Miracle-dz/UniFAR-FTUs huggingface dataset:
A data driven method for congestion mining using big data analytic

Congestion detection is one of the key steps to reduce delays and associated costs in traﬃc management. With the increasing usage of GPS base navigation, promising speed data is now available. This study utilizes such extensive historical probe data to detect spatiotemporal congestion by mining historical speed data. The detected congestion were further classiﬁed as Recurrent and Non Recurrent Congestion (RC, NRC). This paper presents a big data driven expert system for identifying both recurrent and non-recurrent congestion and analyzing the delay and cost associated with them. For this purpose, ﬁrst normal and anomalous days were classiﬁed based on travel rate distribution. Later, we utilized Bayesian change point detection to segment speed signal and detect temporal congestion. Finally according to the type of congestion summary statistics and performance measures including (delays, delay cost, and congestion hours) were analyzed. In this study, a statistical big data mining methodology is developed and the robustness of the proposed methodology is tested on probe data for 2016 calendar year, in Des Moines region, Iowa, US. The proposed framework is self adaptive because it does not rely on additional information for detecting spatio-temporal congestion. Therefore, it addresses the limits of prior work in NRC detection. The optimum value for congestion percentage threshold is identiﬁed by Elbow cut oﬀ method and speed values were temporally denoised.
Hateful Speech Detection on Social Media using Deep Learning : An Overview

Hateful speech detection on Twitter is critical for applications like controversial event extraction, building AI chatter bots, content recommendation, and sentiment analysis. We are going to define that this task is able to classify a tweet as racist, sexist or neither. This task is being very challenging task due to the complexity of the Natural Language constructs. The proposed system carried out the text processing using supervised learning approach for Hateful speech detection in desired tweets. System also use polarity dataset for identify sentiment basis. The proposed system used deep learning approach for classification. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity.
Probabilistic Simulation Methodology for Evaluation of Renewable Resource Intermittency and Variability Impacts in Power System Operations and Planning (3.4)

We report on the development of a comprehensive, stochastic simulation approach for power systems with renewable and storage resources operating in a competitive market environment. The approach explicitly represents the uncertain and timevarying natures of the loads and supply-side resources, as well as the impacts of the transmission constraints on the hourly dayahead markets. We adopt Monte Carlo simulation techniques to emulate the side-by-side power system and transmissionconstrained hourly day-ahead market operations. The approach quantifies the power system economics, emissions and reliability variable effects. We address the implementational aspects of the methodology so as to ensure computational tractability for largescale systems over longer periods. Applications of the approach include planning and investment studies and the formulation and analysis of policy. We illustrate the capabilities and effectiveness of the simulation approach on representative study cases on a modified IEEE 118-bus test system. The results provide valuable insights into the impacts of deepening penetration of wind resources.
A communication-based distributed model predictive control approach for large-scale systems

This work presents a distributed model predictive control strategy as an alternative to conventional centralized approaches, which often suffer from implementation issues when applied to large-scale systems. The overall system is partitioned into minimally coupled subsystems based on its structural properties. Then, the coordination among the subproblems is achieved by means of a communication protocol, which allows each local controller to broadcast its solution to the rest of controllers with a coupled variable. The proposed approach is tested on the quadruple-tank process, and its effectiveness is proved by comparing the obtained results to those documented in an existing benchmark.
Enhanced Photocatalytic Activity of Semiconductor Nanocomposites Doped with Ag Nanoclusters Under UV and Visible Light

Emerging contaminants (ECs) represent a wide range of compounds, whose complete elimination from wastewaters by conventional methods is not always guaranteed, posing human and environmental risks. Advanced oxidation processes (AOPs), based on the generation of highly oxidizing species, lead to the degradation of these ECs. In this context, TiO2 and ZnO are the most widely used inorganic photocatalysts, mainly due to their low cost and wide availability. The addition of small amounts of nanoclusters may imply enhanced light absorption and an attenuation effect on the recombination rate of electron/hole pairs, resulting in improved photocatalytic activity. In this work, we propose the use of silver nanoclusters deposited on ZnO nanoparticles (ZnO–Ag), with a view to evaluating their catalytic activity under both ultraviolet A (UVA) and visible light, in order to reduce energetic requirements in prospective applications on a larger scale. The catalysts were produced and then characterized by scanning electron microscopy (SEM), X-ray diffractometry (XRD) and inductively coupled plasma-optical emission spectrometry (ICP-OES). As proof of concept of the capacity of photocatalysts doped with nanoclusters, experiments were carried out to remove the azo dye Orange II (OII). The results demonstrated the high photocatalytic efficiency achieved thanks to the incorporation of nanoclusters, especially evident in the experiments performed under white light.
Construction of UMLS Metathesaurus with Knowledge-Infused Deep Learning

