OpenRaiser/Intern-Atlas huggingface dataset:
Three Practical Workflow Schedulers for Easy Maximum Parallelism
Venue: arXiv cs.DC 2021
Year: 2021

Runtime scheduling and workflow systems are an increasingly popular algorithmic component in HPC because they allow full system utilization with relaxed synchronization requirements. There are so many special-purpose tools for task scheduling, one might wonder why more are needed. Use cases seen on the Summit supercomputer needed better integration with MPI and greater flexibility in job launch configurations. Preparation, execution, and analysis of computational chemistry simulations at the scale of tens of thousands of processors revealed three distinct workflow patterns. A separate job scheduler was implemented for each one using extremely simple and robust designs: file-based, task-list based, and bulk-synchronous. Comparing to existing methods shows unique benefits of this work, including simplicity of design, suitability for HPC centers, short startup time, and well-understood per-task overhead. All three new tools have been shown to scale to full utilization of Summit, and have been made publicly available with tests and documentation. This work presents a complete characterization of the minimum effective task granularity for efficient scheduler usage scenarios. These schedulers have the same bottlenecks, and hence similar task granularities as those reported for existing tools following comparable paradigms.
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
Venue: arXiv cs.LG 2021
Year: 2021

While Graph Neural Networks have gained popularity in multiple domains, graph-structured input remains a major challenge due to (a) over-smoothing, (b) noisy neighbours (heterophily), and (c) the suspended animation problem. To address all these problems simultaneously, we propose a novel graph neural network FDGATII, inspired by attention mechanism's ability to focus on selective information supplemented with two feature preserving mechanisms. FDGATII combines Initial Residuals and Identity Mapping with the more expressive dynamic self-attention to handle noise prevalent from the neighbourhoods in heterophilic data sets. By using sparse dynamic attention, FDGATII is inherently parallelizable in design, whist efficient in operation; thus theoretically able to scale to arbitrary graphs with ease. Our approach has been extensively evaluated on 7 datasets. We show that FDGATII outperforms GAT and GCN based benchmarks in accuracy and performance on fully supervised tasks, obtaining state-of-the-art results on Chameleon and Cornell datasets with zero domain-specific graph pre-processing, and demonstrate its versatility and fairness.
Future of Smart Classroom in the Era of Wearable Neurotechnology
Venue: arXiv cs.HC 2021
Year: 2021

Interdisciplinary research among engineering, computer science, and neuroscience to understand and utilize the human brain signals resulted in advances and widespread applicability of wearable neurotechnology in adaptive human-in-the-loop smart systems. Considering these advances, we envision that future education will exploit the advances in wearable neurotechnology and move toward more personalized smart classrooms where instructions and interactions are tailored towards. students' individual strengths and needs. In this paper, we discuss the future of smart classrooms and how advances in neuroscience, machine learning, and embedded systems as key enablers will provide the infrastructure for envisioned smart classrooms and personalized education along with open challenges that are required to be addressed.
Viash: from scripts to pipelines
Venue: arXiv cs.SE 2021
Year: 2021

Most bioinformatics pipelines consist of software components that are tightly coupled to the logic of the pipeline itself. This limits reusability of the individual components in the pipeline or introduces maintenance overhead when they need to be reimplemented in multiple pipelines. We introduce Viash, a tool for speeding up development of robust pipelines through "code-first" prototyping, separation of concerns and code generation of modular pipeline components. By decoupling the component functionality from the pipeline logic, component functionality becomes fully pipeline-agnostic, and conversely the resulting pipelines are agnostic towards specific component requirements. This separation of concerns improves reusability of components and facilitates multidisciplinar and pan-organisational collaborations. It has been applied in a variety of projects, from proof-of-concept pipelines to supporting an international data science competition. Viash is available as an open-source project at https://github.com/viash-io/viash and documentation is available at https://viash.io.
Maximum Power Point Tracking Circuit for an Energy Harvester in 130 nm CMOS Technology
Venue: arXiv cs.AR 2021
Year: 2021

