Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain-Specific Semantics Guided Approach to Video Captioning

In video captioning, the description of a video usually relies on the domain to which the video belongs. Typically, the videos belong to wide range domains such as sports, music, news, cooking, etc. In many cases, a video can be associated with more than one domain. In this paper, we propose an approach to video captioning that uses domain-specific decoders. We build a domain classifier to obtain the estimates of probabilities of a video belonging to different domains. For each video, we identify the top − k domains based on the estimated probabilities. Each video in the training data set is shared in training the domain-specific decoders of top−k labels obtained from the domain classifier. The domain-specific decoders use the domain-specific semantic tags for generating captions. The proposed approach uses the Temporal VLAD for preprocessing the features extracted from 2D-CNN and 3D-CNN features. The preprocessed features provide better feature representation of the videos. The effectiveness of the proposed approach is demonstrated through the results of experimental studies on Microsoft Video Description (MSVD) corpus and MSR-VTT dataset.
Stable Intrinsic Auto-Calibration from Fundamental Matrices of Devices with Uncorrelated Camera Parameters

Auto-Calibration is an important task in computer vision and is necessary for many visual applications. Methods like photogrammetry, depth map estimation, metrology, augmented/mixed reality or odometry are strongly dependent on well calibrated devices. While classical calibration relies on tools like checkerboards or additional scene information, auto-calibration only takes epipolar relations into account. Classical calibration is often impractical, tends to de-adjust over time and distributes the error over the entire, limited working volume. Auto-calibration, on the other hand, does not require any information other than the image content itself, has a virtually unlimited working range and usually achieves highest accuracy at the objects’ surfaces. Unfortunately, auto-calibration methods are sensitive to errors in the fundamental matrix and need good initialization to converge to the global solution. In practice this leads to difficulties if optical parameters like principal point or focal length are unconstrained. In such situations, even state-of- the-art auto-calibration methods tend to diverge and do not yield a valid calibration.This work assesses reasons for this behavior, in particular for the initialization method of Bougnoux [3] and Lourakis’ state-of-the-art auto-calibration method [21]. Based on the analysis, a more stable method is proposed. A continuous and smooth energy functional is introduced, providing superior convergence properties. I.e. it can not diverge, converges faster, and has a significantly enlarged convergence region with respect to the global minimum.Finally, a thorough evaluation has been conducted and a detailed comparison with the state of the art is presented.
LOW COMPLEXITY VIDEO CODING FOR SENSOR NETWORS

In the last few years, many research teams have been developing Distributed Video Coding solutions based on a new video coding paradigm founded on the SlepianWolf and Wyner-Ziv theorems. This alternative coding architecture opens the possibility of generating video coding solutions to emerging applications that were not appropriately solved by the available coding solutions, notably wireless cameras, low-consumption video surveillance and mobile surveillance. The objective of this paper is to develop a design, implement and evaluate a specific type of DVC solution , initially proposed by a research team at University of California at Berkeley, well known as PRISM. This solution has the following main characteristics: 1) power-efficiency which means restricted complexity, notably targeting situations of limited battery or memory, 2) inbuilt robustness to channel errors and packet losses; and 3) high-compression efficiency important to make this codec suitable to scenarios with limited bandwidth.
Open-Set Deep Learning for Text Classiﬁcation

—Most research in text classiﬁcation has been done under a closed world assumption. That is, the classiﬁer is tested with unseen examples of the same classes that it was trained with. However, in most real world scenarios, we come across novel data that do not belong to any of the known classes, and hence should not ideally be categorized correctly by the classiﬁer. The goal of open world classiﬁers is to anticipate and be ready to handle test examples of classes unseen during training. The classiﬁer can simply declare that a test example belongs to an unknown class, or alternatively, incorporate it into its knowledge as an example of a new class it has learned. Although substantial research has been done in open world image classiﬁers, its applications in text classiﬁcation is yet to be explored thoroughly.
Sampo: Unsupervised Knowledge Base Construction for Opinions and Implications

