Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden Ciphertext Policy Attribute-Based Encryption With Fast Decryption for Personal Health Record System

Since cloud computing has been playing an increasingly important role in real life, the privacy protection in many fields has been paid more and more attention, especially, in the field of personal health record (PHR). The traditional ciphertext-policy attribute-based encryption (CP-ABE) provides the fine-grained access control policy for encrypted PHR data, but the access policy is also sent along with ciphertext explicitly. However, the access policy will reveal the users’ privacy, because it contains too much sensitive information of the legitimate data users. Hence, it is important to protect users’ privacy by hiding access policies. In most of the previous schemes, although the access policy is hidden, they face two practical problems: 1) these schemes do not support large attribute universe, so their practicality in PHR is greatly limited and 2) the cost of decryption is especially high since the access policy is embedded in the ciphertext. To address these problems, we construct a CP-ABE scheme with efficient decryption, where both the size of public parameters and the cost of decryption are constant. Moreover, we also show that the proposed scheme achieves full security in the standard model under static assumptions by using the dual system encryption method.
Book Reviews

In 1974, when Professor Muhammed Yunus first lent some poor village women $27, and then founded the Grameen Bank two years later, he could not have known that its success would lead to a revolution in how international economic development agencies would treat the poor in Low Income Countries or view the role of the informal sector in the economy, and the Nobel Peace Prize in 2006. Microlending (or microcredit or microfinance; the terms are virtually interchangeable in most contexts) is the process of making small loans to individuals/firms traditionally outside mainstream financial processes because they are too poor or lack the proper documentation to qualify for formal loans. Since the 1980s, informal sector microlending has been viewed by many in international donor agencies, and by NGOs, politicians, and the media, as a vehicle for generating employment and GDP growth, and empowering the poor. In short, if one believed the hype of the true believers, a veritable ‘‘silver bullet’’ had been discovered. A legitimate question is how successful has the ‘‘empowerment debt’’ (Elyachar’s term for microlending) approach actually been? Markets of Dispossession attempts to answer this question. Elyachar, Assistant Professor of Anthropology at the University of California-Irvine, is familiar with Cairo; from 1993 to 1996, she was in Cairo to ‘‘study the interaction of the state, IOs [international organizations] and NGOs in the social field of the informal economy’’ (p. 33). Employing a leftheterodox analysis incorporating Marx, Foucault, and David Harvey, Markets of Dispossession is intended for advanced undergraduates and graduate students studying economic development issues, and the wider academic community. It is an ethnographic study of the world of the selfemployed, small workshop owners in Cairo’s El-Hirafiyeen district. REVIEW OF SOCIAL ECONOMY, VOL. LXVI, NO. 2, JUNE 2008
What Drives Technology Innovation in New Space?: A Preliminary Analysis of Venture Capital Investments in Earth Observation Start-Ups

This article analyzes venture capital (VC) investments in the Earth observation (EO) industry, with the aim of identifying technology trends. We collected tech- and investment-related publicly available data on all private, nonsubsidiary companies using their own satellite constellation or other EO instruments as needed to solve the problem of remote sensing of Earth's surface in the optical part of the electromagnetic spectrum and that were attracting investment and announcing plans for the future, as of 31 December 2016. We refer to these companies as EO startups. In the final sample, there are nine EO start-ups and 21 instruments used in their missions.
Intrinsic Negative Magnetoresistance in Van Der Waals FeNbTe2 Single Crystals

Magnetoresistance, the dependence of resistivity on the applied magnetic field, provides the opportunity to manipulate and utilize the electronic spin degree of freedom, which is not only a long‐term frontier field of solid‐state physics but also the cornerstone of information storage technology. However, the negative magnetoresistance (nMR) is a relatively rare case of magnetoresistance in which the microscopic origin is still elusive and for which it is difficult to define a general interpretation. Herein, an experimental case of an intrinsic unsaturated nMR is demonstrated in van der Waals FeNbTe2 single crystal. The clear‐cut evidence in angle‐resolved photoemission spectroscopy (ARPES), the electronic transport measurement, and DC/AC magnetic susceptibility confirms that the intrinsic unsaturated nMR is derived from the comprehensive effect of Anderson localization and a spin glass state. Taking into consideration that intrinsic unsaturated nMR has so far been rarely reported, especially in van der Waals structures, it is anticipated that this work will not only lead to a deep understanding of the inherent microcosmic mechanism but will also serve as a guide to broaden the research of spintronics and information storage based on magnetoresistance.
The C9orf72 hexanucleotide repeat expansion presents a challenge for testing laboratories and genetic counseling

