Reviewerly/exHarmony huggingface dataset:
Graph Few-Shot Learning via Knowledge Transfer

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.
Deep Clustering: On the Link Between Discriminative Models and K-Means

In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning. It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised deep learning results. On the surface, several recent discriminative models may seem unrelated to K-means. This study shows that these models are, in fact, equivalent to K-means under mild conditions and common posterior models and parameter regularization. We prove that, for the commonly used logistic regression posteriors, maximizing the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> L2 regularized mutual information via an approximate alternating direction method (ADM) is equivalent to minimizing a soft and regularized K-means loss. Our theoretical analysis not only connects directly several recent state-of-the-art discriminative models to K-means, but also leads to a new soft and regularized deep K-means algorithm, which yields competitive performance on several image clustering benchmarks.
Sentiment analysis of extremism in social media from textual information

Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%.
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

Graph structured data has wide applicability in various domains such as physics, chemistry, biology, computer vision, and social networks, to name a few. Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and generalization ability. However, explaining the effectiveness of GNN models is a challenging task because of the complex nonlinear transformations made over the iterations. In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method. GraphLIME is a generic GNN-model explanation framework that learns a nonlinear interpretable model locally in the subgraph of the node being explained. Through experiments on two real-world datasets, the explanations of GraphLIME are found to be of extraordinary degree and more descriptive in comparison to the existing explanation methods.
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification

Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural networks was proposed for classifier building, which can be constructed without bounding boxes or mask ground truth label of training data. The only label required in this method is the classification of mammographic images, which can be relatively easy to collect from diagnostic reports. Because breast lesions usually take up a fraction of the total area visualized in the mammographic image, we propose different pooling structures for convolutional neural networks(CNNs) instead of the common pooling methods, which divide the image into regions and select the few with high probability of malignancy as the representation of the whole mammographic image. The proposed pooling structures can be applied on most CNN-based models, which may greatly improve the models' performance on mammographic image data with the same input. Experimental results on the publicly available INbreast dataset and CBIS dataset indicate that the proposed pooling structures perform satisfactorily on mammographic image data compared with previous state-of-the-art mammographic image classifiers and detection algorithm using segmentation annotations.
A Bot and a Smile: Interpersonal Impressions of Chatbots and Humans Using Emoji in Computer-mediated Communication

Artificially intelligent (AI) agents increasingly occupy roles once served by humans in computer-mediated communication (CMC). Technological affordances like emoji give interactants (humans or bots) the ability to partially overcome the limited nonverbal information in CMC. However, despite the growth of chatbots as conversational partners, few CMC and human-machine communication (HMC) studies have explored how bots’ use of emoji impact perceptions of communicator quality. This study examined the relationship between emoji use and observers’ impressions of interpersonal attractiveness, CMC competence, and source credibility; and whether impressions formed of human versus chatbot message sources were different. Results demonstrated that participants rated emoji-using chatbot message sources similarly to human message sources, and both humans and bots are significantly more socially attractive, CMC competent, and credible when compared to verbal-only message senders. Results are discussed with respect to the CASA paradigm and the human-to-human interaction script framework.
A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional Computationally Expensive Problems

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.
Metasurface‐Enabled Generation of Circularly Polarized Single Photons

Single photons carrying spin angular momentum (SAM), i.e., circularly polarized single photons generated typically by subjecting a quantum emitter (QE) to a strong magnetic field at low temperatures, are at the core of chiral quantum optics enabling nonreciprocal single-photon configurations and deterministic spin-photon interfaces. Here, a conceptually new approach to the room-temperature generation of SAM-coded single photons (SSPs) is described, which entails QE nonradiative coupling to surface plasmons being transformed, by interacting with an optical metasurface, into a collimated stream of SSPs with the designed handedness. Design, fabrication, and characterization of SSP sources, consisting of dielectric circular nanoridges with azimuthally varying widths deterministically fabricated on a dielectric-protected silver film around a nanodiamond containing a nitrogen-vacancy center, are reported. With properly engineered phases of QE-originated fields scattered by nanoridges, the outcoupled photons are characterized by a well-defined SAM (with the chirality >0.8) and high directionality (collection efficiency up to 92%).
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects

Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such datasets is growing continuously. Despite the fact that some of exist, they are scarce and typically have restricted setups, such as a single object per sequence, or they focus on specific object types, such as textureless industrial parts. Besides, two significant components are often ignored: training using only available 3D models instead of real data and scalability, i.e. training one method to detect all objects rather than training one detector per object. Other challenges, such as occlusions, changing light conditions and changes in object appearance, as well precisely defined benchmarks are either not present or are scattered among different datasets. In this paper we present a dataset for 6D pose estimation that covers the above-mentioned challenges, mainly targeting training from 3D models (both textured and textureless), scalability, occlusions, and changes in light conditions and object appearance. The dataset features 33 objects (17 toy, 8 household and 8 industry-relevant objects) over 13 scenes of various difficulty. We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter. We also set a baseline for the presented benchmarks using a state-of-the-art DPOD detector. Considering the difficulty of making such datasets, we plan to release the code allowing other researchers to extend this dataset or make their own datasets in the future.
Pipelined Data-Parallel CPU/GPU Scheduling for Multi-DNN Real-Time Inference

