Reviewerly/exHarmony huggingface dataset:
LBGP: Learning Based Goal Planning for Autonomous Following in Front

This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the "following in front" application. Here, an autonomous robot aims to stay ahead of a person as the person freely walks around. Following in front is a challenging problem as the user’s intended trajectory is unknown and needs to be estimated, explicitly or implicitly, by the robot. In addition, the robot needs to find a feasible way to safely navigate ahead of human trajectory. Our deep RL module makes decisions at a high level by implicitly estimates the human trajectory and produces short-term navigational goals to guide the robot. These goals are used by a trajectory planner, which is responsible for low-level execution, to smoothly navigate the robot to the short-term goals, and eventually in front of the user. We employ curriculum learning in the deep RL module to efficiently achieve a high return. Our system outperforms the state-of-the-art in following ahead and is more reliable compared to end-to-end alternatives in both the simulation and real world experiments. In contrast to a pure deep RL approach, we demonstrate zero-shot transfer of the trained policy from simulation to the real world.
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets

This study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We trained personalized models for 195 individuals with different types and severities of speech impairment with training sets ranging in size from <1 minute to 18-20 minutes of speech data. Word error rate (WER) thresholds were selected to determine Success Percentage (the percentage of personalized models reaching the target WER) in different application scenarios. For the home automation scenario, 79% of speakers reached the target WER with 18-20 minutes of speech; but even with only 3-4 minutes of speech, 63% of speakers reached the target WER. Further evaluation found similar improvement on test sets with conversational and out-of-domain, unprompted phrases. Our results demonstrate that with only a few minutes of recordings, individuals with disordered speech could benefit from personalized ASR.
Interpretable Prediction of Diabetes from Tabular Health Screening Records Using an Attentional Neural Network

Health screening is conducted in numerous countries to observe general health conditions. Machine learning has been applied to health screening records to predict asymptomatic patients' future medical states. However, for medical researchers and physicians, it is crucial to know why machine learning methods made such predictions to understand the underlying mechanism of the disease and prescribe treatments; therefore, predictions must be interpretable. We investigated the ability of an attentional neural network that processes tabular data, namely TabNet, to determine attributes that contribute to making predictions of the aggravation of type 2 diabetes. We used both model-agnostic and model-specific interpretation methods. For the former, we tested SHapley Additive exPlanations (SHAP). For the latter, we used model-specific feature importance and the mask in the attentive transformer of TabNet. We found that this mask provides useful information regarding which items in a biochemical analysis affect the aggravation of type 2 diabetes. The results from model-agnostic and model-specific methods were consistent.
Multilabel Aerial Image Classification With Unsupervised Domain Adaptation

Deep learning (DL) methods are promising for the multilabel aerial image classification (MAIC) task. However, current DL methods face a common problem: the need for large multilabeled datasets. Collecting and annotating raw aerial image datasets can be extremely time- and labor-consuming. To address this concern in MAIC, domain adaptation (DA) provides a novel solution by transferring the knowledge learned from a label-rich dataset (i.e., the source domain) to a label-scarce dataset (i.e., the target domain), while current DA models are mainly designed for single-labeled tasks. In this article, we propose a novel end-to-end MAIC model based on DA techniques, named DA-MAIC. To the best of our knowledge, this article for the first time integrates DA to tackle the label scarcity problem in the MAIC task. Specifically, the proposed DA-MAIC is composed of two main parts: the image classifier and the domain classifier. The image classifier captures task-discriminative features based on the graph convolutional network (GCN) to predict multiple image labels; and the domain classifier extracts domain-invariant representations, which mitigates the domain shift between two underlying distributions. We extensively evaluate the proposed DA-MAIC from different perspectives on three benchmark datasets, including the commonly used UCM dataset, the high-resolution AID dataset, and the recently proposed DFC15 dataset. Both quantitative and qualitative results support that the proposed DA-MAIC can generalize the source domain knowledge to new scenarios and substantially improve the classification performance on the target domain task.
Quantum face recognition protocol with ghost imaging

Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis, and quantum independent component analysis. A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is [Formula: see text]-N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. "interaction-free" imaging or "ghost" imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.
Domain Decomposition Algorithms for Real-Time Homogeneous Diffusion Inpainting in 4K