Abstract C9orf72 hexanucleotide repeat expansions are the most common known cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Genetic testing for C9orf72 expansions in patients with ALS and/or FTD and their relatives has become increasingly available since hexanucleotide repeat expansions were first reported in 2011. The repeat number is highly variable and the threshold at which repeat size leads to neurodegeneration remains unknown. We present the case of an ALS patient who underwent genetic testing through our Motor Neurone Disease Clinic. We highlight current limitations to analysing and interpreting C9orf72 expansion test results and describe how this resulted in discordant reports of pathogenicity between testing laboratories that confounded the genetic counselling process. We conclude that patients with ALS or FTD and their at-risk family members, need to be adequately counselled about the limitations of current knowledge to ensure they are making informed decisions about genetic testing for C9orf72. Greater collaboration between clinicians, testing laboratories and researchers is required to ensure risks to patients and their families are minimised.
Effect of Anesthesia on Fetal-Maternal Heart Rate Variability and Coupling in Pregnant Mice and Fetuses

To evaluate and assess the cardiovascular system during fetal development in the utero of pregnant mouse, it is essential to understand the effect of mandatory anesthesia treatment on sympathetic and parasympathetic nervous system activities. The preliminary study presented in this paper explores the changes in fetal and maternal Heart Rate Variability (HRV) parameters as well as fetal-maternal Heart Rate (HR) coupling measures during anesthesia. ECG signals of 6 pregnant mice and 10 fetuses were recordedfor 15 min. The obtained ECG signals were segmented into three periods, each for a duration of 5 min. Maternal and fetal HRV parameters in addition to fetal-maternal coupling patterns were computed for each of the three segments of the ECG signals. During the first 10 min, results show that mean and root mean square of successive differences (RMSSD) of maternal HR did not change, but significantly decreased after the first 10 min. A similar result was observed for the mean, RMSSD and standard deviation of NN intervals in fetal HR. On the other hand, no significant changes were observed for the coupling patterns between fetal-maternal heartbeats. These observations suggest that fetal nervous system activities were suppressed by anesthesia treatment applied to pregnant mice for more than 10 min.
Security and Accuracy of Fingerprint-Based Biometrics: A Review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.
Reasoning with a Bounded Number of Resources in ATL+

The resource-bounded alternating-time temporal logic RB ± ATL combines strategic reasoning with reasoning about resources. Its model-checking problem is known to be 2 EXPTIME - complete (the same as its proper extension RB ± ATL ∗ ). Several fragments have been identiﬁed to lower the complexity. In this work, we consider the variant RB ± ATL + which permits Boolean combinations of path formulae starting with single temporal operators, but restricted to a single resource, providing an interesting trade-off between temporal expressivity and resource analysis. We show that the model-checking problem for RB ± ATL + restricted to a single agent and a single resource is ∆ p2 -complete, hence the same as for CTL + . In this case reasoning about resources comes at no extra computational cost. Furthermore, we show that, with an arbitrary number of agents and a ﬁxed number of resources, the problem can be solved in EXPTIME using a Turing reduction to the parity game problem for alternating vector addition systems with states.
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations

The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of-concepts for idealized observation setting for fully-observed systems, {\em i.e.} large signal-to-noise ratios and high-frequency sampling of all system variables, we here address the learning of data-driven representations of chaotic dynamics for partially-observed systems, including significant noise patterns and possibly lower and irregular sampling setting. Instead of considering training losses based on short-term prediction error like state-of-the-art learning-based schemes, we adopt a Bayesian formulation and state this issue as a data assimilation problem with unknown model parameters. To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes. Using iterative Expectation-Maximization (EM)-like procedures, the key feature of the proposed inference schemes is the derivation of the posterior of the hidden dynamics. Using a neural-network-based Ordinary Differential Equation (ODE) representation of these dynamics, we investigate two strategies: their combination to Ensemble Kalman Smoothers and Long Short-Term Memory (LSTM)-based variational approximations of the posterior. Through numerical experiments on the Lorenz-63 system with different noise and time sampling settings, we demonstrate the ability of the proposed schemes to recover and reproduce the hidden chaotic dynamics, including their Lyapunov characteristic exponents, when classic machine learning approaches fail.
Meta-Control in Pigeons (Columba livia) and the Role of the Commissura Anterior