The Uniﬁed Medical Language System (UMLS) is a Metathesaurus of biomedical vocabularies developed to integrate a variety of ways the same concepts are expressed by diﬀerent terminologies and to provide cross-walk among them. However, the current process of constructing and inserting new resources to the existing Metathesaurus relies heavily on lexical knowledge, semantic pre-processing, and manual audits by human editors. This project explores the use of supervised Deep Learning approach to identify synonymy and non-synonymy among English UMLS concepts at the atom level. We use a Siamese network with Long Short-Term Memory and Convolutional Neural Network models to learn the similarities and dissimilarities between pairs of atoms from the active subset of 2019AA UMLS. To disambiguate concepts with lexically identical atoms, we contextualize the pairs with various enrichment strategies that reﬂect the information available to the UMLS editors including the source synonymy, hierarchical context, and source semantic group. Learning from base lexical features of the atoms yields an overall F1-score of 75.97%. Infusing source synonymy to the base yields a higher precision and overall F-1 score of 86.54% and 87.63% respectively. Whereas, infusing hierarchical context trades precision for higher recall of 90.38%. Infusing source synonymy, hierarchical context, and semantic group provides an overall increase in accuracy to 95.20%. However, infusing source synonymy of hierarchical context does not yield any noticeable improve-ment. A knowledge-infused learning approach provides a good performance indicating promising potential for emulating the current building process. Future works include evaluation with rule-based normalization approach of constructing the Metathesaurus and investigation of the applicability, maintenance, and scalability of these models.
Study on the drying performance and milling quality of dried paddy using inclined bed dryers in two different paddy mills located in MADA and IADA KETARA

The drying performance of industrial inclined bed dryer (IBD) and the milling quality of dried paddy was evaluated and compared in two main granary areas of Muda Agricultural Development Authority (MADA) and Integrated Agricultural Development Areas (IADA KETARA). A mix of MARDI’s paddy variety (MR 220 CL2 and MR 219) was used and the study on both granary area of MADA and IADA KETARA were carried out in local paddy mills located at Yan, Kedah and Besut, Terengganu respectively. The results indicated that the average drying rate in MADA was 119.93 kg water removed/hr whereas only 73.67 kg water removed/hr was recorded in IADA KETARA. Therefore, normalized water removal value of paddy mill in MADA was found higher than paddy mill in IADA KETARA. In terms of milling quality, average percentage of head rice yield of paddy mill in MADA and IADA KETARA was 76.89% and 73.87% respectively. The milling quality analysis of rice resulted from ANOVA found that there was no significant difference in the HRY and total broken between IBDs in both locations.
University of Birmingham Hybrid Online Model-Based Testing for Communication-Based Train Control Systems

Communication-Based Train Control (CBTC) systems have been increasingly implemented on metro systems because of their characteristics, which result in safety and capacity improvements for metro operations. Automatic testing methods such as Model-Based Testing (MBT) have been applied to solve some particularly simple and ideal case studies. To bring automation to HIL testing, the authors apply MBT to HIL testing and present a novel hybrid online MBT testing platform that combines formal modelling methods with simulation. The theoretical methodology of the hybrid MBT, the platform architecture, and the testing results produced by the platform are described with a case study of a system under test (SUT) of a real Vehicle On-Board Controller (VOBC). Keywords—CBTC; Online testing; HIL testing; Hybrid Model-based testing;
Successful Treatment with Empirical Erlotinib in a Patient with Respiratory Failure Caused by Extensive Lung Adenocarcinoma

We herein describe a 70-year-old woman who presented with respiratory failure due to extensive lung adenocarcinoma. Despite advanced disease, care in the intensive care unit with ventilator support was performed because she was a newly diagnosed patient and was considered to have the potential to recover after cancer treatment. Because prompt control of the cancer was needed to treat the respiratory failure, empirical treatment with an oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor was initiated before confirmation of EGFR-mutant adenocarcinoma, and the patient was successfully treated. Later, EGFR-mutant adenocarcinoma was confirmed.
Further Examination of Youth Leadership Skills Predictors

