OpenRaiser/Intern-Atlas huggingface dataset:
An ns-3 implementation of a battery-less node for energy-harvesting Internet of Things
Venue: arXiv cs.NI 2021
Year: 2021

In the Internet of Things (IoT), thousands of devices can be deployed to acquire data from the environment and provide service to several applications in different fields. In many cases, it is desirable that devices are self-sustainable in terms of energy. Therefore,the research community is exploring the possibility of employing battery-less devices, where the energy is derived solely from external and/or environmental sources, such as solar panels. In this work, we propose an ns-3 model of a (super) capacitor, which can be used as the storage of the harvested energy in a battery-less IoT device, and add the support for the intermittent behavior of devices, turning off/on according to their energy level. To exemplify the use of the model, we apply it to a LoRaWAN node, and compare the simulation outcomes with results in the literature obtained with mathematical analysis, confirming the accuracy of the implementation. Then, we show the importance of analyzing the interaction between energy availability and communication performance, paving the way for more accurate and realistic simulations in the field. The implemented code is made available as open source.
Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms
Venue: arXiv cs.LG 2021
Year: 2021

This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.
MultiScope: Efficient Video Pre-processing for Exploratory Video Analytics
Venue: arXiv cs.DB 2021
Year: 2021

Performing analytics tasks over large-scale video datasets is increasingly common in a wide range of applications. These tasks generally involve object detection and tracking operations that require applying expensive machine learning models, and several systems have recently been proposed to optimize the execution of video queries to reduce their cost. However, prior work generally optimizes execution speed in only one dimension, focusing on one optimization technique while ignoring other potential avenues for accelerating execution, thereby delivering an unsatisfactory tradeoff between speed and accuracy. We propose MultiScope, a general-purpose video pre-processor for object detection and tracking that explores multiple avenues for optimizing video queries to extract tracks from video with a superior tradeoff between speed and accuracy over prior work. We compare MultiScope against three recent systems on seven diverse datasets, and find that it provides a 2.9x average speedup over the next best baseline at the same accuracy level.
BrainPainter v2: Mouse Brain Visualization Software
Venue: arXiv cs.GR 2021
Year: 2021

BrainPainter is a software for the 3D visualization of human brain structures; it generates colored brain images using user-defined biomarker data for each brain region. However, BrainPainter is only able to generate human brain images. In this paper, we present updates to the existing BrainPainter software which enables the generation of mouse brain images. We load meshes for each mouse brain region, based on the Allen Mouse Brain Atlas, into Blender, a powerful 3D computer graphics engine. We then use Blender to color each region and generate images of subcortical, outer-cortical, inner-cortical, top and bottom view renders. In addition to those views, we add new render angles and separate visualization settings for the left and right hemispheres. While BrainPainter traditionally ran from the browser ( https://brainpainter.csail.mit.edu ), we also created a graphical user interface that launches image-generation requests in a user-friendly way, by connecting to the Blender backend via a Docker API. We illustrate a use case of BrainPainter for modeling the progression of tau protein accumulation in a mouse study. Our contributions can help neuroscientists visualize brains in mouse studies and show disease progression. In addition, integration into Blender can subsequently enable the generation of complex animations using a moving camera, generation of complex mesh deformations that simulate tumors and other pathologies, as well as visualization of toxic proteins using Blender's particle system.
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning
Venue: arXiv cs.LG 2021
Year: 2021

Driving in a dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision-making policy. The learning of such a policy requires a state representation that can encode the entire environment. Mid-level representations that encode a vehicle's environment as images have become a popular choice. Still, they are quite high-dimensional, limiting their use in data-hungry approaches such as reinforcement learning. In this article, we propose to learn a low-dimensional and rich latent representation of the environment by leveraging the knowledge of relevant semantic factors. To do this, we train an encoder-decoder deep neural network to predict multiple application-relevant factors such as the trajectories of other agents and the ego car. Furthermore, we propose a hazard signal based on other vehicles' future trajectories and the planned route which is used in conjunction with the learned latent representation as input to a down-stream policy. We demonstrate that using the multi-head encoder-decoder neural network results in a more informative representation than a standard single-head model. In particular, the proposed representation learning and the hazard signal help reinforcement learning to learn faster, with increased performance and less data than baseline methods.
Modeling the Nonsmoothness of Modern Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Modern neural networks have been successful in many regression-based tasks such as face recognition, facial landmark detection, and image generation. In this work, we investigate an intuitive but understudied characteristic of modern neural networks, namely, the nonsmoothness. The experiments using synthetic data confirm that such operations as ReLU and max pooling in modern neural networks lead to nonsmoothness. We quantify the nonsmoothness using a feature named the sum of the magnitude of peaks (SMP) and model the input-output relationships for building blocks of modern neural networks. Experimental results confirm that our model can accurately predict the statistical behaviors of the nonsmoothness as it propagates through such building blocks as the convolutional layer, the ReLU activation, and the max pooling layer. We envision that the nonsmoothness feature can potentially be used as a forensic tool for regression-based applications of neural networks.
An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques
Venue: arXiv cs.CL 2021
Year: 2021

