Reviewerly/exHarmony huggingface dataset:
Geo-distributed efficient deployment of containers with Kubernetes

Software containers are changing the way applications are designed and executed. Moreover, in the last few years, we see the increasing adoption of container orchestration tools, such as Kubernetes, to simplify the management of multi-container applications. Kubernetes includes simple deployment policies that spread containers on computing resources located in the cluster and automatically scale them out or in based on some cluster-level metrics. As such, Kubernetes is not well-suited for deploying containers in a geo-distributed computing environment and dealing with the dynamism of application workload and computing resources. To tackle the problem, in this paper we present ge-kube (Geo-distributed and Elastic deployment of containers in Kubernetes), an orchestration tool that relies on Kubernetes and extends it with self-adaptation and network-aware placement capabilities. Ge-kube introduces flexible and decentralized control loops that can be easily equipped with different deployment policies. Specifically, we propose a two-step control loop, in which a model-based reinforcement learning approach dynamically controls the number of replicas of individual containers on the basis of the application response time, and a network-aware placement policy allocates containers on geo-distributed computing resources. To address the placement issue, we propose an optimization problem formulation and a network-aware heuristic, which explicitly take into account the non-negligible network delays among computing resources so to satisfy Quality of Service requirements of latency-sensitive applications. Using a surrogate CPU-intensive application and a real application (i.e., Redis), we conducted an extensive set of experiments, which show the benefits arising from the combination of elasticity and placement policies, as well as the advantages of using network-aware placement solutions.
Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge

Remote monitoring of older adults and detecting dangers in the state of human health have become essential elements of modern telemedicine. Falls are a frequent reason for deaths or post-traumatic complications in the elderly. Therefore, the early detection of falls can be crucial for the survival of a person or for providing necessary support. However, telemedicine data centers require scalable computing and storage resources for the growing number of monitored people. Dedicated approaches that allow for minimal data transmission of strictly interesting cases are also required. In this paper, we show a scalable architecture of a system that can monitor thousands of older adults, detect falls, and notify caregivers. Scalability tests that disclose requirements to enable large scale system operations were also performed. Moreover, we validated several Machine Learning models to evaluate their suitability in the detection process. Among the tested models, Boosted Decisions Trees resulted in the best classification performance. We also experimentally tested the detection of falls inside a Cloud-based data center and on an Edge IoT device. Results of tests on the device-to-cloud data transmission confirmed that significant reduction in the size of stored and transmitted data can be achieved while performing fall detection on the Edge.
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.
RadixSpline

Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data.
State-Aware Tracker for Real-Time Video Object Segmentation

In this work, we address the task of semi-supervised video object segmentation (VOS) and explore how to make efficient use of video property to tackle the challenge of semi-supervision. We propose a novel pipeline called State-Aware Tracker (SAT), which can produce accurate segmentation results with real-time speed. For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation. SAT achieves a promising result of 72.3% J&F mean with 39 FPS on DAVIS 2017-Val dataset, which shows a decent trade-off between efficiency and accuracy.
Bi-Directional Relationship Inferring Network for Referring Image Segmentation

Most existing methods do not explicitly formulate the mutual guidance between vision and language. In this work, we propose a bi-directional relationship inferring network (BRINet) to model the dependencies of cross-modal information. In detail, the vision-guided linguistic attention is used to learn the adaptive linguistic context corresponding to each visual region. Combining with the language-guided visual attention, a bi-directional cross-modal attention module (BCAM) is built to learn the relationship between multi-modal features. Thus, the ultimate semantic context of the target object and referring expression can be represented accurately and consistently. Moreover, a gated bi-directional fusion module (GBFM) is designed to integrate the multi-level features where a gate function is used to guide the bi-directional flow of multi-level information. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics.
A collaborative filtering recommender system using genetic algorithm

This paper presents a novel genetic-based recommender system (BLIGA) that depends on the semantic information and historical rating data. The main contribution of this research lies in evaluating the possible recommendation lists instead of evaluating items then forming the recommendation list. BLIGA utilizes the genetic algorithm to find the best list of items to the active user. Thus, each individual represents a candidate recommendation list. BLIGA hierarchically evaluates the individuals using three fitness functions. The first function uses semantic information about items to estimates the strength of the semantic similarity between items. The second function estimates the similarity in satisfaction level between users. The third function depends on the predicted ratings to select the best recommendation list. BLIGA results have been compared against recommendation results from alternative collaborative filtering methods. The results demonstrate the superiority of BLIGA and its capability to achieve more accurate predictions than the alternative methods regardless of the number of K-neighbors.
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition

Handwritten text and scene text suffer from various shapes and distorted patterns. Thus training a robust recognition model requires a large amount of data to cover diversity as much as possible. In contrast to data collection and annotation, data augmentation is a low cost way. In this paper, we propose a new method for text image augmentation. Different from traditional augmentation methods such as rotation, scaling and perspective transformation, our proposed augmentation method is designed to learn proper and efficient data augmentation which is more effective and specific for training a robust recognizer. By using a set of custom fiducial points, the proposed augmentation method is flexible and controllable. Furthermore, we bridge the gap between the isolated processes of data augmentation and network optimization by joint learning. An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network. Extensive experiments on various benchmarks, including regular scene text, irregular scene text and handwritten text, show that the proposed augmentation and the joint learning methods significantly boost the performance of the recognition networks. A general toolkit for geometric augmentation is available.
Universal Source-Free Domain Adaptation

There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for real-time adaptation. Devoid of such impractical assumptions, we propose a novel two-stage learning process. 1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model's ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. 2) In the Deployment stage, the goal is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To this end, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighting mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.
Assessing the Threat of Adversarial Examples on Deep Neural Networks for Remote Sensing Scene Classification: Attacks and Defenses

Deep neural networks, which can learn the representative and discriminative features from data in a hierarchical manner, have achieved state-of-the-art performance in the remote sensing scene classification task. Despite the great success that deep learning algorithms have obtained, their vulnerability toward adversarial examples deserves our special attention. In this article, we systematically analyze the threat of adversarial examples on deep neural networks for remote sensing scene classification. Both targeted and untargeted attacks are performed to generate subtle adversarial perturbations, which are imperceptible to a human observer but may easily fool the deep learning models. Simply adding these perturbations to the original high-resolution remote sensing (HRRS) images, adversarial examples can be generated, and there are only slight differences between the adversarial examples and the original ones. An intriguing discovery in our study shows that most of these adversarial examples may be misclassified into the wrong category by the state-of-the-art deep neural networks with very high confidence. This phenomenon, undoubtedly, may limit the practical deployment of these deep learning models in the safety-critical remote sensing field. To address this problem, the adversarial training strategy is further investigated in this article, which significantly increases the resistibility of deep models toward adversarial examples. Extensive experiments on three benchmark HRRS image data sets demonstrate that while most of the well-known deep neural networks are sensitive to adversarial perturbations, the adversarial training strategy helps to alleviate their vulnerability toward adversarial examples.
Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning

Federated learning is promising in enabling large-scale machine learning by massive mobile devices without exposing the raw data of users with strong privacy concerns. Existing work of federated learning struggles for accelerating the learning process, but ignores the energy efficiency that is critical for resource-constrained mobile devices. In this paper, we propose to improve the energy efficiency of federated learning by lowering CPU-cycle frequency of mobile devices who are faster in the training group. Since all the devices are synchronized by iterations, the federated learning speed is preserved as long as they complete the training before the slowest device in each iteration. Based on this idea, we formulate an optimization problem aiming to minimize the total system cost that is defined as a weighted sum of training time and energy consumption. Due to the hardness of nonlinear constraints and unawareness of network quality, we design an experience-driven algorithm based on the Deep Reinforcement Learning (DRL), which can converge to the near-optimal solution without knowledge of network quality. Experiments on a small-scale testbed and large-scale simulations are conducted to evaluate our proposed algorithm. The results show that it outperforms the start-of-the-art by 40% at most.
Control and readout of a superconducting qubit using a photonic link

Delivering on the revolutionary promise of a universal quantum computer will require processors with millions of quantum bits (qubits)1-3. In superconducting quantum processors4, each qubit is individually addressed with microwave signal lines that connect room-temperature electronics to the cryogenic environment of the quantum circuit. The complexity and heat load associated with the multiple coaxial lines per qubit limits the maximum possible size of a processor to a few thousand qubits5. Here we introduce a photonic link using an optical fibre to guide modulated laser light from room temperature to a cryogenic photodetector6, capable of delivering shot-noise-limited microwave signals directly at millikelvin temperatures. By demonstrating high-fidelity control and readout of a superconducting qubit, we show that this photonic link can meet the stringent requirements of superconducting quantum information processing7. Leveraging the low thermal conductivity and large intrinsic bandwidth of optical fibre enables the efficient and massively multiplexed delivery of coherent microwave control pulses, providing a path towards a million-qubit universal quantum computer.
IMUTube

