OpenRaiser/Intern-Atlas huggingface dataset:
SuaCode Africa: Teaching Coding Online to Africans using Smartphones
Venue: arXiv cs.HC 2021
Year: 2021

There is a burgeoning trend of smartphone ownership in Africa due to the low costs of Android smartphones and the global increase in social media usage. Building upon previous works that introduced a smartphone-based coding course to secondary and tertiary students in Ghana via an in-person program and an online course, this work introduced Africans in 37 countries to our online smartphone-based course in 2019. Students in this 8-week course read lesson notes, submitted assignments, collaborated with peers, and facilitators in an online forum and completed open and closed-ended surveys after the course. We performed qualitative and quantitative analyses on the data from the course. Out of the 709 students that applied, 210 were officially admitted to the course after passing the preliminary assignments. And at the end of the course, 72% of the 210 students completed the course. Additionally, students' assignment submissions and self-reports showed an understanding of the programming concepts, with comparable performance between males and females and across educational levels. Also, students mentioned that the lesson notes were easy to understand and they enjoyed the experience of writing code on their smartphones. Moreover, students adequately received help from peers and facilitators in the course forum. Lastly, results of a survey sent to students a year after completing this program showed that they had developed various applications, wrote online tutorials, and learned several tools and technologies. We were successful at introducing coding skills to Africans using smartphones through SuaCode Africa.
Universal Digital Services Through Basic Broadband
Venue: arXiv cs.NI 2021
Year: 2021

In addressing the universal deployment of digital services it is necessary to decide on the breadth of "universality" and the type of functionality of "digital services". These two decisions are linked: certain desirable functionality does not lend itself to being implemented in some environments or with certain constraints. In this paper we define universality as achieving a level of universality in digital service that can bridge the digital divide not only throughout highly industrialized societies but also across the globe and in the face of disruption. We then argue that some of the characteristics of current Internet broadband service, in particular support for synchronous telepresence such as videoconferencing, is a barrier to implementation strategies that could enable cheap and resilient universal deploymen
Dialogue Management for Interactive API Search
Venue: arXiv cs.SE 2021
Year: 2021

API search involves finding components in an API that are relevant to a programming task. For example, a programmer may need a function in a C library that opens a new network connection, then another function that sends data across that connection. Unfortunately, programmers often have trouble finding the API components that they need. A strong scientific consensus is emerging towards developing interactive tool support that responds to conversational feedback, emulating the experience of asking a fellow human programmer for help. A major barrier to creating these interactive tools is implementing dialogue management for API search. Dialogue management involves determining how a system should respond to user input, such as whether to ask a clarification question or to display potential results. In this paper, we present a dialogue manager for interactive API search that considers search results and dialogue history to select efficient actions. We implement two dialogue policies: a hand-crafted policy and a policy optimized via reinforcement learning. We perform a synthetics evaluation and a human evaluation comparing the policies to a generic single-turn, top-N policy used by source code search engines.
Uplink Data Detection Analysis of 1-Bit Quantized Massive MIMO
Venue: arXiv cs.IT 2021
Year: 2021

This paper presents an analytical framework for the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs). Considering the single-user case, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted at the base station (BS) along with their asymptotic behavior at high signal-to-noise ratio (SNR). These results are exploited to enhance the performance of maximum likelihood detection by taking into account the dispersion of the estimated symbols about their expected values. The symbol error rate with 1-bit ADCs is evaluated with respect to the number of BS antennas, the SNR, and the pilot length used for the channel estimation. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right SNR considerably improves the data detection accuracy.
Overview of Bachelors Theses 2021
Venue: arXiv cs.DC 2021
Year: 2021

In this work, we review Bachelors Theses done under the supervision of Vitaly Aksenov at ITMO University. This overview contains the short description of six theses: "Development of a Streaming Algorithm for the Decomposition of Graph Metrics to Tree Metrics" by Oleg Fafurin, "Development of Memory-friendly Concurrent Data Structures" by Roman Smirnov, "Theoretical Analysis of the Performance of Concurrent Data Structures" by Daniil Bolotov, "Parallel Batched Interpolation Search Tree" by Alena Martsenyuk, "Parallel Batched Self-adjusting Data Structures" by Vitalii Krasnov, and "Parallel Batched Persistent Binary Search Trees" by Ildar Zinatulin.
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding
Venue: arXiv cs.NE 2021
Year: 2021

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional deep learning frameworks, since they provide higher computational efficiency in event driven neuromorphic hardware. However, the state-of-the-art (SOTA) SNNs suffer from high inference latency, resulting from inefficient input encoding and training techniques. The most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the temporal learning capabilities of SNNs. This paper presents a training framework for low-latency energy-efficient SNNs that uses a hybrid encoding scheme at the input layer in which the analog pixel values of an image are directly applied during the first timestep and a novel variant of spike temporal coding is used during subsequent timesteps. In particular, neurons in every hidden layer are restricted to fire at most once per image which increases activation sparsity. To train these hybrid-encoded SNNs, we propose a variant of the gradient descent based spike timing dependent back propagation (STDB) mechanism using a novel cross entropy loss function based on both the output neurons' spike time and membrane potential. The resulting SNNs have reduced latency and high activation sparsity, yielding significant improvements in computational efficiency. In particular, we evaluate our proposed training scheme on image classification tasks from CIFAR-10 and CIFAR-100 datasets on several VGG architectures. We achieve top-1 accuracy of $66.46$\% with $5$ timesteps on the CIFAR-100 dataset with ${\sim}125\times$ less compute energy than an equivalent standard ANN. Additionally, our proposed SNN performs $5$-$300\times$ faster inference compared to other state-of-the-art rate or temporally coded SNN models.
Accelerated Gradient Descent Learning over Multiple Access Fading Channels
Venue: arXiv cs.LG 2021
Year: 2021

