OpenRaiser/Intern-Atlas huggingface dataset:
MUSE: Multi-Scale Temporal Features Evolution for Knowledge Tracing
Venue: arXiv cs.AI 2021
Year: 2021

Transformer based knowledge tracing model is an extensively studied problem in the field of computer-aided education. By integrating temporal features into the encoder-decoder structure, transformers can processes the exercise information and student response information in a natural way. However, current state-of-the-art transformer-based variants still share two limitations. First, extremely long temporal features cannot well handled as the complexity of self-attention mechanism is O(n2). Second, existing approaches track the knowledge drifts under fixed a window size, without considering different temporal-ranges. To conquer these problems, we propose MUSE, which is equipped with multi-scale temporal sensor unit, that takes either local or global temporal features into consideration. The proposed model is capable to capture the dynamic changes in users knowledge states at different temporal-ranges, and provides an efficient and powerful way to combine local and global features to make predictions. Our method won the 5-th place over 3,395 teams in the Riiid AIEd Challenge 2020.
Electrotactile Feedback For Enhancing Contact Information in Virtual Reality
Venue: arXiv cs.HC 2021
Year: 2021

This paper presents a wearable electrotactile feedback system to enhance contact information for mid-air interactions with virtual objects. In particular, we propose the use of electrotactile feedback to render the interpenetration distance between the user's finger and the virtual content is touched. Our approach consists of modulating the perceived intensity (frequency and pulse width modulation) of the electrotactile stimuli according to the registered interpenetration distance. In a user study (N=21), we assessed the performance of four different interpenetration feedback approaches: electrotactile-only, visual-only, electrotactile and visual, and no interpenetration feedback. First, the results showed that contact precision and accuracy were significantly improved when using interpenetration feedback. Second, and more interestingly, there were no significant differences between visual and electrotactile feedback when the calibration was optimized and the user was familiarized with electrotactile feedback. Taken together, these results suggest that electrotactile feedback could be an efficient replacement of visual feedback for enhancing contact information in virtual reality avoiding the need of active visual focus and the rendering of additional visual artefacts.
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users' language preferences. In this paper, we utilize the user demographics, and propose EmpathBERT, a demographic-aware framework for empathy prediction based on BERT. Through several comparative experiments, we show that EmpathBERT surpasses traditional machine learning and deep learning models, and illustrate the importance of user demographics to predict empathy and distress in user responses to stimulative news articles. We also highlight the importance of affect information in the responses by developing affect-aware models to predict user demographic attributes.
Deep Learning--Based Scene Simplification for Bionic Vision
Venue: arXiv cs.CV 2021
Year: 2021

Retinal degenerative diseases cause profound visual impairment in more than 10 million people worldwide, and retinal prostheses are being developed to restore vision to these individuals. Analogous to cochlear implants, these devices electrically stimulate surviving retinal cells to evoke visual percepts (phosphenes). However, the quality of current prosthetic vision is still rudimentary. Rather than aiming to restore "natural" vision, there is potential merit in borrowing state-of-the-art computer vision algorithms as image processing techniques to maximize the usefulness of prosthetic vision. Here we combine deep learning--based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision, and measure their ability to support scene understanding of sighted subjects (virtual patients) in a variety of outdoor scenarios. We show that object segmentation may better support scene understanding than models based on visual saliency and monocular depth estimation. In addition, we highlight the importance of basing theoretical predictions on biologically realistic models of phosphene shape. Overall, this work has the potential to drastically improve the utility of prosthetic vision for people blinded from retinal degenerative diseases.
Size and Depth Separation in Approximating Benign Functions with Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. However, not all functions are interesting from a practical viewpoint: functions of interest usually have a polynomially-bounded Lipschitz constant, and can be computed efficiently. We call functions that satisfy these conditions "benign", and explore the benefits of size and depth for approximation of benign functions with ReLU networks. As we show, this problem is more challenging than the corresponding problem for non-benign functions. We give barriers to showing depth-lower-bounds: Proving existence of a benign function that cannot be approximated by polynomial-size networks of depth $4$ would settle longstanding open problems in computational complexity. It implies that beyond depth $4$ there is a barrier to showing depth-separation for benign functions, even between networks of constant depth and networks of nonconstant depth. We also study size-separation, namely, whether there are benign functions that can be approximated with networks of size $O(s(d))$, but not with networks of size $O(s'(d))$. We show a complexity-theoretic barrier to proving such results beyond size $O(d\log^2(d))$, but also show an explicit benign function, that can be approximated with networks of size $O(d)$ and not with networks of size $o(d/\log d)$. For approximation in $L_\infty$ we achieve such separation already between size $O(d)$ and size $o(d)$. Moreover, we show superpolynomial size lower bounds and barriers to such lower bounds, depending on the assumptions on the function. Our size-separation results rely on an analysis of size lower bounds for Boolean functions, which is of independent interest: We show linear size lower bounds for computing explicit Boolean functions with neural networks and threshold circuits.
Recurrent Submodular Welfare and Matroid Blocking Bandits
Venue: arXiv cs.LG 2021
Year: 2021