Meta-control describes an interhemispheric response conflict that results from the perception of stimuli that elicit a different reaction in each hemisphere. The dominant hemisphere for the perceived stimulus class often wins this competition. There is evidence from pigeons that meta-control results from interhemispheric response conflicts that prolong reaction time when the animal is confronted with conflicting information. However, recent evidence in pigeons also makes it likely that the dominant hemisphere can slow down the subdominant hemisphere, such that meta-control could instead result from the interhemispheric speed differences. Since both explanations make different predictions for the effect of commissurotomy, we tested pigeons in a meta-control task both before and after transection of the commissura anterior. This fiber pathway is the largest pallial commissura of the avian brain. The results revealed a transient phase in which meta-control possibly resulted from interhemispheric response conflicts. In subsequent sessions and after commissurotomy, however, the results suggest interhemispheric speed differences as a basis for meta-control. Furthermore, they reveal that meta-control is modified by interhemispheric transmission via the commissura anterior, although it does not seem to depend on it.
Consensus Clustering-Based Undersampling Approach to Imbalanced Learning

Class imbalance is an important problem, encountered in machine learning applications, where one class (named as, the minority class) has extremely small number of instances and the other class (referred as, the majority class) has immense quantity of instances. Imbalanced datasets can be of great importance in several real-world applications, including medical diagnosis, malware detection, anomaly identification, bankruptcy prediction, and spam filtering. In this paper, we present a consensus clustering based-undersampling approach to imbalanced learning. In this scheme, the number of instances in the majority class was undersampled by utilizing a consensus clustering-based scheme. In the empirical analysis, 44 small-scale and 2 large-scale imbalanced classification benchmarks have been utilized. In the consensus clustering schemes, five clustering algorithms (namely, k-means, k-modes, k-means++, self-organizing maps, and DIANA algorithm) and their combinations were taken into consideration. In the classification phase, five supervised learning methods (namely, naïve Bayes, logistic regression, support vector machines, random forests, and k-nearest neighbor algorithm) and three ensemble learner methods (namely, AdaBoost, bagging, and random subspace algorithm) were utilized. The empirical results indicate that the proposed heterogeneous consensus clustering-based undersampling scheme yields better predictive performance.
HIV self‐testing: breaking the barriers to uptake of testing among men and adolescents in sub‐Saharan Africa, experiences from STAR demonstration projects in Malawi, Zambia and Zimbabwe

Social, structural and systems barriers inhibit uptake of HIV testing. HIV self‐testing (HIVST) has shown promising uptake by otherwise underserved priority groups including men, young people and first‐time testers. Here, we use characteristics of HIVST kit recipients to investigate delivery to these priority groups during HIVST scale‐up in three African countries.
Comparative Analysis of Whole-Genome Sequence of African Swine Fever Virus Belgium 2018/1

We analyzed the whole-genome sequence of African swine fever virus Belgium 2018/1. The strain fits into the European genotype II (>99.98% identity). The high-coverage sequence revealed 15 differences compared with an improved African swine fever virus Georgia 2007/1 sequence. However, in the absence of genetic markers, no spatial or temporal correlations could be defined.
Personality Identification of Palmprint Using Convolutional Neural Networks

Overview of personality is needed such employee recruitment, counseling in schools, and the development of the children - for the parents. Palmistry is a technique through the palmprint of the hand that contains personality. Unfortunately, this way is not easy. It requires special expertise in the science of Palmistry. Deep learning one of the developments in machine learning that has a high level of recognition. This research proposed personality identification of palmprint using Convolutional Neural Networks (CNNs). The methods began with learning before being used for identification. Learning used data which were obtained from 11,000 hands that have been labeled customized according to Palmistry science, i.e., Air, Earth, Fire, and Water. The results showed that the accuracy reached 96.7% of new data and 99.7% of training data. The best accuracy was given in five convolution configurations, five Max Pooling, a hidden layer with 1024 hidden neurons, and 512 input vectors. The time required in the identification process is fast enough that it can be used properly on mobile devices for ease of use of the system.
Functional Outcome of Intravenous Thrombolysis in Patients With Lacunar Infarcts in the WAKE-UP Trial.