We consider a distributed learning problem in a wireless network, consisting of N distributed edge devices and a parameter server (PS). The objective function is a sum of the edge devices' local loss functions, who aim to train a shared model by communicating with the PS over multiple access channels (MAC). This problem has attracted a growing interest in distributed sensing systems, and more recently in federated learning, known as over-the-air computation. In this paper, we develop a novel Accelerated Gradient-descent Multiple Access (AGMA) algorithm that uses momentum-based gradient signals over noisy fading MAC to improve the convergence rate as compared to existing methods. Furthermore, AGMA does not require power control or beamforming to cancel the fading effect, which simplifies the implementation complexity. We analyze AGMA theoretically, and establish a finite-sample bound of the error for both convex and strongly convex loss functions with Lipschitz gradient. For the strongly convex case, we show that AGMA approaches the best-known linear convergence rate as the network increases. For the convex case, we show that AGMA significantly improves the sub-linear convergence rate as compared to existing methods. Finally, we present simulation results using real datasets that demonstrate better performance by AGMA.
Adversarial Attacks with Time-Scale Representations
Venue: arXiv cs.CV 2021
Year: 2021

We propose a novel framework for real-time black-box universal attacks which disrupts activations of early convolutional layers in deep learning models. Our hypothesis is that perturbations produced in the wavelet space disrupt early convolutional layers more effectively than perturbations performed in the time domain. The main challenge in adversarial attacks is to preserve low frequency image content while minimally changing the most meaningful high frequency content. To address this, we formulate an optimization problem using time-scale (wavelet) representations as a dual space in three steps. First, we project original images into orthonormal sub-spaces for low and high scales via wavelet coefficients. Second, we perturb wavelet coefficients for high scale projection using a generator network. Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space. We provide a theoretical framework that guarantees a dual mapping from time and time-scale domain representations. We compare our results with state-of-the-art black-box attacks from generative-based and gradient-based models. We also verify efficacy against multiple defense methods such as JPEG compression, Guided Denoiser and Comdefend. Our results show that wavelet-based perturbations consistently outperform time-based attacks thus providing new insights into vulnerabilities of deep learning models and could potentially lead to robust architectures or new defense and attack mechanisms by leveraging time-scale representations.
TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games
Venue: arXiv cs.LG 2021
Year: 2021

Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-like patterning: the placement of game objects in relation to each other to form congruent patterns based on events in the song. Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.
Language Grounding with 3D Objects
Venue: arXiv cs.CL 2021
Year: 2021

Seemingly simple natural language requests to a robot are generally underspecified, for example "Can you bring me the wireless mouse?" Flat images of candidate mice may not provide the discriminative information needed for "wireless." The world, and objects in it, are not flat images but complex 3D shapes. If a human requests an object based on any of its basic properties, such as color, shape, or texture, robots should perform the necessary exploration to accomplish the task. In particular, while substantial effort and progress has been made on understanding explicitly visual attributes like color and category, comparatively little progress has been made on understanding language about shapes and contours. In this work, we introduce a novel reasoning task that targets both visual and non-visual language about 3D objects. Our new benchmark, ShapeNet Annotated with Referring Expressions (SNARE) requires a model to choose which of two objects is being referenced by a natural language description. We introduce several CLIP-based models for distinguishing objects and demonstrate that while recent advances in jointly modeling vision and language are useful for robotic language understanding, it is still the case that these image-based models are weaker at understanding the 3D nature of objects -- properties which play a key role in manipulation. We find that adding view estimation to language grounding models improves accuracy on both SNARE and when identifying objects referred to in language on a robot platform, but note that a large gap remains between these models and human performance.
Proactive Composition of Mobile IoT Energy Services
Venue: arXiv cs.DC 2021
Year: 2021

We propose a novel proactive composition framework of wireless energy services in a crowdsourced IoT environment. We define a new model for energy services and requests that includes providers' and consumers' mobility patterns and energy usage behavior. The proposed composition approach leverages the mobility and energy usage behavior to generate energy services and requests proactively. Preliminary experimental results demonstrate the effectiveness of generating proactive energy requests and composing proactive services.
Probing neural networks with t-SNE, class-specific projections and a guided tour
Venue: arXiv cs.LG 2021
Year: 2021

