OpenRaiser/Intern-Atlas huggingface dataset:
SpatialViewer: A Remote Work Sharing Tool that Considers Intimacy Among Workers
Venue: arXiv cs.HC 2021
Year: 2021

Due to the influence of the new coronavirus disease (COVID-19), teleworking has been expanding rapidly. Although existing interactive remote working systems are convenient, they do not allow users to adjust their spatial distance to team members at will, %"Arbitrarily" is probably not the best word here. It means without apparent reason. A better expression might be "at will." and they ignore the discomfort caused by different levels of intimacy. To solve this issue, we propose a telework support system using spatial augmented reality technology. This system calibrates the space in which videos are projected with real space and adjusts the spatial distance between users by changing the position of projections. Users can switch the projection position of the video using hand-wave gestures. We also synchronize audio according to distance to further emphasize the sense of space within the remote interaction: the distance between projection position and user is inversely proportional to the audio volume. We conducted a telework experiment and a questionnaire survey to evaluate our system. The results show that the system enables users to adjust distance according to intimacy and thus improve the users' comfort.
Limited Associativity Makes Concurrent Software Caches a Breeze
Venue: arXiv cs.AR 2021
Year: 2021

Software caches optimize the performance of diverse storage systems, databases and other software systems. Existing works on software caches automatically resort to fully associative cache designs. Our work shows that limited associativity caches are a promising direction for concurrent software caches. Specifically, we demonstrate that limited associativity enables simple yet efficient realizations of multiple cache management schemes that can be trivially parallelized. We show that the obtained hit ratio is usually similar to fully associative caches of the same management policy, but the throughput is improved by up to X5 compared to production-grade caching libraries, especially in multi-threaded executions.
Augmented Memory Computing: Dynamically Augmented SRAM Storage for Data Intensive Applications
Venue: arXiv cs.AR 2021
Year: 2021

In this paper, we propose a novel memory-centric scheme based on CMOS SRAM for acceleration of data intensive applications. Our proposal aims at dynamically increasing the on-chip memory storage capacity of SRAM arrays on-demand. The proposed scheme called - Augmented Memory Computing allows an SRAM cell to operate in two different modes 1) the Normal mode and 2) the Augmented mode. In the Normal mode of operation, the SRAM cell functions like a standard 6 transistor (6T) SRAM cell, storing one bit of data in static format. While in the Augmented mode, each SRAM cell can store >1 bit of data (in a dynamic fashion). Specifically, we propose two novel SRAM cells - an 8 transistor (8T) dual bit storage augmented cell and a 7 transistor (7T) ternary bit storage augmented cell. The proposed 8T dual bit SRAM cell when operated in the Augmented mode, can store a static bit of data while also, simultaneously, storing another bit in a dynamic form. Thus, when operated in Augmented mode, the 8T SRAM cell can store two bits of data - one SRAM-like data and one DRAM-like data, thereby increasing or augmenting the memory storage capacity. On the other hand, the proposed 7T ternary bit storage augmented cell can either store a single SRAM data in Normal mode or can be configured to operate in Augmented mode, wherein it can store ternary data (3 levels (0,0), (0,1), (1,0)) in a dynamic manner. Thus, based on the mode of operation, the proposed augmented memory bit-cells can either store one static bit of data or >1 bit of data in a dynamic format. We show the feasibility of our proposed bit-cells through extensive simulations at Globalfoundries 22nm FDX node. It is worth mentioning, the novel scheme of augmented memory bit-cells can be seamlessly combined with existing in-memory computing approaches for added energy and throughput benefits.
A drl based distributed formation control scheme with stream based collision avoidance
Venue: arXiv cs.RO 2021
Year: 2021

Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this paper, we propose a deep reinforcement learning (DRL) based distributed formation control scheme for autonomous vehicles. A modified stream-based obstacle avoidance method is applied to smoothen the optimal trajectory, and onboard sensors such as Lidar and antenna arrays are used to obtain local relative distance and angle information. The proposed scheme obtains a scalable distributed control policy which jointly optimizes formation tracking error and average collision rate with local observations. Simulation results demonstrate that our method outperforms two other state-of-the-art algorithms on maintaining formation and collision avoidance.
Blockchain mining in pools: Analyzing the trade-off between profitability and ruin
Venue: arXiv cs.CR 2021
Year: 2021

