Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods to Analyze and Predict Interstate Travel Time Reliability

The Moving Ahead for Progress in the 21st Century Act (MAP-21) defined requirements for system reliability performance measures. Under MAP-21, state departments of transportation are responsible for reporting travel time reliability and for setting targets and showing progress toward those targets. In order to know how to improve travel time reliability and what to expect from investments in transportation infrastructure, these agencies need a better understanding of the factors that affect travel time reliability and methods to predict future travel time reliability. The purpose of this study was to quantify the factors influencing travel time reliability and investigate how to account for these factors in setting reliability targets and communicating progress. To achieve these objectives, this study developed models to estimate quantiles (the 50th, 80th, and 90th) of travel time distributions to quantify the effects of travel time reliability impact factors and predict select reliability measures. First, linear quantile mixed models (LQMMs) were built using both data maintained by the Virginia Department of Transportation (VDOT) and crowdsourced event data. Model results using the crowdsourced data were unstable and difficult to interpret because of data quality issues such as unbalanced spatial density, duplicate reporting, and inconsistent event classification because of individual observer bias. The results using VDOT-maintained data were more reliable and interpretable. Those models showed that frequencies of non-recurrent events, such as incidents and weather, were correlated with higher travel time percentiles. The LQMM was compared with the trend line approach, a common prediction method used in practice, and the results showed that LQMMs significantly improved the accuracy of predictions over the trend line approach based on mean absolute percent error. Generalized random forest (GRF) models were also tested as an alternative prediction method. GRF models improved the prediction accuracy over LQMMs for the 50th and 80th percentiles, but the accuracy was slightly worse for the 90th percentile. In addition, the GRF models could also reflect the impact of variables that were removed from LQMMs because of insignificance, such as the presence of safety service patrols. Before-after studies were conducted to illustrate the application of LQMMs and GRF models. LQMMs captured the changes in the 90th percentile travel times better, and GRF models captured the changes of level of travel time reliability better in most cases. GRF models were more sensitive to the reliability changes caused by non-recurrent events, such as incidents or work zones, and could reflect the impact of variables that were removed from LQMMs because of insignificance. The study recommends that VDOT use the GRF model for predicting travel time reliability on interstate highways. In addition, further research is recommended to extend the GRF models to meet the requirements of MAP-21 federal target setting.
Recognizing persons in television programs using region‐based image features

Technology that can search efficiently for a desired scene is essential for the management of large‐scale TV program archives. A particularly common scenario of this sort is searching TV programs for scenes containing a specific person. There have been many previous studies on recognizing people in video footages. However, the recognition accuracy of their techniques drops significantly when people in the video appear under different lighting conditions, look in different directions, or have different facial expressions. In this paper, we propose a robust technique for recognizing persons in video, especially in television programs. Unlike the feature‐point‐based approach of the previous techniques, our method uses a new region‐based approach inspired by general object recognition. Since it does not require facial feature points to be detected, it improves the robustness of the recognition to image fluctuations that are often found in television programs. In experiments, we verified the effectiveness of our method by using it to conduct searches on approximately 150 TV drama episodes. We also demonstrated the superiority of our method in comparative tests with conventional methods. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Reducing embodied carbon in the construction sector

The increasing drive towards net zero carbon has thrown a spotlight on 'embodied carbon' in the construction industry. Embodied carbon refers to the whole life carbon emissions associated with the materials used in buildings, and includes emissions arising from the resource extraction, material production, transport, installation, maintenance and end of life disposal. In the UK, embodied carbon associated with new construction accounts for 20% of annual carbon emissions from buildings. Reducing this is key to meeting the UK's commitment to achieve net zero by 2050. In this study, recent academic and industry literature on embodied carbon is examined, and interviews are held with nine industry professionals with specialist knowledge on embodied carbon. The findings show that against a background of increasing academic research and industry knowledge on the topic, there remains a lack of clarity over the guidance and methodology used to calculate embodied carbon. There is no comprehensive materials database for embodied carbon and no one calculation tool or approach used by the construction industry. A more coherent and agreed approach is needed if industry is to effectively reduce embodied carbon in new build construction and refurbishment. The UK has no clear legislative requirements or policy framework on the topic. Legislation is urgently needed to drive and incentivise embodied carbon requirements throughout the industry. Since most of the embodied carbon emissions of materials occurs before the construction phase, the focus must be on understanding the implications of material choices with a preference for material reuse where possible.
Information and communication architecture for transmission power line inspections using unmanned aircraft system