A recent line of research focuses on the study of the stochastic multi-armed bandits problem (MAB), in the case where temporal correlations of specific structure are imposed between the player's actions and the reward distributions of the arms (Kleinberg and Immorlica [FOCS18], Basu et al. [NeurIPS19]). As opposed to the standard MAB setting, where the optimal solution in hindsight can be trivially characterized, these correlations lead to (sub-)optimal solutions that exhibit interesting dynamical patterns -- a phenomenon that yields new challenges both from an algorithmic as well as a learning perspective. In this work, we extend the above direction to a combinatorial bandit setting and study a variant of stochastic MAB, where arms are subject to matroid constraints and each arm becomes unavailable (blocked) for a fixed number of rounds after each play. A natural common generalization of the state-of-the-art for blocking bandits, and that for matroid bandits, yields a $(1-\frac{1}{e})$-approximation for partition matroids, yet it only guarantees a $\frac{1}{2}$-approximation for general matroids. In this paper we develop new algorithmic ideas that allow us to obtain a polynomial-time $(1 - \frac{1}{e})$-approximation algorithm (asymptotically and in expectation) for any matroid, and thus to control the $(1-\frac{1}{e})$-approximate regret. A key ingredient is the technique of correlated (interleaved) scheduling. Along the way, we discover an interesting connection to a variant of Submodular Welfare Maximization, for which we provide (asymptotically) matching upper and lower approximability bounds.
Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man
Venue: arXiv cs.NE 2021
Year: 2021

Generative Adversarial Networks (GANs) can generate levels for a variety of games. This paper focuses on combining GAN-generated segments in a snaking pattern to create levels for Mega Man. Adjacent segments in such levels can be orthogonally adjacent in any direction, meaning that an otherwise fine segment might impose a barrier between its neighbor depending on what sorts of segments in the training set are being most closely emulated: horizontal, vertical, or corner segments. To pick appropriate segments, multiple GANs were trained on different types of segments to ensure better flow between segments. Flow was further improved by evolving the latent vectors for the segments being joined in the level to maximize the length of the level's solution path. Using multiple GANs to represent different types of segments results in significantly longer solution paths than using one GAN for all segment types, and a human subject study verifies that these levels are more fun and have more human-like design than levels produced by one GAN.
Linear Computation Coding
Venue: arXiv cs.IT 2021
Year: 2021

We introduce the new concept of computation coding. Similar to how rate-distortion theory is concerned with the lossy compression of data, computation coding deals with the lossy computation of functions. Particularizing to linear functions, we present an algorithm to reduce the computational cost of multiplying an arbitrary given matrix with an unknown column vector. The algorithm decomposes the given matrix into the product of codebook wiring matrices whose entries are either zero or signed integer powers of two. For a typical implementation of deep neural networks, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed.
Multi-access Coded Caching from a New Class of Cross Resolvable Designs
Venue: arXiv cs.IT 2021
Year: 2021

Multi-access coded caching schemes from cross resolvable designs (CRD) have been reported recently \cite{KNRarXiv}. To be able to compare coded caching schemes with different number of users and possibly with different number of caches a new metric called rate-per-user was introduced and it was shown that under this new metric the schemes from CRDs perform better than the Maddah-Ali-Niesen scheme in the large memory regime. In this paper a new class of CRDs is presented and it is shown that the multi-access coded caching schemes derived from these CRDs perform better than the Maddah-Ali-Niesen scheme in the entire memory regime. Comparison with other known multi-access coding schemes is also presented.
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Venue: arXiv cs.CL 2021
Year: 2021