Inpainting-based compression methods are qualitatively promising alternatives to transform-based codecs, but they suffer from the high computational cost of the inpainting step. This prevents them from being applicable to time-critical scenarios such as real-time inpainting of 4K images. As a remedy, we adapt state-of-the-art numerical algorithms of domain decomposition type to this problem. They decompose the image domain into multiple overlapping blocks that can be inpainted in parallel by means of modern GPUs. In contrast to classical block decompositions such as the ones in JPEG, the global inpainting problem is solved without creating block artefacts. We consider the popular homogeneous diffusion inpainting and supplement it with a multilevel version of an optimised restricted additive Schwarz (ORAS) method that solves the local problems with a conjugate gradient algorithm. This enables us to perform real-time in-painting of 4K colour images on contemporary GPUs, which is substantially more efficient than previous algorithms for diffusion-based inpainting.
Adaptive Flexible Optimal Graph for Unsupervised Dimensionality Reduction

Graph-based dimensionality reduction methods are widely used in classification and clustering tasks due to their superior performance. The key to the performance of these methods is how to construct the graph. Although some existing methods can obtain an adaptive graph and a projection matrix simultaneously by combining manifold learning and graph construction in a unified framework, the linear constraint used in manifold learning is too hard . To solve this problem, we propose a novel method named adaptive flexible optimal graph (AFOG) for unsupervised dimensionality reduction. AFOG can obtain an adaptive flexible optimal graph by relaxing the linear constraint in the process of low-dimensional manifold mapping. At the same time, by using the principle of maximum separability, it can also obtain an effective projection matrix, which can solve out-ofsample problems. Experiments on six public benchmark data sets indicate that AFOG outperforms several other state-of-the-art methods.
Geographic Semantic Network for Cross-View Image Geo-Localization

The task of cross-view image geo-localization aims to determine the geo-location (Global Positioning System (GPS) coordinates) of a query ground-view image by matching the image with GPS-tagged aerial (or satellite) images in the reference dataset. Due to the dramatic domain gap between the ground and aerial images, the problem is challenging. The existing approaches mainly adopt convolutional neural networks (CNNs) to learn discriminative features. However, these CNN-based methods mainly leverage appearance and semantic information but fail to jointly model the appearance, positional, and orientation properties of scene objects, which belong to the spatial hierarchy. Since spatial hierarchy information is crucial for cross-view feature correspondence, in this article, we propose an end-to-end network architecture, dubbed GeoNet. GeoNet consists of a ResNetX module and a GeoCaps module. On the one hand, the ResNetX module is developed to learn powerful intermediate feature maps and allows the stable propagation of gradients in deep CNNs. On the other hand, the GeoCaps module utilizes the capsule network to encapsulate the intermediate feature maps into several capsules, whose length and orientation represent the existence probability and spatial hierarchy information of scene objects, respectively. Moreover, by using a dynamic routing-by-agreement mechanism, the GeoCaps module is capable of modeling parts-to-whole relationships between scene objects, which is viewpoint invariant and capable of bridging the cross-view domain gap. In addition to GeoNet, we introduce a simple yet effective metric learning method, based on which two weighted soft margin loss functions with online batch hard sample mining are devised. These functions not only speed up convergence but also improve the generalization ability of the network. Extensive experiments on three well-known datasets demonstrate that our GeoNet not only achieves state-of-the-art results for the ground-to-aerial and aerial-to-ground geo-localization tasks but also outperforms competing approaches for the few-shot geo-localization task.
I Know What You Meant: Learning Human Objectives by (Under)estimating Their Choice Set

Assistive robots have the potential to help people perform everyday tasks. However, these robots first need to learn what it is their user wants them to do. Teaching assistive robots is hard for inexperienced users, elderly users, and users living with physical disabilities, since often these individuals are unable to show the robot their desired behavior. We know that inclusive learners should give human teachers credit for what they cannot demonstrate. But today’s robots do the opposite: they assume every user is capable of providing any demonstration. As a result, these robots learn to mimic the demonstrated behavior, even when that behavior is not what the human really meant! Here we propose a different approach to reward learning: robots that reason about the user’s demonstrations in the context of similar or simpler alternatives. Unlike prior works — which err towards overestimating the human’s capabilities — here we err towards underestimating what the human can input (i.e., their choice set). Our theoretical analysis proves that underestimating the human’s choice set is riskaverse, with better worst-case performance than overestimating. We formalize three properties to generate similar and simpler alternatives. Across simulations and a user study, our resulting algorithm better extrapolates the human’s objective. See the user study here: https://youtu.be/RgbH2YULVRo.
Big Data Processing on Single Board Computer Clusters: Exploring Challenges and Possibilities

