Reviewerly/exHarmony huggingface dataset:
Graph representation learning with encoding edges

Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.
Supervising Unsupervised Open Information Extraction Models

Arpita Roy, Youngja Park, Taesung Lee, Shimei Pan. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging

Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federated Averaging, a novel algorithm for robust federated learning that is designed to detect failures, attacks, and bad updates provided by participants in a collaborative model. We propose a Hidden Markov Model to model and learn the quality of model updates provided by each participant during training. In contrast to existing robust federated learning schemes, we propose a robust aggregation rule that detects and discards bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency. Our experimental evaluation on 4 real datasets show that our algorithm is significantly more robust to faulty, noisy and malicious participants, whilst being computationally more efficient than other state-of-the-art robust federated learning methods such as Multi-KRUM and coordinate-wise median.
Delay-Optimal Traffic Engineering through Multi-agent Reinforcement Learning

Traffic engineering is one of the most important methods of optimizing network performance by designing optimal forwarding and routing rules to meet the quality of service (QoS) requirements for a large volume of traffic flows. End-to-end (E2E) delay is one of the key TE metrics. Optimizing E2E delay, however, is very challenging in large-scale multihop networks due to the profound network uncertainties and dynamics. This paper proposes a model-free TE framework that adopts multi-agent reinforcement learning for distributed control to minimize the E2E delay. In particular, distributed TE is formulated as a multi-agent extension of Markov decision process (MA-MDP). To solve this problem, a modular and composable learning framework is proposed, which consists of three interleaving modules including policy evaluation, policy improvement, and policy execution. Each of component can be implemented using different algorithms along with their extensions. Simulation results show that the combination of several extensions, such as double learning, expected policy evaluation, and on-policy learning, can provide superior E2E delay performance under high traffic load cases.
Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour

This paper proposes a new metaheuristic global optimization algorithm inspired by Wildebeest herding behavior called Wildebeest Herd Optimization (WHO) algorithm. WHO algorithm mimics the way nomadic Wildebeest herds search vast areas of grasslands efficiently for regions of high food density. The WHO algorithm models five principal Wildebeest behaviors: firstly Wildebeests have limited eyesight and can only search for food locally, secondly Wildebeests stick to the herd to escape predators, thirdly Wildebeest herd as a whole migrates to regions of high food availability based on historical knowledge of annual grass growth rates and rainfall patterns, fourthly Wildebeests move out of crowded overgrazed regions and finally Wildebeests move to avoid starvation. The WHO algorithm is compared to Physics inspired, Swarm based, Biologically inspired and Evolution inspired global optimization algorithms on an extended test suite of benchmark optimization problems including rotated, shifted, noisy and high dimensional problems. Extensive simulation results indicate that the WHO algorithm proposed in this paper significantly outperforms state-of-the-art popular metaheuristic optimization algorithms like Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Artificial Bee Colony Algorithm (ABC) and Simulated Annealing (SA) on shifted, high dimensional and large search range problems.
Validating and controlling quantum enhancement against noise by the motion of a qubit

Experimental validation and control of quantum traits for an open quantum system are important for any quantum information purpose. We consider a traveling atom qubit as a quantum memory with adjustable velocity inside a leaky cavity, adopting a quantum witness as a figure of merit for quantumness assessment. We show that this model constitutes an inherent physical instance where the quantum witness does not work properly if not suitably optimized. We then supply the optimal intermediate blind measurements which make the quantum witness a faithful tester of quantum coherence. We thus find that larger velocities protect quantumness against noise, leading to lifetime extension of hybrid qubit-photon entanglement and to higher phase estimation precision. Control of qubit motion thus reveals itself as a quantum enhancer.
Social Assessment of Innovations and Professional Responsibility of Future Engineers

Abstract This article focuses on the social aspects of technological development in the digital age. The assessment of feasibility and effectiveness of implementing new engineering developments implies an assessment of the sociological context of their future use. The position of the authors arises from the general concept of socially responsible innovations. It is emphasized that the global nature of implementing digital technology requires an ethical approach in analyzing the impact of implemented engineering developments on economic, political and social relations. The formation of professional culture of the future professional implies the availability of skills of social assessment of innovations and the readiness to be held responsible for innovative activities.
Simulation and Performance Analysis of Feature Extraction and Matching Algorithms for Image Processing Applications

