Reviewerly/exHarmony huggingface dataset:
Cycle label-consistent networks for unsupervised domain adaptation

Domain adaptation aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution. Previous methods mostly match the distribution between two domains by global or class alignment. However, global alignment methods cannot achieve a fine-grained class-to-class overlap; class alignment methods supervised by pseudo-labels cannot guarantee their reliability. In this paper, we propose a simple yet efficient domain adaptation method, i.e. Cycle Label-Consistent Network (CLCN), by exploiting the cycle consistency of classification label, which applies dual cross-domain nearest centroid classification procedures to generate a reliable self-supervised signal for the discrimination in the target domain. The cycle label-consistent loss reinforces the consistency between ground-truth labels and pseudo-labels of source samples leading to statistically similar latent representations between source and target domains. This new loss can easily be added to any existing classification network with almost no computational overhead. We demonstrate the effectiveness of our approach on MNIST-USPS-SVHN, Office-31, Office-Home and Image CLEF-DA benchmarks. Results validate that the proposed method can alleviate the negative influence of falsely-labeled samples and learn more discriminative features, leading to the absolute improvement over source-only model by 9.4% on Office-31 and 6.3% on Image CLEF-DA.
Modeling the adsorption of chlorobenzene on modified bentonite using an artificial neural network

In this paper, a radial basis function (RBF) feed-forward network has been used to predict the adsorption capacity for chlorobenzene (CB) onto modified Algerian bentonite (HDTMA-bentonite). The input parameters used for training of the RBF model include contact time, amount of adsorbent, pH, and the initial concentrations of chlorobenzene. The adsorption capacity for chlorobenzene onto modified bentonite is considered as an output of the neural network. An RBF model is used to predict the behavior of the adsorption process with the Levenberg–Marquardt algorithm (LMA). The model uses a linear transfer function (purelin) at the output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with four neurons. The values of the determination coefficient (R2 ​= ​0.984) and the root mean square error (RMSE ​= ​0.015) showed good prediction results. Hence, the RBF model as a predictive tool has a great capacity to estimate the effect of operational parameters on the adsorption process.
Online incremental hierarchical classification resonance network

Hierarchical classification is imperative in that almost all objects are described in hierarchical semantics. If a classification method enables incremental class learning to learn new objects online, it will be practically used for real-time applications. In this sense, we propose online incremental hierarchical classification resonance network (OIHCRN) that enables online incremental class learning in hierarchical classification. OIHCRN has a structure that grows horizontally and vertically online according to object classes, so that a newly added object can be classified. By the proposed process of scale-preserving projection and prior label appending, OIHCRN reflects the class dependency between class levels and simultaneously normalizes the input vector online. Additionally, to reduce the model complexity and improve performance, two auxiliary strategies, named OIHCRN with class END and OIHCRN with differentiated class labels, are introduced. To demonstrate the effectiveness of OIHCRNs, experiments are carried out for benchmark datasets and then for a multimedia recommendation system.
Cross-Modal Retrieval With Noisy Labels

Cross-modal retrieval is an important field of study for design of algorithms to effectively retrieve items from one modality when provided with a query from another modality. Recent progress in this field have shown that supervised algorithms perform significantly better than their unsupervised counterparts by utilizing the label information. In real scenarios, the labels are obtained through manual or automatic annotation, and thus are prone to errors. In this work, we systematically study the effect of label corruption on the performance of standard cross-modal algorithms. We propose a very simple, yet effective pre-processing framework which can help to mitigate the performance degradation suffered due to label corruption. First, the potentially more promising modality is automatically chosen, on which two different versions of a noise-resistant classification algorithm is trained to generate the pseudo-labels of the noisy cross-modal training data. The generated pseudo-labels can then be used by any cross-modal supervised approach to improve its performance. Extensive experiments across four cross-modal datasets with different types of label corruption show that the proposed framework gives impressive improvements for this important problem.
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment

Conducting pairwise comparisons is a widely used approach in curating human perceptual preference data. Typically raters are instructed to make their choices according to a specific set of rules that address certain dimensions of image quality and aesthetics. The outcome of this process is a dataset of sampled image pairs with their associated empirical preference probabilities. Training a model on these pairwise preferences is a common deep learning approach. However, optimizing by gradient descent through mini-batch learning means that the “global” ranking of the images is not explicitly taken into account. In other words, each step of the gradient descent relies only on a limited number of pairwise comparisons. In this work, we demonstrate that regularizing the pairwise empirical probabilities with aggregated rankwise probabilities leads to a more reliable training loss. We show that training a deep image quality assessment model with our rank-smoothed loss consistently improves the accuracy of predicting human preferences.
HP-ESN: Echo State Networks Combined with Hodrick-Prescott Filter for Nonlinear Time-Series Prediction