This paper presents design of a Maximum Power Point Tracking (MPPT) circuit and its functionality for tuning the maximum power transfer from an energy harvester (EH) unit. Simple and practical Perturb and Observe algorithm is investigated and implemented. We describe the circuit functionality and the improvements that have been introduced to the original algorithm. The proposed MPPT design is divided into three main blocks. The output signal is being generated by the PWM or PFM block. The tracking speed has been enhanced by implementing a variable step size in the Tracking Block. Finally, the overall power consumption of the MPPT circuit itself is controlled by the Power Management Block, which manages delivering the clock signal to the rest of the circuit. The RTL code of the proposed MPPT has been described in Verilog, then has been synthesized and placed-and-routed in a general purpose 130nm CMOS technology.
Volumetric Data Fusion of External Depth and Onboard Proximity Data For Occluded Space Reduction
Venue: arXiv cs.RO 2021
Year: 2021

In this work, we present a method for a probabilistic fusion of external depth and onboard proximity data to form a volumetric 3-D map of a robot's environment. We extend the Octomap framework to update a representation of the area around the robot, dependent on each sensor's optimal range of operation. Areas otherwise occluded from an external view are sensed with onboard sensors to construct a more comprehensive map of a robot's nearby space. Our simulated results show that a more accurate map with less occlusions can be generated by fusing external depth and onboard proximity data.
Power Saving Evaluation with Automatic Offloading
Venue: arXiv cs.AR 2021
Year: 2021

Heterogeneous hardware other than small-core CPU such as GPU, FPGA, or many-core CPU is increasingly being used. However, heterogeneous hardware usage presents high technical skill barriers such as familiarity with CUDA. To overcome this challenge, I previously proposed environment-adaptive software that enables automatic conversion, automatic configuration, and high-performance and low-power operation of once-written code, in accordance with the hardware to be placed. I also previously verified performance improvement of automatic GPU and FPGA offloading. In this paper, I verify low-power operation with environment adaptation by evaluating power utilization after automatic offloading. I compare Watt*seconds of existing applications after automatic offloading with the case of CPU-only processing.
Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation
Venue: arXiv cs.IR 2021
Year: 2021

Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniques for the top-k retrieval problem. Previous studies comparing document-at-a-time and score-at-a-time approaches have consistently found that the former approach yields lower mean query latency, although the latter approach has more predictable query latency. In our experiments with four different retrieval models that exploit representational learning with bags of words, we find that transformers generate "wacky weights" that appear to greatly reduce the opportunities for skipping and early exiting optimizations that lie at the core of standard document-at-a-time techniques. As a result, score-at-a-time approaches appear to be more competitive in terms of query evaluation latency than in previous studies. We find that, if an effectiveness loss of up to three percent can be tolerated, a score-at-a-time approach can yield substantial gains in mean query latency while at the same time dramatically reducing tail latency.
ReCFA: Resilient Control-Flow Attestation
Venue: arXiv cs.CR 2021
Year: 2021

Recent IoT applications gradually adapt more complicated end systems with commodity software. Ensuring the runtime integrity of these software is a challenging task for the remote controller or cloud services. Popular enforcement is the runtime remote attestation which requires the end system (prover) to generate evidence for its runtime behavior and a remote trusted verifier to attest the evidence. Control-flow attestation is a kind of runtime attestation that provides diagnoses towards the remote control-flow hijacking at the prover. Most of these attestation approaches focus on small or embedded software. The recent advance to attesting complicated software depends on the source code and CFG traversing to measure the checkpoint-separated subpaths, which may be unavailable for commodity software and cause possible context missing between consecutive subpaths in the measurements. In this work, we propose a resilient control-flow attestation (ReCFA), which does not need the offline measurement of all legitimate control-flow paths, thus scalable to be used on complicated commodity software. Our main contribution is a multi-phase approach to condensing the runtime control-flow events; as a result, the vast amount of control-flow events are abstracted into a deliverable size. The condensing approach consists of filtering skippable call sites, folding program-structure related control-flow events, and a greedy compression. Our approach is implemented with binary-level static analysis and instrumentation. We employ a shadow stack mechanism at the verifier to enforce context-sensitive control-flow integrity and diagnose the compromised control-flow events violating the security policy. The experimental results on real-world benchmarks show both the efficiency of the control-flow condensing and the effectiveness of security enforcement.
SciCap: Generating Captions for Scientific Figures
Venue: arXiv cs.CL 2021
Year: 2021

