Reviewerly/exHarmony huggingface dataset:
Universum least squares twin parametric-margin support vector machine

Universum based algorithms involve universum samples in the classification problem to improve the generalization performance. In order to provide prior information about data, we utilized universum data to propose a novel classification algorithm. In this paper, a novel parametric model for universum based twin support vector machine is presented for classification problems. The proposed model is termed as universum least squares twin parametric-margin support vector machine (ULSTPMSVM). The solution of ULSTPMSVM involves a system of linear equations. This makes the ULSTPMSVM efficient w.r.t. training time. In order to verify the performance of the proposed model, various experiments are carried out on real world benchmark datasets. Statistical tests are performed to verify the significance of the proposed method. The proposed ULSTPMSVM performed better than existing algorithms in terms of classification accuracy and training time for most of the datasets. Moreover, an application of proposed ULSTPMSVM is presented for classification of Alzheimer's disease data.
Fast Segmentation-Based Object Tracking Model for Autonomous Vehicles

On-road object tracking is a critical module for both Advanced Driving Assistant System (ADAS) and autonomous vehicles. Commonly, this function can be achieved through single vehicle sensors, such as a camera or LiDAR. Consider the low cost and wide application of optical cameras, a simple image segmentation-based on-road object tracking model is proposed. Different from the detection-based tracking with bounding box, our model improves tracking performance from the following three aspects: 1) the Positional Normalization (PONO) feature is used to enhance the target outline with common convolutional layers. 2) The inter-frame correlation of each target used for tracking relies on mask, this helps the model reducing the influences caused by the background around the targets. 3) By using a bidirectional LSTM module capable of capturing timing correlation information, the forward and reverse matching of the targets in consecutive frames is performed. We also evaluate the presented model on the KITTI MOTS (Multi-Object and Segmentation) task which collected from out door environment for autonomous vehicle. Results show that our model is three times faster than Track RCNN with slightly drop on sMOTSA, and is more suitable for deployment on vehicular low-power edge computing equipment.
RNN-Based Detection of Fault Attacks on RSA

Physical fault injection attacks are becoming an important threat to computer systems, as fault injection equipment becomes more and more accessible.In this work, we propose a new strategy to detect fault attacks in cryptosystems.We use a recurrent neural network (RNN) to detect problems in the program flow caused by injected faults.Our neural network is trained using the instructions of non-faulty operations and therefore, it can protect against both current and future attacks.As a case study, we use two implementations of software RSA.To test the effectiveness of our detector, we propose a collection of fault injection models, where each model represents different types of faults in the instructions.Evaluation results show that we obtain a high detection accuracy in case injected faults lead to changes in the instruction flow and hence, making it difficult to steal secrete keys.Finally, we propose an efficient hardware implementation with only a 6% area overhead compared to a RISC-V processor. II. METHODOLOGYThis section describes the design and evaluation of our RNN-based detector.The methodology consists of six steps, which are described further. Selection of the target application:In general, the detector can be trained to learn any instruction sequence
Towards inductive learning of surgical task knowledge: a preliminary case study of the peg transfer task

Autonomy in robotic surgery will significantly improve the quality of interventions in terms of safety and recovery time for the patient, and reduce fatigue of surgeons and hospital costs. A key requirement for such autonomy is the ability of the surgical system to encode and reason with commonsense task knowledge, and to adapt to variations introduced by the surgical scenarios and the individual patients. However, it is difficult to encode all the variability in surgical scenarios and in the anatomy of individual patients a priori, and new knowledge often needs to be acquired and merged with the existing knowledge. At the same time, it is not possible to provide a large number of labeled training examples in the robotic surgery. This paper presents a framework based on inductive logic programming and answer set semantics for incrementally learning domain knowledge from a limited number of executions of basic surgical tasks. As an illustrative example, we focus on the peg transfer task, and learn state constraints and the preconditions of actions starting from different levels of prior knowledge. We do so using a small dataset comprising human and robotic executions with the da Vinci surgical robot in a challenging simulated scenario.
Design-Time Validation of Runtime Reconfiguration Strategies: An Environmental-Driven Approach