Nonlinear time-series prediction is one of the challenging tasks in machine learning. Recurrent neural networks and their variants have been successful in such a task owing to its ability of storing past inputs in their dynamical states. Echo state networks (ESNs) are a special type of recurrent neural networks, which are capable of high-speed learning. To develop this computational scheme, we propose an HP-ESN method which combines ESNs with a preprocessing based on the Hodrick- Prescott (HP) filter. This filter extracts different components from a single time-series data. The extracted components are processed by ESNs. We show that the proposed method yields better prediction performance compared with other state-of- the-art ESN-based methods in prediction tasks with real-world time-series data. We also demonstrate that the computational performance depends on the setting of the smoothing parameter and the number of decompositions by the HP filter.
Planning with Selective Physics-based Simulation for Manipulation Among Movable Objects

Use of physics-based simulation as a planning model enables a planner to reason and generate plans that involve non-trivial interactions with the world. For example, grasping a milk container out of a cluttered refrigerator may involve moving a robot manipulator in between other objects, pushing away the ones that are moveable and avoiding interactions with certain fragile containers. A physics-based simulator allows a planner to reason about the effects of interactions with these objects and to generate a plan that grasps the milk container successfully. The use of physics-based simulation for planning however is underutilized. One of the reasons for it being that physics-based simulations are typically way too slow for being used within a planning loop that typically requires tens of thousands of actions to be evaluated within a matter of a second or two. In this work, we develop a planning algorithm that tries to address this challenge. In particular, it builds on the observation that only a small number of actions actually need to be simulated using physics, and the remaining set of actions, such as moving an arm around obstacles, can be evaluated using a much simpler internal planning model, e.g., a simple collision-checking model. Motivated by this, we develop an algorithm called Planning with Selective Physics-based Simulation that automatically discovers what should be simulated with physics and what can utilize an internal planning model for pick-and-place tasks.
End-To-End Learned Image Compression With Fixed Point Weight Quantization

Learned image compression (LIC) has reached the traditional hand-crafted methods such as JPEG2000 and BPG in terms of the coding gain. However, the large model size of the network prohibits the usage of LIC on resource-limited embedded systems. This paper presents a LIC with 8-bit fixed-point weights. First, we quantize the weights in groups and propose a non-linear memory-free codebook. Second, we explore the optimal grouping and quantization scheme. Finally, we develop a novel weight clipping fine tuning scheme. Experimental results illustrate that the coding loss caused by the quantization is small, while around 75% model size can be reduced compared with the 32-bit floating-point anchor. As far as we know, this is the first work to explore and evaluate the LIC fully with fixed-point weights, and our proposed quantized LIC is able to outperform BPG in terms of MS-SSIM.
Artificial Bee Colony Algorithm with Modified Operators of Determining the Profitable Food Sources for Identification the Models of Atmospheric Pollution by Nitrogen Dioxide

In the article the method of structural identification of interval discrete dynamic models based on artificial bee colony algorithm is considered. Note, during the model building are used the observations results. The improvement of the computational scheme of this method by the using of new operators to determine the intensity of the investigation of “better” structures of interval discrete dynamic models is proposed. Comparison of the efficiency of these operators using was carried out on the example of the building of discrete models of nitrogen dioxide dynamics due to air pollution by harmful emissions of motor vehicles.
A New Metaheuristic-Based Hierarchical Clustering Algorithm for Software Modularization