The inspection of the transmission lines in the Brazilian electrical system, given the continental dimensions of the country, is a task of a high complexity. The search for technological alternatives that satisfy the need for fast, safe and reliable transmission lines inspection is something that has been sought by the entire sector. The article presents the architecture of a management system using unmanned aircrafts for transmission lines inspection. The proposed architecture in the scope of a project for Celesc Distribuição - Brazil, shows the sub-systems integration such as mission planning and execution, data transmission, image processing and flight control. The architecture was verified by the main project stakeholders and Simulation systems in the aeronautics area showing to be strong and safe for the execution of transmission lines inspection.
Académique Lausannois SERVAL serval.unil.ch

Objectives— We aimed to explore the effects of pharmacologic inhibition of cannabinoid-1 (CB 1 ) receptor in in vivo and in vitro models of doxorubicin (DOX)-induced cardiotoxicity. Background— Doxorubicin is one of the most potent antitumor agents available; however, its clinical use is limited because of the risk of severe cardiotoxicity. Endocannabinoids mediate cardiodepressive effects through CB 1 receptors in various pathophysiological conditions, and these effects can be reversed by CB 1 antagonists. Methods— Left ventricular function was measured by Millar pressure-volume system. Apoptosis markers, CB 1 /CB 2 receptor expression, and endocannabinoid levels were determined by immunohistochemistry, Western blot, reverse transcription-polymerase chain reaction, real-time polymerase chain reaction, flow cytometry, fluorescent microscopy, and liquid chromatography/in-line mass spectrometry techniques. Results— Five days after the administration of a single dose of DOX (20 mg/kg intraperitoneally) to mice, left ventricular systolic pressure, maximum first derivative of ventricular pressure with respect to time (+dP/dt), stroke work, ejection fraction, cardiac output, and load-independent indexes of contractility (end-systolic pressure–volume relation, preload-recruitable stroke work, dP/dt–end-diastolic volume relation) were significantly depressed, and the myocardial level of the endocannabinoid anandamide (but not CB 1 /CB 2 receptor expression) was elevated compared with vehicle-treated control mice. Treatment with the CB 1 antagonists rimonabant or AM281 markedly improved cardiac dysfunction and reduced DOX-induced apoptosis in the myocardium. Doxorubicin also decreased cell viability and induced apoptosis in the H9c2 myocardial cell line measured by flow cytometry and fluorescent microscopy, which were prevented by the preincubation of the cells with either CB 1 antagonist, but not with CB 1 and CB 2 agonists and CB 2 antagonists. Our present findings provide evidence that endocannabinoid AEA is overproduced in a well-established model of DOX-induced acute heart failure, and CB 1 antagonists improve compromised contractile function. Furthermore, we show that CB 1 antagonists exert powerful cytoprotective effect in cardiomyocytes against DOX-induced cardiotoxicity both in vivo and in vitro by reducing apoptosis, offering a novel approach for the prevention of this devastating complication of chemotherapy. 1 cardiotoxicity of the the present findings suggest that CB 1 may represent a new cardioprotective strategy against DOX-induced cardiotoxicity and perhaps other cardiovascular pathologies associated with increased cell death. Further studies are warranted to investigate the underlying signaling mechanisms of these beneficial effects.
Dense motion field estimation involving discontinuities applied to echocardiographic image sequences

A motion estimation algorithm is described, Initial estimates are obtained by block matching. Regularization is anisotropic, controlled by a confidence measure which combines residual matching error and its variance, and is intended to smooth homogenous regions and to avoid smoothing over motion boundaries. This algorithm, initially conceived for video images, is applied to echocardiograms and magnetic resonance images. Non-rigid motion is correctly estimated and motion discontinuities are preserved. Possible improvements are outlined
Presenting a structural model of customer behavioral intention in accepting social media marketing

This study aimed to provide a structural model of customer intentional behavior in accepting social media marketing. The present study uses an applied (purpose) and survey-analytical research methodology (implementation method) carried out with a mixed (quantitative-qualitative) approach. The first part presented the content analysis method and Delphi technique as factors that influence behavioral intention. The research population in the first part was 6045 documents identified on the science website, and in the second part, 30 social media marketing specialists were targeted by sampling method. The findings of the qualitative section were the identification of 20 factors that influence the behavioral intention of customers, which was the most critical comparative advantage. The statistical population in the quantitative part was users of social networks that the sample size was available by sampling method and based on the unlimited Cochran’s formula, 384 people were selected. The data collection tool was a researcher-made questionnaire in the second part. Experts confirmed the validity, and the reliability was confirmed by calculating Cronbach’s alpha coefficient. Data analysis was performed by structural equation modeling. The results showed that trust, perceived ease, comparative advantage, adaptability, mental norm, perceived behavioral control, and attitude toward behavior significantly affect customers’ behavioral intention in accepting social media marketing. The significant relationship between perceived risk and behavioral intent was not confirmed.
The Kerr/CFT Correspondence The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters

Quantum gravity in the region very near the horizon of an extreme Kerr black hole (whose angular momentum and mass are related by J = GM 2 ) is considered. It is shown that consistent boundary conditions exist, for which the asymptotic symmetry generators form one copy of the Virasoro algebra with central charge c L = 12 J ~ . This implies that the near-horizon quantum states can be identiﬁed with those of (a chiral half of) a two-dimensional conformal ﬁeld theory (CFT). Moreover, in the extreme limit, the Frolov-Thorne vacuum state reduces to a thermal density matrix with dimensionless temperature T L = 12 π and conjugate energy given by the zero mode generator, L 0 , of the Virasoro algebra. Assuming unitarity, the Cardy formula then gives a microscopic entropy S micro = 2 πJ ~ for the CFT, which reproduces the macroscopic Bekenstein-Hawking entropy S macro = Area4 ~ G . The results apply to any consistent unitary quantum theory of gravity with a Kerr solution. We accordingly conjecture that extreme Kerr black holes are holographically dual to a chiral two-dimensional conformal ﬁeld theory with central charge c L = 12 J ~ , and in particular that the near-extreme black hole GRS 1915+105 is approximately dual to a CFT with c L ∼ 2 × 10 79 .
The Human Factors and Cybersecurity Policy

Computer security is not limited to technology and systems; it also includes the humans and processes that use and depend on it. Even with strong computer security policies, humans are the weakest link in information security, even if people are aware of the policies, they may not act accordingly, leading to error. This paper presents studies that illustrate how human factors affect the cybersecurity, and explains the demographic features, such as gender, age, personality, and cultural background that are key determinants of an individual's attitude and behavior toward cybersecurity. Based on this review the paper discusses the concept of "insider threat" and how its potential impact on cybersecurity policies. its then proposes recommended solutions to handle insider threats and provides cybersecurity awareness and explains the importance of ensuring training and intervention programs on cybersecurity.
D-GENIES : Dot plot large GENomes in an 1 Interactive, Efﬁcient and Simple way 2

8 Dot plots are widely used to quickly compare sequence sets. They provide a synthetic similarity overview, highlighting repetitions, breaks and inversions. Different tools have been developed to easily generated genomic alignment dot plots, but they are often limited in the input sequence size. D-GENIES is a standalone and WEB application performing large genome alignments using minimap2 software package and generating interactive dot plots. It enables users to sort query sequences along the reference, zoom in the plot and download several image, alignment or sequence ﬁles. D-GENIES is an easy to install open source software package (GPL) developed in Python and JavaScript. The source code is available at https://github.com/genotoul-bioinfo/dgenies and it can be tested at http://dgenies.toulouse. inra.fr/ .
Protein-protein interaction analysis of influenza a virus with the host (homosapiens) to determine the proteins involved in influenza a virus infection mechanism

Influenza A virus (IAV) infection is a serious public health problem all over the world. This virus belongs to the family Orthomyxoviridae and this is the only species of the virus occurring in the genus Alphainfluenzavirus. IAV consists of ss negative sense RNA as its genetic material and its genome comprises eight segments of viral RNA and each segment is complexed with trimeric viral polymerase proteins and nucleoprotein. IAV causes zoonotic infections in birds and severe respiratory infections in humans. The current study determines the various proteins in the biological processes of Influenza A virus in the host (Homosapiens). In this experiment, we retrieved a protein interaction network of Influenza A virus with Homosapiens. To this network cluster analysis was performed which resulted in 6 clusters. Further, gene enrichment analysis was performed for the clustered proteins using the Panther GO database and we, therefore, identified proteins that have a highly effective role in cellular processes and viral infection mechanisms. Hence this study helps to understand the various proteins that can be targeted for further development in drug discovery and also in the prevention of this disease.
DETECTION OF UNFOCUSED RAINDROPS ON CAR WINDSCREEN COMPARATIVE ANALYSIS USING BACKGROUND SUBRACTIONAND AND WATERSHED ALGORTIHM

Use of ADAS in top end cars has been prevalent over past decade. Electronic control and assistance in cars has proven to be a major feature resulting in passenger safety, saving lives as well as preventing fatalities. This system can be trusted or counted upon in clear weather conditions, which by now has been the only limitation questioning the usefulness of ADAS. Current research focuses to strengthen ADAS in rainy climatic conditions. This paper puts forth a novel idea to detect raindrops where ADAS can be used to increase its functionality in rainy condition to control the speed of over-speeding cars. The method basically includes image database on which Background Subtraction and Watershed algorithm are run to find out a numerical data, and to measure performance of both the method. This data can be used to improve ADAS performance in rainy conditions
Story Trees: Representing Documents using Topological Persistence

