Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic modeling of cluttered multi‐room floor plans from panoramic images

We present a novel and light‐weight approach to capture and reconstruct structured 3D models of multi‐room floor plans. Starting from a small set of registered panoramic images, we automatically generate a 3D layout of the rooms and of all the main objects inside. Such a 3D layout is directly suitable for use in a number of real‐world applications, such as guidance, location, routing, or content creation for security and energy management. Our novel pipeline introduces several contributions to indoor reconstruction from purely visual data. In particular, we automatically partition panoramic images in a connectivity graph, according to the visual layout of the rooms, and exploit this graph to support object recovery and rooms boundaries extraction. Moreover, we introduce a plane‐sweeping approach to jointly reason about the content of multiple images and solve the problem of object inference in a top‐down 2D domain. Finally, we combine these methods in a fully automated pipeline for creating a structured 3D model of a multi‐room floor plan and of the location and extent of clutter objects. These contribution make our pipeline able to handle cluttered scenes with complex geometry that are challenging to existing techniques. The effectiveness and performance of our approach is evaluated on both real‐world and synthetic models.
Multi-Speaker Modeling for DNN-based Speech Synthesis Incorporating Generative Adversarial Networks

This paper presents a novel DNN-based speech synthesis method we derived from multi-speaker training data. In general, speaker-dependent modeling techniques based on generative adversarial networks (GANs) improve synthesized speech quality. However, they are inadequate for multi-speaker training because conventional discriminators cannot take into account speaker identity, which degrades anti-spooﬁng performance in GAN discriminators. We introduce two approaches as means to learn GAN speaker characteristics, i.e., auxiliary features and tasks. The ﬁrst uses speaker codes as additional discriminator input. The second uses speaker identiﬁcation as a means to verify that anti-spooﬁng veriﬁcation methods are effective. Experimental results showed that our proposed techniques out-performed conventional and GAN-based methods.
Efficient Video Streaming Using JOKER an Opportunistic Routing Protocol

— The expansion in mixed media administrations has put vitality saving money on the highest point of current requests for cell phones. Shockingly, batteries' lifetime has not been as reached out as it would be alluring. Hence, lessening vitality utilization in each undertaking perform by this gadgets essential. In this work, a novel artful steering convention, called JOKER has been presented. The opinion present in both the applicant choice and coordination stages, has increased the performance in network supporting sight and sound traffic In the proposed system client has to send the request the videos to file server, and file server takes responsibility for distributing the frame to the neighbors. In this system we are considering the two neighbors, neighbour1 and neighbor2 and both neighbors are receiving the frames from file server alternatively, finally client receiving the videos from the neighbors. By using this proposed system we can reduce the time consuming between sender and user.
Losing Control: The 20-Year Decline in Loan Covenant Restrictions

We find that lenders today rely on less restrictive financial covenants than 20 years ago, resulting in a nearly 70% drop in the annual proportion of U.S. public firms reporting a loan covenant violation. To study this decline, we develop a simple model of optimal covenant design that balances the costs associated with violations that occur when a firm is not in danger of financial distress (“false positives”) with the costs of failing to detect a borrower in danger of financial distress (“false negatives”). Our evidence suggests that lenders have eased the restrictiveness of covenants in ways that greatly reduce the ratio of false positives relative to false negatives, including by switching to covenant packages with higher signal-to-noise ratios.
Does peacekeeping only work in easy environments? An analysis of conflict characteristics, mission profiles, and civil war recurrence

ABSTRACT Peacekeeping is widely considered to be an effective means of preventing civil war recurrence. However, as peace has collapsed in a considerable number of cases despite peacekeeping efforts, we are left with the question which combinations of peacekeeping environments and peacekeeping missions lead to lasting peace. This article compares 22 peacekeeping missions between 1990 and 2012. While prominent United Nations documents assume that the success of post-conflict peacekeeping primarily depends on the features of the mission itself, the analysis shows that characteristics of the terminated civil war have a strong influence on whether peace endures. Restrained peacekeeping, defined by low troop density, non-robustness, and a lead nation that is not a permanent member of the Security Council, only succeeds in preserving peace in conducive environments. Inconclusive war endings, evenly distributed military capabilities at war’s end, ethnic conflicts, and high intensity create a particularly difficult context for peacekeeping.
How Satisfaction Disabled People Engaging in Tourism Activities? a Case Study of Disability Tourism in Kuala Lumpur City

