Reviewerly/exHarmony huggingface dataset:
Evaluating Automatic Term Extraction Methods on Individual Documents

Automatic Term Extraction (ATE) extracts terminology from domain-specific corpora. ATE is used in many NLP tasks, including Computer Assisted Translation, where it is typically applied to individual documents rather than the entire corpus. While corpus-level ATE has been extensively evaluated, it is not obvious how the results transfer to document-level ATE. To fill this gap, we evaluate 16 state-of-the-art ATE methods on full-length documents from three different domains, on both corpus and document levels. Unlike existing studies, our evaluation is more realistic as we take into account all gold terms. We show that no single method is best in corpus-level ATE, but C-Value and KeyConceptRelatendess surpass others in document-level ATE.
Real-Time Reinforcement Learning

Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection. As RL systems based on MDPs begin to find application in real-world safety critical situations, this mismatch between the assumptions underlying classical MDPs and the reality of real-time computation may lead to undesirable outcomes. In this paper, we introduce a new framework, in which states and actions evolve simultaneously and show how it is related to the classical MDP formulation. We analyze existing algorithms under the new real-time formulation and show why they are suboptimal when used in real-time. We then use those insights to create a new algorithm Real-Time Actor Critic (RTAC) that outperforms the existing state-of-the-art continuous control algorithm Soft Actor Critic both in real-time and non-real-time settings.
Learning Conditional Deformable Templates with Convolutional Networks

We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or \textit{conditional} templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at http://voxelmorph.csail.mit.edu.
Root Mean Square Layer Normalization

Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. However, the computational overhead introduced by LayerNorm makes these improvements expensive and significantly slows the underlying network, e.g. RNN in particular. In this paper, we hypothesize that re-centering invariance in LayerNorm is dispensable and propose root mean square layer normalization, or RMSNorm. RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability. RMSNorm is computationally simpler and thus more efficient than LayerNorm. We also present partial RMSNorm, or pRMSNorm where the RMS is estimated from p% of the summed inputs without breaking the above properties. Extensive experiments on several tasks using diverse network architectures show that RMSNorm achieves comparable performance against LayerNorm but reduces the running time by 7%~64% on different models. Source code is available at https://github.com/bzhangGo/rmsnorm.
Data Cleansing for Models Trained with SGD

Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose an algorithm that can identify influential instances without using any domain knowledge. The proposed algorithm automatically cleans the data, which does not require any of the users' knowledge. Hence, even non-experts can improve the models. The existing methods require the loss function to be convex and an optimal model to be obtained, which is not always the case in modern machine learning. To overcome these limitations, we propose a novel approach specifically designed for the models trained with stochastic gradient descent (SGD). The proposed method infers the influential instances by retracing the steps of the SGD while incorporating intermediate models computed in each step. Through experiments, we demonstrate that the proposed method can accurately infer the influential instances. Moreover, we used MNIST and CIFAR10 to show that the models can be effectively improved by removing the influential instances suggested by the proposed method.
LACC: A Linear-Algebraic Algorithm for Finding Connected Components in Distributed Memory

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a parallel connected-components algorithm that can run on distributed-memory computers. Our algorithm uses linear algebraic primitives and is based on a PRAM algorithm by Awerbuch and Shiloach. We show that the resulting algorithm, named LACC for Linear Algebraic Connected Components, outperforms competitors by a factor of up to 12× for small to medium scale graphs. For large graphs with more than 50B edges, LACC scales to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperforms previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.
On a Low Cost Fault Injection Framework for Security Assessment of Cyber-Physical Systems: Clock Glitch Attacks

Fault injection methods as a type of physical attack have gained significant importance in the security of MCU-based Internet-of-Things (IoTs) systems and they continue to become more and more important as the value of assets continues to increase. These attacks can pose severe security risks to the entire IoT system and their effects can quickly lead to security breaches. However, embedded software developers most often do not have the necessary expertise concerning existing vulnerabilities against such attacks. This makes it necessary to have a practical evaluation platform for measuring the degree of security, in a rapid and accurate way. These platforms are important not only from the performance and capability point of view, but also they need to be cost-effective, which is a critical factor to consider during their design. We present in this work a generic low cost and open platform, called HackMyMCU framework. While this platform offers both side channel and fault injection capabilities, this paper focuses on its clock glitch attacks. A first review of existing clock-based fault injectors is initially performed. Then we present two different clock glitchers and we use them to evaluate a modern MCU. The suggested methods consider the necessary parameters for the development of cost-effective and easy to use evaluation platforms which utilize easily accessible equipment. The findings show that a common and low-cost evaluation platform can be implemented with the goal to validate appropriate countermeasures against such attacks.
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization

In this paper, we address the problem of weakly supervised object localization, which trains a detection network on the dataset with only image-level annotations. The proposed approach is built on the observation that the proposal set from the training dataset is a collection of background, object parts, and objects. Several strategies are taken to adaptively eliminate the noisy proposals and generate pseudo object-level annotations for the weakly labeled dataset. A multiple instance learning algorithm enhanced by mask-out strategy is adopted to collect the class-specific object proposals, which are then utilized to adapt a pre-trained classification network to a detection network. In addition, the detection results from the detection network are re-weighted by jointly considering the detection scores and the overlap ratio of proposals in a proposal subset optimization framework. The optimal proposals work as object-level labels that enable a pseudo-strongly supervised dataset for training the detection network. Consequently, we establish a fully adaptive detection network. Extensive evaluations on the PASCAL VOC 2007 and 2012 datasets demonstrate a significant improvement compared with the state-of-the-art methods.
U-net like deep autoencoders for deblurring atmospheric turbulence

A method for geometric distortion correction and space and time-varying blur reduction is proposed, which can recover the high-quality image from a single-frame image distorted by atmospheric turbulence. First, the U-net like deep-stacked autoencoder neural network model is proposed, which is composed of two deep convolutional autoencoder (CAE) neural networks and a U-net. The first CAE is used for feature extraction, the U-net is used for feature deconvolution, and the second CAE is used for image reconstruction. For the loss reduction of image information, transposed convolution instead of upsampling is selected in U-net networks. Moreover, in order to obtain sufficient feature information for reconstruction, the first CAE and the last CAE are symmetric skip connected. This not only enables the fusion of low-level and high-level information but also ensures the integrity of image information greatly. Then, a method of gradually mature training from simple to complex is proposed to overcome the difficulty of convergence on smaller training sets. It makes the network be converged and mature by increasing the complexity of training data gradually so as to restore the high turbulence-degraded image. Experimental results of actual observation data and simulation data show that the algorithm has a stronger antinoise ability and can recover image details and sharpen image edges more effectively. In particular, for atmospheric turbulence severely degraded image restoration, the peak signal-to-noise ratio index is increased by about 10% on average compared with state-of-the-art methods.
Adjustment and Calibration of Dome Port Camera Systems for Underwater Vision

Dome ports act as spherical windows in underwater housings through which a camera can observe objects in the water. As compared to flat glass interfaces, they do not limit the field of view, and they do not cause refraction of light observed by a pinhole camera positioned exactly in the center of the dome. Mechanically adjusting a real lens to this position is a challenging task, in particular for those integrated in deep sea housings. In this contribution a mechanical adjustment procedure based on straight line observations above and below water is proposed that allows for accurate alignments. Additionally, we show a chessboard-based method employing an underwater/above-water image pair to estimate potentially remaining offsets from the dome center to allow refraction correction in photogrammetric applications. Besides providing intuition about the severity of refraction in certain settings, we demonstrate the methods on real data for acrylic and glass domes in the water.
Self-Adaptation for Unsupervised Domain Adaptation

Lack of labelled data in the target domain for training is a common problem in domain adaptation.To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches.Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain.Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction.We then use the trained classifier to predict pseudo labels for the target domain unlabelled data.Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels.Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.
Towards Serverless as Commodity

Serverless computing promises to evolve cloud computing architecture from VMs and containers-as-a-service (CaaS) to function-as-a-service (FaaS). This takes away complexities of managing and scaling underlying infrastructure and can result in simpler code, cheaper realization of services, and higher availability. Nonetheless, one of the primary drawbacks customers face when making decision to move their software to a serverless platform is the potential for getting locked-in with a particular provider. This used to be a concern with Platform-as-a-Service (PaaS) offerings too. However with Kubernetes emerging as the industry standard PaaS layer, PaaS is closer to becoming commodity with the Kubernetes API as its common interface. The question is if a similar unification for the API interface layer and runtime contracts can be achieved for serverless. If achieved, this would free up serverless users from their fears of platform lock-in. Our goal in this paper is to extract a minimal common denominator model of execution that can move us closer to a unified serverless platform. As contributors to Knative [13] with in-depth understanding of its internal design, we use Knative as the baseline for this comparison and contrast its API interface and runtime contracts against other prominent serverless platforms to identify commonalities and differences. Influenced by the work in Knative, we also discuss challenges as well as the necessary evolution we expect to see as serverless platforms themselves reach commodity status.
Attitude restricted back-stepping anti-disturbance control for vision based quadrotors with visibility constraint

