Miracle-dz/UniFAR-FTUs huggingface dataset:
The Meaning of “Most” for Visual Question Answering Models

The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms. For the example of “most”, we discuss two strategies which rely on fundamentally different cognitive concepts. Our aim is to identify what strategy deep learning models for visual question answering learn when trained on such questions. To this end, we carefully design data to replicate experiments from psycholinguistics where the same question was investigated for humans. Focusing on the FiLM visual question answering model, our experiments indicate that a form of approximate number system emerges whose performance declines with more difficult scenes as predicted by Weber’s law. Moreover, we identify confounding factors, like spatial arrangement of the scene, which impede the effectiveness of this system.
Resolving the Measurement Uncertainty Paradox in Ecological Management

Ecological management and decision-making typically focus on uncertainty about the future, but surprisingly little is known about how to account for uncertainty of the present: that is, the realities of having only partial or imperfect measurements. Our primary paradigms for handling decisions under uncertainty—the precautionary principle and optimal control—have so far given contradictory results. This paradox is best illustrated in the example of fisheries management, where many ideas that guide thinking about ecological decision-making were first developed. We find that simplistic optimal control approaches have repeatedly concluded that a manager should increase catch quotas when faced with greater uncertainty about the fish biomass. Current best practices take a more precautionary approach, decreasing catch quotas by a fixed amount to account for uncertainty. Using comparisons to both simulated and historical catch data, we find that neither approach is sufficient to avoid stock collapses under moderate observational uncertainty. Using partially observed Markov decision process (POMDP) methods, we demonstrate how this paradox arises from flaws in the standard theory, which contributes to overexploitation of fisheries and increased probability of economic and ecological collapse. In contrast, we find that POMDP-based management avoids such overexploitation while also generating higher economic value. These results have significant implications for how we handle uncertainty in both fisheries and ecological management more generally.
AIR5: Five Pillars of Artificial Intelligence Research

In this paper, we provide an overview of what we consider to be some of the most pressing research questions currently facing the fields of <italic>artificial and computational intelligence</italic> (AI and CI). While AI spans a range of methods that enable machines to learn from data and operate autonomously, CI serves as a means to this end by finding its niche in algorithms that are inspired by complex natural phenomena (including the working of the brain). In this paper, we demarcate the key issues surrounding these fields using five unique <italic>Rs</italic>, namely, <italic>rationalizability</italic>, <italic>resilience</italic>, <italic>reproducibility</italic>, <italic>realism</italic>, and <italic>responsibility</italic>. Notably, just as <italic>air</italic> serves as the basic element of biological life, the term AIR<sub>5</sub>—cumulatively referring to the five aforementioned <italic>Rs</italic>—is introduced herein to mark some of the basic elements of artificial life, <italic>for sustainable AI and CI</italic>. A brief summary of each of the <italic>Rs</italic> is presented, highlighting their relevance as pillars of future research in this arena.
Multilingual Constituency Parsing with Self-Attention and Pre-Training

We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).
K-Nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search is an important problem in data mining and knowledge discovery. Inspired by the huge success of tree-based methodology and ensemble methods over the last decades, we propose a new method for kNN search, random projection forests (rpForests). rpForests finds nearest neighbors by combining multiple kNN-sensitive trees with each constructed recursively through a series of random projections. As demonstrated by experiments on a wide collection of real datasets, our method achieves a remarkable accuracy in terms of fast decaying missing rate of kNNs and that of discrepancy in the k-th nearest neighbor distances. rpForests has a very low computational complexity as a tree-based methodology. The ensemble nature of rpForests makes it easily parallelized to run on clustered or multicore computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights on rpForests by showing the exponential decay of neighboring points being separated by ensemble random projection trees when the ensemble size increases. Our theory can also be used to refine the choice of random projections in the growth of rpForests; experiments show that the effect is remarkable.
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

