Reviewerly/exHarmony huggingface dataset:
Automated Machine Learning on Graph

Machine learning on graphs has been extensively studiedin both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attentions from the research community. In this tutorial, we discuss AutoML on graphs, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. To the best of our knowledge, this tutorial is the first to systematically and comprehensively review automated machine learning on graphs, possessing a great potential to draw a large amount of interests in the community.
Bigger than Bitcoin: A Theoretical Typology and Research Agenda for Digital Currencies

Abstract Currency is the fundamental economic technology that makes promises credible among actors within and across societies. From shells, to metals, to paper, the technology of money has continually evolved to meet the changing needs of human society. The twenty-first century is witnessing yet another evolution in the technology of money: digital currencies. Although political economy scholarship has begun to focus on digital currencies, this research has largely focused on single early examples like Bitcoin. I argue that this generally narrow focus has obscured important degrees of variation among digital currencies and, by extension, has omitted important lines of research on digital currencies as a familiar evolution in the technology of money. In this article, I revisit the history of digital currencies with explicit attention to not only economic inefficiencies but also political power structures and offer a new typology for theoretically organizing digital currencies along dimensions relevant to practitioners of political economy. I illustrate that variation along these typological dimensions produces important differences among different digital currencies and, relatedly, I explore the implications this has for digital currencies’ externalities and governance demands. Drawing on this typology, I conclude with a proposed research agenda for the political economy of digital currencies.
GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results. Code, Data, and Study Materials Availability: https://github.com/amcrisan/GEVitRec.
Searching for Unknown Anomalies in Hierarchical Data Streams

We consider the problem of anomaly detection among a large number of processes, where the probabilistic models of anomalies are unknown. At each time, aggregated noisy observations can be taken from a chosen subset of processes, where the chosen subset conforms to a tree structure. The observation distribution depends on the chosen subset and the absence/presence of anomalies. We develop a sequential search strategy using a hierarchical Kolmogorov-Smirnov (KS) statistics. Referred to as Tree-based Anomaly Search using KS statistics (TASKS), the proposed strategy is order-optimal with respect to the size of the search space and the detection accuracy.
Image source identification with known post-processed based on convolutional neural network

Image source identification is important to verify the origin and authenticity of digital images. However, when images are altered by some post-processing, the performance of the existing source verification methods may degrade. In this paper, we propose a convolutional neural network (CNN) to solve the above problem. Specifically, we present a theoretical framework for different tampering operations, to confirm whether a single operation has affected photo response non-uniformity (PRNU) contained in images. Then, we divide these operations into two categories: non-influential operation and influential operation. Besides, the images altered by the combination of non-influential and influential operations are equal to images that have only undergone a single influential operation. To make our introduced CNN robust to both non-influential operation and influential operation, we define a multi-kernel noise extractor that consists of a high-pass filter and three parallel convolution filters of different sizes. The features generated by the parallel convolution layers are then fed to subsequent convolutional layers for further feature extraction. The experimental results provide the effectiveness of our method.
A Study of One-Class Classification Algorithms for Wearable Fall Sensors

In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.
How do we feel? User Perceptions of a Soft Robot Surface for Regulating Human Emotion in Confined Living Spaces

Pandemic or not, many of us are spending more time indoors, apart from others and from nature. We report on user perceptions of "pheB," a non-anthropomorphic, bio-inspired, pneumatic-actuated robot surface aiming to help regulate our emotional states when inhabiting confined spaces, such as our homes. A survey (N = 50) tested perceived stress levels before and after performing guided breathing exercises under two conditions: led by the pheB prototype and a 2D vector graphic. We learned that perceived stress levels were significantly lower after performing the pheB led exercises. Comments from respondents who did not prefer pheB suggested a possible "Uncanny Valley" effect. The same survey elicited feedback on possible design features for pheB related to color, scale, orientation, and edge complexity. Beyond reporting on a soft robotic artifact of our own design, the research reported here offers an exemplar for conducting user studies online of novel robot designs, highlights user perceptions of bio-inspired robots in HRI research and considers biophilia and the uncanny valley for non-anthropomorphic robots supporting human-centered design applications.
A High-Level Modeling Framework for Estimating Hardware Metrics of CNN Accelerators

