Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological, Thermal, and Electrical Characterization of Syndiotactic Polypropylene/Multiwalled Carbon Nanotube Composites

In this work, syndiotactic polypropylene/multiwalled carbon nanotubes (MWCNT) nanocomposites, in various concentrations, were produced using melt mixing. The influence of the addition of MWCNT on the morphology, crystalline form, and the thermal and electrical properties of the polymer matrix was studied. To that aim, scanning electron microscopy, Raman spectroscopy, X-ray diffraction, differential scanning calorimetry, and dielectric relaxation spectroscopy were employed. Significant alterations of both the crystallization behavior and the thermal properties of the matrix were found on addition of the carbon nanotubes: conversion of the disordered crystalline form I to the ordered one, increase of the crystallization temperature and the degree of crystallinity, and decrease of the glass transition temperature and the heat capacity jump. Finally, the electrical percolation threshold was found between 2.5–3.0 wt. % MWCNT. For comparison purposes, the results of the system studied here are also correlated with the findings from a previous work on the isotactic polypropylene/MWCNT system.
Deficits in Coordinative Bimanual Timing Precision in Children With Specific Language Impairment.

Purpose Our objective was to delineate components of motor performance in specific language impairment (SLI); specifically, whether deficits in timing precision in one effector (unimanual tapping) and in two effectors (bimanual clapping) are observed in young children with SLI. Method Twenty-seven 4- to 5-year-old children with SLI and 21 age-matched peers with typical language development participated. All children engaged in a unimanual tapping and a bimanual clapping timing task. Standard measures of language and motor performance were also obtained. Results No group differences in timing variability were observed in the unimanual tapping task. However, compared with typically developing peers, children with SLI were more variable in their timing precision in the bimanual clapping task. Nine of the children with SLI performed greater than 1 SD below the mean on a standardized motor assessment. The children with low motor performance showed the same profile as observed across all children with SLI, with unaffected unimanual and impaired bimanual timing precision. Conclusions Although unimanual timing is unaffected, children with SLI show a deficit in timing that requires bimanual coordination. We propose that the timing deficits observed in children with SLI are associated with the increased demands inherent in bimanual performance.
Effectiveness of All-Oral Antiviral Regimens in 996 Human Immunodeficiency Virus/Hepatitis C Virus Genotype 1–Coinfected Patients Treated in Routine Practice

Background. Large cohorts are needed to assess human immunodeficiency virus (HIV)/hepatitis C virus (HCV) real-world treatment outcomes. We examined the effectiveness of ledipasvir/sofosbuvir with or without ribavirin (LDV/SOF ± RBV) and ombitasvir/ paritaprevir/ritonavir plus dasabuvir (OPrD) ± RBV in HIV/HCV genotype 1 (GT1)-coinfected patients initiating HCV therapy in clinical practice. Methods. Observational intent-to-treat cohort analysis using the Veterans Affairs Clinical Case Registry to identify HIV/HCV GT1-coinfected veterans initiating 12 weeks of LDV/SOF ± RBV or OPrD ± RBV. Multivariate models of sustained virologic response (SVR) included age, race, cirrhosis, proton pump inhibitor (PPI) prescription, prior HCV treatment, body mass index, genotype subtype, and HCV treatment regimen. Results. Nine hundred ninety-six HIV/HCV GT1-coinfected veterans initiated therapy: 757 LDV/SOF, 138 LDV/SOF + RBV, 28 OPrD, and 73 OPrD + RBV. Overall SVR was 90.9% (823/905); LDV/SOF 92.1% (631/685), LDV/SOF + RBV 86.3% (113/131), OPrD 88.9% (24/27), and OPrD + RBV 88.7% (55/62). SVR was 85.9% (176/205) and 92.4% (647/700) in those with and without cirrhosis (P = .006). SVR was similar between African Americans (90.5% [546/603]) and all others (91.7% [277/302]). PPI use with LDV/SOF ± RBV did not affect SVR (89.7% [131/146] with PPI and 91.5% [613/670] without PPI). Cirrhosis was predictive of reduced SVR (0.51 [95% confidence interval {CI}, .31-.87]; P = .01). Median creatinine change did not differ among patients receiving LDV/SOF and tenofovir disoproxil fumarate (TDF) without a protease inhibitor (PI) (0.18 [interquartile range {IQR}, 0.08-0.30]; n = 372), LDV/SOF and TDF/PI (0.17 [IQR, 0.04-0.30]; n = 100), and LDV/SOF without TDF (0.15 [IQR, 0.00-0.30]; n = 423). Conclusions. SVR rates in HIV/HCV GT1-coinfected patients were high. African American race or PPI use with LDV/SOF ± RBV was not associated with lower SVR rates, but cirrhosis was. Renal function did not worsen on LDV/SOF regimens with TDF.
OTO: online trust oracle for user-centric trust establishment