Software refactoring is a software maintenance action to improve the software internal quality without changing its external behavior. During the maintenance process, structural refactoring is performed by remodularizing the source code. Software clustering is a modularization technique to remodularize artifacts of source code aiming to improve readability and reusability. Due to the NP hardness of the clustering problem, evolutionary approaches such as the genetic algorithm have been used to solve this problem. In the structural refactoring literature, there exists no search-based algorithm that employs a hierarchical approach for modularization. Utilizing global and local search strategies, in this paper, a new search-based top-down hierarchical clustering approach, named TDHC, is proposed that can be used to modularize the system. The output of the algorithm is a tree in which each node is an artifact composed of all artifacts in its subtrees and is a candidate to be a software module (i.e., cluster). This tree helps a software maintainer to have better vision on source code structure to decide appropriate composition points of artifacts aiming to create modules (i.e., files, packages, and components). Experimental results on seven folders of Mozilla Firefox with different functionalities and five other software systems show that the TDHC produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software maintainer for better remodularization of a source code. The source codes and dataset related to this paper can be accessed at https://github.com/SoftwareMaintenanceLab.
Cryptographic Accelerators for Trusted Execution Environment in RISC-V Processors

The trusted execution environment protects data by taking advantage of memory isolation schemes. Most of the software implementations on security enclaves offer a framework that can be implemented on any processor architecture. Assuming that privilege escalation is not possible through software means, the only way to access protected data is over authentication over a driver in kernel mode. However, the use of hardware back-doors cannot prevent processor execution in more privileged modes. Implementation of kernel-mode allows the reading of sensitive data over the protected regions of memory. In this work, a proposal of crypto-accelerator is described. The peripheral bus in the proposed architecture features a write-only secure memory. That means the cryptography operations on the software level can not read the sensitive data from that secure memory. This approach suppresses any cache coherence manipulator and fault execution-related attacks against reading sensitive data. The peripheral can be useful to accelerate the cryptography operations, and store securely intermediate calculations as well as storing secure keys. The time of execution compared to the software counterpart can be reduced down to 2.5 decades, and the throughput is risen to 3 decades, reaching speeds of 30MB/s for large chunks of data. The total area represents 10.7% of the total area of a dual-core RISC-V processor with RV64IMAFC extensions and TileLink buses.
A Classification Framework for Practice Exercises in Adaptive Learning Systems

Learning systems can utilize many practice exercises, ranging from simple multiple-choice questions to complex problem-solving activities. In this article, we propose a classification framework for such exercises. The framework classifies exercises in three main aspects: 1) the primary type of interaction; 2) the presentation mode; and 3) the integration in the learning system. For each of these aspects, we provide a systematic mapping of available choices and pointers to relevant research. For developers of learning systems, the framework facilitates the design and implementation of exercises. For researchers, the framework provides support for the design, description, and discussion of experiments dealing with student modeling techniques and algorithms for adaptive learning. One of the aims of the framework is to facilitate replicability and portability of research results in adaptive learning.
Neighborhood-Aware Attention Network for Semi-supervised Face Recognition

Although face recognition has achieved fairly remarkable results in recent years, it heavily relies on large- scale labeled data to train the high-capacity deep convolutional neural networks. It is unrealistic to collect larger labeled datasets to further boost the performance, which requires burdensome and expensive annotation efforts. Meanwhile, there exist numerous unlabeled face images. It is challenging but promising to jointly utilize limited labeled and abundant unlabeled data to obtain higher performance gain, which is the target of semi-supervised learning. In this paper, we propose a bottom- up method, Neighborhood-Aware Attention Network (NAAN), for semi-supervised face recognition. It clusters unlabeled face images by collaboratively predicting pairwise relations based on their neighborhood information, where the neighborhood is defined as a k-hop ego network centered in the given sample called "ego". Considering the different importance of neighbors, we employ the graph attention network to learn the ego's representation. We evaluate our model on two face recognition datasets MegaFace and IJB-A, and it yields favorably comparable performance to the fully-supervised results.
A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines

Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs.Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults.We present a data-to-text generation system utilising transformers for generating corrective maintenance strategies for faults using SCADA data capturing the operational status of turbines.We achieve this in two stages: a first stage identifies faults based on SCADA input features and their relevance.A second stage performs content selection for the language generation task and creates maintenance strategies based on phrase-based natural language templates.Experiments show that our dual transformer model achieves an accuracy of up to 96.75% for alarm prediction and up to 75.35% for its choice of maintenance strategies during content-selection.A qualitative analysis shows that our generated maintenance strategies are promising.We make our humanauthored maintenance templates publicly available, and include a brief video explaining our approach.
Intention Mining Data preprocessing based on Multi-Agents System

