Reviewerly/exHarmony huggingface dataset:
DCGNN: a single-stage 3D object detection network based on density clustering and graph neural network

Abstract Currently, single-stage point-based 3D object detection network remains underexplored. Many approaches worked on point cloud space without optimization and failed to capture the relationships among neighboring point sets. In this paper, we propose DCGNN, a novel single-stage 3D object detection network based on density clustering and graph neural networks. DCGNN utilizes density clustering ball query to partition the point cloud space and exploits local and global relationships by graph neural networks. Density clustering ball query optimizes the point cloud space partitioned by the original ball query approach to ensure the key point sets containing more detailed features of objects. Graph neural networks are very suitable for exploiting relationships among points and point sets. Additionally, as a single-stage 3D object detection network, DCGNN achieved fast inference speed. We evaluate our DCGNN on the KITTI dataset. Compared with the state-of-the-art approaches, the proposed DCGNN achieved better balance between detection performance and inference time.
End-to-end Contextual Speech Recognition Using Class Language Models and a Token Passing Decoder

End-to-end modeling (E2E) of automatic speech recognition (ASR) blends all the components of a traditional speech recognition system into a single, unified model. Although it simplifies the ASR systems, the unified model is hard to adapt when training and testing data mismatches. In this work, we focus on contextual speech recognition, which is particularly challenging for E2E models because contextual information is only available in inference time. To improve the performance in the presence of contextual information during training, we propose to use class-based language models (CLM) that can populate context-dependent information during inference. To enable this approach to scale to a large number of class members and minimize search errors, we propose a token passing algorithm with an efficient token recombination for E2E systems. We evaluate the proposed system on general and contextual ASR tasks, and achieve relative 62% Word Error Rate (WER) reduction for the contextual ASR task without hurting recognition performance for the general ASR task. We also show that the proposed method performs well without modification of the decoding hyper-parameters across tasks, making it a desirable solution for E2E ASR.
Classifying Arabic Tweets Based on Credibility Using Content and User Features

Social Media services, such as Facebook and Twitter, have recently become a huge and continuous source of daily news. People all around the world rely heavily on news published via social media to know more about current events and activities. As a result, many users have started to exploit social media by broadcasting misleading news for financial and political purposes, which has an adverse impact on society. In this paper, we utilize machine learning to identify fake news from Arabic tweets based on a supervised classification model. Twitter content published in Arabic is very noisy with a high level of uncertainty, where little work has been accomplished to process and extract important features for classification purposes. In this paper, we utilize content-and user-related features, and employ sentiment analysis to generate new features for the detection of fake Arabic news. Sentiment analysis led to improving the accuracy of the prediction process. Among a number of machine learning algorithms used to train the classification models, four algorithms are chosen, namely Random Forest, Decision Tree, AdaBoost, and Logistic Regression. The experimental evaluation shows that our system can filter out fake news with an accuracy of 76%.
Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images

Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their prediction. However, most of the proposed CNNs require an expensive state-of-the-art hardware which substantially limits their use in practical scenarios and commercial systems, especially for clinical, biomedical and other applications that require on-the-fly analysis. In this paper, we investigate the possibility of making CNNs lighter by parametrizing the architecture and decreasing the number of trainable weights of a popular CNN: U-Net. In order to demonstrate that comparable results can be achieved with substantially less trainable weights than the original U-Net we used a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images with minimal annotations (i.e. consisting only of the virus particle centers or centerlines). We explored 4 U-Net hyper-parameters: the number of base feature maps, the feature maps multiplier, the number of the encoding-decoding levels and the number of feature maps in the last 2 convolutional layers. Our experiments lead to two main conclusions: 1) the architecture hyper-parameters are pivotal if less trainable weights are to be used, and 2) if there is no restriction on the trainable weights number using a deeper network generally gives better results. However, training larger networks takes longer, typically requires more data and such networks are also more prone to overfitting. Our best model achieved an accuracy of 82.2% which is similar to the original U-Net while using nearly 4 times less trainable weights (7.8 M in comparison to 31.0 M). We also present a network with < 2 M trainable weights that achieved an accuracy of 76.4%. The proposed U-Net hyper-parameter exploration can be adapted to other CNNs and other applications. It allows a comprehensive CNN architecture designing with the aim of a more efficient trainable weight use. Making the networks faster and lighter is crucial for their implementation in many practical applications. In addition, a lighter network ought to be less prone to over-fitting and hence generalize better.
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts

Timo Schick, Hinrich Schütze. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Simplicity Creates Inequity

Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.
Optimizing ensemble weights and hyperparameters of machine learning models for regression problems

Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting focus more on reducing variance and bias, respectively, stacking approaches target both by finding the optimal way to combine base learners. In stacking with the weighted average, ensembles are created from weighted averages of multiple base learners. It is known that tuning hyperparameters of each base learner inside the ensemble weight optimization process can produce better performing ensembles. To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed. Besides, Bayesian search was used to speed-up the optimizing process and a heuristic was implemented to generate diverse and well-performing base learners. The algorithm is shown to be generalizable to real data sets through analyses with ten publicly available data sets.
ROBUST DISCRIMINATION OF LEUKOCYTES PROTUBERANT TYPES FOR EARLY DIAGNOSIS OF LEUKEMIA

Advanced laboratory technology has made blood testing more automated, robust and tests are being implemented comprehensively. Leukocytes type differentiation is a critical hematological images analysis step of blood film as it delivers valuable information in diagnosis of several diseases. At present, the morphological examination of leukocytes is done manually and this process is very tedious, inefficient and slow. Although many white blood cells detection or classification techniques are presented by different researchers, there is still a need of fully automated and an efficient detection system of blood cells with its particular types for an early diagnosis of leukemia. This paper presents a technique for the classification of protuberant types of leukocytes and early diagnosis of leukemia. The work is divided into the following main stages: (a) image augmentation, (b) wavelet composition and decomposition for attaining high and low frequency bands of the cell image, (c) convolutional neural network (CNN) training model for the classification of leukocytes categories and (d) prediction of leukemia. The main intention behind this study is to develop an automated, robust and efficient classification and detection system of leukocytes for microscopic blood images.
Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks

Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.
StructPool: Structured Graph Pooling via Conditional Random Fields

Learning high-level representations for graphs is of great importance for graph analysis tasks. In addition to graph convolution, graph pooling is an important but less explored research area. In particular, most of existing graph pooling techniques do not consider the graph structural information explicitly. We argue that such information is important and develop a novel graph pooling technique, know as the StructPool, in this work. We consider the graph pooling as a node clustering problem, which requires the learning of a cluster assignment matrix. We propose to formulate it as a structured prediction problem and employ conditional random fields to capture the relationships among assignments of different nodes. We also generalize our method to incorporate graph topological information in designing the Gibbs energy function. Experimental results on multiple datasets demonstrate the effectiveness of our proposed StructPool.
Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres

Particle swarm optimization (PSO) algorithms have low-quality initial particle swarm, which is generated by a random method when handling the problem of task scheduling in networked data centres. Such algorithms also fall easily into local optimum when searching for the optimal solution. To address these problems, this study proposes combining opposition-based learning (OBL) and tentative perception (TP) with PSO; the proposed method is called OBL-TP-PSO. This algorithm uses reverse learning to generate the initial population, such that the quality of the initial particle swarm can be improved. Before the particle speed and location are updated, the TP method is used to search for the individual optimum around each particle, thereby reducing the possibility of missing the potential optimal solution during the process of searching. In this manner, the problem in which the PSO algorithm easily falls into the local optimal is effectively solved. To evaluate the performance of the proposed algorithm, simulation experiments are performed on CloudSim toolkit. Experimental results show that in comparison with other algorithms (namely, Min-Min, Max-Min and PSO algorithm), the proposed OBL-TP-PSO algorithm has better performance in terms of the total execution time, load balancing and quality of service.
Playing games with multiple access channels

Abstract Communication networks have multiple users, each sending and receiving messages. A multiple access channel (MAC) models multiple senders transmitting to a single receiver, such as the uplink from many mobile phones to a single base station. The optimal performance of a MAC is quantified by a capacity region of simultaneously achievable communication rates. We study the two-sender classical MAC, the simplest and best-understood network, and find a surprising richness in both a classical and quantum context. First, we find that quantum entanglement shared between senders can substantially boost the capacity of a classical MAC. Second, we find that optimal performance of a MAC with bounded-size inputs may require unbounded amounts of entanglement. Third, determining whether a perfect communication rate is achievable using finite-dimensional entanglement is undecidable. Finally, we show that evaluating the capacity region of a two-sender classical MAC is in fact NP-hard.
Artificial Intelligence Applications in Telecommunications and other network industries

Artificial intelligence applications in network industries have the potential to reduce network roll-out and operating costs, improve performance, enhance customer service, and support the development and introduction of new services. This paper identifies and analyzes the range of AI applications already in place and those expected to be in place in the near future. After analyzing the applications found in the literature, we have conducted interviews with senior executives responsible for AI in telecommunications, electricity, gas and water companies headquartered in North America and Europe, as well as with suppliers incorporating AI into their equipment. These interviews have provided information on which AI applications are actually being used in these network industries and which are expected to be implemented in the future. While some of these AI applications, like predictive maintenance, have already been implemented today by most operators, more complex applications, such as traffic management AIs for 5G, are being considered by only a few large operators. Finally, the paper explores the consequences of AI for regulation. At the least, regulators will need to understand AI in order to determine if AI applications raise regulatory concerns. At this point, however, as AI becomes increasingly integrated into network operations, regulatory issues are only beginning to emerge.
Group Activity Detection from Trajectory and Video Data in Soccer