Malware continues to thrive on the Internet. Besides automated mechanisms for detecting malware, we provide users with trust evidence information to enable them to make informed trust decisions. To scope the problem, we study the challenge of assisting users with judging the trustworthiness of software downloaded from the Internet. Through expert elicitation, we deduce indicators for trust evidence, then analyze these indicators with respect to scalability and robustness. We design OTO, a system for communicating these trust evidence indicators to users, and we demonstrate through a user study the effectiveness of OTO, even with respect to IE's SmartScreen Filter (SSF). The results from the between-subjects experiment with 58 participants confirm that the OTO interface helps people make correct trust decisions compared to the SSF interface regardless of their security knowledge, education level, occupation, age, or gender.
An Improved Dictatorship Test with Perfect Completeness

A Boolean function f:{0,1}^n\->{0,1} is called a dictator if it depends on exactly one variable i.e f(x_1, x_2, ., x_n) = x_i for some i in [n]. In this work, we study a k-query dictatorship test. Dictatorship tests are central in proving many hardness results for constraint satisfaction problems. The dictatorship test is said to have perfect completeness if it accepts any dictator function. The soundness of a test is the maximum probability with which it accepts any function far from a dictator. Our main result is a k-query dictatorship test with perfect completeness and soundness (2k + 1)/(2^k), where k is of the form 2^t -1 for any integer t > 2. This improves upon the result of [Tamaki-Yoshida, Random Structures & Algorithms, 2015] which gave a dictatorship test with soundness (2k + 3)/(2^k).
Understanding Parallelization Tradeoffs for Linear Pipelines

Pipelining techniques execute some loops with cross-iteration dependences in parallel, by partitioning the loop body into a sequence of stages such that the data dependences are not violated. Obtaining good performance for all kinds of loops is challenging and current techniques, e.g., PS-DSWP and LBPP, have difficulties handling load-imbalanced loops. Particularly, for loop iterations that differ substantially in execution time, these techniques achieve load-balancing by assigning work to threads using round-robin scheduling. Algorithms that rely on work-stealing e.g., Piper, efficiently handle load-imbalanced loops, but the high overhead of the scheduler implies poor performance for fine-grained loops. In this paper, we present Proteas, a programming model to allow tradeoffs between load-balancing, partitioning, mapping, synchronization, chunking, and scheduling. Proteas provides a set of simple directives to express the different mappings to handle a loop's parallelism. Then, a source-to-source compiler generates parallel code to support experimentation with Proteas. The directives allow us to investigate various tradeoffs and achieve good performance according to PS-DSWP and LBPP. In addition, the directives make a meaningful comparison to Piper possible. We present a performance evaluation on a 32-core system for a set of popular pipelined programs selected from three widely-used benchmark suites. The results show the tradeoffs of the different techniques and their parameters. Moreover, the results show that efficient handling of load-imbalanced fine-grained loops remains the main challenge.
Towards Moral Autonomous Systems

Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss the main challenges which, in our view, machine ethics posses to moral philosophy. We them consider different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.
Towards measuring warning readability

Security systems frequently rely on warning messages to convey important information, especially when a machine is not able to assess a situation automatically. For a long time, researchers have investigated the effects of warning messages to optimise their reception by a user. Design guidelines and best practises help the developer or interaction designer to adequately channel urgent information. In this poster, we investigate the application of readability measures to assess the difficulty of the descriptive text in warning messages. Adapting such a measure to fit the needs of warning message design allows objective feedback on the quality of a warning's descriptive text. An automated process will be able to assist software developers and designers in creating more readable and hence more understandable security warning messages. We present an initial exploration of the use of readability measures on the descriptive text of warning messages. Existing measures were evaluated on warning messages extracted from current browsers using an experimental study with 15 undergrad students. While our data did not yield conclusive results yet, we argue that readability measures can provide valuable assistance when implementing security systems.
A Hybrid Method for Incomplete Data Imputation