For more than a decade, “big data” has been an industry and academia buzz phrase. Over this time, many companies adopted Apache Hadoop and Spark frameworks for their massive data storage and analysis efforts, using powerful, energy-hungry, general-purpose server as their big data processing platforms. But not all industry or academic fields want, or even need, such large systems. Moreover, capital costs aside, power consumption has also become a primary data center concern. Consequently, lower-cost, lower-power microservers have emerged as viable alternatives in many settings. Now, the latest generation Raspberry Pi (RPi), model 4B, exhibits significant computational performance improvements over its predecessors, and is presently considered a sufficiently powerful single board computer (SBC) to run many mainstream operating systems and accommodate heavy workloads. This paper reexamines SBC cluster big data processing possibilities by integrating the most powerful (presently) RPi model–the RPi 4B with 4 Gigabytes (GB) main memory. We examine external storage’s performance impact on such an SBC cluster’s big data processing performance by employing three different external storage solutions with measurably distinct performance characteristics. Moreover, we discuss challenges we encountered and identify further SBC cluster performance optimizations. We perform several representative big data application benchmarks and measure various key performance metrics such as execution time, power consumption, throughput, performance-per-dollars, etc. Our extensive experiments and comprehensive studies conclude this current, fourth-generation RPi has evolved to become the first generation to effectively run massive (i.e., more than 100GB) workloads in big data processing applications.
Factors Influencing the Adoption of Blockchain Technology in the Construction Industry: A System Dynamics Approach

The construction industry is always a slow adopter of innovative technologies than other sectors of the economy. Although some technologies, such as Building Information Modelling (BIM), robotics, among others, have been implemented, their adoption has faced some challenges. Blockchain technology is also considered a game-changer for the construction sector with the functionalities and capabilities to improve the construction supply chain, improve transparency, sustainability, and the like. Hence, this study using the system dynamics approach aims to conceptualize the complex causal interrelationship of the key factors influencing blockchain technology adoption in the construction industry. The analytical findings revealed that stakeholders’ awareness and satisfaction, support from top management, and the development of standardized and compatible blockchain solutions would enhance its adoption in construction firms and the construction industry. The study also emphasizes the need to integrate blockchain technology with the existing technologies towards facilitating the delivery of smart buildings and cities as well as enhancing the operation of modular integrated construction (MiC) projects both in Hong Kong and overseas
Joint Learning for Relationship and Interaction Analysis in Video with Multimodal Feature Fusion

To comprehend long duration videos, the deep video understanding (DVU) task is proposed to recognize interactions on scene level and relationships on movie level and answer questions on these two levels. In this paper, we propose a solution to the DVU task which applies joint learning of interaction and relationship prediction and multimodal feature fusion. Our solution handles the DVU task with three joint learning sub-tasks: scene sentiment classification, scene interaction recognition and super-scene video relationship recognition, all of which utilize text features, visual features and audio features, and predict representations in semantic space. Since sentiment, interaction and relationship are related to each other, we train a unified framework with joint learning. Then, we answer questions for video analysis in DVU according to the results of the three sub-tasks. We conduct experiments on the HLVU dataset to evaluate the effectiveness of our method.
Automated Trust Analysis of Copland Specifications for Layered Attestations✱

In distributed systems, trust decisions are often based on remote attestations in which evidence is gathered about the integrity of subcomponents. Layered attestations leverage hierarchical dependencies among the subcomponents to bolster the trustworthiness of evidence. Copland is a declarative, domain-specific language for specifying complex layered attestations. How phrases are composed bears directly on the trustworthiness of the evidence they produce, and complex phrases become quite difficult to analyze by hand. We introduce an automated method for analyzing executions of attestations specified by Copland phrases in an adversarial setting. We develop a general theory of executions with adversarial corruption and repair events. Our approach is to enrich the Copland semantics according to this theory. Using the model finder Chase, we characterize all executions consistent with a set of initial assumptions. From this set of models, an analyst can discover all ways an active adversary can corrupt subcomponents without being detected by the attestation. These efforts afford trust policymakers the ability to compare attestations expressed as Copland phrases against trust policy in a way that encompasses both static and runtime concerns.
Efficient Sparse Attacks on Videos using Reinforcement Learning