The resource-consuming mining of blocks on a blockchain equipped with a proof of work consensus protocol bears the risk of ruin, namely when the operational costs for the mining exceed the received rewards. In this paper we investigate to what extent it is of interest to join a mining pool that reduces the variance of the return of a miner for a specified cost for participation. Using methodology from ruin theory and risk sharing in insurance, we quantitatively study the effects of pooling in this context and derive several explicit formulas for quantities of interest. The results are illustrated in numerical examples for parameters of practical relevance.
Convex Influences
Venue: arXiv cs.CC 2021
Year: 2021

We introduce a new notion of influence for symmetric convex sets over Gaussian space, which we term "convex influence". We show that this new notion of influence shares many of the familiar properties of influences of variables for monotone Boolean functions $f: \{\pm1\}^n \to \{\pm1\}.$ Our main results for convex influences give Gaussian space analogues of many important results on influences for monotone Boolean functions. These include (robust) characterizations of extremal functions, the Poincaré inequality, the Kahn-Kalai-Linial theorem, a sharp threshold theorem of Kalai, a stability version of the Kruskal-Katona theorem due to O'Donnell and Wimmer, and some partial results towards a Gaussian space analogue of Friedgut's junta theorem. The proofs of our results for convex influences use very different techniques than the analogous proofs for Boolean influences over $\{\pm1\}^n$. Taken as a whole, our results extend the emerging analogy between symmetric convex sets in Gaussian space and monotone Boolean functions from $\{\pm1\}^n$ to $\{\pm1\}$
Sparse Distributed Memory using Spiking Neural Networks on Nengo
Venue: arXiv cs.NE 2021
Year: 2021

We present a Spiking Neural Network (SNN) based Sparse Distributed Memory (SDM) implemented on the Nengo framework. We have based our work on previous work by Furber et al, 2004, implementing SDM using N-of-M codes. As an integral part of the SDM design, we have implemented Correlation Matrix Memory (CMM) using SNN on Nengo. Our SNN implementation uses Leaky Integrate and Fire (LIF) spiking neuron models on Nengo. Our objective is to understand how well SNN-based SDMs perform in comparison to conventional SDMs. Towards this, we have simulated both conventional and SNN-based SDM and CMM on Nengo. We observe that SNN-based models perform similarly as the conventional ones. In order to evaluate the performance of different SNNs, we repeated the experiment using Adaptive-LIF, Spiking Rectified Linear Unit, and Izhikevich models and obtained similar results. We conclude that it is indeed feasible to develop some types of associative memories using spiking neurons whose memory capacity and other features are similar to the performance without SNNs. Finally we have implemented an application where MNIST images, encoded with N-of-M codes, are associated with their labels and stored in the SNN-based SDM.
Infusing Future Information into Monotonic Attention Through Language Models
Venue: arXiv cs.CL 2021
Year: 2021

Simultaneous neural machine translation(SNMT) models start emitting the target sequence before they have processed the source sequence. The recent adaptive policies for SNMT use monotonic attention to perform read/write decisions based on the partial source and target sequences. The lack of sufficient information might cause the monotonic attention to take poor read/write decisions, which in turn negatively affects the performance of the SNMT model. On the other hand, human translators make better read/write decisions since they can anticipate the immediate future words using linguistic information and domain knowledge.Motivated by human translators, in this work, we propose a framework to aid monotonic attention with an external language model to improve its decisions.We conduct experiments on the MuST-C English-German and English-French speech-to-text translation tasks to show the effectiveness of the proposed framework.The proposed SNMT method improves the quality-latency trade-off over the state-of-the-art monotonic multihead attention.
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
Venue: arXiv cs.CV 2021
Year: 2021