Topological Data Analysis (TDA) focuses on the inherent shape of (spatial) data. As such, it may provide useful methods to explore spatial representations of linguistic data (embeddings) which have become central in NLP. In this paper we aim to introduce TDA to researchers in language technology. We use TDA to represent document structure as so-called story trees. Story trees are hierarchical representations created from semantic vector representations of sentences via persistent homology. They can be used to identify and clearly visualize prominent components of a story line. We showcase their potential by using story trees to create extractive summaries for news stories.
A survey on various machine learning approaches for human electrocardiograms identification

Human identification is a critical function that can aid in data security protection. Developing deep learning models for human identification from electrocardiogram (ECG) data is one of the most promising strategies. It has a number of specific advantages, including the identification of liveness, insensitivity, ease of collecting, and greater security. On the other hand, present classifier-based methods can only identify closed sets, whilst existing matching-based methods are computationally intensive. Additionally, virtually all algorithms analyze only one-shot identification, which is subject to noise. In light of the fact that the electrocardiogram (ECG) is the most often used diagnostic tool for monitoring electrical activity in the heart, it is critical to use it to find early detection and diagnosis signals. The rapid growth and adoption of electronic health records, which include a systematized collection of various types of digitalized medical data, along with the development of new methods for quickly evaluating this massive amount of data, has resurrected interest in the fields of machine learning and deep learning in recent decades. The purpose of this article is to provide an overview of the EKG’s significance in terms of learning approaches, as well as a comparison of the most well-known research and technical phrases relating to the electrocardiogram.
Majority Logic-based Approximate Recording Adders for High-radix Booth Multipliers

Approximate computing permits the improvement of hardware efficiency with a relaxation of accuracy for nanoscale technologies and devices. Instead of conventional Boolean logic, voter-based majority logic (ML) is widely applicable to many emerging nanotechnologies. High-radix Booth multipliers, such as radix-8 and radix-16 multipliers, suffer from high complexity when generating odd multiples of the multiplicand. In this paper, designs of approximate recording adders (ARAs) based on ML with no carry propagation are proposed to alleviate this issue. For calculating the triple and 5×of the multiplicand, a 2-bit ARA and a 3-bit ARA are designed to compute the sum of 1× and 2× multiplicand, and the sum of 1× and 4× multiplicand, respectively. Moreover, a 4-bit ARA is especially developed for computing 7× of the multiplicand as the addition of −1× and 8× of the multiplicand. The proposed ARAs show advantages in hardware evaluated by delay and gate complexity, as well as in accuracy; for example, in a 16 × 16 radix-8 multiplier, the use of 2-bit ARAs achieves a reduction of 77% in the area-delay product with a normalized mean error distance of 7.51 × 10–4for computing the triple multiplicand.
Understanding High-Level Properties of Low-Level Programs Through Transformers

—Transformer models have enabled breakthroughs in the field of natural language processing largely because unlike other models, Transformers can be trained on a large corpus of unlabeled data. One can then perform fine-tuning on the model to fit a specific task. Unlike natural language, which is somewhat tolerant of minor differences in word choices or ordering, the structured nature of programming languages means that program meaning can be completely redefined or be invalid if even one token is altered. In comparison to high-level languages, low-level languages are less expressive and more repetitive with more details from the computer microarchitecture. Whereas recent literature has examined how to effectively use Transformer models on high-level programming semantics, this project explores the effectiveness of applying Transformer models on low-level representations of programs that can shed light on better optimizing compilers. In this paper, we show that Trans-former models can translate C to LLVM-IR with high accuracy, by training on a parallel corpus of functions extract from 1 million compilable, open-sourced C programs (AnghaBench) and its corresponding LLVM-IR after compiling with Clang. Our model shows a 49.57% verbatim match when performed on the AnghaBench dataset and a high BLEU score of 87.68. We also present another case study that analyzes x86 64 basic blocks for estimating their throughput and match the state of the art. We show through ablation studies that a collection of preprocessing simplifications of the low-level programs especially improves the model’s ability to generate low level programs and discuss data selection, network architecture, as well as limitations to the use of Transformers on low-level programs.
ANALYSIS ON PROSPECTOR, DEFENDER, ANALYZER AND REACTOR STRATEGY APPLICATION FOR SMALL AND MEDIUM ENTERPRISES

In the last five years, small and medium enterprises (SMEs) received more attention from the government and banks. SMEs’ ability to withstand waves of financial crisis in Indonesia, its significant contribution for national economy, and its capability in absorbing workforce made SMEs play an important part in Indonesia’s economy. Every SMEs are surely implementing business strategy to keep their market share and expand their business. Using business strategy typology by Jabnoun – prospector, defender, analyzer and reactor – this research aims to map business strategy types used by SMEs in Batu city, the most used business strategy, and the result of the business strategy used on their business’ success, both partially and simultaneously. The result of this research shows that prospector, defender, analyzer, and reactor business strategy partially and simultaneously affect business’ success where prospector strategy being the most dominant factor.