Validating the effectiveness of reconfiguration strategies of Self-Adaptive Systems (SAS) regarding their impact on runtime quality properties is a challenging problem at design time. Since quality properties, such as performance or reliability, are effectively observable at runtime, it is inherently difficult to validate reconfiguration strategies at design-time during their design (e.g., during the definition of the software architecture). Furthermore, engineering and validating SAS at design-time involves uncertainties that are difficult to manage due to a dynamic operating environment. Therefore, we propose a novel model-based analysis approach that is driven by a temporal probabilistic model which captures the stochastic nature of the operating environment. The sampled trajectories through the state space serve as a basis for validation. Software engineers benefit from the framework by validating their reconfiguration strategy regarding quality objectives before implementation. The validated strategy serves as starting point for further model-based analyses such as correctness verification of adaptation logic or scenario-based analysis for local optimization.
Tunneling Trust Into the Blockchain: A Merkle Based Proof System for Structured Documents

The idea of Smart contracts foresees the possibility of automating contractual clauses using hardware and software tools and devices. One of the main perspectives of their implementation is the automation of interactions such as bets, collaterals, prediction markets, insurances. As blockchain platforms, such as Ethereum, offer very strong guarantees of untampered, deterministic execution, that can be exploited as smart contracts substrate, the problem of how to provide reliable information from the “outside world” into the contracts becomes central. In this article, we propose a system based on a Merkle tree representation of structured documents (such as all XML), with which it is possible to generate compact proofs on the content of web documents. The proofs can then be efficiently checked on-chain by a smart contract, to trigger contract action. We provide an end-to-end proof of concept, applying it to real use case scenarios, which allows us to give an estimate of the costs.
BizDevOps: A Multivocal Literature Review

BizDevOps as an extension of DevOps, reinforces the collaboration between business, development, and operation stakeholders in the organization in order to enhance the software cycle. While BizDevOps has not yet received much attention in academic circles, it has gained considerable prestige in the industry area. This situation reflects a gap between theory and practice in this context. In this work and by means of a Multivocal Literature Review authors gather visions from both academic and industry spheres on the topic. The result is a gathered image of BizDevOps, including definition, characteristics, related motivating issues, and potential challenges and benefits.
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets

Rigorous mathematical investigation of learning rates used in back-propagation in shallow neural networks has become a necessity. This is because experimental evidence needs to be endorsed by a theoretical background. Such theory may be helpful in reducing the volume of experimental effort to accomplish desired results. We leveraged the functional property of Mean Square Error, which is Lipschitz continuous to compute learning rate in shallow neural networks. We claim that our approach reduces tuning efforts, especially when a significant corpus of data has to be handled. We achieve remarkable improvement in saving computational cost while surpassing prediction accuracy reported in literature. The learning rate, proposed here, is the inverse of the Lipschitz constant. The work results in a novel method for carrying out gene expression inference on large microarray data sets with a shallow architecture constrained by limited computing resources. A combination of random sub-sampling of the dataset, an adaptive Lipschitz constant inspired learning rate and a new activation function, A-ReLU helped accomplish the results reported in the paper.
$$\epsilon $$-Differential Privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility)

Differential privacy (DP) is a privacy model that was designed for interactive queries to databases. Its use has then been extended to other data release formats, including microdata. In this paper we show that setting a certain \(\epsilon \) in DP does not determine the confidentiality offered by DP microdata, let alone their utility. Confidentiality refers to the difficulty of correctly matching original and anonymized data, and utility refers to anonymized data preserving the correlation structure of original data. Specifically, we present two methods for generating \(\epsilon \)-differentially private microdata. One of them creates DP synthetic microdata from noise-added covariances. The other relies on adding noise to the cumulative distribution function. We present empirical work that compares the two new methods with DP microdata generation via prior microaggregation. The comparison is in terms of several confidentiality and utility metrics. Our experimental results indicate that different methods to enforce \(\epsilon \)-DP lead to very different utility and confidentiality levels. Both confidentiality and utility seem rather dependent on the amount of permutation performed by the particular SDC method used to enforce DP. Thus suggests that DP is not a good privacy model for microdata releases.
Adversarial Vulnerability in Doppler-based Human Activity Recognition