GPUs became the reference platform for both training and inference phases of Convolutional Neural Networks (CNN) due to their tailored architecture to the CNN operators.However, GPUs are power-hungry architectures.A path to enable the deployment of CNNs in energy-constrained devices is adopting hardware accelerators for the inference phase.The design space exploration of CNNs using standard approaches, such as RTL, is limited due to their complexity.Thus, designers need frameworks enabling design space exploration that delivers accurate hardware estimation metrics to deploy CNNs.This work proposes a framework to explore CNNs design space, providing power, performance, and area (PPA) estimations.The heart of the framework is a system simulator.The system simulator front-end is TensorFlow, and the back-end is performance estimations obtained from the physical synthesis of hardware accelerators, not only from components like multipliers and adders.The first set of results evaluate the CNN accuracy using integer quantization, the accelerators PPA after physical synthesis, and the benefits of using a system simulator.These results allow a rich design space exploration, enabling selecting the best set of CNN parameters to meet the design constraints.
Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion

Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes.
Contextual modal types for algebraic effects and handlers

Programming languages with algebraic effects often track the computations’ effects using type-and-effect systems. In this paper, we propose to view an algebraic effect theory of a computation as a variable context; consequently, we propose to track algebraic effects of a computation with contextual modal types . We develop ECMTT, a novel calculus which tracks algebraic effects by a contextualized variant of the modal □ (necessity) operator, that it inherits from Contextual Modal Type Theory (CMTT). Whereas type-and-effect systems add effect annotations on top of a prior programming language, the effect annotations in ECMTT are inherent to the language, as they are managed by programming constructs corresponding to the logical introduction and elimination forms for the □ modality. Thus, the type-and-effect system of ECMTT is actually just a type system. Our design obtains the properties of local soundness and completeness, and determines the operational semantics solely by β-reduction, as customary in other logic-based calculi. In this view, effect handlers arise naturally as a witness that one context (i.e., algebraic theory) can be reached from another, generalizing explicit substitutions from CMTT. To the best of our knowledge, ECMTT is the first system to relate algebraic effects to modal types. We also see it as a step towards providing a correspondence in the style of Curry and Howard that may transfer a number of results from the fields of modal logic and modal type theory to that of algebraic effects.
T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection

Making correct decisions as to whether code chunks should be considered similar becomes increasingly important in software design and education and not only can improve the quality of computer programs, but also help assure the integrity of student assessments. In this paper we test numerous source code similarity detection tools on pairs of code fragments written in the data science-oriented functional programming language R. Contrary to mainstream approaches, instead of considering symmetric measures of “how much code chunks A and B are similar to each other”, we propose and study the nonsymmetric degrees of inclusion “to what extent A is a subset of B” and “to what degree B is included in A”. Overall, t-norms yield better precision (how many suspicious pairs are actually similar), t-conorms maximise recall (how many similar pairs are successfully retrieved), and custom aggregation functions fitted to training data provide a good balance between the two. Also, we find that program dependence graph-based methods tend to outperform those relying on normalised source code text, tokens, and names of functions invoked.
Alleviation of COVID by means of Social Distancing &amp; Face Mask Detection Using YOLO V4

This paper consists of social distancing & face mask detection for the events of coronavirus, alleviation in such pandemic can be solved by social distancing as well as putting on a face mask. This small step of wearing a face mask as well as following social distancing would save lots of lives as the spread of the virus could be mitigated. YOLO stands for You Only Look Once, this algorithm is used for Object Detection as well as Object Tracking, this research uses YOLO for calculating the social distancing & identifying face mask on people's face with the help of Object Detection, whereas tracking the face is done by Object Tracking. The minimum distance to keep while adhering for social distancing is 6 Feet, keeping this as the base for calculating distance, the model was trained and used for object detection as well as for object tracking. There are different types of algorithms available, YOLO stands out from all the other present currently. The custom datasets were used for the understanding the face masks and it was trained on those datasets for detection and tracking. For evaluation of the trained model, mAP (Mean Average Precision) was calculated for both the use cases (Social Distancing & Face Mask Detection), it works by comparing the ground-truth bounding box vs the detected box and, in the end, returns the score. The higher the mAP score would be, the better model is in the detection of objects. Mean Average Precision was calculated for two different thresholds (0.25 % & 0.50 %) with 101 recall points. Three different classes were created for classification those were Good, Bad & None, for which True Positive & False Positive values were calculated with ROC Curve for better understanding.
Linecounter: Learning Handwritten Text Line Segmentation By Counting