With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI's functionality rests on four pillars: (i) a distinctive value assigned to impute missing values initially, (ii) the stacked auto-encoder(SAE) applied to locate principal features, (iii) a new incremental clustering utilized to partition incomplete data set, and (iv) the top nearest neighbors' weighted values designed to refill the missing values. Most importantly, stages (ii)~(iv) iterate until convergence condition is satisfied. Experimental results demonstrate that the proposed scheme not only imputes the missing data values effectively, but also has better time performance. Moreover, this work is suitable for distributed data processing framework, which can be applied to the imputation of incomplete big data.
The Effect of Different Alkaline Treatment Condition on Flexural Properties of Kenaf Bast-Unsaturated Polyester Composite

The biocomposites were prepared by using kenaf bast fiber mat as reinforcing materials at different percentage. The kenaf bast fiber was treated with alkaline at different sodium hydroxide (NaOH) percentage. From the results obtained, surface morphology of the treated kenaf bast fiber shows less impurity than untreated kenaf bast fiber. As for composites prepared from alkaline treated kenaf bast fiber, it showed higher mechanical properties as compared to those prepared from untreated kenaf bast fiber. The percentage of kenaf fiber in composites also plays a crucial role in determining the composite properties.
Estimating Rice LAI Based on Digital Camera and Image Processing

A fast, low-cost method for rice canopy leaf area index (LAI) estimation is proposed. Take photos of rice canopy with a 57° view angle from above using a common digital camera. Extract canopy gap fraction by digital image processing technology. Then LAI can be estimated using canopy gap fraction based on optical transmission model and Leaf angle distribution model. AccuPAR-LP80 and direct measurement were employed to provide Comparative data. Comparison of the three methods, we obtained high correlation coefficients (R²≥0.6). The result shows that the method is especially suitable for estimating LAI in early growth stage of rice.
Phosphorylation and activity of the tumor suppressor Merlin and the ERM protein Moesin are coordinately regulated by the Slik kinase

Merlin and Moesin are closely related members of the 4.1 Ezrin/Radixin/Moesin domain superfamily implicated in regulating proliferation and epithelial integrity, respectively. The activity of both proteins is regulated by head to tail folding that is controlled, in part, by phosphorylation. Few upstream regulators of these phosphorylation events are known. In this study, we demonstrate that in Drosophila melanogaster, Slik, a Ste20 kinase, controls subcellular localization and phosphorylation of Merlin, resulting in the coordinate but opposite regulation of Merlin and Moesin. These results suggest the existence of a novel mechanism for coordinate regulation of cell proliferation and epithelial integrity in developing tissues.
Consensus algorithm of multi-agent system with binary-valued communication

This paper studies the consensus of multi-agent systems with binary-valued communication. We consider a group of agents on an undirected graph with a fixed topology, but differing from most existing work, each agent cannot get the true value of its neighbors' states. What information each agent gets from its neighbors is binary-valued measurement. A two-scale control algorithm is constructed: Each agent in the information network estimates the states of its neighbors' based on the binary-valued measurements for a short time, during which every node's state will keep constant; Based on the estimation of its neighbors' states and its own state, each agent designs its own control, by which the states will be updated and finally the system will achieve consensus. The state estimation algorithm is analyzed to have convergent and asymptotically efficient properties. As a result, the system is shown to be weak consensus by using a stochastic Lyapunov analysis.
Automatic detection system of the fallen down for the parked bicycle in the underground parking garage using optical flow method

Recently, there is a new parking device for the bicycles, which are built on the underground because of good landscape, space saving and more in the urban towns. However, there are a few accidents such that the bicycles parked with high speed by the powerful robot arm are sometimes fallen down in the underground garage after parking. This is serious problem for the use. So a real time detection system for the fallen down bicycles is requested, extremely. We propose a new and unique detection algorithm with applied optical flow method to solve this serious problem. We propose its detection mechanism and show its effectiveness using real monitoring image of the parked bicycle. Finally, we implement this detection algorithm to a Linux computer board and show its practical feasibility.
An agile V-model for medical device software development to overcome the challenges with plan-driven software development lifecycles

Through the use of semi structured interviews with medical device software organizations it emerged that medica device software organizations are experiencing difficulties when following plan driven Software Development Life Cycles (SDLC) particularly in the area of requirements management. To attempt to resolve these issues an examination of the non-regulated industry was performed to determine if lessons learned there could be applied to the development of medical device software This examination revealed that agile methods are being widely adopted in the non-regulated software industry. To learn if agile methods could be adopted when developing medical device software a mapping study was performed which looked for instances of where agile methods have been used in regulated industries and where they have been adopted, to what success This mapping study revealed that incorporating agile practices with the existing plan driven SDLC is the most favourable choice for medical device software organizations. This research aims to develop a SDLC which has a foundation of a plan driven SDLC which incorporates agile practices which can be followed when developing regulatory compliant software.
Longitudinal Monitoring and Detection of Alzheimer's Type Dementia from Spontaneous Speech Data

