Miracle-dz/UniFAR-FTUs huggingface dataset:
BIM, INFORMATION ON SUSTAINABLE BUILDING AND PERFORMANCES

Sustainability thresholds generating Value There is an expanding literature on the value of sustainability features in buildings (European Commission, 2013; World GBC, 2013; World GBC, 2014). While several publications focus on the price differentiation between buildings with sustainability credentials and buildings with no sustainability credentials (Eichholtz et al. , 2010; etc.), others examine the costs and benefits of sustainability features individually referring to both monetary and intangible values (Heerwagen, 2000). These various studies usually refer to different concepts of value and encompass benefits that may not always directly profit the investors themselves. The value of sustainability can thus be interpreted differently according to the stakeholders and scope considered. On the one hand, market value refers to the price at which the good would be traded in a perfect open market. On the other hand, a concept of total value could be defined to encompass all the benefits associated with sustainability features for the various stakeholders (investors as well as users, local authorities, citizens, etc.). This notion would include a wider range of benefits which may not all be priced by the market. However, examining the mechanisms through which benefits for the different stakeholders could impact investors helps better understand the financial gains investors can expect from sustainability.
How Long Do Vulnerabilities Live in the Code? A Large-Scale Empirical Measurement Study on FOSS Vulnerability Lifetimes

How long do vulnerabilities live in the repositories of large, evolving projects? Although the question has been identified as an interesting problem by the software community in online forums, it has not been investigated yet in adequate depth and scale, since the process of identifying the exact point in time when a vulnerability was introduced is particularly cum-bersome. In this paper, we provide an automatic approach for accurately estimating how long vulnerabilities remain in the code (their lifetimes ). Our method relies on the observation that while it is difficult to pinpoint the exact point of introduction for one vulnerability, it is possible to accurately estimate the average lifetime of a large enough sample of vulnerabilities, via a heuristic approach. With our approach, we perform the first large-scale measurement of Free and Open Source Software vulnerability lifetimes, going beyond approaches estimating lower bounds prevalent in previous research. We find that the average lifetime of a vulnerability is around 4 years, varying significantly between projects (~2 years for Chromium, ~7 years for OpenSSL). The distribution of lifetimes can be approximately described by an exponential distribution. There are no statistically significant differences between the lifetimes of different vulnerability types when considering specific projects. Vulnerabilities are getting older, as the average lifetime of fixed vulnerabilities in a given year increases over time, influenced by the overall increase of code age. However, they live less than non-vulnerable code, with an increasing spread over time for some projects, suggesting a notion of maturity that can be considered an indicator of quality. While the introduction of fuzzers does not significantly reduce the lifetimes of memory-related vulnerabilities, further research is needed to better understand and quantify the impact of fuzzers and other tools on vulnerability lifetimes and on the security of codebases.
Geological parameterization of a reservoir model for history matching

Geological structure can have a large effect on hydrocarbon flow. In structurally complex reservoirs, it is often one of the biggest uncertainties in reservoir modeling. Geological and structural features can be used as parameters when history matching a reservoir model. History matching with geologic parameters can enhance reservoir management by identifying non-swept regions and optimizing the placement of infill wells.
A Review of Offset and Noise Reduction Techniques for CMOS Amplifiers

—Input referred offset voltage, 1/f noise and thermal noise are amplifier properties that directly restrict the ability of discerning signals beyond a certain limit. The ever-increasing spectrum of applications of integrated circuits and trends in the semiconductor market have pushed engineers to design circuits with successively lower voltage, less power consumption, higher dynamic range, accurate gain and wider bandwidth, preferably altogether. Amplifiers input errors are key properties, which have to be minimized; however, with the least negative impact upon the other equally important properties. This paper reviews some of the most relevant techniques applied to reducing input errors of CMOS amplifiers, aiming to provide a condensed set of information that can help designers at the starting point of a new design of a precision analog circuit. The focus in all cases that were selected to be studied in this review work was the reduction of offset and noise regardless of any commitment of the used technique with other characteristics of the amplifier and its impacts on figures of merit like NEF and PEF.
EMPLOYEES ENTRY MONITORING SYSTEM TO AVOID SPREAD OF COVID -19