Researchers use figures to communicate rich, complex information in scientific papers. The captions of these figures are critical to conveying effective messages. However, low-quality figure captions commonly occur in scientific articles and may decrease understanding. In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures. To this end, we introduce SCICAP, a large-scale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020. After pre-processing - including figure-type classification, sub-figure identification, text normalization, and caption text selection - SCICAP contained more than two million figures extracted from over 290,000 papers. We then established baseline models that caption graph plots, the dominant (19.2%) figure type. The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.
High-performance Estimation of Jamming Covariance Matrix for IRS-aided Directional Modulation Network with a Malicious Attacker
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate the anti-jamming problem of a directional modulation (DM) system with the aid of intelligent reflecting surface (IRS). As an efficient tool to combat malicious jamming, receive beamforming (RBF) is usually designed to be on null-space of jamming channel or covariance matrix from Mallory to Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimate, three JCM estimation methods, including eigenvalue decomposition (EVD), parametric estimation method by gradient descend (PEM-GD) and parametric estimation method by alternating optimization (PEM-AO), are proposed. Here, the proposed EVD is under rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that in low and medium jamming-noise ratio (JNR) regions, the proposed three methods perform better than the existing sample covariance matrix method. The proposed PEM-GD and PEM-AO outperform EVD method and existing clutter and disturbance covariance estimator RCML.
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we consider the task of semantic segmentation in autonomous driving applications. Specifically, we consider the cross-domain few-shot setting where training can use only few real-world annotated images and many annotated synthetic images. In this context, aligning the domains is made more challenging by the pixel-wise class imbalance that is intrinsic in the segmentation and that leads to ignoring the underrepresented classes and overfitting the well represented ones. We address this problem with a novel framework called Pixel-By-Pixel Cross-Domain Alignment (PixDA). We propose a novel pixel-by-pixel domain adversarial loss following three criteria: (i) align the source and the target domain for each pixel, (ii) avoid negative transfer on the correctly represented pixels, and (iii) regularize the training of infrequent classes to avoid overfitting. The pixel-wise adversarial training is assisted by a novel sample selection procedure, that handles the imbalance between source and target data, and a knowledge distillation strategy, that avoids overfitting towards the few target images. We demonstrate on standard synthetic-to-real benchmarks that PixDA outperforms previous state-of-the-art methods in (1-5)-shot settings.
ListReader: Extracting List-form Answers for Opinion Questions
Venue: arXiv cs.CL 2021
Year: 2021

Question answering (QA) is a high-level ability of natural language processing. Most extractive ma-chine reading comprehension models focus on factoid questions (e.g., who, when, where) and restrict the output answer as a short and continuous span in the original passage. However, in real-world scenarios, many questions are non-factoid (e.g., how, why) and their answers are organized in the list format that contains multiple non-contiguous spans. Naturally, existing extractive models are by design unable to answer such questions. To address this issue, this paper proposes ListReader, a neural ex-tractive QA model for list-form answer. In addition to learning the alignment between the question and content, we introduce a heterogeneous graph neural network to explicitly capture the associations among candidate segments. Moreover, our model adopts a co-extraction setting that can extract either span- or sentence-level answers, allowing better applicability. Two large-scale datasets of different languages are constructed to support this study. Experimental results show that our model considerably outperforms various strong baselines. Further discussions provide an intuitive understanding of how our model works and where the performance gain comes from.
UBR$^2$S: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised domain adaptation (UDA) deals with the adaptation process of a model to an unlabeled target domain while annotated data is only available for a given source domain. This poses a challenging task, as the domain shift between source and target instances deteriorates a model's performance when not addressed. In this paper, we propose UBR$^2$S - the Uncertainty-Based Resampling and Reweighting Strategy - to tackle this problem. UBR$^2$S employs a Monte Carlo dropout-based uncertainty estimate to obtain per-class probability distributions, which are then used for dynamic resampling of pseudo-labels and reweighting based on their sample likelihood and the accompanying decision error. Our proposed method achieves state-of-the-art results on multiple UDA datasets with single and multi-source adaptation tasks and can be applied to any off-the-shelf network architecture. Code for our method is available at https://gitlab.com/tringwald/UBR2S.
Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes
Venue: arXiv cs.CV 2021
Year: 2021

Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalisation requirement for unseen classes. While recent progress has been particularly encouraging, we discover that existing methods tend to have poor performance in terms of meanIoU when query images contain other semantic classes besides the target class. To address this issue, we propose a novel self-supervised task that generates random pseudo-classes in the background of the query images, providing extra training data that would otherwise be unavailable when predicting individual target classes. To that end, we adopted superpixel segmentation for generating the pseudo-classes. With this extra supervision, we improved the meanIoU performance of the state-of-the-art method by 2.5% and 5.1% on the one-shot tasks, as well as 6.7% and 4.4% on the five-shot tasks, on the PASCAL-5i and COCO benchmarks, respectively.
Semantic Identifiers and DNS Names for IoT
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we propose a scheme for representing semantic metadata of IoT devices in compact identifiers and DNS names to enable simple discovery and search with standard DNS servers. Our scheme defines a binary identifier as a sequence of bits: a Context to use and several bits of fields corresponding to semantic properties specific to the Context. The bit string is then encoded as base32 characters and registered in DNS. Furthermore, we use the compact semantic DNS names to offer support for search and discovery. We propose to take advantage of the DNS system as the basic functionality for querying and discovery of semantic properties related to IoT devices. We have defined three specific Contexts for hierarchical semantic properties as well as logical and geographical locations. For this last part, we have developed two prototypes for managing geo-identifiers in LoRa networks, one based on Node and the Redis in-memory database, the other one based on the CoreDNS server.
Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning
Venue: arXiv cs.CV 2021
Year: 2021

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images, which requires a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator. To solve this problem, we propose a novel image captioning method by exploiting the Cross-modal Prediction and Relation Consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the commonly semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency. CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling; 2) Relation consistency. CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semi-supervised scenario.
Clustering of Bank Customers using LSTM-based encoder-decoder and Dynamic Time Warping
Venue: arXiv cs.LG 2021
Year: 2021

Clustering is an unsupervised data mining technique that can be employed to segment customers. The efficient clustering of customers enables banks to design and make offers based on the features of the target customers. The present study uses a real-world financial dataset (Berka, 2000) to cluster bank customers by an encoder-decoder network and the dynamic time warping (DTW) method. The customer features required for clustering are obtained in four ways: Dynamic Time Warping (DTW), Recency Frequency and Monetary (RFM), LSTM encoder-decoder network, and our proposed hybrid method. Once the LSTM model was trained by customer transaction data, a feature vector of each customer was automatically extracted by the encoder.Moreover, the distance between pairs of sequences of transaction amounts was obtained using DTW. Another vector feature was calculated for customers by RFM scoring. In the hybrid method, the feature vectors are combined from the encoder-decoder output, the DTW distance, and the demographic data (e.g., age and gender). Finally, feature vectors were introduced as input to the k-means clustering algorithm, and we compared clustering results with Silhouette and Davies-Bouldin index. As a result, the clusters obtained from the hybrid approach are more accurate and meaningful than those derived from individual clustering techniques. In addition, the type of neural network layers had a substantial effect on the clusters, and high network error does not necessarily worsen clustering performance.
Handling Concurrency in Behavior Trees
Venue: arXiv cs.RO 2021
Year: 2021

This paper addresses the concurrency issues affecting Behavior Trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry. BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to combine concurrent behaviors that has high potential, since composing pre-existing BTs in parallel results easier than composing in parallel classical control architectures, as finite state machines or teleo-reactive programs. However, BT designers rarely use such composition due to the underlying concurrency problems similar to the ones faced in concurrent programming. As a result, the parallel composition, despite its potential, finds application only in the composition of simple behaviors or where the designer can guarantee the absence of conflicts by design. In this paper, we define two new BT nodes to tackle the concurrency problems in BTs and we show how to exploit them to create predictable behaviors. In addition, we introduce measures to assess execution performance and show how different design choices affect them. We validate our approach in both simulations and the real world. Simulated experiments provide statistically significant data, whereas real-world experiments show the applicability of our method on real robots. We provided an open-source implementation of the novel BT formulation and published all the source code to reproduce the numerical examples and experiments.