Knowledge bases (KBs) have long been the backbone of many real-world applications and services. Most KB construction (KBC) methods today capture only factual information explicitly stated in the text and cannot model opinions and the implications between opinions, which are abundant in user-generated text such as reviews. Our goal is to develop techniques to build KBs that can capture both the opinions and their implications. A major challenge in developing such techniques is that most KBC methods are based on weak or distant supervision. These require human-annotated examples for training, which can be prohibitively expensive to obtain. We propose an unsupervised KBC system, S AMPO , which learns to identify implications between opinions from reviews based on matrix factorization techniques. Speciﬁcally, S AMPO is tailored to build KBs for domains where many reviews on the same domain are available. We generate KBs for 20 different domains using S AMPO and manually evaluate KBs for 6 domains. Our experiments show that KBs generated using S AMPO capture information otherwise missed by other KBC meth-ods. We show that our KBs can complement language models and beneﬁt downstream tasks such as review comprehension.
Extrapolation and learning equations – Abstract

In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, ﬁnding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a new type of function learning network, called equation learner (EQL), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efﬁcient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. We also devise a model selection strategy tailored to the function learning setting, such that often the true underlying source expression can be identiﬁed. In this way the behavior of physical systems can be
E-Government Implementation and Practice in Developing Countries

The recent revolution in technology has encouraged governments throughout the world to change their approaches to providing services to their citizens. Accordingly, some countries have set up e-government portals as one such approach to deliver services efficiently, economically, and promptly. The synergy of information with communication technology has the potential to make e-government a major advance; some citizens, however, are not satisfied with e-services introduced in several countries due to factors including lack of computer skills, limited availability of the Internet, and less than total coverage of necessary infrastructure. Effective E-government has several immediate benefits: 1) to lessen the need of travelling to and queuing in the various government departments; and 2) to combine access to different departments together in one government portal. A case study might be the e-government developments in Saudi Arabia, where, in the last few years, huge amounts of money have been expended to establish e-government service. In this context, the question arises whether the expenditure has been worthwhile. In the current research, the satisfaction levels with these services were surveyed among a set of users in order to determine drawback to using the system or any obstacles to access. Objectives were established in order to achieve the aims, and research questions devised to examine the usage of e-government and the service’s usability, as well as to identify the barriers and factors that can affect the use of e-government. The relationship between these barriers and overall satisfaction levels with respect to e-government projects is also analyzed.
Designing Intelligent Cognitive Assistants With Teachers to Support Classroom Orchestration of Collaborative Inquiry

: Because orchestration in collaborative inquiry learning requires teachers to coordinate multiple activities and access diverse resources at the same time, it is an exceptionally complex task, not only for teachers to engage in but for designers to develop orchestration supporting tools. The present study explored what classroom teachers perceived to be critical areas in classroom orchestration that require support and describes our initial design of an intelligent cognitive assistant through a participatory design approach. Thirteen classroom teachers were interviewed to address what teachers identify as integral to support orchestration in collaborative inquiry learning. We present the design tensions between teacher and designer goals in working toward a collective vision of what it means to design intelligent cognitive assistants for classroom orchestration and discuss the implications for our design
EFFECTS OF THE BIOMASS BURNING IN THE THUNDERSTORM DEVELOPMENT: AN ANALYSIS IN THE AMAZON BASIN

Cloud-to-ground lightning activity in thunderstorms are related to several environmental features that accounts to their development: microphysics (changes in droplet size distributions due to pollution, which consequently changes the life cycle and ice particles formation), thermodynamics (local convection) and large-scale forcings (seasonal humidity conditions and motions configurations, such as the South Atlantic Convergence Zone, squall lines that propagate through the Amazon, Bolivian high), as studied by Rosenfeld (1999), Petersen et al. (2001, 2002), Cifelli et al. (2002), Williams et al. (2002), among others. These features modify the development of thunderstorms that is observed in its life cycle and lightning flash density. This study investigates the impact of such dependencies on the development of the thunderstorms in the Amazon region based on four 4 years of cloud-to-ground lightning measurements over the state of Rondonia, Brazil. A special attention will be given between the dry and wet season (September to November), where biomass burning take place, and can contribute or not to the development of thunderstorms or even modify the electrical charge center.
Investigating Company Logo Memorability with Convolutional Neural Embedding Models

The present study compared several state of the art neural embedding models for the correlation of their embeddings with human judgements, relating to both human memory and relevance ranking. These models included two embedding models, DeepRank and Ranknet; two classiﬁcation models, ConvNet and VisNet; and a Variational Au-toencoder. To assess each model’s performance, two custom evaluation metrics were developed: a ﬁne detail coeﬃcient and a coarse detail co-eﬃcient. These measures revealed that the embeddings produced by the DeepRank model had the highest correlation with human judgement. This design combination of a tri-linear architecture, triplet loss function and semi-hard negative sampling did best at capturing the similarities between the images, achieving the highest overall result for both the ﬁne detail and coarse detail coeﬃcients. The embeddings produced by the DeepRank model were then used to investigate the memorability of each company logo. However, as image memorability cannot be characterised by low-level features alone our results suﬀered. In addition, the results show that deep features extracted from the embedding models show markedly better results on ﬁne classiﬁcation and retrieval tasks than their classiﬁcation counterparts.
Rethinking Causality in Neural Mechanisms: Constraints and Control Jason Winning and William Bechtel