Accessible tourism is considered new-partially conceptual that being implemented in the tourism industry. This study carry out to investigate and to determine disabled people in engage with the tourism activities in Kuala Lumpur City. This study uses qualitative of face-to-face interview and quantitative of questionnaire survey. Five domains had been set for interview purposes, while questionnaire survey involved with two main parts, namely demographic profile and the experienced of disabled people in expose to tourism activities in Kuala Lumpur city. This questionnaire uses Thurstone scale and Likert scale. Complete collection data will be input using SPSS v.23 for analysis. The result indicates majority participants are positively agreed that infrastructure or services, as well as information map should be prepared in appropriated ways would increase the accessible tourism. The education also plays an important role that able to sustain the accessible tourism industry for longer term. Nevertheless, most of participants are partially disagreed in the facilities provided, which likely to refer to the public toilets and transportations, as well as hotels management that should serve specialty to disabled people. They do opinion that public facilities should be upgrade, as well as comment on the hotel staffs should undergo for training and behave friendlier attitude, which important to provide comfortable condition towards the disabled people. This study suggests disabled people have self-encouraged in engage with the tourism activities in Kuala Lumpur. At the same time, further study in exploratory, understandable, and predictable on accessible tourism are required, especially involved in the sub-urban and rural area that provide facilities and services for the tourism industry.
High Resolution 2-D Electromagnetic Vortex Imaging Using Uniform Circular Arrays

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) has been introduced in radar imaging to take full advantage of its special phase front. It has been demonstrated that the azimuthal and range resolution are decided by the number of OAM mode and frequency bandwidth respectively. In this paper, theelevation resolution of the vortex wave transmitted by a uniform circular array is derived for the first time through the calculation of correlation function of two closely adjacent points. Since large number of OAM modes should be applied to achieve high-resolution image, which is difficult to realize in practice, the sparse Bayesian learning (SBL) is introduced to reconstruct the targets for 2-D EM vortex imaging. Simulation results show that the targets located at different elevation angle can be reconstructed correctly and higher resolution can be achieved based on the SBL reconstruction algorithm.
Cardiopulmonary Exercise Testing

Background: The Cardiopulmonary Exercise Test (CPET or CPX) is an important stress test for the evaluation of patients with heart failure (HF). A CPET assesses how well the heart, lungs, and muscles are working individually, and how they are working together. Most exercise tests produce a single measurement; CPET quantitatively links metabolic, cardiovascular, and pulmonary responses to exercise. Since a CPET uses a stationary bicycle, this allows for a wider range of patients to complete an exercise stress test that may not otherwise be able.
A heuristic model for pedestrian intention estimation

Understanding pedestrian behaviour and controlling interactions with pedestrians is of critical importance for autonomous vehicles, but remains a complex and challenging problem. This study infers pedestrian intent during possible road-crossing interactions, to assist autonomous vehicle decisions to yield or not yield when approaching them, and tests a simple heuristic model of intent on pedestrian-vehicle trajectory data for the first time. It relies on a heuristic approach based on the observed positions of the agents over time. The method can predict pedestrian crossing intent, crossing or stopping, with 96% accuracy by the time the pedestrian reaches the curbside, on the standard Daimler pedestrian dataset. This result is important in demarcating scenarios which have a clear winner and can be predicted easily with the simple heuristic, from those which may require more complex game-theoretic models to predict and control.
Discovering and Characterizing Interdomain Links Between Access ISPs and Netflix CDN

Internet traffic sourcing from content delivery increases vastly by each year. Netflix is one of the biggest content providers and it deployed its own content delivery network (CDN) in 2011. As a successful provider, Netflix distributes its servers in various locations in US and adopts effective algorithms to deliver video services to its users. To explore the connections between users and Netflix, we conducted traceroute measurements from 29 vantage points in US access ISPs to 92 Netflix CDN servers to discover and characterize the Interdomain links along the source-destination paths. We discovered over 600 interdomain links and found that most AS (autonomous systems) paths from access ISPs to Netflix servers are one or two hops long. In addition, most of the 2-hop paths traverse a Tier 1 ISP. We conduct detailed analysis of the discovered Interdomain links and observe that 1) AS pairs often establish multiple interdomain links and interconnect at diverse locations; 2) Some interdomain links have negative delay based on RTT calculation, indicating asymmetric path to/from the far end routers; and 3) RTT span of Netflix server/routers typically have lower RTT span than routers belonging to access ISPs and transit ISPs, which can be accredited to Netflix’s effective load balancing and path selection algorithms.
Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance

Software bug localization is very important in software engineering, but it is also complicated and time consuming. To improve the efficiency of developers, researchers have developed various traditional bug localization and machine learning bug localization methods. In this paper, we propose a novel method that improves bug localization performance. First, surface lexical correlation matching between bug reports and source code files is used to obtain features by deep neural network. Second, to solve the lexical gap between bug reports and source code files, semantic correlation matching between them is used to obtain features based on word embedding and sentence embedding by deep neural network. Then, the joint features obtained by the surface lexical and semantic correlation matching are fused into a unified feature representation for bug reports and source code files. In addition, since our experimental datasets are imbalanced data, we use a focal loss function to solve the impact of data imbalance. Finally, our method obtains the relatively high bug localization performance compared to other classic methods.
Design and Optimization of a Homopolar Permanent-Magnet Linear Tubular Motor Equipped With the E-Core Stator