Computer vision relies on labeled datasets for training and evaluation in detecting and recognizing objects. The popular computer vision program, YOLO ("You Only Look Once"), has been shown to accurately detect objects in many major image datasets. However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e.g. ambient lighting) changes. This paper describes a novel effort to evaluate YOLO's consistency for large-scale applications. It does so by working (a) at large scale and (b) by using consecutive images from a curated network of public video cameras deployed in a variety of real-world situations, including traf? c intersections, national parks, shopping malls, university campuses, etc. We speci? cally examine YOLO's ability to detect objects in different scenarios (e.g., daytime vs. night), leveraging the cameras' ability to rapidly retrieve many successive images for evaluating detection consistency. Using our camera network and advanced computing resources (supercomputers), we analyzedmorethan5millionimagescapturedby140network cameras in 24 hours. Compared with labels marked by humans (considered as "ground truth"), YOLO struggles to consistently detect the same humans and cars as their positions change from one frame to the next; it also struggles to detect objects at night time. Our ? ndings suggest that state-of-the art vision solutions should be trained by data from network camera with contextual information before they can be deployed in applications that demand high consistency on object detection.
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks

Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region. However, Siamese trackers still have accuracy gap compared with state-of-the-art algorithms and they cannot take advantage of feature from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on four large tracking benchmarks, including OTB2015, VOT2018, UAV123, and LaSOT. Our model will be released to facilitate further studies based on this problem.
Visibly Pushdown Languages over Sliding Windows

We investigate the class of visibly pushdown languages in the sliding window model. A sliding window algorithm for a language $L$ receives a stream of symbols and has to decide at each time step whether the suffix of length $n$ belongs to $L$ or not. The window size $n$ is either a fixed number (in the fixed-size model) or can be controlled by an adversary in a limited way (in the variable-size model). The main result of this paper states that for every visibly pushdown language the space complexity in the variable-size sliding window model is either constant, logarithmic or linear in the window size. This extends previous results for regular languages.
Recognizing Unconstrained Vietnamese Handwriting By Attention Based Encoder Decoder Model

Inspired by recent successes in neural machine translation and image caption generation, we present an attention based encoder decoder model (AED) to recognize Vietnamese Handwritten Text. The model composes of three parts: a convolution neural network (CNN) for extracting invariant features, a Bidirectional Long Short-Term Memory network (BLSTM) for encoding extracted features (BLSTM encoder), and a Long Short-Term Memory network (LSTM) with an attention model incorporated for generating output text (LSTM decoder), which are connected from the CNN part to the BLSTM encoder and finally the LSTM decoder. The input of the CNN part is a handwritten text image and the target of the LSTM decoder is the corresponding text of the input image. Our model is trained end-to-end to predict the text from a given input image since all the parts are differential components. In the experiment section, we evaluate our proposed AED model on the VNOnDB-Word database to verify its efficiency. The experiential results show that our model achieves 12.30% of word error rate without using language model. This result is competitive with the handwriting recognition system provided by Google in the Vietnamese Online Handwritten Text Recognition competition.
Review of Current Technologies and Proposed Intelligent Methodologies for Water Distributed Network Leakage Detection

Water is a precious resource that should be managed carefully. However, due to leakages in water distributed networks (WDNs), a large amount of water is lost each year that suggests the need for reliable and robust leak detection and localization system. This paper attempts to review the current technologies for leakage detection in WDN as well as several proposed intelligent methodologies (such as support vector machine, neural network, and convolution neural network) over the past few years. The current methodologies and their limitations are discussed. Uncertainties involved in the implementation of WDN leakage detection are also discussed, and several suggestions to overcome such uncertainties are provided for future implementations.
Single Image Super-Resolution Using Deep CNN with Dense Skip Connections and Inception-ResNet

Recent studies have shown that the use of deep convolutional neural networks (Deep CNNs) can significantly improve the performance of single-image super-resolution reconstruction. In this paper, we propose a highly accurate and fast single-image super-resolution reconstruction (SISR) method by introducing dense skip connections and Inception-ResNet in deep convolutional neural networks. In the proposed network, all previous layer feature maps are used as input for each subsequent layer to promote feature reuse and alleviate the vanishing-gradient problem. In addition, the parallelized CNNs structure used reduces the size of the output of the previous layer, thereby accelerating the calculation speed and reducing the feature loss. Moreover, we only learn the residuals between the high-resolution images and the low-resolution images, and use the adjustable gradient cropping to achieve a very high learning rate. Results on benchmark datasets demonstrate that the proposed model not only achieves higher accuracy but also enables faster and more efficient calculations than the state-of-the-art methods.
Distributed and On-demand Cache for CMS Experiment at LHC

