age at which they were able to complete an SCP test was 8.3 years (range 4.5 17.4). 44% were tested with the Full Field Peritest (FFP), 23% with the Goldmann VF test, 29% with the Central Peritest (CP) and 4% with the Humphrey Field Analyzer. Among the FFP 44% had “good” reliability versus 22% in Goldmann tests and 17% in CP (p < 0.001). The mean age of NI children performing SCP was 8.3 years versus 4.6 years with BV F (p < 0.001). Conclusion: The Full Field Peritest is the most reliable VF test in this cohort of NI children. Use of the BVF test significantly reduces (average 3.7 years) the time to diagnosis of a peripheral VFD.
Fetal Origins of Mental Health: Evidence from Africa

Mental health disorders represent a substantial portion of the global disease burden, and the treatment gap is higher in developing countries. Accounting for location and year-of-birth fixed effects and using data on 19 African countries, we find temperature shocks in utero increase depressive symptoms in adulthood. Effects are present for several depressive symptoms and are greatest in younger cohorts.
The Italian version of HoNOS (Health of the Nation Outcome Scales), a scale for evaluating the outcomes and the severity in mental health services

1 Dipartimento di Salute Mentale dell 'Azienda Ospedaliera di Vimercate, Vimercate (MI) Dipartimento di Salute Mentale dell'Azienda Ospedaliera di Legnano, Legnano (MI) 3 Dipartimento di Salute Mentale dell 'Azienda Ospedaliera di Busto Arsizio, Busto Arsizio (MI) 'Direzione Generate Sanita Regione Lombardia 'Dipartimento di Salute Mentale dell'Azienda Ospedaliera Niguarda Ca' Granda, Milano 'Dipartimento di Salute Mentale dell'Azienda Ospedaliera S. Carlo, Milano Dipartimento di Salute Mentale dell'Azienda Ospedaliera S. Paolo, Milano 'Istituto di Economia Sanitaria ' Unita di Biometria, Istituto M.Negri, Milano '"Istituto Superiore di Sanita
CVS2KG: Transforming Tabular Data into Semantic Knowledge

A large portion of structured data does not yet reap the ben-eﬁts of the Semantic Web, or Web 2.0, as it is not semantically annotated. In this paper, we propose a system to generates semantic knowledge, available on DBPedia, from common CSV ﬁles. The “Tabular Data to Knowledge Graph Matching” competition, consisting of three diﬀerent subchallenges, was used to evaluate the proposed methodology.
Automatic Annotation Service: Utilizing a Named Entity Linking Tool in Legal Domain

Texts referencing court decisions, statutes, and EU directives can be difficult to understand without context. It can be time consuming and expensive to find related statutes or to learn about context specific terminology. As a solution, we utilized an automatic annotation tool, Nelli, for extracting information and tailored it to a service that can automatically annotate legal documents to provide context to the readers. The service can identify and link named entities and references to legal texts to corresponding vocabularies and data sources by combining statisticsand rule-based named entity recognition with named entity linking. The results provide users with enhanced reading experience with contextual information and possibility to access related materials such as statutes and court decisions.
Graphene textiles towards soft wearable interfaces for electroocular remote control of objects

Study of eye movements (EMs) and measurement of the resulting biopotentials, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human-computer interaction (HCI) applications, and personalized medicine provided that the limitations of conventional “wet” electrodes are addressed. To overcome the limitations of conventional electrodes, this work, reports for the first time the use and characterization of graphene-based electroconductive textile electrodes for EOG acquisition using a custom-designed embedded eye tracker. This self-contained wearable device consists of a headband with integrated textile electrodes and a small, pocket-worn, battery-powered hardware with real-time signal processing which can stream data to a remote device over Bluetooth. The feasibility of the developed gel-free, flexible, dry textile electrodes was experimentally authenticated through side-by-side comparison with pre-gelled, wet, silver/silver chloride (Ag/AgCl) electrodes, where the simultaneously and asynchronous recorded signals displayed correlation of up to ~87% and ~91% respectively over durations reaching hundred seconds and repeated on several participants. Additionally, an automatic EM detection algorithm is developed and the performance of the graphene-embedded “all-textile” EM sensor and its application as a control element toward HCI is experimentally demonstrated. The excellent success rate ranging from 85% up to 100% for eleven different EM patterns demonstrates the applicability of the proposed algorithm in wearable EOG-based sensing and HCI applications with graphene textiles. The system-level integration and the holistic design approach presented herein which starts from fundamental materials level up to the architecture and algorithm stage is highlighted and will be instrumental to advance the state-of-the-art in wearable electronic devices based on sensing and processing of electrooculograms.
Statistical Verification of Learning-Enabled Controlled Systems