The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone. To address this problem, we introduce IMUTube, an automated processing pipeline that integrates existing computer vision and signal processing techniques to convert videos of human activity into virtual streams of IMU data. These virtual IMU streams represent accelerometry at a wide variety of locations on the human body. We show how the virtually-generated IMU data improves the performance of a variety of models on known HAR datasets. Our initial results are very promising, but the greater promise of this work lies in a collective approach by the computer vision, signal processing, and activity recognition communities to extend this work in ways that we outline. This should lead to on-body, sensor-based HAR becoming yet another success story in large-dataset breakthroughs in recognition.
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study

The freedom of expression given by social media has a dark side: the growing proliferation of abusive contents on these platforms. Misogynistic speech is a kind of abusive language, which can be simplified as hate speech targeting women, and it is becoming a more and more relevant issue in recent years. AMI IberEval 2018 and AMI EVALITA 2018 were two shared tasks which mainly focused on tackling the problem of misogyny in Twitter, in three different languages, namely English, Italian, and Spanish. In this paper, we present an in-depth study on the phenomena of misogyny in those three languages, by focusing on three main objectives. Firstly, we investigate the most important features to detect misogyny and the issues which contribute to the difficulty of misogyny detection, by proposing a novel system and conducting a broad evaluation on this task. Secondly, we study the relationship between misogyny and other abusive language phenomena, by conducting a series of cross-domain classification experiments. Finally, we explore the feasibility of detecting misogyny in a multilingual environment, by carrying out cross-lingual classification experiments. Our system succeeded to outperform all state of the art systems in all benchmark AMI datasets both subtask A and subtask B. Moreover, intriguing insights emerged from error analysis, in particular about the interaction between different but related abusive phenomena. Based on our cross-domain experiment, we conclude that misogyny is quite a specific kind of abusive language, while we experimentally found that it is different from sexism. Lastly, our cross-lingual experiments show promising results. Our proposed joint-learning architecture obtained a robust performance across languages, worth to be explored in further investigation.
Service robots in the hospitality industry: The case of Henn-na hotel, Japan

Services are changing at an impressive pace boosted by the technological advances felt in Robotics, Big Data, and Artificial Intelligence (AI) that have uncovered new research opportunities. Our objective is to contribute to the literature by exploring the pros and cons of the use of service robots in the hospitality industry and to practice, by presenting the architectural and technological characteristics of a fully automated plant based on a relevant case. To achieve such goal, this article uses a systematic literature review to assess the state-of-the-art, characterize the unit of analysis, and find new avenues for further research. The results indicate that, in high customer contact settings, service robots tend to outperform humans when performing standardized tasks, because of their mechanical and analytical nature. Evidence also shows that, in some cases, service robots have not yet achieved the desired technological maturity to proficiently replace humans. In other words, the technology is not quite there yet, but this does not contradict the fact that new robot technologies, enabled by AI, will be able to replace the employees’ empathetic intelligence. In practical terms, organizations are facing challenges where they have to decide whether service robots are capable of completely replacing human labor or if they should rather invest in balanced options, such as human-robot systems, that seem to be a much more rational choice today.
A zero-knowledge-proof-based digital identity management scheme in blockchain

The traditional centralized digital identity management system (DIMS) has been subject to threats such as fragmented identity, single point of failure, internal attacks and privacy leakage. Emerging blockchain technology allows DIMSs to be deployed in it, which largely alleviates the problems caused by the centralized third party, but its inherent transparency and lack of privacy pose a huge challenge to DIMSs. In this regard, we leverage the smart contracts and zero-knowledge proof (ZKP) algorithms to improve the existing claim identity model in blockchain to realize the identity unlinkability, effectively avoiding the exposure of the ownership of attributes. Furthermore, we implement a system prototype named BZDIMS that includes a challenge-response protocol, which allows users to selectively disclose their ownership of attributes to service providers to protect users’ behavior privacy. Performance evaluation and security analysis show that our scheme achieves effective attribute privacy protection and a wider application scope compared with the prior model.
MotionSqueeze: Neural Motion Feature Learning for Video Understanding

Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows extracted by a separate off-the-shelf method. As the frame-by-frame optical flows require heavy computation, incorporating motion information has remained a major computational bottleneck for video understanding. In this work, we replace external and heavy computation of optical flows with internal and light-weight learning of motion features. We propose a trainable neural module, dubbed MotionSqueeze, for effective motion feature extraction. Inserted in the middle of any neural network, it learns to establish correspondences across frames and convert them into motion features, which are readily fed to the next downstream layer for better prediction. We demonstrate that the proposed method provides a significant gain on four standard benchmarks for action recognition with only a small amount of additional cost, outperforming the state of the art on Something-Something-V1 & V2 datasets.
Content-Aware Unsupervised Deep Homography Estimation

Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real world applications. To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art including deep solutions and feature-based solutions.