In the CMS [1] computing model the experiment owns dedicated resources around the world that, for the most part, are located in computing centers with a well defined Tier hierarchy. The geo-distributed storage is then controlled centrally by the CMS Computing Operations. In this architecture data are distributed and replicated across the centers following a preplacement model, mostly human controlled. Analysis jobs are then mostly executed on computing resources close to the data location. This of course allow to avoid CPU wasting due to I/O latency, although it does not allow to optimize the available job slots.
Modeling of a New Sensorless Suction Detection System for the Rotary Left Ventricular Assist Device

The rotary left ventricular assist device (LVAD) has been successfully and clinically used for congestive heart failure patients. However, patients may be at significant risk for ventricular suction while using a LVAD. Suction may lead to ventricular collapse when the blood pump of the LVAD is working at higher rotational speeds to draw more blood than available. Some suction detection systems typically required the external implantation of sensors, which were not reliable for long-term use because of failure, baseline drift, and short lifespan of sensors. In this study, a new sensorless suction detection system only using the intrinsic pump parameter (pump speed) to eliminate all external sensors is proposed. Three features (suction indicators) are extracted from the pump speed signals without and with measurement noise and adopted as inputs to different classifiers. Two pumping states as no suction or suction are classified with each classifier. The in-silico results with a combined human circulatory system and LVAD model show that the proposed strategy can detect ventricular suction effectively with high accuracy and stability. It could provide theoretical basis and technology support for developing the LVAD controller with high reliability.
Assessing the Determinants of Adopting Component-Based Development in a Global Context: A Client-Vendor Analysis

Component-based software (CBS) development is an attractive proposition to globally distributed software development organizations because of its potential to integrate reusable components in new products. Several organizations have adopted CBS development practices to support their global development processes, a phenomenon referred to as globally distributed CBS development. Many factors influence an organization’s decision to adopt globally distributed CBS development practices. The objective of this paper is to systematically assess the determinants that influence the adoption of CBS development practices in global software development organizations. We develop a conceptual research model based on the diffusion of innovation theory and the technology-organization-environment framework. Data collected from 115 participants are used to test the conceptual model. The findings from our study indicate that relative advantage, complexity, technology competence, and top management support are the key determinants for organizations to adopt globally distributed CBS development practices. The assessment of both the direct and total effects of the determinants offers insight into the organization’s decision to adopt globally distributed CBS development practices.
A Benchmark for Distance Measurements

The need to analyze and visualize distances between objects arises in many use cases. Although the problem to calculate the distance between two polygonal objects may sound simple, real-world scenarios with large models will always be challenging, but optimization techniques - such as space partitioning - can reduce the complexity of the average case significantly. Our contribution to this problem is a publicly available benchmark to compare distance calculation algorithms. Furthermore, we evaluated the two most important techniques (hierarchical tree structures versus grid-based approaches).
A Multiple Quantum Well Structure Simulator

A simulator was developed for modeling and designing multiple quantum wells structures such as quantum well infrared photodetectors and quantum cascade laser, based on a single-electron effective mass Schrödinger equation. It employs box integration finite differences and transfer matrix approaches to find energies of bound and scattering states in the structures. The graphical user interface allows the user to vary easily design parameters including effective mass, layer thickness and number of layers as well as simulation parameters in order to optimize the structure for different device applications and functionality. Three simulation examples were performed on: three coupled quantum wells structure, two-color asymmetric quantum well infrared photodetector structure and on a quantum cascade laser design. The results show an accuracy which is comparable to more complicated simulations.
Single-stage Bridgeless Buck-boost PFC Converter with DC Split for Low Power LED applications

