Miracle-dz/UniFAR-FTUs huggingface dataset:
YODA: Enabling computationally intensive contracts on blockchains with Byzantine and Selfish nodes

One major shortcoming of permissionless blockchains such as Bitcoin and Ethereum is that they are unsuitable for running Computationally Intensive smart Contracts (CICs). This prevents such blockchains from running Machine Learning algorithms, Zero-Knowledge proofs, etc. which may need non-trivial computation. In this paper, we present YODA, which is to the best of our knowledge the first solution for efficient computation of CICs in permissionless blockchains with guarantees for a threat model with both Byzantine and selfish nodes. YODA selects one or more execution sets (ES) via Sortition to execute a particular CIC off-chain. One key innovation is the MultI-Round Adaptive Consensus using Likelihood Estimation (MIRACLE) algorithm based on sequential hypothesis testing. M I RACLE allows the execution sets to be small thus making YODA efficient while ensuring correct CIC execution with high probability. It adapts the number of ES sets automatically depending on the concentration of Byzantine nodes in the system and is optimal in terms of the expected number of ES sets used in certain scenarios. Through a suite of economic incentives and technical mechanisms such as the novel Randomness Inserted Contract Execution (RICE) algorithm, we force selfish nodes to behave honestly. We also prove that the honest behavior of selfish nodes is an approximate Nash Equilibrium. We present the system design and details of YODA and prove the security properties of MIRACLE and RICE. Our prototype implementation built on top of Ethereum demonstrates the ability of YODA to run CICs with orders of magnitude higher gas per unit time as well as total gas requirements than Ethereum currently supports. It also demonstrates the low overheads of RICE.
A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios

In this paper, we propose GeoSaW, a delay-tolerant routing protocol for Airborne Networks in Search and Rescue scenarios. The protocol exploits the geographical information of UAVs to make appropriate message forwarding decisions. More precisely, the information about the future UAV’s motion path is exploited to select the best UAV carrying the message towards the destination. Simulation results show that the proposed solution outperforms the classic DTN routing protocols in terms of several performance metrics.
Self-Correlation Analysis Framework with Property Data in Master Data Management — A Case on Power Utility Equipment Retire Analysis

Causing Reason Locating (CRL) is a negative going decision making process. It provides specific individual causing reasons to events or abnormal variation so that decision maker can find out clear-cut points for updates or maintenance. Traditional CRL is usually implemented with self-correlation analysis on Value Comparable Data (VCD). Property Data (PD) is not covered in CRL for its nominal evaluations. This paper initiates a new scheme for PD data correlation analysis, including virtual data creation method and correlation balancing method for Kendall correlation computation. A numerical study is implemented for model support on equipment retire analysis.
Clinical Reasoning: A State of the Science Report

Abstract Clinical reasoning is the cognitive process that nurses use to gather and incorporate information into a larger bank of personal knowledge. This incorporated information guides therapeutic actions, and helps determine client care. Since the process guides therapeutic actions regarding client care, failure to use the process effectively leads to poor clinical decision-making, inappropriate actions, or inaction. Because of the criticality of this process, this paper presents an analysis of the literature that reveals the current state of the science of clinical reasoning, identifies gaps in knowledge, and elucidates areas for future research. A systematic review of the databases the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Educational Resources Information Center (ERIC), PsychInfo, the Education Full Text (H.W. Wilson), and PubMed revealed 873 articles on the topic of clinical reasoning. Quality appraisal narrowed the field to 27 pieces of literature. Appendix A gives the State of the Science Coding Sheet used to identify the selections used in this research. Appendix B contains a summary of this literature. Although analysis of this literature shows that three theories exist on how to utilize most effectively the clinical reasoning process presently; a clear consistent definition is lacking. Additional research should focus on closing gaps that exist in defining the process, understanding the process, establishing linkages to non-clinical reasoning processes, and developing measures to both develop and accurately measure clinical reasoning.
Rural Microgrid Load Profile Benefit Assessment