Modern Cyber-Physical Systems (CPSs) which are known as one of the most complex and important categories of Probabilistic Uncertain Systems (PUSs) become increasingly dependent on controllers derived from machine learning. In this work, we proposed a statistical model checking framework for verifying the safety of the learning-enabled controllers. The scalability problem, the existing constraints on using the verification tools and reachability methods, inaccuracy of the linear models, and dependency of the existing methods on the mathematical model of the system are regarded as the main drawbacks of the employed conventional methods for verification of the learning-enabled control systems. To cope with these problems, a Statistical Model Checking (SMC) approach is employed to verify the specification which has been expressed by the Probabilistic Computation Tree Logic (PCTL) formulas. Herewith this method, a grey-box model of the system will be used for the verification. Less relying on the system’s model and its parameters and using arsenals of well-developed techniques and tools in the SMC will address the aforementioned problems. To illustrate the performance of the proposed technique three case studies have been evaluated.
Analysis of Torque Pulsations of Inverter Fed Five-Phase Induction Machines

The analysis of torque pulsations in five-phase induction machines is presented. The pulsations arise from the mutual interaction of certain spatial harmonics present in the air gap of the machine. Simulation results are validated by experimental measurements for various voltage supply methods. A voltage supply method that significantly reduces the unfavorable effects of the third space harmonic in five-phase machines is described in the paper.
Ultrasonography Diagnostics Using Gaussian Mixture Model

The problem of diagnostics of diseases of thyroid gland is addressed, namely Hashimoto's Lymphotitic thyroiditis, which is one of most usual thyroid gland diseases. As opposed to expensive diagnosis using magnetic resonance or by painful invasive methods, we base our method on using ultrasound images. Sonographic imaging of thyroid gland is done rou- tinely and these images can be used to find new cases which can help to start treatment as soon as possible. It is hard for physician, to make thyroid gland diagnostics based on ultrasound image only. However, we have found out (see (2, 3)), that Hashimoto's Lymphotitic thyroiditis changes the pattern of the ultrasound scan. For diagnostics, we use the statistics method. Database of ultrasound images with known diagnosis is used for learning. Pattern features are calculated and Gaussian mixture model in the space of pattern features is used. In diagnostics stage, the sonogram of person with unknown diagnostics is taken and the most probable diagnosis is inferred using Bayesian inference with on Gaussian clusters of pattern features. Hashimoto's lymphocytic thyroiditis is ideally suited for this approach, however, we research possibilities to use it for diagnostics of another diseases of thyroid gland or even other internal diseases.
Canadian Diabetes Association Clinical Practice Guidelines Expert Committee

If glycemic targets are not achieved within 2 to 3 months of lifestyle management, antihyperglycemic pharmacotherapy should be initiated. Timely adjustments to, and/or additions of, antihyperglycemic agents should be made to attain target glycated hemoglobin (A1C) within 3 to 6 months. In patients with marked hyperglycemia (A1C 8.5%), antihyperglycemic agents should be initiated concomitantly with lifestyle management, and consideration should be given to initiating combination therapy with 2 agents, 1 of which may be insulin. Unless contraindicated, metformin should be the initial agent of choice, with additional antihyperglycemic agents selected on the basis of clinically relevant issues, such as contraindication to drug, glucose lowering effectiveness, risk of hypoglycemia and effect on body weight.
Variations in the anterolateral approach to the hip

A pictorial questionnaire distributed to 56 hip surgeons who use an anterolateral approach to the hip revealed that only 46% use an approach which conforms closely to the original eponymous description. This is important because problems associated with anterolateral approaches such as the bare trochanter, bursitis, gluteal detachment, persistent limp and superior gluteal nerve palsy may be associated with particular variants. We suggest a classification to append to eponymous descriptions which may assist in proper evaluation of these approaches in future, as well as being helpful to any surgeon required to carry out revisions on such cases.
FDTD Analysis of Cross Polarization Characteristics for 4.4 GHz-band Radio Propagation Inside Aircraft Cabin

Electromagnetic field (EMF) distributions generated by a 4.4 GHz-band wireless transmitter for Wireless Avionics Intra Communications (WAIC) system inside an aircraft cabin are analyzed and fundamental propagation characteristics such as path-loss and Cross Polarization Power Ratio (XPR), are discussed from the analysis results. Keyword Wireless Avionics Intra Communications, Aircraft, Propagation characteristics, Large-scale FDTD analysis.
Sharing control in direct style

Administrative normal form (ANF) promises to reap the benefits of continuation-passing style (CPS) while retaining the advantages of direct style. We believe ANF falls short of this ideal because it does not provide a satisfactory way to describe shared control flow, as CPS does naturally. We show how CPS itself can guide us in adding a control effect to ANF and taming it to regain purity, guaranteeing an efficient implementation. Interestingly, even though CPS is usually used for compiling call-by-value languages, our technique is independent of the evaluation strategy. In short, we propose that the known advantages of CPS can be achieved by extending ANF or another direct-style representation. Does this mean that CPS is finally out? No: whatever the compiler’s chosen representation, we suspect CPS will reveal new insights about program manipulation.
Clinical Symptoms of Minor Head Trauma and Abnormal Computed Tomography Scan