Human activity recognition (HAR) is an important task in many internet of things (IoT) applications. In recent years, significant efforts have been made towards achieving the highest possible recognition performance (accuracy and robustness) by using advanced machine learning techniques, including deep learning. However, to the best of our knowledge, the adversarial vulnerability of the Doppler sensor-based HAR systems has not been studied. In other domains such as computer vision, the vulnerability of deep learning algorithms to adversarial samples has attracted tremendous research interests in the past few years. In this work, we investigate the adversarial vulnerability of the Doppler-based human activity recognition system. Using a case study we demonstrate that the adversarial examples can significantly degrade the performance of the human activity recognition. Specifically, the basic iterative method (BIM) attack can reduce classification accuracy by as much as 85%. We also discuss different types of attacks, e.g., data poisoning attacks and potential strategies of protecting the Doppler-based HAR systems against adversarial attacks.
Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images

In this work, we introduce a novel, end-to-end motion planner for quadrotor navigation. Informed by a rough path to goal in partially unknown environments, our method creates desirable motion plans using raw depth images from a front-facing camera. It exploits correlations between local spatial portions of these images to generate desirable motion primitive sequences on the fly without conducting explicit sensing-reconstructing-planning. We evaluate our method through an extensive comparison with three competitor algorithms over ten different environments in AirSim simulations. Our method outperforms its competitors in terms of safe navigation distance, navigation time, and crash rate over 50 flights. We also deploy our method for real flight tests with DJI F330 Quadrotor equipped with Intel RealSense D435, and demonstrate its real-time ap-plicability. Our method successfully performs 15 real flights in three different environment settings with increasing complexity. The experiments can be found at https://youtu.be/hw0sxNwliqs.
Implementation of IoT Device on Public Fitness Equipment for Health Physical Fitness Improvement

In this paper, an IoT architecture for public fitness equipment with software and hardware is proposed and implemented. We utilize reed switch and MCU to implement hardware on public fitness equipment. Furthermore, we also implement web service, database and user interface which provide thorough individual exercise prescription service. From the experimental results, all the participants' physical fitness does improve significantly. It shows that public fitness equipment with IoT device can improve body health by providing an efficient exercise way.
Interpretability vs. Complexity: The Friction in Deep Neural Networks

Saliency maps have been used as one possibility to interpret deep neural networks. This method estimates the relevance of each pixel in the image classification, with higher values representing pixels which contribute positively to classification. The goal of this study is to understand how the complexity of the network affects the interpretabilty of the saliency maps in classification tasks. To achieve that, we investigate how changes in the regularization affects the saliency maps produced, and their fidelity to the overall classification process of the network.The experimental setup consists in the calculation of the fidelity of five saliency map methods that were compare, applying them to models trained on the CIFAR-10 dataset, using different levels of weight decay on some or all the layers. Achieved results show that models with lower regularization are statistically (significance of 5%) more interpretable than the other models. Also, regularization applied only to the higher convolutional layers or fully-connected layers produce saliency maps with more fidelity.
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks

We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks. The framework is based on the theory of Lipschitz continuity, specifically utilizing the relationship between learning rate and Lipschitz constant of the loss function. Based on experimentation, we have found that the adaptive learning rate policy enables up to 20× faster convergence compared to a constant learning rate policy.
Human-Centric Active Perception for Autonomous Observation

As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems - free-flying cameras capable of actively tracking human activity within some predefined area of interest. In this work, we formulate the autonomous observation problem through multi-objective optimization, presenting a novel Semi-MDP formulation of the autonomous human observation problem that maximizes observation rewards while accounting for both human- and robot-centric costs. We demonstrate that the problem can be solved with both scalarization-based Multi-Objective MDP methods and Constrained MDP methods, and discuss the relative benefits of each approach. We validate our work on activity tracking using a NASA Astrobee robot operating within a simulated International Space Station environment.
Inferring Student Engagement in Collaborative Problem Solving from Visual Cues