Importance The rationale for intravenous thrombolysis in patients with lacunar infarcts is debated, since it is hypothesized that the microvascular occlusion underlying lacunar infarcts might not be susceptible to pharmacological reperfusion treatment. Objective To study the efficacy and safety of intravenous thrombolysis among patients with lacunar infarcts. Design, Setting, and Participants This exploratory secondary post hoc analysis of the WAKE-UP trial included patients who were screened and enrolled between September 2012 and June 2017 (with final follow-up in September 2017). The WAKE-UP trial was a multicenter, double-blind, placebo-controlled randomized clinical trial to study the efficacy and safety of intravenous thrombolysis with alteplase in patients with an acute stroke of unknown onset time, guided by magnetic resonance imaging. All 503 patients randomized in the WAKE-UP trial were reviewed for lacunar infarcts. Diagnosis of lacunar infarcts was based on magnetic resonance imaging and made by consensus of 2 independent investigators blinded to clinical information. Main Outcomes and Measures The primary efficacy variable was favorable outcome defined by a score of 0 to 1 on the modified Rankin Scale at 90 days after stroke, adjusted for age and severity of symptoms. Results Of the 503 patients randomized in the WAKE-UP trial, 108 patients (including 74 men [68.5%]) had imaging-defined lacunar infarcts, whereas 395 patients (including 251 men [63.5%]) had nonlacunar infarcts. Patients with lacunar infarcts were younger than patients with nonlacunar infarcts (mean age [SD], 63 [12] years vs 66 [12] years; P = .003). Of patients with lacunar infarcts, 55 (50.9%) were assigned to treatment with alteplase and 53 (49.1%) to receive placebo. Treatment with alteplase was associated with higher odds of favorable outcome, with no heterogeneity of treatment outcome between lacunar and nonlacunar stroke subtypes. In patients with lacunar strokes, a favorable outcome was observed in 31 of 53 patients (59%) in the alteplase group compared with 24 of 52 patients (46%) in the placebo group (adjusted odds ratio [aOR], 1.67 [95% CI, 0.77-3.64]). There was 1 death and 1 symptomatic intracranial hemorrhage according to Safe Implementation of Thrombolysis in Stroke-Monitoring Study criteria in the alteplase group, while no death and no symptomatic intracranial hemorrhage occurred in the placebo group. The distribution of the modified Rankin Scale scores 90 days after stroke also showed a nonsignificant shift toward better outcomes in patients with lacunar infarcts treated with alteplase, with an adjusted common odds ratio of 1.94 (95% CI, 0.95-3.93). Conclusions and Relevance While the WAKE-UP trial was not powered to demonstrate the efficacy of treatment in subgroups of patients, the results indicate that the association of intravenous alteplase with functional outcome does not differ in patients with imaging-defined lacunar infarcts compared with those experiencing other stroke subtypes.
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2

The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).
A Vector Meshless Method for Transient Vector Wave Equations

Based on the vector radial basis function (RBF) which is theoretical divergence free, we propose a meshless method for solving the transient vector wave equation. Unlike the conventional radial point interpolation meshless (RPIM) method that solves the field components separately, the proposed method solves the vector wave equation directly. Therefore, easy source and boundary implementation is achieved due to the direct solution of the vector wave equation, which is a challenge issue in the traditional RPIM method. Several numerical experiments are taken to validate the accuracy of the proposed solver.
Carrollian physics at the black hole horizon

We show that the geometry of a black hole horizon can be described as a Carrollian geometry emerging from an ultra-relativistic limit where the near-horizon radial coordinate plays the role of a virtual velocity of light tending to zero. We prove that the laws governing the dynamics of a black hole horizon, the null Raychaudhuri and Damour equations, are Carrollian conservation laws obtained by taking the ultra-relativistic limit of the conservation of an energy–momentum tensor; we also discuss their physical interpretation. We show that the vector fields preserving the Carrollian geometry of the horizon, dubbed Carrollian Killing vectors, include BMS-like supertranslations and superrotations and that they have non-trivial associated conserved charges on the horizon. In particular, we build a generalization of the angular momentum to the case of non-stationary black holes. Finally, we discuss the relation of these conserved quantities to the infinite tower of charges of the covariant phase space formalism.