Feature extraction and matching has the limelight in all almost all the fields ranging from biomedical to exploratory research. It has ubiquitous applications in present world that is moving at a breathtaking pace towards automation. The algorithms used for feature extraction are application specific, i.e. the one that yields better performance for face recognition does not guarantee the same performance for lane detection. A lot of time is invested in identifying algorithms that are best suited for an application. In the interest of time, an attempt has been made to develop few good algorithm combinations that assist in the selection of algorithms. The features of input image and the target image are extracted, described and matched using various algorithm combinations like SURF, FAST, MSER, and Harris Corner Detector. The combination of all these algorithms is simulated on MATLAB using computer vision and image processing toolboxes. A Graphical User Interface (GUI) is developed for better user experience. Face recognition is considered as an example to perform the simulation. The results reflect that the combination of SURF and MSER performs better compared to other algorithm combinations when an image is scaled and rotated, however there are no good matches when there is also a pose variation. Proper thresholding of ‘Match Threshold’, ‘Reject Ratio’, and ‘Inlier Threshold’ must be carried out through trial and error method to get better results.
Near-term performance of quantum repeaters with imperfect ensemble-based quantum memories

Four different quantum repeater schemes built with ensemble-based quantum memories are analyzed in order to guide experimentalists to achieve real-world implementations. The work focuses on repeaters devoid of error correction and suggests that meaningful proof-of-principle demonstrations are within reach of current technology.
Quantum metrology in a non-Markovian quantum evolution

The problem of quantum metrology under the context of a particular non-Markovian quantum evolution is explored. We study the dynamics of the quantum Fisher information (QFI) of a composite quantum probe coupled to a Lorentzian environment, for a full variety of different classes of parameters. We are able to find the best metrological state which is not maximally entangled but is the one which evolves fastest. This is shown by evidencing a connection between QFI and different quantum speed limits. At the same time, by optimizing a control field acting over the probes, we show how the total information flow is actively manipulated by the control such as to enhance the parameter estimation at a given final evolution time. At last, under this controlled scenario, a sharp interplay between the dynamics of QFI, non-Markovianity, and entanglement is revealed within different control schemes.
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic, and it is essential to develop a methodology that works in a task-free manner. Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resources to learn new data. In this work, we propose an expansion-based approach for task-free continual learning. Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data. CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework. With extensive experiments, we show that our model successfully performs task-free continual learning for both discriminative and generative tasks such as image classification and image generation.
Comparison of Electric Power Output Observed and Estimated from Floating Photovoltaic Systems: A Case Study on the Hapcheon Dam, Korea

An interest in floating photovoltaic (PV) is growing drastically worldwide. To evaluate the feasibility of floating PV projects, an accurate estimation of electric power output (EPO) is a crucial first step. This study estimates the EPO of a floating PV system and compares it with the actual EPO observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPOs. The monthly estimated EPOs were lower than the monthly observed EPOs. This result is ascribed to the cooling effect of the water environment on the floating PV module, which makes the floating PV efficiency higher than overland PV efficiency. Unfortunately, most commercial PV software, including the SAM, was unable to consider this effect in estimating EPO. The error results showed it was possible to estimate the monthly EPOs with an error of less than 15% (simply by simulation) and 9% (when considering the cooling effect: 110% of the estimated monthly EPOs). This indicates that the approach of using empirical results can provide more reliable estimation of EPO in the feasibility assessment stage of floating PV projects. Furthermore, it is necessary to develop simulation software dedicated to the floating PV system.
Invariant Representations through Adversarial Forgetting

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves state-of-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.
Off-Policy Evaluation in Partially Observable Environments