Automatic analysis of students' collaborative interactions in physical settings is an emerging problem with a wide range of applications in education. However, this problem has been proven to be challenging due to the complex, interdependent and dynamic nature of student interactions in real-world contexts. In this paper, we propose a novel framework for the classification of student engagement in open-ended, face-to-face collaborative problem-solving (CPS) tasks purely from video data. Our framework i) estimates body pose from the recordings of student interactions; ii) combines face recognition with a Bayesian model to identify and track students with a high accuracy; and iii) classifies student engagement leveraging a Team Long Short-Term Memory (Team LSTM) neural network model. This novel approach allows the LSTMs to capture dependencies among individual students in their collaborative interactions. Our results show that the Team LSTM significantly improves the performance as compared to the baseline method that takes individual student trajectories into account independently.
Joint Representation Learning and Clustering: A Framework for Grouping Partial Multiview Data

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and Clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.
Super-Pixel Sampler: a Data-driven Approach for Depth Sampling and Reconstruction

Depth acquisition, based on active illumination, is essential for autonomous and robotic navigation. LiDARs (Light Detection And Ranging) with mechanical, fixed, sampling templates are commonly used in today's autonomous vehicles. An emerging technology, based on solid-state depth sensors, with no mechanical parts, allows fast and adaptive scans. In this paper, we propose an adaptive, image-driven, fast, sampling and reconstruction strategy. First, we formulate a piece-wise planar depth model and estimate its validity for indoor and outdoor scenes. Our model and experiments predict that, in the optimal case, adaptive sampling strategies with about 20-60 piece-wise planar structures can approximate well a depth map. This translates to requiring a single depth sample for every 1200 RGB samples (less than 0.1%), providing strong motivation to investigate an adaptive framework. Second, we introduce SPS (Super-Pixel Sampler), a simple, generic, sampling and reconstruction algorithm, based on super-pixels. Our sampling improves grid and random sampling, consistently, for a wide variety of reconstruction methods. Third, we propose an extremely simple and fast reconstruction for our sampler. It achieves state-of-the-art results, compared to complex image- guided depth completion algorithms, reducing the required sampling rate by a factor of 3-4. A single-pixel prototype sampler built in our lab illustrates the concept.
Reinforcement Learning for Adaptive Illumination with X-rays

We propose a learning algorithm for automating image sampling in scientific applications. We consider settings where images are sampled by controlling a probe beam's scanning trajectory over the image surface. We explore alternatives to obtaining images by the standard rastering method. We formulate the scanner control problem as a reinforcement learning (RL) problem and train a policy to adaptively sample only the highest value regions of the image, choosing the acquisition time and resolution for each sample position based on an observation of previous readings. We use convolutional neural network (CNN) policies to control the scanner as a way to generalize our approach to larger samples. We show simulation results for a simple policy on both synthetic data and real world data from an archaeological application.
Residual Encoder-Decoder Network For Deep Subspace Clustering

Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to learn the linear representation coefficients from data. However, the training of current deep methods converges slowly, which is extremely expensive. We propose a novel Residual Encoder-Decoder network for deep Subspace Clustering (RED-SC) with skip-layer connections to accelerate the convergence, using a new strategy to generate the linear coefficients by learning the linearity of data in multiple latent spaces. Experiments show the superiority of RED-SC in training efficiency and clustering accuracy.
Transformation Based Deep Anomaly Detection in Astronomical Images

In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform).The model assumes that the anomaly class is unknown and that only inlier samples are available for training.We introduce new filter based transformations useful for detecting anomalies in astronomical images, that highlight artifact properties to make them more easily distinguishable from real objects.In addition, we propose a transformation selection strategy that allows us to find indistinguishable pairs of transformations.This results in an improvement of the area under the Receiver Operating Characteristic curve (AUROC) and accuracy performance, as well as in a dimensionality reduction.The models were tested on astronomical images from the High Cadence Transient Survey (HiTS) and Zwicky Transient Facility (ZTF) datasets.The best models obtained an average AUROC of 99.20% for HiTS and 91.39% for ZTF.The improvement over the original GeoTransform algorithm and baseline methods such as One-Class Support Vector Machine, and deep learning based methods is significant both statistically and in practice.