Considering the substantial upcoming transfer of leadership to younger generations, it is critical to study the unique trait, skill, and behavioral associations of youth leaders. The purpose of this two-phase study was to quantitatively examine the relationship between positive psychological capacities (PsyCap), personality, and empathy with leadership skills in youth. Regression results from both phases revealed cognitive empathy and academic PsyCap as significant predictors of youth leadership life skills. These results follow earlier research that identified trait-based emotional intelligence as a significant predictor of leadership skills in youth. The combined results offer important considerations as leadership scholars and practitioners attempt to accurately predict and plan for the leadership transfer landscape over the next two decades. These combined results also serve as helpful considerations for youth leadership practitioners as program outcomes, learning objectives, and activities targeting the development of cognitive empathy and PsyCap will perhaps allow for more productive youth leadership development efforts and better documentation of their impact.
Discriminative System Identification via the Principle of Maximum Causal Entropy

System identification addresses the important problem of estimating the answers to what will happen if. questions. In black-box system identification, minimal assumptions are made about the unknown system and its dynamics are estimated entirely from control input and observational data. Black-box methods for identifying linear systems are well-established (Ljung, 1999). The perspective of these methods is often taken in nonlinear black-box system identification as well, with a regression-based focus on squared loss minimization (Sjöberg et al., 1995).
Tackling Hardware/Software co-design from a database perspective

Hardware is evolving at a very fast pace due to diverse trends in the IT industry. In the area of data processing, it is fair to say that software often just reacts to these changes, trying to accommodate developments that are not always an immediate step forward in terms of either performance or functionality. In this paper we report on two ongoing, long-term projects: Enzian, an experimental hardware platform to explore the design of software systems on future hardware, and doppioDB, a research database engine built to explore how to to co-design hardware and software from a data procesisng perspective. The paper focuses on the possibilities oﬀered by the combination of Enzian+doppioDB in terms of enabling novel data processing systems.
Natural Compounds Inhibiting Pseudomonas aeruginosa Biofilm Formation by Targeting Quorum Sensing Circuitry

The biofilm lifestyle mode certainly represents one of the most successful behaviors to facilitate bacterial survival in diverse inhospitable environments. Conversely, the ability of bacteria to develop effective biofilms represents one of the major obstacles in the fight against bacterial infections. In Pseudomonas aeruginosa, the biofilm formation is intimately connected to the quorum sensing (QS) mechanisms, a mode of cell-to-cell communication that allows many bacteria to detect their population density in order to coordinate common actions. In this chapter, we propose an overview (i) on P. aeruginosa QS mechanisms and their implication in biofilm formation, and (ii) on natural products that are known to interfere with these QS mechanisms, subsequently disrupting biofilm formation. The concluding remarks focus on perspectives of these compounds as possible antibiotherapy adjuvants.
Development of an IoT-Based Indoor Air Quality Monitoring Platform

In this paper, an IoT-based indoor air quality monitoring platform, consisting of an air quality-sensing device called “Smart-Air” and a web server, is demonstrated. This platform relies on an IoT and a cloud computing technology to monitor indoor air quality in anywhere and anytime. Smart-Air has been developed based on the IoT technology to efficiently monitor the air quality and transmit the data to a web server via LTE in real time. The device is composed of a microcontroller, pollutant detection sensors, and LTE modem. In the research, the device was designed to measure a concentration of aerosol, VOC, CO, CO2, and temperature-humidity to monitor the air quality. Then, the device was successfully tested for reliability by following the prescribed procedure from the Ministry of Environment, Korea. Also, cloud computing has been integrated into a web server for analyzing the data from the device to classify and visualize indoor air quality according to the standards from the Ministry. An application was developed to help in monitoring the air quality. Thus, approved personnel can monitor the air quality at any time and from anywhere, via either the web server or the application. The web server stores all data in the cloud to provide resources for further analysis of indoor air quality. In addition, the platform has been successfully implemented in Hanyang University of Korea to demonstrate its feasibility.
PACIFIC RIM REAL ESTATE SOCIETY SEVENTH ANNUAL CONFERENCE JANUARY 21-23, 2002 CHRISTCHURCH, NEW ZEALAND Murders, Externalities, and Market Efficiency – Empirical Evidence From Hong Kong