The nervous system in animals contains a multitude of control mechanisms that use information from various sources to regulate muscles and other tissues. The most basic level of control is exhibited in reflex arcs, which in the simplest case involves a synapse between a sensory and motor neuron. In organisms with brains one finds local hierarchies of control, with higher-level controllers exercising their effects on more specific, lower-level ganglia and nuclei. Lower levels of control are often revealed when researchers remove higher-level control mechanisms (e.g., the cortex) and observe what control the organism still has over muscle movements. Comparable reliance on lower-level control systems is familiar to all of us who have, for example, navigated a highway while attending to something else and have no awareness of taking various actions.
2Gather4Health: Web Crawling and Indexing System Implementation

In healthcare, patients tend to be more aware of their needs than market producers. So, it is only normal to start seeing innovative behavior emerging from them, or from their caregivers, to help them cope with their health conditions, before producers do. Today, it is believed that these users share their innovative health-related solutions, also called patient-driven solutions, on the Internet. However, the size of the Internet makes it hard to efficiently manually browse for these solutions. A focused crawler is a system that automatically browses the Web, focusing its search on a topic of interest. This Master thesis proposes a focused crawler that searches the Web for patientdriven solutions, storing and indexing them to ease a further medical screening. To perform the focusing task, it was developed a distiller and a classifier. The distiller ranks the URLs to visit, sorting them by a given score. This is a linear combination of three components, that depend on the content, URL context, or scores of the pages where the URL was found. The classifier automatically classifies visited webpages, verifying if they concern patient-driven solutions. In this thesis, it is shown that the developed system outperforms a breadth-first crawling and common focused approaches on measures of harvest rate and target recall, while searching for patient-driven solutions. The proposed classifier’s results on crawled data deviate from its validation results. However, it is proposed an approach to re-train the classifier with crawled data that improves its performance.
Engagement Based Mood Prediction

To interpret the engagement of an individual using various features such as facial affects or mood has been an important research problem in many ﬁelds such as academia and human-robot interactions. While much research has been focused on predicting engagement from a wide variety of features, the use of engagement in predicting a feature such as mood is a relatively unexplored area. In this work, we attempt to verify whether any correlation may exist between mood sub-scale scores and self-reported engagement. We also attempt to verify correlations for engagement with the total mood disturbance scores of the mood-states from our dataset and the change in mood in our dataset from before and after a web session. We use Support Vector Machine (SVM) regression in order to analyze to what degree, if any, these mood scores are related to engagement. Furthermore, we model mood as a sequence learning problem using facial Action Units (AUs) and engagement as input into a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) in order to analyze to what degree engagement may aid in predicting mood. We evaluate our work on a rich dataset with many features comprising of 110 subjects who participated in a trauma recovery web-intervention. Our experiments using SVM regression indicate the correlation between mood and engagement is not signiﬁcant. Furthermore, our results from testing with AUs and engagement as input to our LSTM were inconclusive due to not attaining results as good as or better than the work this builds on: Dhamija, S. , and Boult, T. E. 2017a. Automated mood aware engagement prediction. In 2017 Seventh International Conference on, Affective Computing and Intelligent Interaction (ACII), 18. IEEE.
Soteria: A Provably Compliant User Right Manager Using a Novel Two-Layer Blockchain Technology

Soteria is a user right management system designed to safeguard user-data privacy in a transparent and provable manner in compliance to regulations such as GDPR and CCPA. Soteria represents user data rights as formal executable sharing agreements, which can automatically be translated into a human readable form and enforced as data are queried. To support revocation and to prove compliance, an indelible, audited trail of the hash of data access and sharing agreements are stored on a two-layer distributed ledger. The main chain ensures partition tolerance and availability (PA) properties while side chains ensure consistency and availability (CA), thus providing the three properties of the CAP (consistency, availability, and partition tolerance) theorem. Besides depicting the two-layer architecture of Soteria, this paper evaluates representative consensus protocols and recommends side-chain and inter-chain management strategies for improving latency and throughput.
Learning Physics with Neural Stethoscopes