Handwritten Text Line Segmentation (HTLS) is a low-level but important task for many higher-level document processing tasks like handwritten text recognition. It is often formulated in terms of semantic segmentation or object detection in deep learning. However, both formulations have serious shortcomings. The former requires heavy post-processing of splitting/merging adjacent segments, while the latter may fail on dense or curved texts. In this paper, we propose a novel Line Counting formulation for HTLS – that involves counting the number of text lines from the top at every pixel location. This formulation helps learn an end-to-end HTLS solution that directly predicts per-pixel line number for a given document image. Furthermore, we propose a deep neural network (DNN) model LineCounter to perform HTLS through the Line Counting formulation. Our extensive experiments on the three public datasets (ICDAR2013-HSC [1], HIT-MW [2], and VML-AHTE [3]) demonstrate that LineCounter outperforms state-of-the-art HTLS approaches. Source code is available at https://github.com/Leedeng/LineCounter.
Embodied Language Learning with Paired Variational Autoencoders

Language acquisition is an integral part of developmental robotics, which aims at understanding the key components in human development and learning to utilise them in artificial agents. Similar to human infants, robots can learn language while interacting with objects in their environments and receiving linguistic input. This process, also coined as embodied language learning, can enhance language acquisition in robots via multiple modalities including visual and sensorimotor input. In this work, we explore ways to translate a simple action in a tabletop environment into various linguistic commands based on an existing approach which exploits the idea of multiple autoencoders. While the existing approach focuses on strict one-to-one mappings between actions and descriptions by implicitly binding two standard autoencoders in the latent space, we propose a variational autoencoder model to facilitate one-to-many mapping between actions and descriptions. Additionally, for extracting visual features, we employ channel-separated convolutional autoencoders to better handle complex visual input. The results show that our model outperforms the existing approach in associating multiple commands with the corresponding action.
Imperceptible Adversarial Examples For Fake Image Detection

Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society. Many methods have been proposed to detect fake images, but they are vulnerable to adversarial perturbations – intentionally designed noises that can lead to the wrong prediction. Existing methods of attacking fake image detectors usually generate adversarial perturbations to perturb almost the entire image. This is redundant and increases the perceptibility of perturbations. In this paper, we propose a novel method to disrupt the fake image detection by determining key pixels to a fake image detector and attacking only the key pixels, which results in the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> and the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norms of adversarial perturbations much less than those of existing works. Experiments on two public datasets with three fake image detectors indicate that our proposed method achieves state-of the-art performance in both white-box and black-box attacks.
Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning

In this study, reinforcement learning was applied to learning two-dimensional path planning including obstacle avoidance by unmanned aerial vehicle (UAV) in an indoor environment. The task assigned to the UAV was to reach the goal position in the shortest amount of time without colliding with any obstacles. Reinforcement learning was performed in a virtual environment created using Gazebo, a virtual environment simulator, to reduce the learning time and cost. Curriculum learning, which consists of two stages was performed for more efficient learning. As a result of learning with two reward models, the maximum goal rates achieved were 71.2% and 88.0%.
Adversarially Robust One-class Novelty Detection

One-class novelty detectors are trained with examples of a particular class and are tasked with identifying whether a query example belongs to the same known class. Most recent advances adopt a deep auto-encoder style architecture to compute novelty scores for detecting novel class data. Deep networks have shown to be vulnerable to adversarial attacks, yet little focus is devoted to studying the adversarial robustness of deep novelty detectors. In this article, we first show that existing novelty detectors are susceptible to adversarial examples. We further demonstrate that commonly-used defense approaches for classification tasks have limited effectiveness in one-class novelty detection. Hence, we need a defense specifically designed for novelty detection. To this end, we propose a defense strategy that manipulates the latent space of novelty detectors to improve the robustness against adversarial examples. The proposed method, referred to as Principal Latent Space (PrincipaLS), learns the incrementally-trained cascade principal components in the latent space to robustify novelty detectors. PrincipaLS can purify latent space against adversarial examples and constrain latent space to exclusively model the known class distribution. We conduct extensive experiments on eight attacks, five datasets and seven novelty detectors, showing that PrincipaLS consistently enhances the adversarial robustness of novelty detection models.