Guessing games are a prototypical instance of the "learning by interacting" paradigm. This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA). We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL). We evaluate the ability of both procedures to generalize: an in-domain evaluation shows an increased accuracy (+7.79) compared with competitors on the evaluation suite CompGuessWhat?!; a transfer evaluation shows improved performance for VQA on the TDIUC dataset in terms of harmonic average accuracy (+5.31) thanks to more fine-grained object representations learned via SPIEL.
A SDN/OpenFlow Framework for Dynamic Resource Allocation based on Bandwidth Allocation Model
Venue: arXiv cs.NI 2021
Year: 2021

The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number of users, applications, and services that are highly distributed with distinct and heterogeneous communications requirements. Resource allocation in this context requires dynamic, efficient, and customized solutions and Bandwidth Allocation Models (BAMs) are an alternative to support this new trend. This paper proposes the BAMSDN (Bandwidth Allocation Model through Software-Defined Networking) framework that dynamically allocates resources (bandwidth) for a MPLS (MultiProtocol Label Switching) network using a SDN (Software-Defined Networking)/OpenFlow strategy with BAM. The framework adopts an innovative implementation approach for BAM systems by controlling the MPLS network using SDN with OpenFlow. Experimental results suggest that using SDN/OpenFlow with BAM for bandwidth allocation does have effective advantages for MPLS networks requiring flexible resource sharing among applications and facilitates the migration path to a SDN/OpenFlow network.
Adversarial Contrastive Pre-training for Protein Sequences
Venue: arXiv cs.CL 2021
Year: 2021

Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks. These ideas have been adapted to other domains, including the analysis of the amino acid sequences of proteins. However, to date most attempts on protein sequences rely on direct masked language model style pre-training. In this work, we design a new, adversarial pre-training method for proteins, extending and specializing similar advances in NLP. We show compelling results in comparison to traditional MLM pre-training, though further development is needed to ensure the gains are worth the significant computational cost.
Urban Change Detection by Fully Convolutional Siamese Concatenate Network with Attention
Venue: arXiv cs.CV 2021
Year: 2021

Change detection (CD) is an important problem in remote sensing, especially in disaster time for urban management. Most existing traditional methods for change detection are categorized based on pixel or objects. Object-based models are preferred to pixel-based methods for handling very high-resolution remote sensing (VHR RS) images. Such methods can benefit from the ongoing research on deep learning. In this paper, a fully automatic change-detection algorithm on VHR RS images is proposed that deploys Fully Convolutional Siamese Concatenate networks (FC-Siam-Conc). The proposed method uses preprocessing and an attention gate layer to improve accuracy. Gaussian attention (GA) as a soft visual attention mechanism is used for preprocessing. GA helps the network to handle feature maps like biological visual systems. Since the GA parameters cannot be adjusted during network training, an attention gate layer is introduced to play the role of GA with parameters that can be tuned among other network parameters. Experimental results obtained on Onera Satellite Change Detection (OSCD) and RIVER-CD datasets confirm the superiority of the proposed architecture over the state-of-the-art algorithms.
Fine-tuning Handwriting Recognition systems with Temporal Dropout
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces a novel method to fine-tune handwriting recognition systems based on Recurrent Neural Networks (RNN). Long Short-Term Memory (LSTM) networks are good at modeling long sequences but they tend to overfit over time. To improve the system's ability to model sequences, we propose to drop information at random positions in the sequence. We call our approach Temporal Dropout (TD). We apply TD at the image level as well to internal network representation. We show that TD improves the results on two different datasets. Our method outperforms previous state-of-the-art on Rodrigo dataset.
A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning researchers and practitioners usually leverage GPUs to help train their deep neural networks (DNNs) faster. However, choosing which GPU to use is challenging both because (i) there are many options, and (ii) users grapple with competing concerns: maximizing compute performance while minimizing costs. In this work, we present a new practical technique to help users make informed and cost-efficient GPU selections: make performance predictions with the help of a GPU that the user already has. Our technique exploits the observation that, because DNN training consists of repetitive compute steps, predicting the execution time of a single iteration is usually enough to characterize the performance of an entire training process. We make predictions by scaling the execution time of each operation in a training iteration from one GPU to another using either (i) wave scaling, a technique based on a GPU's execution model, or (ii) pre-trained multilayer perceptrons. We implement our technique into a Python library called Habitat and find that it makes accurate iteration execution time predictions (with an average error of 11.8%) on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across six different GPU architectures. Habitat supports PyTorch, is easy to use, and is open source.
Bandgap optimization in combinatorial graphs with tailored ground states: Application in Quantum annealing
Venue: arXiv cs.DS 2021
Year: 2021