: In this digital era, face recognition systems plays a vital role in almost every sector. Face recognition is one among the mostly used biometrics. It can used for security, authentication and identification. As compare to iris recognition and fingerprint recognition, it is being widely used because of its noncontact and non-invasive process. Face recognition system also can be used for attendance marking in schools, colleges, offices, etc. This method aims to make a class attendance system which uses the concept of face detection and face recognition as existing manual attendance system is time consuming and also there is a chances of proxy attendance. Thus, the need for this system increases. So, in the present situation created by Covid-19 pandemic, temperature and oxygen level measurement is also important to allow the students to attend the class. We propose a concept where together with face recognition, temperature and oxygen level is measure to mark the attendance record and also, sanitization system is added for safety purposes. This system will design using ultrasonic sensors, temperature sensor, oximeter, camera module that is interfaced with Raspberry Pi. In this manner attendance are often marked automatically and at an identical time system can scan individual student to spot potential patients of Covid-19.
SysMIC: A Blueprint for Interdisciplinary Online Training in the Life Sciences

‘Big Data’ and a surge in quantitative methodologies increasingly urge bioresearchers to train and improve their mathematical and computational skills. In response to this challenge the Biotechnology and Biological Sciences Research Council in the United Kingdom offered funds to develop web-based training in mathematics and computation for life scientists in all fields. The SysMIC consortium won the bid and implemented a new type of online course, which so far has been delivered to more than 1400 scientists, mostly in the UK. Researchers train self-paced or in cohorts in a media rich environment with materials based around intuitive biological examples. We describe the course, its trainee spectrum, and the modes of trainee engagement. We propose that tailored interactive online training is a scalable model for continuing professional development in the interdisciplinary life sciences in the 21st century.
Improvement of Initialized Decadal Predictions over the North Paciﬁc Ocean by Systematic Anomaly Pattern Correction

The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean–atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is due to the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of actionof thedominantdecadalclimate oscillations, such as thePaciﬁc decadaloscillation(PDO)and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Paciﬁc Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projection method (SPPM) based on the data in an independent training period, the prediction skill of sea surface temperature (SST) is greatly enhanced over the North Paciﬁc Ocean. The SST prediction skill over the North Paciﬁc Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.
TO DETECT BEHAVIOUR BASED MALWARE USING INDEPENDENT COMPONENT ANALYSIS

: Malware has been a concern to enterprises for a long time, and organisations have made little progress in identifying malware in a timely manner [1]. Malware can easily affect the system by executing unwanted services that add to the system's burden and obstruct its smooth operation. There are basically two approaches for identifying malware [2] the traditional method of detecting malware based on signatures and the new method of detecting malware based on behaviour. The operation that the malware conducts when it is active in the machine determines the malware's behaviour [3], such as launching the Operating System services or downloading infected files from the internet. Based on the malware's activity, the suggested method detects it. The proposed model in this paper is a hybrid of Support Vector Machines and Principle Component Analysis. During validation, our suggested model achieved a 97.75 percent accuracy with 97 percent precision, 99 percent recall, and a f1-score of.98 for genuine Malwares.
Investigating the thermal behavior of 3D printer extruder of Fused Deposition Modeling

—In the present work, different optimization techniques used for 3D printing or fused deposition modeling processes are studied and after effects of various input parameters on some performance parameters of 3D printed component like surface finish have been reviewed. The aim of this work is to investigate the effect of input parameters like filament diameter, extruder temperature, feed rate, raster angle, characteristic of working material, nozzle angle, distance between parallel faces on output parameters like surface finishing, moving speed or movement of nozzle head, material volumetric concentration, cooling of print, strength, number of shells and deposition rate in 3D printing through different optimization techniques. Considerable amount of work has been reported by the researchers on 3D printing and optimization of various input parameters. Several approaches are proposed in the literature to optimize these parameters hence it is felt that a review of the various approaches developed would help to compare their main features and their relative advantages and limitations to allow choose the most suitable approach for a particular application. In view of above, this paper presents a review of development done in the area of optimization of process parameter of 3D printed components
Semantic Event Detection in Videos

