Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic Compensation Method for Accepting Different Recording Devices in Body-Conducted Voice Conversion

This paper presents an acoustic compensation method in body-conducted speech conversion that automati- cally compensates for acoustic differences caused by changes in recording conditions. An enhancement process for body- conducted speech recorded with a Non-Audible Murmur (NAM) microphone has successfully applied a statistical voice conver- sion technique. Speech waveforms are generated from acoustic parameters of normal speech estimated from those of body- conducted speech with a conversion model previously trained using stereo data of those two types of speech. This framework suffers from mismatched conditions between training and con- version processes. To alleviate this issue, an unsupervised acoustic compensation method based on constrained maximum likelihood linear regression (CMLLR) has been proposed and its effective- ness has been reported in the compensation of acoustic differences caused by attachment location of the NAM microphone. This paper further applies the CMLLR-based acoustic compensation method to the compensation of acoustic differences caused by different recording devices and evaluates its effectiveness. The experimental results demonstrate that the proposed method effectively reduces quality degradation and converted speech caused by differences in recording devices as well as attachment location of the NAM microphone.
Registration of Laser Scanning Point Clouds: A Review

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.
Programmer Experience Design and the Spreadsheet Paradigm

This doctoral consortium report proposes using the continuing evolution of the spreadsheet paradigm to frame a discussion about research into the usability of programming languages. In particular it argues that research into programming language design needs to be hardened, and should adopt a perspective similar to that described in The Prospects for Psychological Science in Human-Computer Interaction (Newell & Card, 1985). In summary there are three components, the need to work on good applied problems, using empirical studies to provide the basis for theories to aid programming language designers.
Shock Hipovolemico en Pediatria

Choque es un sindrome clinico, consistente en la subita disruption de la funcion circulatoria e insuficiencia general de la perfusion tisular la cual ultimamente conduce a alteration de los mecanismos homeostaticos. En general, todos los estados de choque eventualmente cnvuelven entrega disminuida o falla en la utilization de substratos celulares esenciales, los cuales causan disfunciony eventual perdida del metabolismo y funcion celular. La funcion circulatoria depende del volumen sanguineo, tono vascular y funcion cardiaca, y todos los estados de choque resultan de anormalidades en uno o mas de estos factores. (16)
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

Using pre-trained word embeddings as input layer is a common practice in many natural language processing (NLP) tasks, but it is largely neglected for neural machine translation (NMT). In this paper, we conducted a systematic analysis on the effect of using pre-trained source-side monolingual word embedding in NMT. We compared several strategies, such as fixing or updating the embeddings during NMT training on varying amounts of data, and we also proposed a novel strategy called dual-embedding that blends the fixing and updating strategies. Our results suggest that pre-trained embeddings can be helpful if properly incorporated into NMT, especially when parallel data is limited or additional in-domain monolingual data is readily available.
Stimuli-Responsive Microgels and Microgel-Based Systems: Advances in the Exploitation of Microgel Colloidal Properties and Their Interfacial Activity

In this paper, recent developments in the chemical design of functional microgels are summarized. A wide range of available synthetic methods allows the incorporation of various reactive groups, charges, or biological markers inside the microgel network, thus controlling the deformation and swelling degree of the resulting smart microgels. These microgels can respond to various stimuli, such as temperature, pH, light, electric field, etc. and can show unique deformation behavior at the interface. Due to their switchability and interfacial properties, these smart microgels are being extensively explored for various applications, such as antifouling coatings, cell encapsulation, catalysis, controlled drug delivery, and tissue engineering.
Does Informal Caregiving Lead to Parental Burnout? Comparing Parents Having (or Not) Children With Mental and Physical Issues

Introduction: Parenting a child with special needs (CSN) may be an important challenge. Previous research has highlighted an increased risk of parental burnout among parents caring for their CSN. Yet, these studies only focused on children with specific issues and did not consider the wide variety of CSN. There is thus a need to take a more global approach to assessing the impact of caring for a CSN on parental burnout. In addition, the impact on parental burnout of personality and parenting (dis)agreement needs to be measured to have a better understanding of parent-caregivers’ (PCgs) burnout. Method: An online survey was completed by a large sample of parents from which a subsample of PCgs was identified. Results: T-tests highlighted significantly more parental burnout among parents of CSN. However, further analyses showed that parents with only one child with one special need did not experience significantly more burnout than parents with typical children. The significant difference lay in the presence of comorbidity or the presence of multiple CSN in the family. Hierarchical regressions showed an important impact of Neuroticism for every burnout facet, along with co-parenting (dis)agreement. Subjective consequences of having to care for a CSN were also related to the burnout facets of both emotional exhaustion and emotional distancing. Discussion: The presence of comorbidity and of multiple CSN in the family were related to more PCg burnout, emphasizing the need to consider these situations in further research. The role of neuroticism in PCg burnout confirms previous research both in parental and professional contexts. Parenting (dis)agreement also highlights the importance of dyadic support among parents. Finally, the importance of subjective aspects suggests that parental perception of their situation remains a central element in understanding the consequences of caregiving.
Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction

In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incremental-prediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.
Enhancements to Google Course Builder: Assessments Visualisation, YouTube Events Collector and Dummy Data Generator

This paper presents some extensions to the functionality of Google Course Builder (GCB) for improving its learning analytics support. The contributions of our work are the following: we add the functionality of detecting and storing YouTube events, visualise complex data related to the performance of the students, and provide automatic suggestions to the teachers regarding which specific parts of material need revision. Finally, we test our approach by implementing a dummy data generating program, which creates a big number of fictitious students who can interact with the platform in a fashion identical to real students.
Revealing Common Statistical Behaviors in Heterogeneous Populations

In many areas of neuroscience and biological data analysis, it is desired to reveal common patterns among a group of subjects. Such analyses play important roles e.g., in detecting functional brain networks from fMRI scans and in identifying brain regions which show increased activity in response to certain stimuli. Group level techniques usually assume that all subjects in the group behave according to a single statistical model, or that deviations from the common model have simple parametric forms. Therefore, complex subject-specific deviations from the common model severely impair the performance of such methods. In this paper, we propose nonparametric algorithms for estimating the common covariance matrix and the common density function of several variables in a heterogeneous group of subjects. Our estimates converge to the true model as the number of subjects tends to infinity, under very mild conditions. We illustrate the effectiveness of our methods through extensive simulations as well as on real-data from fMRI scans and from arterial blood pressure and photoplethysmogram measurements.
Learning Reliable and Scalable Representations Using Multimodal Multitask Deep Learning

Those were the words from one of the pioneers of AI when asked to comment on the progress of robotics in the twentieth century. This shows the high expectations and unforeseen challenges that we are faced with for deploying robots in complex real-world environments. One of the primary impediments has been the robustness of scene understanding models as it is a prerequisite for any action execution or planning. The tremendous progress made in machine learning in the last decade has enabled us to learn representations from raw sensor data, rather than relying on hand-engineered features. However, these models still perform inconsistently, especially in challenging weather conditions. The current dominant paradigms rely on camera images or depth data. However, alternate modalities such as infrared [1] and sound [2] need to be exploited for learning the most comprehensive information about the scene that will enable us to reduce perceptual ambiguity in challenging conditions. State-of-art deep learning models rely on thousands to millions of annotated training data and acquiring this data is an arduous task, if not impossible for every foreseeable scenario and for every task. Moreover, learning task-specific models on task-specific datasets, limits the overall learning ability of the robot as most models are trained in a supervised fashion and independently, therefore they have no ability to share cross-domain information and exploit training signals from complementary tasks. In order to address this limitation, our models should be able to learn representations across different modalities as well as reuse and share the learned representations across different tasks. My work enables models to effectively learn fused representations from multiple modalities and across tasks, exploiting complementary features and cross-modal interdependencies. Despite that fact that we are still far away from creating robots with human-level intelligence, equipping them with these basic capabilities will enable robots to learn new tasks from limited amount of data by leveraging transfer learning which facilitates self-supervised model adaptation. Advancing self-supervised learning techniques to autonomous learning is a strong starting point that will enable robots to continuously learn from what it experiences and perceives in the real-world. Modality 1 Modality 2 Unimodal Seg. Multimodal Seg.
Capacity and Entropy of a Retro-Causal Channel Observed in a Twin Mach-Zehnder Interferometer During Measurements of Pre- and Post-Selected Quantum Systems

Simple intuitive models are presented for the capacity and entropy of retro-causal channels in measured ensembles of quantum systems which can be represented as statistical mixtures of pre-selected only and pre- and post-selected systems. Measurement data from a twin Mach-Zehnder interferometer experiment are used in these models to discuss the capacity and entropy of an apparent retro-causal channel observed in the experimental data. It is noted that low capacity/low entropy retro-causal channels can exist in strong measurement systems.
Transporting objects by multiagent cooperation in crowd simulation

This paper focuses on simulating agents transporting passive objects in a virtual environment with pedestrians. The agents adopt the pushing manipulation patterns to transport the passive objects. We propose techniques to achieve two kinds of cooperation. First, the agents transport the passive objects to the goal positions. During the transportation process, the agents may adjust their formation to transport the passive objects effectively. Second, the agents avoid collision with each other and passive objects in an unfreezing manner. Experiment results show that our technique avoids the freezing problem while the agents are transporting multiple objects.
User experiences in a visual analytics business

We report on an ongoing ethnographic study conducted at a data analytics company and discuss the multiple facets of user experience observed in this context. We describe in detail two interaction episodes of analysts working with visual analytics software and characterize them through the Patterns of User Experience framework. We discuss the implications of our observations and make some recommendations for future tool design.
Experimental performance evaluation of WebRTC video services over mobile networks

With the rapid growing market for smartphones, and user's confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. WebRTC is a new and evolving standard that has been developed specifically to meet this demand and enable a high-quality experience for mobile users of real time communication services. However, little systematic experimental studies have been carried out so far to assess the service experienced by users in a realistic mobile setting. In this work, we describe measurements collected from a WebRTC implementation operated from real mobile nodes within the pan-European MONROE platform. Using data from application and network, in different wireless environments, we experimentally investigate and compare the performance of WebRTC for static and mobile cellular users of several networks across Europe.
Development and Evaluation of a Social Media Health Intervention to Improve Adolescents’ Knowledge About and Vaccination Against the Human Papillomavirus