Visual servoing is an effective strategy for autonomous flight of quadrotor. However, it faces the requirement of visibility constraint, i.e., holding the observed object inside the field of view (FOV) of camera. To accomplish this goal, we present an attitude restricted back-stepping anti-disturbance controller. Before the controller design, proper perspective moment features are selected to establish an uncoupled vision-quadrotor model without image singularity from a novel image plane, i.e., virtual image plane. Considering the structured vision-quadrotor model, an attitude restricted anti-disturbance control strategy is developed via back-stepping, where the unknown uncertainties are estimated via designing a model-assisted ESO and compensated in feedforward loop, and the drastic attitude motions are restricted by the designed saturation compensated integral barrier Lyapunov function(iBLF) control law. Its superiorities lie in: (1) by designing strong anti-disturbance mechanism and strict attitude limitation arithmetic, the controller can guarantee the observed object in the FOV of camera, (2) considering the deterioration of transient-state behavior due to the strict attitude constraints, a saturation compensator is constructed to improve the system transient-state performance, (3) by incorporating the model information into observer design, the estimation burden of ESO relieves greatly. Stability analysis based on the Lyapunov theory proves the boundedness of attitude states and the convergence of closed-loop system. And we design many simulations to confirm the controller performance.
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation

State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader.
Identification of patchouli oil chemical components as the results on distillation using GC-MS

Abstract The main purpose of this study is to identify the chemical components contained in patchouli oil. The method used is water vapor distillation, water distillation. Patchouli plant material used which has been dried by aeration process and chopped above 20 kg. Then it is distilled using several distillation methods. The distillation process is carried out to extract the chemical components of patchouli oil contained in the ingredients. The three distillation methods are used using water because it does not react with patchouli, and can remove patchouli oil chemical components from its patchouli plant tissue, besides that water is also easy to use, has simple set of tools, and has low operating costs. The distillation process is carried out after the material that need to be distilled is put into the kettle and added water then connected to the condenser and heated, then the oil will be condensed and separated with a separating funnel. Analysis using GC-MS was carried out to identify the chemical components of patchouli oil, the result shows that there are 16 constituent components of patchouli oil, with 3 dominant peaks and other components as follow: Patchouli alcohol (42.75%), Delta-Guaiene (28.30 ), Azulene (20.48%), Trans Caryophellene (11.84%), Seychellence (CAS) (10.77%), Nephtalene (8.02%), Cycloheptane (6.02%) and Caryophyllene (5, 73%). The main component of patchouli oil is Patchouli alcohol about 42.75% that is obtained from the distillation process using water distillation method with distillation time about 5 hours.
On the Value of Target Data in Transfer Learning

We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy. To this aim, we establish the first minimax-rates in terms of both source and target sample sizes, and show that performance limits are captured by new notions of discrepancy between source and target, which we refer to as transfer exponents. Interestingly, we find that attaining minimax performance is akin to ignoring one of the source or target samples, provided distributional parameters were known a priori. Moreover, we show that practical decisions -- w.r.t. minimizing sampling costs -- can be made in a minimax-optimal way without knowledge or estimation of distributional parameters nor of the discrepancy between source and target.
FDnCNN-based image denoising for multi-labfel localization measurement

Recently, the discriminant learning denoising model has deserved special attention due to its outstanding denoising property. This paper designs the RFID multi-label dynamic localization system based on visual measurement to avoid electromagnetic interference. Firstly, the potential sharp images are estimated via denoising images acquired by dual CCD. Then the labels are matched by template. Finally, Shaft Alignment Method (SAM) models the multi-label 3D coordinates in world space and image pixel. Flexible Feed-forward Denoising Convolutional Neural Network (FDnCNN) is proposed to reach the equilibrium between denoising effect and image particulars without producing artifacts in GPU effectively and flexibly. FDnCNN increases the image quality by at least 0.5 dB than WNNM and DnCNN. The 3D coordinate measurement system is evaluated by error, which proves the feasibility and effectiveness of the positioning system.
AutoQ: Automated Kernel-Wise Neural Network Quantization

Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy. The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead. To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs. However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large. The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results. It is difficult for even deep reinforcement learning (DRL) DDPG-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy. In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer. Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06%, and decrease the inference energy consumption by 50.69%, while achieving the same inference accuracy.
The intriguing role of module criticality in the generalization of deep networks

We study the phenomenon that some modules of deep neural networks (DNNs) are more \emph{critical} than others. Meaning that rewinding their parameter values back to initialization, while keeping other modules fixed at the trained parameters, results in a large drop in the network's performance. Our analysis reveals interesting properties of the loss landscape which leads us to propose a complexity measure, called {\em module criticality}, based on the shape of the valleys that connects the initial and final values of the module parameters. We formulate how generalization relates to the module criticality, and show that this measure is able to explain the superior generalization performance of some architectures over others, whereas earlier measures fail to do so.