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.
Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users' Requests: a Case Study of the Android Operating System
Venue: arXiv cs.SE 2021
Year: 2021

Text mining approaches are being used increasingly for business analytics. In particular, such approaches are now central to understanding users' feedback regarding systems delivered via online application distribution platforms such as Google Play. In such settings, large volumes of reviews of potentially numerous apps and systems means that it is infeasible to use manual mechanisms to extract insights and knowledge that could inform product improvement. In this context of identifying software system improvement options, text mining techniques are used to reveal the features that are mentioned most often as being in need of correction (e.g., GPS), and topics that are associated with features perceived as being defective (e.g., inaccuracy of GPS). Other approaches may supplement such techniques to provide further insights for online communities and solution providers. In this work we augment text mining approaches with social network analysis to demonstrate the utility of using multiple techniques. Our outcomes suggest that text mining approaches may indeed be supplemented with other methods to deliver a broader range of insights.
LSTM Based Sentiment Analysis for Cryptocurrency Prediction
Venue: arXiv cs.CL 2021
Year: 2021

Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.
An Introduction to Robust Graph Convolutional Networks
Venue: arXiv cs.LG 2021
Year: 2021

Graph convolutional neural networks (GCNs) generalize tradition convolutional neural networks (CNNs) from low-dimensional regular graphs (e.g., image) to high dimensional irregular graphs (e.g., text documents on word embeddings). Due to inevitable faulty data collection instruments, deceptive data manipulation, or other system errors, the data might be error-contaminated. Even a small amount of error such as noise can compromise the ability of GCNs and render them inadmissible to a large extent. The key challenge is how to effectively and efficiently employ GCNs in the presence of erroneous data. In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly. Experimental results on various real-world datasets demonstrate the superiority of the proposed model over the baseline methods and its robustness against different types of error.
Determination of weight coefficients for additive fitness function of genetic algorithm
Venue: arXiv cs.NE 2021
Year: 2021

The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function. This algorithm is the basis for an evolutionary process, which forms a stable and effective query population in a search engine to obtain highly relevant results. The paper gives a formal description of an algorithm fitness function, which is a weighted sum of three heterogeneous criteria. The selected methods for analytical determining of weight factors are described in detail. It is noted that expert assessment methods are impossible to use. The authors present a research methodology using the experimental results from earlier in the discussed project "Data Warehouse Support on the Base Intellectual Web Crawler and Evolutionary Model for Target Information Selection". There is a description of an initial dataset with data ranges for calculating weights. The calculation order is illustrated by examples. The research results in graphical form demonstrate the fitness function behavior during the genetic algorithm operation using various weighting options.
Teaching Information Security Management Using an Incident of Intellectual Property Leakage
Venue: arXiv cs.CR 2021
Year: 2021

Case-based learning is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information Systems. The real-world case is described in a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei Technologies Co. LTD, alleging theft of intellectual property and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies.
AR Mapping: Accurate and Efficient Mapping for Augmented Reality
Venue: arXiv cs.CV 2021
Year: 2021

Augmented reality (AR) has gained increasingly attention from both research and industry communities. By overlaying digital information and content onto the physical world, AR enables users to experience the world in a more informative and efficient manner. As a major building block for AR systems, localization aims at determining the device's pose from a pre-built "map" consisting of visual and depth information in a known environment. While the localization problem has been widely studied in the literature, the "map" for AR systems is rarely discussed. In this paper, we introduce the AR Map for a specific scene to be composed of 1) color images with 6-DOF poses; 2) dense depth maps for each image and 3) a complete point cloud map. We then propose an efficient end-to-end solution to generating and evaluating AR Maps. Firstly, for efficient data capture, a backpack scanning device is presented with a unified calibration pipeline. Secondly, we propose an AR mapping pipeline which takes the input from the scanning device and produces accurate AR Maps. Finally, we present an approach to evaluating the accuracy of AR Maps with the help of the highly accurate reconstruction result from a high-end laser scanner. To the best of our knowledge, it is the first time to present an end-to-end solution to efficient and accurate mapping for AR applications.
Team-oriented Consistency Checking of Heterogeneous Engineering Artifacts
Venue: arXiv cs.SE 2021
Year: 2021