This study describes the formative research, execution, and evaluation of a social media health intervention to improve adolescents’ knowledge about and vaccination against human papillomavirus (HPV). Based on the results from formative focus groups with adolescents (N = 38) to determine intervention feasibility, parameters, and message preferences, we developed and conducted a pretest/posttest evaluation of a 3-month social media health intervention for adolescents who had not completed the HPV vaccine series (N = 108). Results revealed that adolescents who fully engaged with the intervention improved in their knowledge compared with a control group, and many were also likely to have interpersonal discussions with others about what they learned. Adolescents are generally interested in receiving information about HPV and the vaccine, along with other relevant health information, through social media channels if messages are considered interesting, their privacy is protected, and the source is credible.
Modeling Impact of Human Errors on the Data Unavailability and Data Loss of Storage Systems

Data storage systems (DSSs) and their availability play a crucial role in contemporary datacenters. Despite using mechanisms such as automatic failover in datacenters, the role of human agents and consequently their destructive errors is inevitable. Due to very large number of disk drives used in exascale datacenters and their high failure rates, the disk subsystem in storage systems has become a major source of <italic>data unavailability</italic> (DU) and <italic>data loss</italic> (DL) initiated by human errors. In this paper, we investigate the effect of <italic>incorrect disk replacement service</italic> (IDRS) on the availability and reliability of DSSs. To this end, we analyze the consequences of IDRS in a disk array, and conduct Monte Carlo simulations to evaluate DU and DL during mission time. The proposed modeling framework can cope with different storage array configurations and <italic> data object survivability</italic>, representing the effect of system-level redundancies such as remote backups and mirrors. In the proposed framework, the model parameters are obtained from industrial and scientific reports alongside field data, which have been extracted from a datacenter operating with 70 storage racks. The results show that ignoring the impact of IDRS leads to unavailability underestimation by up to three orders of magnitude. Moreover, our study suggests that by considering the effect of human errors, the conventional beliefs about the dependability of different <italic>redundant array of independent disks</italic> (RAID) mechanisms should be revised. The results show that <inline-formula><tex-math notation="LaTeX">$\text{RAID}1$</tex-math></inline-formula> can result in lower availability compared to <inline-formula><tex-math notation="LaTeX">$\text{RAID}5$</tex-math></inline-formula> in the presence of human errors. The results also show that employing automatic fail-over policy (using hot spare disks) can reduce the drastic impacts of human errors by two orders of magnitude.
Information Aggregation via Dynamic Routing for Sequence Encoding

While much progress has been made in how to encode a text sequence into a sequence of vectors, less attention has been paid to how to aggregate these preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector. Usually, a simple max or average pooling is used, which is a bottom-up and passive way of aggregation and lack of guidance by task information. In this paper, we propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence. Following the work of Capsule Network, we design two dynamic routing policies to aggregate the outputs of RNN/CNN encoding layer into a final encoding vector. Compared to the other aggregation methods, dynamic routing can refine the messages according to the state of final encoding vector. Experimental results on five text classification tasks show that our method outperforms other aggregating models by a significant margin. Related source code is released on our github page.Related source code is released on our github page.
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications

In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other region-based approaches use probabilistic analysis such as clustering and Markov random fields, but those methods cannot be used to analyze edges and their interaction. In fact, only image segmentation can produce regions based on edges, but it requires thresholding by simply separating the regions into binary in-out information. Hence, there is currently a gap between edge-based and region-based algorithms, since edges cannot be used to study the properties of a region and vice versa. The objective of this paper is to present a novel spatial probability analysis that allows determining the probability of inclusion inside a set of partial contours (strokes). To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods. The developed method is consistent with the fundamental properties of inclusion probabilities and its results are validated by comparing an image with the probability-based estimation given by our algorithm. The method can also be generalized to take into consideration the intensity of the edges or to be used for 3D shapes. This is the first documented method that allows computing a space of probability based on interacting edges, which opens the path to broader applications such as image segmentation and contour completion.
Equitable Coloring and Equitable Choosability of Graphs with Small Maximum Average Degree

Abstract A graph is said to be equitably k-colorable if the vertex set V (G) can be partitioned into k independent subsets V1, V2, . , Vk such that ||Vi|−|Vj || ≤ 1 (1 ≤ i, j ≤ k). A graph G is equitably k-choosable if, for any given k-uniform list assignment L, G is L-colorable and each color appears on at most ⌈|V(G)|k⌉ $\left\lceil {{{\left| {V(G)} \right|} \over k}} \right\rceil$ vertices. In this paper, we prove that if G is a graph such that mad(G) < 3, then G is equitably k-colorable and equitably k- choosable where k ≥ max{Δ(G), 4}. Moreover, if G is a graph such that 125 ${{12} \over 5}$ , then G is equitably k-colorable and equitably k-choosable where k ≥ max{Δ (G), 3}.