In order to achieve the objective of universal electricity access by 2030, new microgrids need to serve 630 million people globally. To achieve this goal, new finance models need to be developed and implemented. Load profiles of rural consumers are crucial for off-grid systems capacity planning and control. The combination of detailed load profiles with benefit assessment of rural electricity can provide important insights into the social dynamics of communities ready to adopt microgrid technologies and an important incentive to guide demand side management. We propose a Load Profile - Benefit Assessment (LPBA) methodology that integrates microgrid data, analyzed through mean shift clustering clustering technique, with mixed method instrumentation to adequately assess the impact electricity use has on consumers. Enabled by wellbeing assessment methodology, LPBA could change the perceived investment risk by taking a deeper look at the reasons why a rural electrification business succeeds or fails. Preliminary data from a case study of a rural secondary school in Tanzania is used here to illustrate the methodology. While microgrids connected to a reliable grid seek to reduce cost or maximize the hours of power output when the price per kWh is higher, the LPBA provides a general guideline for microgrid users to manage power consumption with the goal of maximizing wellbeing benefits. Initial results indicate that in order to scale up methodologies such as LPBA, innovation is needed in affordable smart meters, near-real time signal processing tools, and transactive energy appropriate technology.
Doc2Im: document to image conversion through self-attentive embedding

Text classification is a fundamental task in NLP applications. Latest research in this field has largely been divided into two major sub-fields. Learning representations is one sub-field and learning deeper models, both sequential and convolutional, which again connects back to the representation is the other side. We posit the idea that the stronger the representation is, the simpler classifier models are needed to achieve higher performance. In this paper we propose a completely novel direction to text classification research, wherein we convert text to a representation very similar to images, such that any deep network able to handle images is equally able to handle text. We take a deeper look at the representation of documents as an image and subsequently utilize very simple convolution based models taken as is from computer vision domain. This image can be cropped, re-scaled, re-sampled and augmented just like any other image to work with most of the state-of-the-art large convolution based models which have been designed to handle large image datasets. We show impressive results with some of the latest benchmarks in the related fields. We perform transfer learning experiments, both from text to text domain and also from image to text domain. We believe this is a paradigm shift from the way document understanding and text classification has been traditionally done, and will drive numerous novel research ideas in the community.
Defect-Induced Tunable Permittivity of Epsilon-Near-Zero in Indium Tin Oxide Thin Films

Defect-induced tunable permittivity of Epsilon-Near-Zero (ENZ) in indium tin oxide (ITO) thin films via annealing at different temperatures with mixed gases (98% Ar, 2% O2) was reported. Red-shift of λENZ (Epsilon-Near-Zero wavelength) from 1422 nm to 1995 nm in wavelength was observed. The modulation of permittivity is dominated by the transformation of plasma oscillation frequency and carrier concentration depending on Drude model, which was produced by the formation of structural defects and the reduction of oxygen vacancy defects during annealing. The evolution of defects can be inferred by means of X-ray diffraction (XRD), atomic force microscopy (AFM), and Raman spectroscopy. The optical bandgaps (Eg) were investigated to explain the existence of defect states. And the formation of structure defects and the electric field enhancement were further verified by finite-difference time domain (FDTD) simulation.
Distributed exact weighted all-pairs shortest paths in near-linear time

