Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Image Denoising using Deep Convolutional Neural Network

Abstract A performance analysis of conventional Convolutional Neural Network (CNN) based denoising method is proposed. In this image denoising method, the contrast of images is adaptively enhanced. Generally, it is not possible to capture the imageswith good quality for all situations. Because they are captured in various light conditions.So, the captured images are suffered by noise, which results in poor perceived image quality. Thus, it is necessary to improve the quality of images with edge detail preservation as much as possible. The convolutional neural network model for low light image enhancement is already developed and is named as DnCNNs. Here, the performance analysis of image denoising using the DnCNNmodel is presented. The DnCNN implicitly removes the noise in the image. The simulation results afford better reference for application developers.
From context-aware to knowledge-aware: Boosting OOV tokens recognition in slot tagging with background knowledge

Neural-based context-aware models for slot tagging tasks in language understanding have achieved state-of-the-art performance, especially deep contextualized models, such as ELMo, BERT. However, the presence of out-of-vocab (OOV) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-aware slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge. Besides, we use multi-level graph attention to explicitly reason via lexical relations. We aim to leverage both linguistic regularities covered by deep language models (LM) and high-quality background knowledge derived from curated knowledge bases (KB). Consequently, our model could infer rare and unseen words in the test dataset by incorporating contextual semantics learned from the training dataset and lexical relations from ontology. The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets. We also show through detailed analysis that incorporating background knowledge effectively alleviates issues of data scarcity.
A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition

This paper addresses the problem of recognizing human-human interaction from skeletal sequences. Existing methods are mainly designed to classify single human action. Many of them simply stack the movement features of two characters to deal with human interaction, while neglecting the abundant relationships between characters. In this paper, we propose a novel two-stream recurrent neural network by adopting the geometric features from both single actions and interactions to describe the spatial correlations with different discriminative abilities. The first stream is constructed under pairwise joint distance (PJD) in a fully-connected mesh to categorize the interactions with explicit distance patterns. To better distinguish similar interactions, in the second stream, we combine PJD with the spatial features from individual joint positions using graph convolutions to detect the implicit correlations among joints, where the joint connections in the graph are adaptive for flexible correlations. After spatial modeling, each stream is fed to a bi-directional LSTM to encode two-way temporal properties. To take advantage of the diverse discriminative power of the two streams, we come up with a late fusion algorithm to combine their output predictions concerning information entropy. Experimental results show that the proposed framework achieves state-of-the-art performance on 3D and comparable performance on 2D interaction datasets. Moreover, the late fusion results demonstrate the effectiveness of improving the recognition accuracy compared with single streams.
Reducing Cognitive Workload in Telepresence Lunar - Martian Environments Through Audiovisual Feedback in Augmented Reality

Navigating through an unknown, and perhaps resource-constrained environment, such as Lunar terrain or through a disaster region can be detrimental - e.g. both physically and cognitively exhausting. Difficulties during navigation can cost time, operational resources, or even a life. To this end, the interaction with a robotic exploration system in lunar or Martian environments could be key to successful exploration extravehicular activities (X-EVA). Through the use of augmented reality (AR) we can afford an astronaut with various capabilities. In particular, we focus on two: (1) The ability to obtain and display information on their current position, on important locations, and on essential objects in an augmented space. (2) The ability to control an exploratory robot system, or smart robotic tools using AR interfaces. We present our ongoing development of such AR robot control interfaces and the feedback system being implemented. This work extends the augmented reality robot navigation and audio spatial feedback components presented at the 2020 National Aeronautics and Space Administration (NASA) SUITS Challenge.
Tracking Buggy Files: New Efficient Adaptive Bug Localization Algorithm

Upon receiving a new bug report, developers need to find its cause in the source code.Bug localization can be helped by a tool that ranks all source files according to how likely they include the bug.This problem was thoroughly examined by numerous scientists.We introduce a novel adaptive bug localization algorithm.The concept behind it is based on new feature weighting approaches and an adaptive selection algorithm utilizing pointwise learn-to-rank method.The algorithm is evaluated on publicly available datasets, and is competitive in terms of accuracy and required computational resources compared to state-of-the-art.Additionally, to improve reproducibility we provide extended datasets that include computed features and partial steps, and we also provide the source code.
ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification

The learning of classification models to predict class labels of new and previously unseen data instances is one of the most essential tasks in data mining.A popular approach to classification is ensemble learning, where a combination of several diverse and independent classification models is used to predict class labels.Ensemble models are important as they tend to improve the average classification accuracy over any member of the ensemble.However, classification models are also often required to be explainable to reduce the risk of irreversible wrong classification.Explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc.Unfortunately, ensemble learning decreases the level of explainability of the classification, as the analyst would have to examine many decision models to gain insights about the causality of the prediction.The aim of the research presented in this paper is to create an ensemble method that is explainable in the sense that it presents the human analyst with a conditioned view of the most relevant model aspects involved in the prediction.To achieve this aim the authors developed a rule-based explainable ensemble classifier termed Ranked ensemble G-Rules (ReG-Rules) which gives the analyst an extract of the most relevant classification rules for each individual prediction.During the evaluation process ReG-Rules was evaluated in terms of its theoretical computational complexity, empirically on benchmark datasets and qualitatively with respect to the complexity and readability of the induced rule sets.The results show that ReG-Rules scales linearly, delivers a high accuracy and at the same time delivers a compact and manageable set of rules describing the predictions made.
Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks

The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.
A Hybrid True Random Number Generator using Ring Oscillator and Digital Clock Manager

The modern cryptographic systems demand a random number generator to be of low-complexity and also be undeterminable simultaneously. The applications range from key generation, random padding bits, PIN/password generation, authentication protocols, and nonce generation. This paper introduces a new hybrid method that uses jitter as the randomness source. The proposed design incorporates a Ring Oscillators with Programmable Delay Lines and a Digital clock manager. Reduction in the correlation between the equal length ring oscillators is achieved by programmable delay lines and the Digital Clock Manager allows on-the-fly tunability which eventually enhances the randomness quality. A Von Neumann corrector is employed as a post-processor to eliminate any bias if present in the output bitstream and further improves the quality of randomness. The proposed TRNG achieves high throughput and low hardware footprint with a minimum cost of hardware. An analysis of the National Institute of Standards and Technology (NIST) tests demonstrates the effectiveness of the proposed architecture.
LSTM for Dialogue Breakdown Detection: Exploration of Different Model Types and Word Embeddings

One of the principal problems of human-computer interaction is miscommunication. Occurring mainly on behalf of the dialogue system, miscommunication can lead to dialogue breakdown, i.e., a point when the dialogue cannot be continued. Detecting breakdown can facilitate its prevention or recovery after breakdown occurred. In the paper, we propose a multinomial sequence classifier for dialogue breakdown detection. We explore several LSTM models each different in terms of model type and word embedding models they use. We select our best performing model and compare it with the performance of the best model and with the majority baseline from the previous challenge. We conclude that our detector outperforms the baselines during the offline testing.
Manifold-Regularized Multitask Fuzzy System Modeling With Low-Rank and Sparse Structures in Consequent Parameters

Multitask modeling methods for Takagi–Sugeno–Kang (TSK) fuzzy systems exhibit better generalization ability attributed to the utilization of the knowledge of intertask correlation. However, existing methods usually ignore the balance between the sharing of the common knowledge across multiple tasks and the preservation of the task-specific characteristics of each rule. To this end, we propose a novel manifold-regularized multitask modeling method for TSK fuzzy system by introducing low-rank and sparse structures into consequent parameters across multiple tasks. Specifically, we decompose the consequent parameters into two components—a task–shared component that represents similar structure across multiple tasks, and a task-specific component that encodes the sparse characteristics of the individual tasks. This can be implemented by imposing low-rank constraints on the task-shared component and applying the sparse constraints on the task-specific component. A new manifold regularization is further devised to reflect the feature-feature relation, which provides prior knowledge in multitask learning. An efficient augmented Lagrange multiplier is developed to solve the optimization problem. The experimental results demonstrate that the proposed model significantly outperforms the existing methods.
Research on Human Sports Rehabilitation Design Based on Object-Oriented Technology

In order to improve the effect of human motion rehabilitation, a design model of human motion rehabilitation based on object-oriented technology is proposed. The entire model design process includes the following steps. First, a visual dynamic tracking model for human motion rehabilitation is established, and then a fuzzy PID (Proportion Integration Differentiation) superheterodyne control method is used to design the bone training control for human motion rehabilitation. The bone tracking control and adaptive training are under the control of object-oriented technology; it is analyzed by collecting human activity data during training. The 6-DOF kinematics problem of human movement rehabilitation is decomposed into the bone training control problem in the subspace. Combining object-oriented technology, visual blur recognition of human sports rehabilitation training, and adopting an adaptive kinematics model to design sports rehabilitation can improve the control convergence and global stability of the human sports rehabilitation process. The simulation results show that the method has a good overall steady state and the sports rehabilitation training effect is obvious.
The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0&lt;3 and ε1&lt;0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.
Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and sh.PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs).For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO.These CHTs distinguish feasible solutions from infeasible ones.Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance.Therefore, a PSO is adapted for constrained optimization, yielding two constrained variants, denoted SF-PSO and VCH-PSO.Both SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems: the Himmelblau's nonlinear optimization, the welded beam design, the spring design, the pressure vessel design, and the three-bar truss design.The simulation results show that both algorithms are consistent in terms of their solutions to these problems, including their different available versions.Comparison of the SF-PSO and the VCH-PSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used.We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.
Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd.