More and more deep neural network models have been deployed in real-time video systems. However, it is proved that deep models are susceptible to the crafted adversarial examples. The adversarial examples are imperceptible and can make the normal deep models misclassify them. Although there exist a few works aiming at the adversarial examples of video recognition in the black-box attack mode, most of them need large perturbations or hundreds of thousands of queries. There are still lack of effective adversarial methods to produce adversarial videos with small perturbations and limited query numbers at the same time.
Rethinking the Impacts of Overfitting and Feature Quality on Small-scale Video Classification

While Transformers have yielded impressive results for video classification on large datasets recently, simpler models without the transformer architecture can be promising for small datasets. In this paper, we propose three major techniques to improve feature quality and another three to alleviate overfitting in an attempt to make lightweight models achieve higher performances. In particular, we enhance features of Image Flow by combining temporal information, multi-level features of CNNs, and Text embedding. We alleviate overfitting by removing redundant modal, fine-tuning dropout rate, and augmenting data. In the 2021 Tencent Advertisement Algorithm Competition, the baseline model achieved a GAP score of 0.8019 offline with our strategies. It's worth mentioning that our design works well with the 10-fold method, which produces our final submitting model with a GAP score of 0.8210 online, ranking the 5th among 287 teams. In addition, our solution is among the fastest within the top 10 teams.
Towards Efficient Multiview Object Detection with Adaptive Action Prediction

Active vision is a desirable perceptual feature for robots. Existing approaches usually make strong assumptions about the task and environment, thus are less robust and efficient. This study proposes an adaptive view planning approach to boost the efficiency and robustness of active object detection. We formulate the multi-object detection task as an active multiview object detection problem given the initial location of the objects. Next, we propose a novel adaptive action prediction (A2P) method built on a deep Q-learning network with a dueling architecture. The A2P method is able to perform view planning based on visual information of multiple objects; and adjust action ranges according to the task status. Evaluated on the AVD dataset, A2P leads to 21.9% increase in detection accuracy in unfamiliar environments, while improving efficiency by 22.7%. On the T-LESS dataset, multi-object detection boosts efficiency by more than 30% while achieving equivalent detection accuracy.
NMAP: Power Management Based on Network Packet Processing Mode Transition for Latency-Critical Workloads

Processor power management exploiting Dynamic Voltage and Frequency Scaling (DVFS) plays a crucial role in improving the data-center's energy efficiency. However, we observe that current power management policies in Linux (i.e., governors) often considerably increase tail response time (i.e., violate a given Service Level Objective (SLO)) and energy consumption of latency-critical applications. Furthermore, the previously proposed SLO-aware power management policies oversimplify network request processing and ignore the fact that network requests arrive at the application layer in bursts. Considering the complex interplay between the OS and network devices, we propose a power management framework exploiting network packet processing mode transitions in the OS to quickly react to the processing demands from the received network requests. Our proposed power management framework tracks the transitions between polling and interrupt in the network software stack to detect excessive packet processing on the cores and immediately react to the load changes by updating the voltage and frequency (V/F) states. Our experimental results show that our framework does not violate SLO and reduces energy consumption by up to 35.7% and 14.8% compared to Linux governors and state-of-the-art SLO-aware power management techniques, respectively.
TransRPN: Towards the Transferable Adversarial Perturbations using Region Proposal Networks and Beyond

The adversarial perturbation for object detectors has drawn increasing attention due to the application in video surveillance and autonomous driving. However, few works have explored the transferability of adversarial perturbations across different object detectors. In this paper, we describe a simple but effective method, namely TransRPN, to generate adversarial perturbations that can reliably transfer among different object detectors—different categories (e.g., SSD, Faster-RCNN, YOLO) and different base networks (e.g., VGG16, ResNet, MobileNet), and even other tasks such as instance segmentation methods. Our method targets the Region Proposal Network (RPN) as the common bottleneck of the existing object detectors and disrupts the RPN by attacking the intermediate features. Moreover, as RPNs have no constraint on the size of the input image, our method can generate the adversarial images directly fitting into object detectors with arbitrary input size, which thereby improves the feasibility of our method in practical applications. We study four types of RPNs and validate our method on each type of RPN on MSCOCO dataset with nine object detectors and two instance segmentation methods, as well as the real-world API, which demonstrates the effectiveness of our method regarding transferability.