In the distributed all-pairs shortest paths problem (APSP), every node in the weighted undirected distributed network (the CONGEST model) needs to know the distance from every other node using least number of communication rounds (typically called time complexity). The problem admits (1+o(1))-approximation Θ(n)-time algorithm and a nearly-tight Ω(n) lower bound [Nanongkai, STOC’14; Lenzen and Patt-Shamir PODC’15]. For the exact case, Elkin [STOC’17] presented an O(n5/3 log2/3 n) time bound, which was later improved to Õ(n5/4) in [Huang, Nanongkai, Saranurak FOCS’17]. It was shown that any super-linear lower bound (in n) requires a new technique [Censor-Hillel, Khoury, Paz, DISC’17], but otherwise it remained widely open whether there exists a Õ(n)-time algorithm for the exact case, which would match the best possible approximation algorithm. This paper resolves this question positively: we present a randomized (Las Vegas) Õ(n)-time algorithm, matching the lower bound up to polylogarithmic factors. Like the previous Õ(n5/4) bound, our result works for directed graphs with zero (and even negative) edge weights. In addition to the improved running time, our algorithm works in a more general setting than that required by the previous Õ(n5/4) bound; in our setting (i) the communication is only along edge directions (as opposed to bidirectional), and (ii) edge weights are arbitrary (as opposed to integers in {1, 2, . poly(n)}). The previously best algorithm for this more difficult setting required Õ(n3/2) time [Agarwal and Ramachandran, ArXiv’18] (this can be improved to Õ(n4/3) if one allows bidirectional communication). Our algorithm is extremely simple and relies on a new technique called Random Filtered Broadcast. Given any sets of nodes A,B⊆ V and assuming that every b ∈ B knows all distances from nodes in A, and every node v ∈ V knows all distances from nodes in B, we want every v∈ V to know DistThroughB(a,v) = minb∈ B dist(a,b) + dist(b,v) for every a∈ A. Previous works typically solve this problem by broadcasting all knowledge of every b∈ B, causing super-linear edge congestion and time. We show a randomized algorithm that can reduce edge congestions and thus solve this problem in Õ(n) expected time.
Beyond Visual Retargeting: A Feature Retargeting Approach for Visual Recognition and Its Applications

The popularity of mobile applications has greatly enriched and facilitated our lives. However, the rapid increase of digital images and the problem of narrow bandwidth of the wireless network call for an appropriate approach to reduce the amount of data transmitted over the wireless network (i.e., low bit-rate transmission) while ensuring high recognition accuracy at the cloud. We propose a simple and effective feature retargeting (FR) approach for retargeting an image while preserving the representative local features (e.g., SIFT, SURF, and BRIEF) in the image. Our feature retargeting approach aims at low bit-rate visual recognition instead of high-quality visual perception that visual retargeting methods dedicate to. Our algorithm consists of two key novelties: estimating feature saliency and retargeting image: Estimating feature saliency focuses on predicting the relative importance of different features in an image by analyzing uniqueness in a specific context; Retargeting image aims at finding the optimal resolution for the retargeted image to maximize feature-saliency energy. We evaluate the proposed approach for two different applications in three large data sets and observe that our FR approach consistently outperforms state-of-the-art retargeting algorithms, resulting in both higher precision and lower bit-rates. We also demonstrate that even when the resolution of source image is reduced greatly, e.g., 1/7 original size, our algorithm produces superior results as compared with other approaches.
Afadin Downregulation by Helicobacter pylori Induces Epithelial to Mesenchymal Transition in Gastric Cells

Afadin is a cytoplasmic protein of the adherens junctions, which regulates the formation and stabilization of both the adherens and the tight junctions. Aberrant expression of Afadin has been shown in cancer and its loss has been associated with epithelial-to-mesenchymal transition (EMT). EMT is characterized by the change from an epithelial to a mesenchymal phenotype, with modifications on the expression of adhesion molecules and acquisition of a migratory and invasive cell behavior. While it is known that Helicobacter pylori disrupts the tight and the adherens junctions and induces EMT, the effect of the bacteria on Afadin is still unknown. The aim of this study was to disclose the effect of H. pylori on Afadin and its impact in the induction of an EMT phenotype in gastric cells. Using two different cell lines, we observed that H. pylori infection decreased Afadin protein levels, independently of CagA, T4SS, and VacA virulence factors. H. pylori infection of cell lines recapitulated several EMT features, displacing and downregulating multiple proteins from cell–cell junctions, and increasing the expression of ZEB1, Vimentin, Slug, N-cadherin, and Snail. Silencing of Afadin by RNAi promoted delocalization of junctional proteins from the cell–cell contacts, increased paracellular permeability, and decreased transepithelial electrical resistance, all compatible with impaired junctional integrity. Afadin silencing also led to increased expression of the EMT marker Snail, and to the formation of actin stress fibers, together with increased cell motility and invasion. Finally, and in line with our in vitro data, the gastric mucosa of individuals infected with H. pylori showed decrease/loss of Afadin membrane staining at cell–cell contacts significantly more frequently than uninfected individuals. In conclusion, Afadin is downregulated by H. pylori infection in vitro and in vivo, and its downregulation leads to the emergence of EMT and to the acquisition of an aggressive phenotype in gastric cells, which can contribute to gastric carcinogenesis.
Effective Data Driven Coding Unit Size Decision Approaches for HEVC INTRA Coding