Background: Minor head trauma accounts for 70% to 90% of all head traumas. Previous studies stated that minor head traumas were associated with 7% 20% significant abnormal findings in brain computed tomography (CT)-scans. Objectives: The aim of this study was to reevaluate clinical criteria of taking brain CT scan in patients who suffered from minor head trauma. Patients and Methods: We enrolled 680 patients presented to an academic trauma hospital with minor head trauma in a prospective manner. All participants underwent brain CT scan if they met the inclusion criteria and the results of scans were compared with clinical examination finding. Results: Loss of consciousness (GCS drop or amnesia) was markedly associated with abnormal brain CT scan (P < 0.05). Interestingly, we found 7 patients with normal clinical examination but significant abnormal brain CT scan. Conclusions: According to the results of our study, we recommend that all patients with minor head trauma underwent brain CT scan in order not to miss any life-threatening head injuries.
Brand Inertia in Seed Demand: State Dependence and Heterogeneity

A commonly observed feature of differentiated product markets is brand inertia, the tendency of consumers to purchase brands they have purchased in the past. In this paper, we develop and estimate a micro-level random coefficients logit model to study two competing explanations of brand inertia, state dependence and heterogeneity, in the U.S. soybean seed industry. Specifically, heterogeneity is captured by brand-specific random coefficients and state dependence is incorporated through a brand purchase history variable. We further deal with two important issues in the identification: we apply a correction to the initial conditions problem similar to the procedure outlined in Wooldridge (2005); and to deal with price endogeneity, we use the control function approach in our nonlinear regression environment. The model is estimated using a large dataset of more than 200,000 seed purchase decisions by roughly 28,000 farmers over the period 1996-2016. We find that state dependence and heterogeneity are both important features of seed demand. On average, farmers are willing to pay (WTP) an additional $6.77/unit for a brand if it was purchased in the previous period, equivalent to about 15% of the average retail price. We also find that farmers are willing to pay large premiums for brand labels and the glyphosate tolerance (GT) technology, however there is considerable heterogeneity in this willingness. To investigate the implications of state dependence, especially as it relates to the introduction and diffusion of the GT innovation, we simulate several counterfactual scenarios (with/without state dependence and/or the GT technology). Our simulations show that state dependence has little effect on the diffusion of the GT technology, but it functions as a cushion for the structural benefits brought about by the innovation—it reduces the gains/losses in brands’ market shares. We also show that there is an “early adoption” advantage associated with the marketing of the GT trait, which is reinforced by state dependence.
Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention

Charge prediction which aims to determine appropriate charges for criminal cases based on textual fact descriptions, is an important technology in the ﬁeld of AI&Law. Previous works focus on improving prediction accuracy, ignoring the interpretability, which limits the methods’ applicability. In this work, we propose a deep neural framework to extract short but charge-decisive text snippets – rationales – from input fact description, as the interpretation of charge prediction. To solve the scarcity problem ofrationale annotatedcorpus, rationalesare extractedinareinforcement stylewiththe only supervision in the form of charge labels. We further propose a dynamic rationale attention mechanism to better utilize the information in extracted rationales and predict the charges. Experimental results show that besides providing charge prediction interpretation, our approach can also capture subtle details to help charge prediction.
Parental Involvement and the Educational Strategies of Youth In Russia

This paper studies the influence of parental involvement in the educational process on the educational achievements of Russian students and their educational strategies, such as studying in high school and successful admission to university. We argue that the patterns of parental involvement represent a link between the formal (school) and informal (family) educational institutions and can have a beneficial effect on academic performance and contribute to the choice of the educational pathway to higher education. Based on data from the longitudinal study ‘Trajectories in Education and Careers’, it was shown that the results of school state examinations are positively associated with the active participation of parents in school meetings, the employment of tutors (except for the Unified State Exam score in mathematics), and the provision of additional literature for the child. A negative relationship was found between homework control and student success. In general, the factor of ‘rational’ (not excessive) involvement is positively associated with educational achievement and educational choice, which may indicate the non-linear nature of the relationship. Parental involvement itself depends on the family characteristics, such as mother’s education, family income and the number of books at home. In addition, family has a positive impact on educational success and educational strategies, and high school characteristics are especially important for the results of the Unified State Exam and the university choice.
Stable predictive representations with general value functions for continual learning

The objective of continual learning is to build agents that continually learn about their world, building on prior learning. In this paper, we explore an approach to continual learning based on making and updating many predictions formalized as general value functions (GVFs). The idea behind GVFs is simple: if we can cast the task of representing predictive knowledge as a prediction of future reward, then computationally efficient policy evaluation methods from reinforcement learning can be used to learn a large collection of predictions while the agent interacts with the world. We explore this idea further by analyzing how GVF predictions can be used as predictive features, and introduce two algorithmic techniques to ensure the stability of continual prediction learning. We illustrate these ideas with a small experiment in the cycle world domain.