There are a lot of different factors affecting the market value of properties. However, to the best of my knowledge, there has been no study on the effect of brutal murders in apartments on their market values and the market values of nearby properties. This study attempts to examine the effects of murders on property prices of nearby units and how such effects change over time. In a Chinese society, a murder is believed to have a bad influence on the feng shui of the property, and thus diminishes its market value. This effect will also spread to nearby properties, and thereby creates a negative externality. This external effect is likely to be much more noticeable in high development density areas such as Hong Kong, where residential units are predominately high-rise apartments. Five murder cases that occurred in high density housing estates were chosen for analysis. The results showed that not only were the property values of the incident apartments negatively affected, but the negative impact also extended to nearby apartments. After the murder cases were revealed to the public, the price levels of all the units within the same building of the incident property declined. Moreover, this adverse effect is sometimes so strong that nearby buildings (within the same estate) were also affected. Furthermore, the magnitude of this negative effect is directly related to distance from the incident property. The effect diminishes as the
Leveraging Adversarial training for Monocular Depth Estimation

In this project, we tackle the problem of estimating the depth of a scene given a single 2D RGB image. Approaches based on convolutional neural networks (CNNs) have been widely used for depth estimation in recent years. We propose a generative adversarial approach to extend the recent work done by Hu et al. [baseline]. A discriminator network is added to their proposed architecture which is fed with ground truth depth maps as well as the depth maps estimated by the generator network. The discriminator network learns to differentiate between the real depth maps and artificial depth maps that are generated by the generator. We also utilize the structural similarity between the generated depth maps and ground truth as a loss term for depth estimation training. We then evaluate our proposed approach on the NYU v2. dataset [23]. The experimental results show that when adversarial training is used, the performance of the existing method is improved. Also, we report the results of employing different combinations of loss functions in the literature to show how these terms would contribute to the performance.
PII: S0167-8809(01)00283-3

Much of the increased atmospheric concentration of ammonia (NH 3) is derived from dairy farms. In order to better understand the potential for controlling emissions, a comparison was made of NH 3 emissions from dairy farms in the United Kingdom and New Zealand using recently derived emission factors. This desk study demonstrated distinct differences between the two farms: total N input, off-take and surplus in the UK were 1.7, 1.2 and 1.8 times greater than in New Zealand. More sources of NH3 were identified in the UK which were, in the main, associated with the housing phase, and therefore manure generation. Total NH3 losses were equivalent to 57 and 24 kg N ha −1 in the UK and New Zealand farms, respectively: these represented similar proportions of the total N inputs to both systems but because of stocking density in New Zealand and its overall greater milk production per ha, losses of NH 3 whether expressed per LU or unit of milk, were at least two times greater in the UK. Options for controlling emissions and their impact included improved slurry storage and application methods, efficient grazing patterns and increased N fertilizer use efficiency in the UK. Options were fewer in New Zealand, the most practical being more effective fertilizer application. In both cases, farming practices which increase N use efficiency, or improve C:N dietary balance for highly productive cows have much potential for reducing volatilization. The approach taken illustrated some important differences between the two farms and provided an easily adopted method to make assessments of NH3 loss. Additionally, a set of indicators was derived to allow quantifiable comparisons. © 2002 Elsevier Science B.V. All rights reserved.
Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design

This paper presents a case study of migrating a privacy-safe information extraction system in production for Gmail from a traditional rule-based architecture to a machine-learned Software 2.0 architecture. The key idea is to use the extractions from the existing rule-based system as training data to learn models that in turn replace all the machinery for the rule-based system. The resulting system a) delivers better precision and recall, b) is signiﬁcantly smaller in terms of lines of code, c) is easier to maintain and improve, and d) allowed us to leverage machine learning advances to build a cross-language extraction system even though our original training data was only in English. We describe challenges encountered during this migration around generation and management of training data, evaluation of models, and report on many traditional“Software 1.0”components we built to address them.
Farkas Lemma made easy

In this paper, we present fkcc, a scripting tool to prototype program analyses and transformations exploiting the affine form of Farkas lemma. Our language is general enough to prototype in a few lines sophisticated termination and scheduling algorithms. The tool is freely avalailable and may be tried online via a web interface. We believe that fkcc is the missing chain to accelerate the development of program analyses and transformations exploiting the affine form of Farkas lemma.