In this study, a new homopolar permanent-magnet (PM) linear tubular motor is designed, analyzed and optimized. Firstly, a mechanical structure of the linear oscillating motor (LOM) is proposed and the main working principles are introduced. A basic dynamic mathematic model of the motor is given. Secondly, the magnetic circuit of the motor is analyzed and equivalent magnetic modes are obtained to estimate the magnetic characteristic and force outputs. Thirdly, optimizations including the tooth stricter of the E-core stator, the size of the PM and the air gap length are carried out to improve the force output performance of the motor, increasing force outputs and reducing force ripples during the stroke. Finally, the motor prototype is manufactured according to the design and experimental tests involving force outputs and working efficiency are measured. The average force output and efficiency of the motor can achieve 60 N and 67 %, respectively. Results obtained by finite element method (FEM) and experiments prove the correctness of the design and the effectiveness of the LOM.
E-learning Technologies

As information professionals, we have different reasons for focusing our attention on e-learning (learning online or through electronic media). Some of us struggle with a high demand for our services and seek alternatives to face-to-face teaching and one-on-one support. There are also expectations for digital just-in-time content, as individuals have an increasing number of educational opportunities to choose from. We may wish to take advantage of technologies to improve engagement and motivation, such as through gamification, the application of game design to different contexts.
Modeling and Forecasting the Infrastructure Investments Needs in Lebanon

The aim of this paper is to model and forecast the infrastructure investment needs in Lebanon. It divides the Lebanese infrastructure into ten main sectors, and it represents an analysis of each sector. The article collects data of the government expenditures on projects for each sector from the Lebanese council of development and reconstruction. The expenditure on each sector represents a different impact on the Lebanese growth and the Gross Domestic Product, the article analyses the impact in an assessment approach. Findings emphasis the importance of social infrastructure in promoting growth, especially Education sector. Furthermore, findings stress the high volume of expenditure on waste water and solid waste management sector. The article proceeds by modeling the infrastructure investment need using Dynamic OLS equilibrium. The study encloses ten Dynamic OLS models, one for each sector, having the investment needs as the dependent variable, and as independent variables the Lebanese Gross Domestic Product and the Lebanese population. Eventually, the paper forecasts the Lebanese infrastructure investment needs for each sector until the year 2025. Results emphasis the unfair low expenditure on Education and other social infrastructures, and the high expenditures on inefficient sectors such as the waste water and solid waste sector, and the electricity sector. The study eventually recommends a better distribution of infrastructure investments within the Lebanese expenditures.
Eight crime factor model for youths in Mexican violence contexts

The impact of youth violent interactions promoted by: collective fear, and individual coping strategies: youth coping mechanisms, posttraumatic stress traits, measured individually but considered at a collective level and identified as PTSD traits in a community, and social vulnerability as predictors of multifactorial high violence social contexts, are the main psychological mechanisms that Diagnostic on Vulnerability Effectiveness model (DOVE-8) consider as multifactorial factors underpinning cycles of violence, the priority for this diagnostic model examine abovementioned constructs and their association in youth’s efficacy, as a collective outcome, DOVE-8 analysis model states that teenagers dealing with specific or widespread fear in violent scenarios, develop certain coping strategies, that furthermore strengthen collective fear. Gradually, an impact on life quality is established, promoting unhealthier mental states and poor social relationships, that consolidate a collective inefficacy, in addition, particular associations were found between high impact crimes and above cited main constructs, that provide a research tool to provide insight into: main fears, coping mechanisms and youth outcomes translated into social ineffectiveness such as: low cohesion, tolerance, participation and respect to law and social norms. Youth cope can be explained by cognitive and physical strategies. Several traits of posttraumatic stress can promote physiological arousal, followed by emotional or affective coping at violent stimuli or criminal events and, all abovementioned become predictors of crime fear, also known as social anxiety, especially in youth communities with high socio economic and cultural vulnerability. Confronting cognitively a context of violence triggers a rise on risk perception, basically, in privileged- low vulnerability groups with crucial health implications, unnecessary high expenses to cope with crime expectancies in private security at a household level, and stressful fearful coping strategies such as: information distortion, discriminatory processes, low social cohesion, fear based reactions to confront crime, a factor that seen at a glance, might diminish criminal widespread or emotional fear, undoubtedly, this fear type was not found in high vulnerability groups under study. Risk perception was a specific fear commonly found in youth groups with low vulnerability. Both diverse type of fears: specific (risk perception) and widespread fear (social anxiety /crime fear), gather together in a heterogeneous youth community, diminishing collective effectiveness: promoting poor cohesion, low tolerance, discrimination processes, lower levels of social agency, recurrent violent cycles arousals in the community, among other consequences.
Respiratory Control Disorders in Infants and Children