: The redundancy of the information in a video contents can be reduced using the process of video event detection. Here the aim is to construct a reliable and scalable solution for the large scale video event detection systems. With the analytical and statistical learning models used for the existing event detection procedures, its focusing on the low level features of video content. In such methods, the capability of analyzing and interpreting the content of complex video events lacks accuracy. So, the intermediary-level representation of semantic concept of real time events has been introduced as a method for perceiving video events. High accuracy can only be achieved when various features including the high level and low level features of different modalities is used. Event identification can be used effectively in real time video processing technologies. Hence, accuracy rate has to be improved using the newly introduced methodology.
Blockchain Based Vehicle Insurance Management

— The automotive industry is blooming day by day with the advancement of technology that is helping in connecting people and non-living things. Electric vehicles and autonomous vehicles are already in the edge technology that’s keeping everyone waiting to bring it on road. There is a new frontier that can lend its abilities to the experiences built around vehicles and it is the blockchain technology. Blockchain technology is an enabler. It can act as a transaction medium between interacting parties. It can also be used as a tamper-free ledger to store a history of transactions. With these two simple abilities, blockchains can enable several applications to make vehicle-related experiences better. In this paper, we will give a diverse knowledge into how blockchain as a new trending technology will help in keeping a track of vehicle insurance management. This insurance record system can include all aspects of insurance transactions. It will act as evidence in the event of dispute.
Repair Suggestions for Planning Domains with Missing Actions Effects

In Automated Planning (AP) a proper definition of the domain and problem files is assumed. However, producing complete model descriptions is a time consuming and challenging task, especially for non-experts. It is easy to make mistakes when creating formal models, turning the planning task un-solvable for the planners. This can happen if the initial state is not fully and properly specified, some actions are missing, or some actions are incomplete. Explaining the absence of solution in such cases is essential to help humans in the development of AP tasks. In this paper we focus on repairing planning models where the effects of some actions are incomplete. We propose a compilation of the unsolvable task to a new extended planning task, where the actions are allowed to insert possible missing predicates in their effects. The so-lution to such task is a plan that achieves the goals of the original problem while also warning about the modifications that were necessary to do so. Experimental results show this approach can be effectively used to repair incomplete planning tasks across different planning domains.
A Reliable Methodology for Multi-objective Voltage Regulation in the Presence of Renewable Power Generation

Abstract This article proposes a reliable computational paradigm for solving the optimal voltage regulation problem in power distribution systems in the presence of distributed generators. The main idea is to integrate elements of multi-objective optimization theory and decision analysis for computing a reliable problem solution, considering all the system uncertainties, and describing the multi-criteria aspects of the regulation problem. Detailed numerical simulations obtained from both balanced and unbalanced power systems are presented and discussed to demonstrate the effectiveness of the proposed methodology in the task of solving the optimal voltage regulation problem under different operating scenarios.
PROCESS WIND TUNNEL FOR IMPROVING BUSINESS PROCESS

EXTENDED ABSTRACT In this talk, we will introduce a simulation-based process improvement framework and methodology called the Process Wind Tunnel. We will describe this framework and introduce the underlying technologies namely process mapping and data collection, data wrangling, exploratory data analysis and visualization, process mining, discrete-event simulation optimization and solution implementation. We will discuss how Process Wind Tunnel framework was utilized to improve a critical business process namely, the post-execution trade settlement process. The work builds upon and generalizes the Lean Document Production solution (2008 Edelman finalist) for optimizing printshops to more general and complex business processes found within the insurance and financial services industry. A complex business process typically requires multiple teams with different skills to coordinate their activities to deliver a service. The overall task is broken down into sub-tasks and a team is assigned to each sub-task. Each subtask takes inputs and delivers an output which is then consumed by another team who does the same. The service request thus flows from origin to the end until the service is delivered. As the business process complexity increases, the team that handles each sub-task gets siloed with communication with other teams occurring through interfaces. Thus while each team works effectively within the silo, the overall process tends to get more and more fragmented and the overall visibility into the end-to-end process gets less transparent. Thus inefficiencies creep into the process over time, making it difficult to diagnose and fix.
ESD reliability issues in RF CMOS circuits

The ESD reliability issues in conventional ESD protection circuit elements in CMOS technology are investigated. Due to the stringent load limitations, the success of implementing a robust ESD protection depends on careful selection, design and layout of the devices. Further, at frequencies above 2.5GHz, the design window for application of most of these devices rapidly deteriorates. This paper highlights the benefits and limitations of the standard ESD protection methodologies.
Comparison of the bite force and occlusal contact area of the deviated and non-deviated sides after intraoral vertical ramus osteotomy in skeletal Class III patients with mandibular asymmetry: Two-year follow-up