This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is inherently prone to bias, with risk of arbitrarily large errors. We define the problem of off-policy evaluation for Partially Observable Markov Decision Processes (POMDPs) and establish what we believe is the first off-policy evaluation result for POMDPs. In addition, we formulate a model in which observed and unobserved variables are decoupled into two dynamic processes, called a Decoupled POMDP. We show how off-policy evaluation can be performed under this new model, mitigating estimation errors inherent to general POMDPs. We demonstrate the pitfalls of off-policy evaluation in POMDPs using a well-known off-policy method, Importance Sampling, and compare it with our result on synthetic medical data.
BertMCN: Mapping colloquial phrases to standard medical concepts using BERT and highway network

In the last few years, people started to share lots of information related to health in the form of tweets, reviews and blog posts. All these user generated clinical texts can be mined to generate useful insights. However, automatic analysis of clinical text requires identification of standard medical concepts. Most of the existing deep learning based medical concept normalization systems are based on CNN or RNN. Performance of these models is limited as they have to be trained from scratch (except embeddings). In this work, we propose a medical concept normalization system based on BERT and highway layer. BERT, a pre-trained context sensitive deep language representation model advanced state-of-the-art performance in many NLP tasks and gating mechanism in highway layer helps the model to choose only important information. Experimental results show that our model outperformed all existing methods on two standard datasets. Further, we conduct a series of experiments to study the impact of different learning rates and batch sizes, noise and freezing encoder layers on our model.
Wearable XR-technology: literature review, conceptual framework and future research directions

Extended reality (XR) has revamped the way people experience the physical and the virtual environments, from observation to immersion. XR is an umbrella term that encompasses both augmented reality (AR) and virtual reality (VR), among others. Despite the promising outlook, this nascent technology has been shrouded by uncertain possibilities, making the adoption of XR technology much slower than expected. Moreover, the interdisciplinary applications of XR technology have led to scattered scholarly works and fragmentary insights to be translated into practice. Thus, there is a pressing need for a critical review and synthesis of prior XR research in order to strengthen this emerging field in IS. To accomplish this, the current study identifies and analyses a total of 45 articles through an extensive literature search. As a result, this study identifies the major drivers, barriers, and boundary conditions to XR adoption, classifies and map them into a holistic conceptual model.
PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network

Federated learning can complete an enormous training task efficiently by inviting participants to train a deep learning model collaboratively, and the user privacy will be well preserved for the users only upload model parameters to the centralized server. However, the attackers can initiate poisoning attacks by uploading malicious updates in federated learning. Therefore, the accuracy of the global model will be impacted significantly after the attack. To address this vulnerability, we propose a novel poisoning defense generative adversarial network (PDGAN) to defend the poising attack. The PDGAN can reconstruct training data from model updates and audit the accuracy for each participant model by using the generated data. Precisely, the participant whose accuracy is lower than a predefined threshold will be identified as an attacker and model parameters of the attacker will be removed from the training procedure in this iteration. Experiments conducted on MNIST and Fashion-MNIST datasets demonstrate that our approach can indeed defend the poisoning attacks in federated learning.
O‐MedAL: Online active deep learning for medical image analysis

Abstract Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under: Technologies &gt; Machine Learning Technologies &gt; Classification Application Areas &gt; Health Care
Hybrid DBLSTM-SVM Based Beta-Elliptic-CNN Models for Online Arabic Characters Recognition

The deep learning-based approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid Beta-Elliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long short-term memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of feature-based systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and Online-KHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the on-line and off-line systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other state-of-the-art systems.
Quantum random number generation using a hexagonal boron nitride single photon emitter

Abstract Quantum random number generation (QRNG) harnesses the intrinsic randomness of quantum mechanical phenomena. On-chip photonic circuitry provides a robust and versatile platform that can address and explore fundamental questions in quantum as well as classical physics. Likewise, integrated waveguide-based architectures hold the potential for intrinsically scalable, efficient and compact implementations of photonic QRNG. Here, we harness the quantum emission from the two-dimensional material hexagonal boron nitride an emerging atomically thin medium that can generate single photons on demand while operating at room temperature. By means of a customized splitter arrangement, we achieve true random number generation through the measurement of single photons exiting one of four designated output ports, and subsequently verify the randomness of the sequences in accordance with the National Institute of Standards and Technology benchmark suite. Our results clearly demonstrate the viability and efficiency of this approach to on-chip deterministic random number generators.