Neural control of breathing chemical synaptic transmission respiratory muscles and chest wall upper airway neuromuscular control maturation of breathing control thermometabolism and cardiorespiratory control maturation of sleep ventilatory and arousal responses polysomnography periodic breathing apnea of prematurity sudden infant death syndrome alveolar hypoventilation syndromes neuromuscular disorders arnold-chiari malformation inborn errors in metabolism craniofacial syndrome respiratory control in chronic lung disease gastroesophageal reflux obesity drug therapy ventilatory treatment at home diaphragm pacing home monitoring.
Improving Speech Synthesis with Discourse Relations

This paper explores whether adding Discourse Relation (DR) features improves the naturalness of neural statistical parametric speech synthesis (SPSS) in English. We hypothesize ﬁrst - in the light of several previous studies - that DRs have a dedicated prosodic encoding. Secondly, we hypothesize that encoding DRs in a speech synthesizer’s input will improve the naturalness of its output. In order to test our hypotheses, we prepare a dataset of DR-annotated transcriptions of audiobooks in English. We then perform an acoustic analysis of the corpus which supports our ﬁrst hypothesis that DRs are acoustically encoded in speech prosody. The analysis reveals signiﬁcant correlation between speciﬁc DR categories and acoustic features, such as F0 and intensity. Then, we use the corpus to train a neural SPSS system in two conﬁgurations: a baseline conﬁguration making use only of conventional linguistic features, and an experimental one where these are supplemented with DRs. Augmenting the inputs with DR features improves objective acoustic scores on a test set and leads to signiﬁcant preference by listeners in a forced choice AB test for naturalness.
Understanding the Non-medical Costs of Healthcare: Evidence from Inpatient Care for Older People in China

Abstract Non-medical costs, including costs associated with carers, travel, food and accommodation for family members who care for older people during their medical visits, can constitute a substantial part of total healthcare costs, especially for older people. Using data from the 2015 China Health and Retirement Longitudinal Survey, this study examines the effects of such non-medical costs on catastrophic health payments and health payment-induced poverty among older people in China. Results indicate that non-medical costs account for approximately 18 per cent of total inpatient costs. The percentage is highest for those in the lowest economic brackets. Rural populations are more likely than urban populations to incur catastrophic health payments and suffer from health payment-induced poverty. Non-medical costs increase the chances of older people incurring catastrophic health payments and suffering from health payment-induced poverty. These findings suggest that policymakers should look to develop new policies that facilitate reimbursement of non-medical costs, particularly for the rural population.
Extraction of Fetal ECG Signal with Ectopic Beats using Blind Source Separation Based Null Space Approach

The aim of this paper is to apply blind source separation (BSS) to extract fetal electrocardiogram (FECG) signal with ectopic beat. We use a novel deterministic BSS algorithm type null space transformation matrix (NSITM). The ECG signals are used to compute the ITM. Then, the FECG signal and maternal ECG (MECG) signals are extracted from the null space of the ITM. Results from Physionet synthesized ECG data show considerable improvement in extraction performance (quality signal-to-noise ratio qSNR and correlation $r$) over other algorithms used in this work, when the fetal-to-maternal signal-to-noise ratio (fmSNR) increases from −30 dB to 0 dB. Using the NSITM algorithm, the maximum values of qSNR and $r$ are 5.95 dB and 0.871, respectively, when fmSNR is equal to 0 dB. The minimum values of qSNR and $r$ are 2.27 dB and 0.726, respectively, when fmSNR is equal to −30 dB. The study demonstrates that the BSS type NSITM is a feasible algorithm for extracting FECG signals for subjects with ectopic beats.
Towards methodological principles for user studies in Human-Robot Interaction

Robots are becoming more and more present in our everyday life at work or for entertainment. This raised the need for a principled and well-understood interaction between humans and robots which led to the creation of the Human-Robot Interaction (HRI) research field. In order to assess the models, designs and algorithms elaborated in these researches, evaluation techniques have to be employed in the context of user studies. In this work, we aim to adapt from other research fields some principles that might be used in HRI user studies. More precisely we discuss around some frequent issues concerning recruited users, evaluation methods and replication of the studies, and how some methodological practices could circumvent them. We will finally discuss more generally on HRI studies, claiming that they need methods and assessment techniques specific to their particularities.