High Efficiency Video Coding (HEVC) INTRA coding improves compression efficiency by adopting advanced coding technologies, such as multi-level quad-tree block partitioning and up to 35-mode INTRA prediction. However, it significantly increases the coding complexity, memory access, and power consumption, which goes against its widely applications, especially for ultra-high definition and/or mobile video applications. To tackle this problem, we propose effective data driven coding unit (CU) size decision approaches for HEVC INTRA coding, which consists of two stages of support vector machine-based fast INTRA CU size decision schemes at four CU decision layers. At the first stage classification, a three output classifier with offline learning is developed to early terminate the CU size decision or early skip checking the current CU depth. As for the samples that neither early skipped nor early terminated, the second stage of binary classification, which learns online from previous coded frames, is proposed to further refine the CU size decision. Representative features for the CU size decision are explored at different decision layers and stages of classifications. Finally, the optimal parameters derived from the training data are achieved to reasonably allocate complexity among different CU layers at given total rate-distortion degradation constraint. Extensive experiments show that the proposed overall algorithm can achieve 27.95%–80.53% and 52.48% on average complexity reduction for the CU size decision as compared with the original HM16.7 model. Meanwhile, the average Bjonteggard delta peak-signal-to-noise ratio degradation is only −0.08 dB, which is negligible. The overall performance of the proposed algorithm outperforms the state-of-the-art benchmark schemes.
Hard tick (Acari: Ixodidae) species of livestock and their seasonal activity in Boyer-Ahmad and Dena cities of Kohgiluyeh and Boyer-Ahmad Province, Southwest of Iran

Aim: The present study was carried out to identify the Ixodidae ticks fauna of livestock and their seasonal activity in the cities of Boyer-Ahmad and Dena of Kohgiluyeh Province, south-west of Iran. Materials and Methods: Hard ticks from sheep, goats, and cattle were collected manually, stored in 70% ethanol, and identified using morphological characters. Results: During the study, a total of 1273 hard ticks from four genera, including Rhipicephalus, Hyalomma, Dermacentor, and Haemaphysalis, were collected. Rhipicephalus sanguineus (s.l.) had the highest frequencies in both cities with 62.08 and 62.88% of collected specimens, followed by Hyalomma scupense with 14.36 and 13.54% in Boyer-Ahmad and Dena, respectively. Furthermore, Hyalomma marginatum with only one sample or 0.12% of collected ticks showed the lowest frequencies in the studied areas. Dermacentor marginatus with three samples or 0.37% was recorded only in Boyer-Ahmad, and Haemaphysalis sulcata with two samples or 0.43% was recorded only in Dena. In both cities, sheep were the most infested ruminant, and the ears in sheep and goats were the most affected areas. The highest activity was observed in spring, and the lowest activity was observed in winter and autumn. Conclusion: The results of the present study showed that Hyalomma and Rhipicephalus genera were the most widespread genera in the study areas. Regarding the importance of genera, such as Rhipicephalus, Hyalomma, and Haemaphysalis, in transmitting disease agents and the location of Kohgiloyeh and Boyer-Ahmad Province in the routes of migrant birds, further studies are required to elucidate their exact roles in human and livestock health in these areas.
BAR: Bayesian Activity Recognition using variational inference

Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable uncertainty estimates in safety and security critical applications will help to establish trust in the AI system. Our contribution is to apply Bayesian deep learning framework to visual activity recognition application and quantify model uncertainty along with principled confidence. We utilize the stochastic variational inference technique while training the Bayesian DNNs to infer the approximate posterior distribution around model parameters and perform Monte Carlo sampling on the posterior of model parameters to obtain the predictive distribution. We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to conventional DNNs. We also show that our method improves the visual activity recognition precision-recall AUC by 6.2% compared to non-Bayesian baseline. We evaluate our models on Moments-In-Time (MiT) activity recognition dataset by selecting a subset of in- and out-of-distribution video samples.
Telerehabilitation for Chronic Obstructive Pulmonary Disease Patients: An Underrecognized Management in Tertiary Care

Pulmonary rehabilitation (PR) is proved to be best supportive management in chronic obstructive pulmonary disease (COPD) individuals. The literature claims the reduction of dyspnea, fatigue, exacerbations, and improved functional capacity and quality of life. Home-based PR is being prescribed widely than hospital-based rehab due to be less cost and ease of caregiver burden, but efficacy is usually questioned. The poor efficacy may be probably due to recurrent exacerbation and poor quality of life even after years of home rehabilitation. Telerehabilitation is an excellent rehab measure where the COPD patients exercise at his home, while expertise from the tertiary care centers monitors the rehab sessions remotely. In India, the tele-PR is at its budding state. This review shall enable the readers with the basics of telerehabilitation in comparison with the other available rehab measures and evidence in the management of COPD.
Towards Optimal Network Planning for Software-Defined Networks

Supporting on-line and adaptive traffic engineering in software-defined networks entails the fast, robust control message forwarding from software-defined switches to the controller(s). In-band control using the existing infrastructure is cost-efficient, but imposes a substantial barrier to timely transmissions of control messages. Also, due to the limited computational capability of a single controller, only the use of multiple controllers is practically viable for large-scaled networks. Therefore, in this paper, the optimal software-defined network planning is investigated with multi-controllers. First, the network planning problem is formulated as a nonlinear multi-objective optimization, which aims to simultaneously minimize the number of controllers and the control traffic delay for each switch. This planning problem is then partitioned into two sub-problems, i.e., multi-controller placement and control traffic balancing, which are respectively solved by the proposed fast-convergent algorithms. Furthermore, an adaptive feedback control mechanism is proposed to iteratively work out the two sub-problems and enable the dynamic network replanning, subject to the time-varying traffic volume and network topology. Simulations validate the adaptivity of our control scheme, which significantly reduces delay with maximum throughput for control flows, brings minimal impact to normal data flows, and requires the minimum controllers.
Initial microbial community of the neonatal stomach immediately after birth

ABSTRACT The purpose of this prospective cross-sectional cohort pilot study is to explore the initial microbial community of gastric aspirate fluid as collected immediately after birth and its relationships with mode of delivery and preterm birth. Twenty-nine gastric aspirate samples collected immediately after birth from infants born between 24–40 weeks gestation were analyzed for microbial composition. Total microbial content was low in many samples, with a substantial number sharing taxonomic composition with negative controls. qPCR targeting the 16S rRNA gene showed that infants delivered vaginally had a higher microbial load than infants delivered by C-section. Some pre-term samples showed high relative abundance of genus Ureaplasma, consistent with previous literature that has implicated infections with this taxon as a potential cause of pre-term birth. Vaginally born term infant samples, by contrast, had significantly higher levels of genus Lactobacillus with Lactobacillus crispatus the most dominant species. Microbial evaluation showed that vaginally born term infant gastric aspirate samples had higher levels of lactobacilli than pre-terms. Samples from many infants had low microbial load near the edge of the detection limit.