Today vast and diverse event records of applications exist for almost every scientific domain, making their integration and intelligent exploitation challenging. Indeed, complex data require expressive data representation models. This work deals with the intention mining that is a very active and promising research area. Performance, flexibility, and adaptation are the biggest challenges for intention mining. Those issues have been illustrated from research on method engineering and guidance. Intention mining is the ability to predict a user’s goals. Knowing the user’s intention can support the decision-making of the network administrators. In addition, the user-friendliness is another big challenge to the intention mining in this paper. For this, based on the communication and coordination of intelligent Agents, a new multi-Agents based System approach is gathered in order to discover an intentional process model and provide specific recommendations. In addition, the input of intention mining is the trace of activities, which is an unstructured file because the activities are distributed from different sources. However, the dataset that will be used in intention mining must be well structured and filtered, so often some efforts are required to filter the relevant data. The main input of all algorithm used to discover intentional process model is the log file (traces activities), which is unstructured dataset and not ready to be feed as-is to machine learning algorithm. Therefore, this paper aims to describe the data preprocessing steps, which transform the unstructured log file to a structured one.
EEG feature learning with Intrinsic Plasticity based Deep Echo State Network

In this paper, deep EEG feature learning method is proposed for emotion recognition. It is well known that EEG signals dramatically vary from person to person, thereby making subject-independent emotion recognition very challenging. To address the above challenge, this work presents a deep echo state network (DeepESN) to learn temporal representation from raw EEG data. DeepESN as an input-driven discrete time non-linear dynamical system allows to process the temporal information at each time step in a deep temporal fashion by means of a hierarchical composition of multiple levels of recurrent neurons. To make the DeepESN robust, we pre-train the reservoir connections with an unsupervised intrinsic plasticity rule to generate activities following a desired Gaussian distribution. Then, we propose a hybrid learning algorithm for training the output weights which benefits from both the ridge regression and the online delta rule. Our leaky DeepESN achieved encouraging results when tested on the well-known affective benchmarks DEAP and DREAMER.
Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study

A defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a solution for predicting how the future will unfold visually, and thus, many models have been proposed that are capable of predicting future frames based on a history of observed frames (and sometimes robot actions). However, a comprehensive method for determining the fitness of different video prediction models at guiding the selection of actions is yet to be developed.Current metrics assess video prediction models based on human perception of frame quality. In contrast, we argue that if these systems are to be used to guide action, necessarily, the actions the robot performs should be encoded in the predicted frames. In this paper, we are proposing a new metric to compare different video prediction models based on this argument. More specifically, we propose an action inference system and quantitatively rank different models based on how well we can infer the robot actions from the predicted frames. Our extensive experiments show that models with high perceptual scores can perform poorly in the proposed action inference tests and thus, may not be suitable options to be used in robot planning systems.
An approach to seismic inversion with quantum annealing

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020An approach to seismic inversion with quantum annealingAuthors: Sarah GreerDaniel O’MalleySarah GreerLos Alamos National Laboratory and Massachusetts Institute of TechnologySearch for more papers by this author and Daniel O’MalleyLos Alamos National LaboratorySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3424413.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractQuantum computing has advanced enough that solving basic inverse problems in exploration seismology may be beneficial to show proof-of-concept results. The problems solved should be simple enough to work with the current early state of quantum computing hardware, which would be comparable to problems solved in the early stages of classical computing. We introduce an iterative algorithm for solving PDE-constrained optimization problems on a quantum annealer. We then use this method to invert for subsurface P-wave velocity—using a hybrid of classical and quantum computing—in a simple seismic experiment.Presentation Date: Tuesday, October 13, 2020Session Start Time: 9:20 AMPresentation Time: 10:10 AMLocation: Poster Station 11Presentation Type: PosterKeywords: processing, least squares, inversionPermalink: https://doi.org/10.1190/segam2020-3424413.1FiguresReferencesRelatedDetailsCited byQuantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimationMarcin Dukalski, Diego Rovetta, Stan van der Linde, Matthias Möller, Niels Neumann, and Frank Phillipson14 February 2023 | GEOPHYSICS, Vol. 88, No. 23D seismic data processing using cloud computingJournal of the Japanese Association for Petroleum Technology, Vol. 87, No. 1 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Sarah Greer and Daniel O’Malley, (2020), "An approach to seismic inversion with quantum annealing," SEG Technical Program Expanded Abstracts : 2845-2849. https://doi.org/10.1190/segam2020-3424413.1 Plain-Language Summary Keywordsprocessingleast squaresinversionPDF DownloadLoading ...