Optical Character Recognition (OCR) systems have been widely used in various of application scenarios. Designing an OCR system is still a challenging task. In previous work, we proposed a practical ultra lightweight OCR system (PP-OCR) to balance the accuracy against the efficiency. In order to improve the accuracy of PP-OCR and keep high efficiency, in this paper, we propose a more robust OCR system, i.e. PP-OCRv2. We introduce bag of tricks to train a better text detector and a better text recognizer, which include Collaborative Mutual Learning (CML), CopyPaste, Lightweight CPUNetwork (LCNet), Unified-Deep Mutual Learning (U-DML) and Enhanced CTCLoss. Experiments on real data show that the precision of PP-OCRv2 is 7% higher than PP-OCR under the same inference cost. It is also comparable to the server models of the PP-OCR which uses ResNet series as backbones. All of the above mentioned models are open-sourced and the code is available in the GitHub repository PaddleOCR which is powered by PaddlePaddle.
PEEK: A Large Dataset of Learner Engagement with Educational Videos
Venue: arXiv cs.IR 2021
Year: 2021

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great potential to improve learning gains. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In this work, we release a large, novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature. The video lectures have been associated with Wikipedia concepts related to the material of the lecture, thus providing a humanly intuitive taxonomy. We believe that granular learner engagement signals in unison with rich content representations will pave the way to building powerful personalization algorithms that will revolutionise educational and informational recommendation systems. Towards this goal, we 1) construct a novel dataset from a popular video lecture repository, 2) identify a set of benchmark algorithms to model engagement, and 3) run extensive experimentation on the PEEK dataset to demonstrate its value. Our experiments with the dataset show promise in building powerful informational recommender systems. The dataset and the support code is available publicly.
The pyglaf argumentation reasoner (ICCMA2021)
Venue: arXiv cs.AI 2021
Year: 2021

The pyglaf reasoner takes advantage of circumscription to solve computational problems of abstract argumentation frameworks. In fact, many of these problems are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription. Within pyglaf, Python is used to build the encodings and to control the execution of the external circumscription solver, which extends the SAT solver glucose and implements algorithms taking advantage of unsatisfiable core analysis and incremental computation.
Self-adaptive Architectures in IoT Systems: A Systematic Literature Review
Venue: arXiv cs.NI 2021
Year: 2021

Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in bandwidth consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with simultaneous events of different types. In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Venue: arXiv cs.AI 2021
Year: 2021

Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns are barely fully enumerated. This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program, in order to improve prediction performance. Compared with a generic GNN, our enhancements include a synthesis of multiple representations learned from the several parsed graphs of a program, and a new training loss metric that leverages the fine granularity of labeling. Our model outperforms multiple text, image and graph-based approaches, across two real-world datasets.
Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose
Venue: arXiv cs.HC 2021
Year: 2021

We outline the role of forward and inverse modelling approaches in the design of human--computer interaction systems. Causal, forward models tend to be easier to specify and simulate, but HCI requires solutions of the inverse problem. We infer finger 3D position $(x,y,z)$ and pose (pitch and yaw) on a mobile device using capacitive sensors which can sense the finger up to 5cm above the screen. We use machine learning to develop data-driven models to infer position, pose and sensor readings, based on training data from: 1. data generated by robots, 2. data from electrostatic simulators 3. human-generated data. Machine learned emulation is used to accelerate the electrostatic simulation performance by a factor of millions. We combine a Conditional Variational Autoencoder with domain expertise/models experimentally collected data. We compare forward and inverse model approaches to direct inference of finger pose. The combination gives the most accurate reported results on inferring 3D position and pose with a capacitive sensor on a mobile device.
RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management
Venue: arXiv cs.CV 2021
Year: 2021

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.
From Cloud to Edge: A First Look at Public Edge Platforms
Venue: arXiv cs.NI 2021
Year: 2021