Deep neural networks (DNNs) have been showing significant success in various applications, such as autonomous driving, mobile devices, and Internet of Things. Although much research has been conducted to optimize the structure of DNNs, limited attention has been given to their timely execution, specifically on the scheduling of real-time inference requests to various DNN models. For instance, existing DNN frameworks, such as Caffe, TensorFlow and Torch, only provide a single-level priority, one-DNN-per-process execution model and sequential inference interfaces. They can be particularly problematic when used in edge computing and in-vehicle intelligence systems for multiple DNNs, as response time may become unpredictably long in the worst case while leaving system resources underutilized. This paper presents DART, a DNN scheduling framework that offers deterministic response time to real-time tasks and increased throughput to best-effort tasks. DART employs a pipeline-based scheduling architecture with data parallelism, where heterogeneous CPUs and GPUs are arranged into nodes with different parallelism levels. DART also includes pipeline stage design and node configuration schemes, admission control, execution time profiling, and runtime enforcement techniques. We evaluated DART on Intel x86 Xeon and Nvidia ARM platforms with GPUs. Experimental results indicate that DART significantly outperforms the existing approaches, by up to 98.5% shorter worst-case response time for real-time tasks while simultaneously achieving up to 17.9% higher throughput for best-effort tasks.
He Is Just Like Me

Over the past few years, the technological vision of the HCI and UbiComp communities regarding conversational devices has become manifest in the form of smart speakers such as Google Home and Amazon Echo. Even though millions of households have adopted and integrated these devices into their daily lives, we lack a deep understanding of how different members of a household use such devices. To this end, we conducted interviews with 18 families and collected their Google Home Activity logs to understand the usage patterns of adults and children. Our findings reveal that there are substantial differences in the ways smart speakers are used by adults and children in families over an extended period of time. We report on how parents influence children's use and how different users perceive the devices. Finally, we discuss the implications of our findings and provide guidelines for improving the design of future smart speakers and conversational agents.
A Solution for Large-Scale Multi-Object Tracking

A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed.The algorithm is capable of tracking a very large, unknown and timevarying number of objects simultaneously, in the presence of a high number of false alarms, as well as missed detections and measurement origin uncertainty due to closely spaced objects.The algorithm is demonstrated on a simulated tracking scenario, where the peak number objects appearing simultaneously exceeds one million.Additionally, we introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric to determine a meaningful distance between two sets of tracks.We also develop an efficient strategy for its exact computation in large-scale scenarios to evaluate the performance of the proposed tracker.
CLITE: Efficient and QoS-Aware Co-Location of Multiple Latency-Critical Jobs for Warehouse Scale Computers

Large-scale data centers run latency-critical jobs with quality-of-service (QoS) requirements, and throughput-oriented background jobs, which need to achieve high perfor-mance. Previous works have proposed methods which cannot co-locate multiple latency-critical jobs with multiple back-grounds jobs while: (1) meeting the QoS requirements of all latency-critical jobs, and (2) maximizing the performance of the background jobs. This paper proposes CLITE, a Bayesian Optimization-based, multi-resource partitioning technique which achieves these goals. CLITE is publicly available at https://github.com/GoodwillComputingLab/CLITE.
Fractal: Post-quantum and Transparent Recursive Proofs from Holography

We present a new methodology to efficiently realize recursive composition of succinct non-interactive arguments of knowledge (SNARKs). Prior to this work, the only known methodology relied on pairing-based SNARKs instantiated on cycles of pairing-friendly elliptic curves, an expensive algebraic object. Our methodology does not rely on any special algebraic objects and, moreover, achieves new desirable properties: it is post-quantum and it is transparent (the setup is public coin). We exploit the fact that recursive composition is simpler for SNARKs with preprocessing, and the core of our work is obtaining a preprocessing zkSNARK for rank-1 constraint satisfiability (R1CS) that is post-quantum and transparent. We obtain this latter by establishing a connection between holography and preprocessing in the random oracle model, and then constructing a holographic proof for R1CS. We experimentally validate our methodology, demonstrating feasibility in practice. (The full version of this work is available at https://ia.cr/2019/1076 .)
BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos

Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely "unseen" videos is undocumented in the literature. In this work, we propose a new, supervised, background-subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.
A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks

Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.
A Tensor-Based Multiattributes Visual Feature Recognition Method for Industrial Intelligence

Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial manufacturing through industrial intelligence by incorporating production equipment, mobile terminals, and smart devices with wireless or wired networks. However, industrial visual information, such as images, videos, graphs, and texts, generated and collected from the industrial processes, contains various kinds of hidden value for industrial intelligence. Therefore, for the trend of providing ubiquitous industrial intelligence, new paradigms of perception and processing technologies of visual information such as recognition methods are required. However, industrial visual information is heterogeneous and complex with multiattributes, which presents significant challenges on visual information perception and processing technologies such as multiattributes recognition method. In this article, to provide industrial intelligence, a tensor-based visual feature recognition method is used to recognize the object from the perspective of multiattributes with the combination of attributes. To demonstrate its practical implementation, a case study about the industrial intelligence on the faulty location and diameter of bearings in the IIoT is described. Also, experiments on object recognition are carried out on the public image set COIL-100 to demonstrate the performance of the proposed method.
Performance Evaluation of Docker Container and Virtual Machine

Server virtualization is a technological innovation broadly used in IT enterprises. Virtualization provides a platform to run different services of operating systems on the cloud. It facilitates to build multiple virtual machines on a single basic physical machine either in the form of hypervisors or containers. To host many microservice applications, the emergent technology has introduced a model which consists of different operations performed by smaller individual deployed services. Thus, the demand for low-overhead virtualization technique is rapidly developing. There are many lightweight virtualization technologies; docker is one among them, which is an open-source platform. This technology allows developers and system admins to build, create, and run applications using docker engine. This paper provides the performance evaluation of Docker containers and virtual machines using standard benchmark tools such as Sysbench, Phoronix, and Apache benchmark, which include CPU performance, Memory throughput, Storage read/write performance, load test, and operation speed measurement.