Consistency checking of interdependent heterogeneous engineering artifacts, such as requirements, specifications, and code, is a challenging task in large-scale engineering projects. The lack of team-oriented solutions allowing a multitude of project stakeholders to collaborate in a consistent manner is thus becoming a critical problem. In this context, this work proposes an approach for team-oriented consistency checking of collaboratively developed heterogeneous engineering artifacts.
Equivariant Filtering Framework for Inertial-Integrated Navigation
Venue: arXiv cs.RO 2021
Year: 2021

This paper proposes a equivariant filtering (EqF) framework for the inertial-integrated state estimation problem. As the kinematic system of the inertial-integrated navigation can be naturally modeling on the matrix Lie group $SE_2(3)$, the symmetry of the Lie group can be exploited to design a equivariant filter which extends the invariant extended Kalman filtering on the group affine system. Furthermore, details of the analytic state transition matrices for left invariant error and right invariant error are given.
On TasNet for Low-Latency Single-Speaker Speech Enhancement
Venue: arXiv cs.SD 2021
Year: 2021

In recent years, speech processing algorithms have seen tremendous progress primarily due to the deep learning renaissance. This is especially true for speech separation where the time-domain audio separation network (TasNet) has led to significant improvements. However, for the related task of single-speaker speech enhancement, which is of obvious importance, it is yet unknown, if the TasNet architecture is equally successful. In this paper, we show that TasNet improves state-of-the-art also for speech enhancement, and that the largest gains are achieved for modulated noise sources such as speech. Furthermore, we show that TasNet learns an efficient inner-domain representation, where target and noise signal components are highly separable. This is especially true for noise in terms of interfering speech signals, which might explain why TasNet performs so well on the separation task. Additionally, we show that TasNet performs poorly for large frame hops and conjecture that aliasing might be the main cause of this performance drop. Finally, we show that TasNet consistently outperforms a state-of-the-art single-speaker speech enhancement system.
Continuous Conditional Generative Adversarial Networks (cGAN) with Generator Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Conditional Generative Adversarial Networks are known to be difficult to train, especially when the conditions are continuous and high-dimensional. To partially alleviate this difficulty, we propose a simple generator regularization term on the GAN generator loss in the form of Lipschitz penalty. Thus, when the generator is fed with neighboring conditions in the continuous space, the regularization term will leverage the neighbor information and push the generator to generate samples that have similar conditional distributions for each neighboring condition. We analyze the effect of the proposed regularization term and demonstrate its robust performance on a range of synthetic and real-world tasks.
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
Venue: arXiv cs.AI 2021
Year: 2021

Recently, deep Reinforcement Learning (RL) algorithms have achieved dramatically progress in the multi-agent area. However, training the increasingly complex tasks would be time-consuming and resources-exhausting. To alleviate this problem, efficient leveraging the historical experience is essential, which is under-explored in previous studies as most of the exiting methods may fail to achieve this goal in a continuously variational system due to their complicated design and environmental dynamics. In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design. We employ the knowledge distillation paradigm to transfer the knowledge among agents with the goal to accelerate the training phase for new tasks, while improving the asymptotic performance of agents. To empirically demonstrate the robustness and effectiveness of KnowRU, we perform extensive experiments on state-of-the-art multi-agent reinforcement learning (MARL) algorithms on collaborative and competitive scenarios. The results show that KnowRU can outperform the recently reported methods, which emphasizes the importance of the proposed knowledge reusing for MARL.
Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection
Venue: arXiv cs.CL 2021
Year: 2021

The datasets most widely used for abusive language detection contain lists of messages, usually tweets, that have been manually judged as abusive or not by one or more annotators, with the annotation performed at message level. In this paper, we investigate what happens when the hateful content of a message is judged also based on the context, given that messages are often ambiguous and need to be interpreted in the context of occurrence. We first re-annotate part of a widely used dataset for abusive language detection in English in two conditions, i.e. with and without context. Then, we compare the performance of three classification algorithms obtained on these two types of dataset, arguing that a context-aware classification is more challenging but also more similar to a real application scenario.
Automated Backend-Aware Post-Training Quantization
Venue: arXiv cs.CV 2021
Year: 2021

Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment. However, different hardware backends such as x86 CPU, NVIDIA GPU, ARM CPU, and accelerators may demand different implementations for quantized networks. This diversity calls for specialized post-training quantization pipelines to built for each hardware target, an engineering effort that is often too large for developers to keep up with. We tackle this problem with an automated post-training quantization framework called HAGO. HAGO provides a set of general quantization graph transformations based on a user-defined hardware specification and implements a search mechanism to find the optimal quantization strategy while satisfying hardware constraints for any model. We observe that HAGO achieves speedups of 2.09x, 1.97x, and 2.48x on Intel Xeon Cascade Lake CPUs, NVIDIA Tesla T4 GPUs, ARM Cortex-A CPUs on Raspberry Pi4 relative to full precision respectively, while maintaining the highest reported post-training quantization accuracy in each case.