Public edge platforms have drawn increasing attention from both academia and industry. In this study, we perform a first-of-its-kind measurement study on a leading public edge platform that has been densely deployed in China. Based on this measurement, we quantitatively answer two critical yet unexplored questions. First, from end users' perspective, what is the performance of commodity edge platforms compared to cloud, in terms of the end-to-end network delay, throughput, and the application QoE. Second, from the edge service provider's perspective, how are the edge workloads different from cloud, in terms of their VM subscription, monetary cost, and resource usage. Our study quantitatively reveals the status quo of today's public edge platforms, and provides crucial insights towards developing and operating future edge services.
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago
Venue: arXiv cs.LG 2021
Year: 2021

Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, which, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machine-learning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the model's prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop a clustering framework to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.
Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images
Venue: arXiv cs.CV 2021
Year: 2021

Scene text detection has drawn the close attention of researchers. Though many methods have been proposed for horizontal and oriented texts, previous methods may not perform well when dealing with arbitrary-shaped texts such as curved texts. In particular, confusion problem arises in the case of nearby text instances. In this paper, we propose a simple yet effective method for accurate arbitrary-shaped nearby scene text detection. Firstly, a One-to-Many Training Scheme (OMTS) is designed to eliminate confusion and enable the proposals to learn more appropriate groundtruths in the case of nearby text instances. Secondly, we propose a Proposal Feature Attention Module (PFAM) to exploit more effective features for each proposal, which can better adapt to arbitrary-shaped text instances. Finally, we propose a baseline that is based on Faster R-CNN and outputs the curve representation directly. Equipped with PFAM and OMTS, the detector can achieve state-of-the-art or competitive performance on several challenging benchmarks.
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Quantization has been proven to be a vital method for improving the inference efficiency of deep neural networks (DNNs). However, it is still challenging to strike a good balance between accuracy and efficiency while quantizing DNN weights or activation values from high-precision formats to their quantized counterparts. We propose a new method called elastic significant bit quantization (ESB) that controls the number of significant bits of quantized values to obtain better inference accuracy with fewer resources. We design a unified mathematical formula to constrain the quantized values of the ESB with a flexible number of significant bits. We also introduce a distribution difference aligner (DDA) to quantitatively align the distributions between the full-precision weight or activation values and quantized values. Consequently, ESB is suitable for various bell-shaped distributions of weights and activation of DNNs, thus maintaining a high inference accuracy. Benefitting from fewer significant bits of quantized values, ESB can reduce the multiplication complexity. We implement ESB as an accelerator and quantitatively evaluate its efficiency on FPGAs. Extensive experimental results illustrate that ESB quantization consistently outperforms state-of-the-art methods and achieves average accuracy improvements of 4.78%, 1.92%, and 3.56% over AlexNet, ResNet18, and MobileNetV2, respectively. Furthermore, ESB as an accelerator can achieve 10.95 GOPS peak performance of 1k LUTs without DSPs on the Xilinx ZCU102 FPGA platform. Compared with CPU, GPU, and state-of-the-art accelerators on FPGAs, the ESB accelerator can improve the energy efficiency by up to 65x, 11x, and 26x, respectively.
DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation
Venue: arXiv cs.LG 2021
Year: 2021

Trip itinerary recommendation finds an ordered sequence of Points-of-Interest (POIs) from a large number of candidate POIs in a city. In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-k alternative itineraries for given source and destination POIs. These alternative itineraries would be not only popular given the historical routes adopted by past users but also dissimilar (or diverse) to each other. The DeepAltTrip consists of two major components: (i) Itinerary Net (ITRNet) which estimates the likelihood of POIs on an itinerary by using graph autoencoders and two (forward and backward) LSTMs; and (ii) a route generation procedure to generate k diverse itineraries passing through relevant POIs obtained using ITRNet. For the route generation step, we propose a novel sampling algorithm that can seamlessly handle a wide variety of user-defined constraints. To the best of our knowledge, this is the first work that learns from historical trips to provide a set of alternative itineraries to the users. Extensive experiments conducted on eight popular real-world datasets show the effectiveness and efficacy of our approach over state-of-the-art methods.