We use graphical methods to probe neural nets that classify images. Plots of t-SNE outputs at successive layers in a network reveal increasingly organized arrangement of the data points. They can also reveal how a network can diminish or even forget about within-class structure as the data proceeds through layers. We use class-specific analogues of principal components to visualize how succeeding layers separate the classes. These allow us to sort images from a given class from most typical to least typical (in the data) and they also serve as very useful projection coordinates for data visualization. We find them especially useful when defining versions guided tours for animated data visualization.
A Biomedically oriented automatically annotated Twitter COVID-19 Dataset
Venue: arXiv cs.IR 2021
Year: 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontraditional sources of clinical data to characterize the disease in near real-time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-COVID). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations do not generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021
Venue: arXiv cs.CV 2021
Year: 2021

This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track. Temporal Action Localization (TAL) requires to not only precisely locate the temporal boundaries of action instances, but also accurately classify the untrimmed videos into specific categories. However, Weakly-Supervised TAL indicates locating the action instances using only video-level class labels. In this paper, to train a supervised temporal action localizer, we adopt Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through ``local and global" temporal context aggregation and complementary as well as progressive boundary refinement. As for the WSTAL, a novel framework is proposed to handle the poor quality of CAS generated by simple classification network, which can only focus on local discriminative parts, rather than locate the entire interval of target actions. Further inspired by the transfer learning method, we also adopt an additional module to transfer the knowledge from trimmed videos (HACS Clips dataset) to untrimmed videos (HACS Segments dataset), aiming at promoting the classification performance on untrimmed videos. Finally, we employ a boundary regression module embedded with Outer-Inner-Contrastive (OIC) loss to automatically predict the boundaries based on the enhanced CAS. Our proposed scheme achieves 39.91 and 29.78 average mAP on the challenge testing set of supervised and weakly-supervised temporal action localization track respectively.
Cross-lingual Transferring of Pre-trained Contextualized Language Models
Venue: arXiv cs.CL 2021
Year: 2021

Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant. In this work, building upon the recent works connecting cross-lingual model transferring and neural machine translation, we thus propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate ``TRILayer" structure that learns from these differences and creates a better transfer in our primary translation direction, as well as a new cross-lingual language modeling objective for transfer training. Additionally, we showcase an embedding aligning that adversarially adapts a PrLM's non-contextualized embedding space and the TRILayer structure to learn a text transformation network across languages, which addresses the vocabulary difference between languages. Experiments on both language understanding and structure parsing tasks show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency. Moreover, despite an insignificant performance loss compared to pre-training from scratch in resource-rich scenarios, our cross-lingual model transferring framework is significantly more economical.
Co-Transport for Class-Incremental Learning
Venue: arXiv cs.CV 2021
Year: 2021

Traditional learning systems are trained in closed-world for a fixed number of classes, and need pre-collected datasets in advance. However, new classes often emerge in real-world applications and should be learned incrementally. For example, in electronic commerce, new types of products appear daily, and in a social media community, new topics emerge frequently. Under such circumstances, incremental models should learn several new classes at a time without forgetting. We find a strong correlation between old and new classes in incremental learning, which can be applied to relate and facilitate different learning stages mutually. As a result, we propose CO-transport for class Incremental Learning (COIL), which learns to relate across incremental tasks with the class-wise semantic relationship. In detail, co-transport has two aspects: prospective transport tries to augment the old classifier with optimal transported knowledge as fast model adaptation. Retrospective transport aims to transport new class classifiers backward as old ones to overcome forgetting. With these transports, COIL efficiently adapts to new tasks, and stably resists forgetting. Experiments on benchmark and real-world multimedia datasets validate the effectiveness of our proposed method.
Continual Learning with Neuron Activation Importance
Venue: arXiv cs.LG 2021
Year: 2021

Continual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. In this paper, we propose a neuron activation importance-based regularization method for stable continual learning regardless of the order of tasks. We conduct comprehensive experiments on existing benchmark data sets to evaluate not just the stability and plasticity of our method with improved classification accuracy also the robustness of the performance along the changes of task order.
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN
Venue: arXiv cs.AR 2021
Year: 2021

Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature \textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to \textbf{8.3}$\times$ memory saving and \textbf{42.9}$\times$ computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show $\sim$\textbf{70} milliseconds latency on Qualcomm Snapdragon 865 chipset.
Time-Varying Fuzzy Contour Trees
Venue: arXiv cs.HC 2021
Year: 2021

We present a holistic, topology-based visualization technique for spatial time series data based on an adaptation of Fuzzy Contour Trees. Common analysis approaches for time dependent scalar fields identify and track specific features. To give a more general overview of the data, we extend Fuzzy Contour Trees, from the visualization and simultaneous analysis of the topology of multiple scalar fields, to time dependent scalar fields. The resulting time-varying Fuzzy Contour Trees allow the comparison of multiple time steps that are not required to be consecutive. We provide specific interaction and navigation possibilities that allow the exploration of individual time steps and time windows in addition to the behavior of the contour trees over all time steps. To achieve this, we reduce an existing alignment to multiple sub-alignments and adapt the Fuzzy Contour Tree-layout to continuously reflect changes and similarities in the sub-alignments. We apply time-varying Fuzzy Contour Trees to different real-world data sets and demonstrate their usefulness.