A mixed-integer linear programming (MILP) formulation is presented for parameter estimation of the Potts model. Two algorithms are developed; the first method estimates the parameters such that the set of ground states replicate the user-prescribed data set; the second method allows the user to prescribe the ground states multiplicity. In both instances, the optimization process ensures that the bandgap is maximized. Consequently, the model parameter efficiently describes the user data for a broad range of temperatures. This is useful in the development of energy-based graph models to be simulated on Quantum annealing hardware where the exact simulation temperature is unknown. Computationally, the memory requirement in this method grows exponentially with the graph size. Therefore, this method can only be practically applied to small graphs. Such applications include learning of small generative classifiers and spin-lattice model with energy described by Ising hamiltonian. Learning large data sets poses no extra cost to this method; however, applications involving the learning of high dimensional data are out of scope.
Multi-Agent Reinforcement Learning with Temporal Logic Specifications
Venue: arXiv cs.AI 2021
Year: 2021

In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour. From a learning perspective these specifications provide a rich formal language with which to capture tasks or objectives, while from a logic and automated verification perspective the introduction of learning capabilities allows for practical applications in large, stochastic, unknown environments. The existing work in this area is, however, limited. Of the frameworks that consider full linear temporal logic or have correctness guarantees, all methods thus far consider only the case of a single temporal logic specification and a single agent. In order to overcome this limitation, we develop the first multi-agent reinforcement learning technique for temporal logic specifications, which is also novel in its ability to handle multiple specifications. We provide correctness and convergence guarantees for our main algorithm - ALMANAC (Automaton/Logic Multi-Agent Natural Actor-Critic) - even when using function approximation. Alongside our theoretical results, we further demonstrate the applicability of our technique via a set of preliminary experiments.
Tale of Seven Alerts: Enhancing Wireless Emergency Alerts (WEAs) to Reduce Cellular Network Usage During Disasters
Venue: arXiv cs.HC 2021
Year: 2021

In weather disasters, first responders access dedicated communication channels different from civilian commercial channels to facilitate rescues. However, rescues in recent disasters have increasingly involved civilian and volunteer forces, requiring civilian channels not to be overloaded with traffic. We explore seven enhancements to the wording of Wireless Emergency Alerts (WEAs) and their effectiveness in getting smartphone users to comply, including reducing frivolous mobile data consumption during critical weather disasters. We conducted a between-subjects survey (N=898), in which participants were either assigned no alert (control) or an alert framed as Basic Information, Altruism, Multimedia, Negative Feedback, Positive Feedback, Reward, or Punishment. We find that Basic Information alerts resulted in the largest reduction of multimedia and video services usage; we also find that Punishment alerts have the lowest absolute compliance. This work has implications for creating more effective WEAs and providing a better understanding of how wording can affect emergency alert compliance.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Venue: arXiv cs.CV 2021
Year: 2021

False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countless target domains. Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way. In this paper, we model an object detection task into a ranking task among positive and negative boxes innovatively, and thus transform a false positive suppression problem into a box re-ranking problem elegantly, which makes it feasible to solve without manual annotation. An attached problem during box re-ranking appears that no labeled validation data is available for cherrypicking. Considering we aim to keep the detection of true positive unchanged, we propose box number alignment, a self-supervised evaluation metric, to prevent the optimized model from capacity degeneration. Extensive experiments conducted on cross-domain pedestrian detection datasets have demonstrated the effectiveness of our proposed framework. Furthermore, the extension to two general unsupervised domain adaptive object detection benchmarks also supports our superiority to other state-of-the-arts.