In the digital marketplaces, businesses can micro-monitor sales worldwide and in real-time. Due to the vast amounts of data, there is a pressing need for tools that automatically highlight changing trends and anomalous (outlier) behavior that is potentially interesting to users. In collaboration with Danmark Music Group Ltd. we developed an unsupervised system for this problem based on a predictive neural network. To make the method transparent to developers and users (musicians, music managers, etc.), the system delivers two levels of outlier explanations: the deviation from the model prediction, and the explanation of the model prediction. We demonstrate both types of outlier explanations to provide value to data scientists and developers during development, tuning, and evaluation. The quantitative and qualitative evaluation shows that the users find the identified trends and anomalies interesting and worth further investigation. Consequently, the system was integrated into the production system. We discuss the challenges in unsupervised parameter tuning and show that the system could be further improved with personalization and integration of additional information, unrelated to the raw outlier score.
A Deep Learning Framework Supporting Model Ownership Protection and Traitor Tracing

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits. We demonstrate that SecureMark_DL is robust against various attacks including fingerprints collusion and network transformation (e.g., model compression and model fine-tuning). Extensive experiments conducted on MNIST and CIFAR10 datasets, as well as various types of deep neural network show the superiority of SecureMark_DL in terms of training accuracy and robustness against various types of attacks.
Optimal filtering for state space model with time-integral measurements

Filtering for state space model with time-integral measurement is studied. In this model, the measurement is an integral function, rather than an algebraic function, of the state vector. To be more specific, in the problem studied, the measurement is the between-epoch integral of a linear combination of the state vector. This is a new measurement type which arises in several navigation applications with velocity as part of the state vector and with between-epoch displacement as the measurement of the state space model. A rigorous discrete state space model is constructed from first principles to correctly represent the problem to be solved. Compared to this rigorous model, the model used in conventional method is inferior due to the following: a) the covariance of the measurement noise is wrongly determined; b) the between-epoch correlation in the measurement noise is ignored; and c) the cross correlation between the process noise and the measurement noises is ignored. Optimal filtering algorithm is designed according to the rigorous model. A simulation study is conducted to validate the superior performance of the proposed method to the conventional method.
Research on Pedestrian Detection System based on Tripartite Fusion of "HOG+SVM+Median filter"

Aiming at the problem of low accuracy of traditional pedestrian detection methods in a complex monitoring environment, based on the histogram of oriented gradient (HOG) and support vector machine (SVM) pedestrian detection algorithms, incorporating median filter (MF), a pedestrian detection model based on tripartite fusion (PDMTF) is proposed. The model first uses the median filter algorithm to denoise the image, effectively reducing the impact of noise on the HOG feature descriptor. Then, uses the extracted pedestrian features to train the SVM classifier. In addition, in order to optimize the SVM classifier, the model conducts a secondary training on the misidentified pedestrian area. The final experimental results show that the pedestrian false detection rate of the PDMTF model is only 7%, which has a good pedestrian recognition rate in complex environment.
Detailed Study of Deep Learning Models for Natural Language Processing

Natural Language Processing involves computational processing, and understanding of human languages. With the increase in computation power, deep learning models are being used for various NLP tasks. Further availability of large datasets of various languages enables the training of deep learning models. Multiple processing layers are used by the deep learning methods for learning representations of data which are hierarchical in nature and which gives excellent results for different NLP tasks. This paper reviews the important models and methods in deep learning which are applied to natural language problems. In particular, Convolutional Neural Network, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Recursive Neural Network have been described. Also, their advantages and suitability to various natural language processing applications such as text classification, sentiment analysis, etc. have been reviewed.
Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks

Machine learning predictors have been increasingly applied in production settings, including in one of the world's largest hiring platforms, Hired, to provide a better candidate and recruiter experience. The ability to provide actionable feedback is desirable for candidates to improve their chances of achieving success in the marketplace. Until recently, however, methods aimed at providing actionable feedback have been limited in terms of realism and latency. In this work, we demonstrate how, by applying a newly introduced method based on Generative Adversarial Networks (GANs), we are able to overcome these limitations and provide actionable feedback in real-time to candidates in production settings. Our experimental results highlight the significant benefits of utilizing a GAN-based approach on our dataset relative to two other state-of-the-art approaches (including over 1000x latency gains). We also illustrate the potential impact of this approach in detail on two real candidate profile examples.
Ritual Drones

Through a critical design approach, I suggest new perspectives on social drones, particularly companion drones. Supported by philosophies such as slow technology, I propose the design of anti-solutionist ritual drones and the study of their impact on the lives of users, particularly in domestic contexts. I intend to fill some of the methodological gaps identified, such as longitudinal studies in drone user experience through ethnography and auto-ethnography. I propose a "Research through Design" process of custom domestic probes for children and their families.