Single-stage power factor correction (PFC) solutions for low power LED applications are facing challenges of how to achieve lower cost, higher efficiency and longer lifetime. Mainly focusing on efficiency improvement, this paper proposes a single-stage bridgeless buck-boost converter with DC split configuration to minimize conduction losses. Secondly, two inductors in the proposed topology are alternately working in discontinuous conduction mode (DCM) when connecting to AC side, and working in continuous conduction mode (CCM) when connecting to DC side. The inductor in CCM helps filter switching frequency ripple, while the inductor in DCM allows the converter to utilize simple single loop control scheme in order to achieve output current regulation and near unity power factor (PF). Thirdly, harmonic injection is applied to further reduce output current ripple. A tradeoff between PF and lower output current ripple is given. Experimental validations are presented with 12.5 W prototypes to verify the theoretical analyses.
Short-Term Load Forecasting based on ResNet and LSTM

Recent development of artificial intelligence (AI) makes AI applicable to diverse fields, and the smart grid is not an exception. In particular, there have been extensive researches on load forecasting using deep learning. Most existing studies have been conducted on deep neural network (DNN) and recurrent neural network (RNN). Very recently, CNN with shallow network has been studied for short-term load forecasting (STLF). In this paper, we propose a novel framework based on ResNet/LSTM combined model. The proposed model has two steps. First, ResNet extracts latent features of daily and weekly load data. Then, LSTM is applied to train the encoded feature vector with dynamics, and make prediction suitable for volatile load data. By leveraging ResNet and LSTM, the proposed model has the advantage of forecasting load data that has both regularity and inconsistency. To demonstrate the performance, we compare the proposed model with other deep learning models: multi-layer perceptron (MLP), ResNet, LSTM and ResNet/MLP combined model. The results show that the proposed ResNet/LSTM combined model has 21.3% of MAPE improvement in overall, and 25.8% of MAPE improvement for the bottom 25% group in terms of MAPE compared to MLP.
Dynamically adaptive simulation based on expertise and cognitive load

Augmented and Virtual Reality (AR/VR) simulated environments offer an unprecedented opportunity to engage users in fully immersive, highly interactive environments that offer high fidelity, and authentic, virtual representations of the real world, supporting learner development without risk to the learner, or to the entities the learner is engaging with. Cognitive load theory (CLT) describes the degree to which learners’ available cognitive capacity, both in working memory and in information processing, is being utilized. Dynamically assessing and responding to cognitive load in simulated environments is, we suggest, a powerful new tool for supporting more effective learning. By combining dynamic cognitive load assessment with other performance measures, data analytics and artificial intelligence (AI), we create the potential opportunity to establish a new partnership between the learner and the learning environment. In addition to providing adaptive and dynamic responses to the learner, continuous and pervasive awareness of the learner’s cognitive load allows the environment to customize certain parameters to individual learners, optimizing learning outcomes. Finally, utilizing AI and machine learning algorithms, simulated environments may be able to predict and anticipate learner cognitive states, which in turn can help further support the learning process.
Deep Recurrent Q-Network with Truncated History

Reinforcement Learning is a kind of machine learning method which learns through agents' interaction with the environment. Deep Q-Network (DQN), which is a model of reinforcement learning based on deep neural networks, succeeded in learning human-level control policies on various kinds of Atari 2600 games with image pixel inputs. Because an input of DQN is the game frames of the last four steps, DQN had difficulty on mastering such games that need to remember events earlier than four steps in the past. To alleviate the problem, Deep Recurrent Q-Network (DRQN) and Deep Attention Recurrent Q-Network (DARQN) were proposed. In DRQN, the first fully-connected layer just after convolutional layers is replaced with an LSTM to incorporate past information. DARQN is a model with visual attention mechanisms on top of DRQN. We propose two new reinforcement learning models: Deep Recurrent Q-Network with Truncated History (T-DRQN) and Deep Attention Recurrent Q-Network with Truncated History (T-DARQN). T-DRQN uses a truncated history so that we can control the length of history to be considered. T-DARQN is a model with visual attention mechanism on top of T-DRQN. Experiments of our models on six games of Atari 2600 shows a level of performance between DQN and D(A) RQN. Furthermore, results show the necessity of using past information with a truncated length, rather than using only the current information or all of the past information.