Objective The objectives of this study were to compare the time-dependent changes in occlusal contact area (OCA) and bite force (BF) of the deviated and non-deviated sides in mandibular prognathic patients with mandibular asymmetry before and after orthognathic surgery and investigate the factors associated with the changes in OCA and BF on each side. Methods The sample consisted of 67 patients (33 men and 34 women; age range 15–36 years) with facial asymmetry who underwent 2-jaw orthognathic surgery. OCA and BF were taken before presurgical orthodontic treatment, within 1 month before surgery, and 1 month, 3 months, 6 months, 1 year, and 2 years after surgery. OCA and BF were measured using the Dental Prescale System. Results The OCA and BF decreased gradually before surgery and increased after surgery on both sides. The OCA and BF were significantly greater on the deviated side than on the non-deviated side before surgery, and there was no difference after surgery. According to the linear mixed-effect model, only the changes in the mandibular plane angle had a significant effect on BF (p < 0.05). Conclusions There was a difference in the amount of the OCA and BF between the deviated and non-deviated sides before surgery. The change in mandibular plane angle affects the change, especially on the non-deviated side, during the observation period.
The relationship between poor sleep quality measured by the Pittsburgh Sleep Quality Index and smoking status according to sex and age: an analysis of the 2018 Korean Community Health Survey

OBJECTIVES Multiple studies have found that cigarette smokers are more likely to experience sleep disturbances than non-smokers. This study aimed to examine various associations between smoking and sleep quality according to sex and age, which have yet to be sufficiently examined in prior studies. METHODS Data analysis was conducted using a nationally representative sample of 224,986 Korean adults who participated in the 2018 Korea Community Health Survey. Sleep quality, as the dependent variable, was measured using the Pittsburgh Sleep Quality Index (PSQI), with PSQI scores indicating either good (≤4 points) or poor (>5 points) sleep quality. Multiple logistic regression analysis was performed considering socio-demographic factors, health behaviors, comorbidities, and psychological factors as covariates. RESULTS The overall weighted prevalence of poor sleep quality was 39.4% (95% confidence interval, 39.1 to 39.7). In the multivariate model that excluded psychological factors, poor sleep quality positively correlated to smoking for both sexes and all age groups except for male aged ≥65 years. However, in the full model that included psychological factors, statistically significant odds ratios (approximately 1.5) for poor sleep quality according to smoking status were only observed for female under 65 years of age. CONCLUSIONS The relationship between poor sleep quality and smoking status differed according to sex and age. In order to improve the quality of sleep, it is necessary to intervene smoking cessation along with solving psychological problems, especially female in middle age and younger.
Refractory Short-Lasting Unilateral Neuralgiform Headache Attacks With Conjunctival Injection and Tearing (SUNCT) Responding to Erenumab Adjuvant Therapy: A Case Report

Many patients with short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) fail to respond to the first-line treatment of lamotrigine. Additionally, data for other treatments are limited in this rare headache disorder. SUNCT involves activation of the trigeminal nerve which uses the neuropeptide calcitonin gene-related peptide (CGRP); thus CGRP-targeted treatments may be beneficial in this disorder. We present a patient with SUNCT who failed to respond optimally to 10 medications and four surgical treatments. However, she had minimal attacks after erenumab 140 mg was added to carbamazepine 200 mg three times daily and pregabalin 75 mg twice daily. Decreasing any of these three medications worsened her attacks. Our case represents the second case report of a SUNCT patient responding to a CGRP monoclonal antibody, suggesting this treatment may be a consideration in refractory SUNCT.
Empathic Conversations: A Multi-level Dataset of Contextualized Conversations

Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI, but it is unclear how different forms of empathy (e.g., self-report vs counterpart other-report, concern vs. distress) interact with other affective phenomena or demographics like gender and age. To better understand this, we created the {\it Empathic Conversations} dataset of annotated negative, empathy-eliciting dialogues in which pairs of participants converse about news articles. People differ in their perception of the empathy of others. These differences are associated with certain characteristics such as personality and demographics. Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed. This dataset is the first to present empathy in multiple forms along with personal distress, emotion, personality characteristics, and person-level demographic information. We present baseline models for predicting some of these features from conversations.