Group activity detection in soccer can be done by using either video data or player and ball trajectory data. In current soccer activity datasets, activities are labelled as atomic events without a duration. Given that the state-of-the-art activity detection methods are not well-defined for atomic actions, these methods cannot be used. In this work, we evaluated the effectiveness of activity recognition models for detecting such events, by using an intuitive non-maximum suppression process and evaluation metrics. We also considered the problem of explicitly modeling interactions between players and ball. For this, we propose self-attention models to learn and extract relevant information from a group of soccer players for activity detection from both trajectory and video data. We conducted an extensive study on the use of visual features and trajectory data for group activity detection in sports using a large scale soccer dataset provided by Sportlogiq. Our results show that most events can be detected using either vision or trajectory-based approaches with a temporal resolution of less than 0.5 seconds, and that each approach has unique challenges.
Context-Aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training

Mispronunciation detection is an essential component of the Computer-Assisted Pronunciation Training (CAPT) systems. State-of-the-art mispronunciation detection models use Deep Neural Networks (DNN) for acoustic modeling, and a Goodness of Pronunciation (GOP) based algorithm for pronunciation scoring. However, GOP based scoring models have two major limitations: i.e., (i) They depend on forced alignment which splits the speech into phonetic segments and independently use them for scoring, which neglects the transitions between phonemes within the segment; (ii) They only focus on phonetic segments, which fails to consider the context effects across phonemes (such as liaison, omission, incomplete plosive sound, etc.). In this work, we propose the Context-aware Goodness of Pronunciation (CaGOP) scoring model. Particularly, two factors namely the transition factor and the duration factor are injected into CaGOP scoring. The transition factor identifies the transitions between phonemes and applies them to weight the frame-wise GOP. Moreover, a self-attention based phonetic duration modeling is proposed to introduce the duration factor into the scoring model. The proposed scoring model significantly outperforms baselines, achieving 20% and 12% relative improvement over the GOP model on the phoneme-level and sentence-level mispronunciation detection respectively.
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering

Image text carries essential information to understand the scene and perform reasoning. Text-based visual question answering (text VQA) task focuses on visual questions that require reading text in images. Existing text VQA systems generate an answer by selecting from optical character recognition (OCR) texts or a fixed vocabulary. Positional information of text is underused and there is a lack of evidence for the generated answer. As such, this paper proposes a localization-aware answer prediction network (LaAP-Net) to address this challenge. Our LaAP-Net not only generates the answer to the question but also predicts a bounding box as evidence of the generated answer. Moreover, a context-enriched OCR representation (COR) for multimodal fusion is proposed to facilitate the localization task. Our proposed LaAP-Net outperforms existing approaches on three benchmark datasets for the text VQA task by a noticeable margin.
Global optimization based on active preference learning with radial basis functions

Abstract This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis .
High-accuracy Ising machine using Kerr-nonlinear parametric oscillators with local four-body interactions

Abstract A two-dimensional array of Kerr-nonlinear parametric oscillators (KPOs) with local four-body interactions is a promising candidate for realizing an Ising machine with all-to-all spin couplings, based on adiabatic quantum computation in the Lechner–Hauke–Zoller (LHZ) scheme. However, its performance has been evaluated only for a symmetric network of three KPOs, and thus it has been unclear whether such an Ising machine works in general cases with asymmetric networks. By numerically simulating an asymmetric network of more KPOs in the LHZ scheme, we find that the asymmetry in the four-body interactions causes inhomogeneity in photon numbers and hence degrades the performance. We then propose a method for reducing the inhomogeneity, where the discrepancies of the photon numbers are corrected by tuning the detunings of KPOs depending on their positions, without monitoring their states during adiabatic time evolution. Our simulation results show that the performance can be dramatically improved by this method. The proposed method, which is based on the understanding of the asymmetry, is expected to be useful for general networks of KPOs in the LHZ scheme and thus for their large-scale implementation.
Optimal blockchain network construction methodology based on analysis of configurable components for enhancing Hyperledger Fabric performance

Presently, blockchain technology has been widely applied in various application domains such as the Internet of Things (IoT), supply chain management, healthcare, etc. So far, there has been much confusion about whether blockchain performs with scale, and admittedly, a lack of information about best practices that can improve the performance and scale. This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric. As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance. A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance. To demonstrate the significance of the proposed methodology, we set up the experiment environment for the baseline and the test network using optimized parameters, respectively. The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.