Model interpretability and systematic, targeted model adaptation present central challenges in deep learning. In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and inﬂuencing the model’s reasoning. Our contributions are two-fold. Firstly, we introduce neural stethoscopes as a framework for quantifying the degree of importance of speciﬁc factors of inﬂuence in deep networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from training with auxiliary and adversarial losses. Secondly, we deploy the stethoscope framework to provide an in-depth analysis of a state-of-the-art neural network for stability prediction, speciﬁcally examining its physical reasoning. Previous work has shown that neural networks are highly capable of learning physical tasks such as stability prediction. However, unlike approaches using physics simulators
How Designers Design and Prototype Interactive Behaviors

Designers are skilled at sketching and prototyping the look of interfaces, but to explore various behaviors (what the interface does) typically requires writing scripting code using Javascript, Flash or other programming tools. In our survey of 231 designers, 86% said that the behavior is more difficult to prototype than the appearance. Often (79% of the time), designing the behavior requires collaborating with developers, but 40% of designers reported that communicating the design to developers was difficult. Other results include that annotations such as arrows and para-graphs of text are used on top of sketches and storyboards to explain behaviors, and designers want to explore multiple versions of behaviors, but today’s tools make this difficult. The results provide new ideas for future tools.
Implementation and Development of a GPS Monitoring Station

- The global positioning system is now inserted in various areas and applications, enabling the generation of various results. The aim of this work is the implementation and development of a GPS monitoring station, using for it two antennas and two GPS receivers, and a computer. The communication between the computer and the two GPS receivers is achieved using software development, and is also performed the processing of acquired data. With the aim of the work be used in future projects, it was developed an objective and simple interface that allows future users of the station to acquire the data already processed. The final step in the implementation of the station, is develop a web interface. Access through the Internet is universal and intuitive, which is restricted to authorized users. The configuration of the receivers, the viewing of certain data and the possibility to download files with some information is available anywhere in the world. The resulting work serves as a starting point in developing a GPS monitoring station, with some features developed.
FastGSK: Fast and Efﬁcient Sequence Analysis using Gapped String Kernels

String kernel methods achieve strong classiﬁcation performance on DNA, protein, and natural language data using modestly-sized training sets. However, existing kernel function algorithms suffer from slow kernel computation times, as they depend exponentially on the sub-sequence feature length and the task’s alphabet size. In this work, we introduce a new string kernel algorithm using gapped k -mers called FastGSK. Compared to previous string kernels, it uses a simpliﬁed kernel algorithm that decomposes into a set of independent counting operations over the possible mismatch positions. This algorithm is naturally parallelizable, is performant on any sequence analysis task, and allows us to devise a fast Monte Carlo approximation method to scale to much greater feature lengths. We evaluate FastGSK on 10 DNA transcription factor binding site (TFBS) prediction tasks, 10 protein remote homology detection tasks, and 7 English-language medical named entity recognition tasks. FastGSK consistently matches or outperforms the state-of-the-art string kernel algorithms in AUC, while achieving average speedups in kernel computation of ∼ 100 × and speedups of ∼ 800 × for large feature lengths. We further show that FastGSK consistently outperforms character-level recurrent neural networks across these sequence analysis tasks. Our algorithm is available as a Python package and as C++ source code 1 .
Application advantages of high voltage high current IGBTs with punch through technology

New process technologies are extending the application range of IGBT modules. 2500 V and 3300 V/1200 A IGBTs have been developed using an optimized punch-through design that achieves a positive temperature coefficient of saturation voltage and rugged safe operating area of the nonpunch-through approach while retaining the low loss characteristics of punch-through technology. Special wafer processing, optimizing the collector p+ concentration and local lifetime control, is employed to achieve these characteristics. This paper discusses the adaptation of the punch-through design to achieve the desired device characteristics.
Estimating Improvement Curves for Young Skiers

This paper aims to estimate the improvement curves for young skiers. We selected 203 individual skiing participants from two races and obtained 967 repeated measures for these skiers in the recent 6 years (2003-2008). To reduce the confounding factors and make sure skiers’ performance can be compared at the same level, reference race was used to standardize the velocities. After the standardization, non-linear mixed model was employed to fit the data. We find that the girl’s improvement curve is more extended and the boy’s curve is steep, which show the different effects of puberty. But the variations among different skiers for both boys and girls are similar.