A method for detection of Alzheimers type dementia though analysis of vocalisation features that can be easily extracted from spontaneous speech is presented. Unlike existing approaches, this method does not rely on transcriptions of the patients speech. Tests of the proposed method on a data set of spontaneous speech recordings of Alzheimers patients (n=214) and elderly controls (n=184) show that accuracy of 68% can be achieved with a Bayesian classifier operating on features extracted through simple algorithms for voice activity detection and speech rate tracking.
Discrete-Time Nonzero-Sum Games for Multiplayer Using Policy-Iteration-Based Adaptive Dynamic Programming Algorithms

In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.
EvolvingSpace: A Data Centric Framework for Integrating Bioinformatics Applications

The paper presents EvolvingSpace, a data centric distributed system, which is intended to address the data and application integration problem in bioinformatics data centers. The system employs commodity PCs for data storage and computation. EvolvingSpace manages data in a decentralized manner, which is convenient for storing data annotations and can eliminate potential data-access bottlenecks. It indexes distributed data in multilevels to facilitate the construction of complex workflows that consist of applications running on different types of data. In addition, the paper proposes a data locality and workflow aware scheduling algorithm (ES-Scheduling) to balance the data distribution and computing performance as well as throughput and workflow response time. We run extensive experiments using the system with real bioinformatics applications. Our results show that the system is efficient for running integrated bioinformatics applications and has good scalability.
Sentiment Analysis Is a Big Suitcase

Although most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem that requires tackling many natural language processing (NLP) tasks. The expression “sentiment analysis” itself is a big suitcase (like many others related to affective computing, such as emotion recognition or opinion mining) that all of us use to encapsulate our jumbled idea about how our minds convey emotions and opinions through natural language. The authors address the composite nature of the problem via a three-layer structure inspired by the “jumping NLP curves” paradigm. In particular, they argue that there are (at least) 15 NLP problems that need to be solved to achieve human-like performance in sentiment analysis.
Pharmacovigilance in oncology: evaluation of current practice and future perspectives.

RATIONALE, AIMS AND OBJECTIVES Pharmacovigilance (PV), or drug safety monitoring, aims to improve patient safety through the detection and management of drug-related adverse reactions. It is implemented both by spontaneous reporting of adverse drug reactions (ADRs) and by careful detection of signals suggestive of drug toxicity. PV is an important clinical topic in clinical practice and pharmacotherapy, assuring the maintenance of a safe risk/benefit ratio throughout the commercial life cycle of a drug. METHODS We conducted a structured literature search on PubMed, Scopus, Cinahl and the Cochrane Library. We also performed manual searches in international databases of ADR individual reports to outline a structured profile on the topic. Our goal was to review key elements that affect safety monitoring of cancer drugs and their appropriate use, highlighting the strengths and weaknesses of PV in oncology. RESULTS This paper provides an understanding of the methodologies used by PV in current clinical practice and particularly in cancer drug therapy; a focus upon reporting of ADRs by health professionals and patients; and a focus upon methods used by PV to detect new signals of risk/harm related to medicines utilization. CONCLUSION To our knowledge, few articles focus upon the importance of PV and post-marketing surveillance of cancer drug therapies. Structured management of spontaneous reports of ADRs and data collection is essential to monitoring the safe use of drugs in this field in which pharmacotherapy is affected by high incidence of drug-related complications and by a narrow benefit/risk ratio.
Multi-Pedestrian detection and tracking using unified multi-channel features

This paper presents an online multiple pedestrian detection and tracking method using unified multi-channel features. The proposed method efficiently utilizes the multi-channel features by sharing them in each module: pedestrian detection, visual tracking, and data association. The multi-channel features are originally generated from the pedestrian detection module, and they represent sufficiently rich feature information. In the pedestrian detector, feature vectors for the pedestrian classifier are constructed from the unified multi-channel features, and the visual tracker localizes the target pedestrian on the multi-channel feature maps. An appearance model for data association is also established from the unified multi-channel features. Experimental results show that our method outperforms the state-of-the-art method in both accuracy and speed.