Reviewerly/exHarmony huggingface dataset:
Pipe Routing using Reinforcement Learning on Initial Design Stage

Pipe routing is a important part of the whole design process in the shipbuilding industry. It has a lot of constraints and many tasks that should be considered together. Also, the result of this stage affects follow-up works in a wide scope. Therefore, this part requires skilled designers and a lot of time. This study aims to reduce the workload and time during the design process by automating the pipe route design on initial stage. In this study, the reinforcement learning was used for pipe auto-routing. Reinforcement learning has the advantage of dynamically selecting routes, unlike existing algorithms. Therefore, it is suitable for the pipe routing design in ship design process which is frequently modified. At last, the effectiveness of this study was verified by comparing pipelines which were designed by piping designer and reinforcement learning results.
A Multi-feature Weighting Based K-means Algorithm for MOOC Learner Classification

Massive open online courses (MOOC) have recently gained worldwide attention in the field of education. The manner of MOOC provides a new option for learning various kinds of knowledge. A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups. However, most current algorithms mainly focus on the final grade of the learners, which may result in an improper classification. To overcome the shortages of the existing algorithms, a novel multi-feature weighting based K-means (MFWK-means) algorithm is proposed in this paper. Correlations between the widely used feature grade and other features are first investigated, and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm. Experimental results with the Canvas Network Person-Course (CNPC) dataset demonstrate the effectiveness of our method. Moreover, a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.
A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition

Surface electromyography (sEMG) array based gesture recognition, which is widely-used, could provide natural surfaces for human-computer interaction. Currently, most existing gesture recognition methods with sEMG array only work with the fixed and pre-defined electrodes configuration. However, changes in the number of electrodes (i.e., increment or decrement) is common in real scenarios due to the variability of physiological electrodes. In this paper, we study this challenging problem and propose a random forest based ensemble learning method, namely feature incremental and decremental ensemble learning (FIDE). FIDE is able to support continuous changes in the number of electrodes by dynamically maintaining the matrix sketches of every sEMG electrode and spatial structure of sEMG array. To evaluate the performance of FIDE, we conduct extensive experiments on three benchmark datasets, including NinaPro, CSL-hdemg, and CapgMyo. Experimental results demonstrate that FIDE outperforms other state-of-the-art methods and has the potential to adapt to the evolution of electrodes in the changing environments. Moreover, based on FIDE, we implement a multi clients/server collaboration system, namely McS, to support feature adaption in real-world environment. By collecting sEMG using two clients (smartphone and personal computer) and adaptively recognizing gestures in the cloud server, FIDE significantly improves the gesture recognition accuracy in electrode increment and decrement circumstances.
ArabHCI

research-article Share on ArabHCI: five years and counting Authors: Shaimaa Lazem City of Scientific Research and Technology Applications (SRTA-city) Alexandria, Egypt City of Scientific Research and Technology Applications (SRTA-city) Alexandria, EgyptView Profile , Mennatallah Saleh Cairo ACM SIGCHI Chapter (CairoCHI), Egypt Cairo ACM SIGCHI Chapter (CairoCHI), EgyptView Profile , Ebtisam Alabdulqader King Saud University Riyadh, Saudi Arabia King Saud University Riyadh, Saudi ArabiaView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 4April 2021 pp 69–71https://doi.org/10.1145/3447733Online:22 March 2021Publication History 4citation1,176DownloadsMetricsTotal Citations4Total Downloads1,176Last 12 Months159Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks

In this paper, we propose an automated three dimensional (3D) deep learning approach for the segmentation of gliomas in pre-operative brain MRI scans. We introduce a state-of-the-art multi-resolution architecture based on encoder-decoder which comprise of separate branches to incorporate local high-resolution image features and wider low-resolution contextual information. We also used a unified multi-task loss function to provide end-to-end segmentation training. For the task of survival prediction, we propose a regression algorithm based on random forests to predict the survival days for the patients. Our proposed network is fully automated and designed to take input as patches that can work on input images of any arbitrary size. We trained our proposed network on the BraTS 2020 challenge dataset that consists of 369 training cases, and then validated on 125 unseen validation datasets, and tested on 166 unseen cases from the testing dataset using a blind testing approach. The quantitative and qualitative results demonstrate that our proposed network provides efficient segmentation of brain tumors. The mean Dice overlap measures for automatic brain tumor segmentation of the validation dataset against ground truth are 0.87, 0.80, and 0.66 for the whole tumor, core, and enhancing tumor, respectively. The corresponding results for the testing dataset are 0.78, 0.70, and 0.66, respectively. The accuracy measures of the proposed model for the survival prediction tasks are 0.45 and 0.505 for the validation and testing datasets, respectively.
Automatic Modulation Classification Using Cumulants and Ensemble Classifiers

Communication signal recognition has a great implication in data communications to ensure stable, secure, and reliable data exchange. To accomplish this, automatic modulation classification (AMC) is indispensable because it can help users to recognize the modulation format, which benefits to receiver reconfiguration. On the other hand, AMC provides an additional level of encryption to the data and preventing unknown receivers from recovering the information without any awareness of the modulation type. This paper presents an ensemble-based pattern recognition (PR) classifiers for AMC with a combination of decision trees (DT), discriminant analysis, and K-nearest neighbors (KNN) classifiers. In addition, the performance of ensemble-based PRCs is analyzed and compared with that of the existing techniques.
Experimental Investigation of an Optical Reservoir Computing System Based on Two Parallel Time-Delay Reservoirs

We experimentally investigate the performances of an optical reservoir computing (RC) system based on two parallel time-delay reservoirs composed of two semiconductor lasers (SLs) subject to optical feedback.In such a system, the information being processed is split into two parts to send into two reservoirs through directly modulating the pump currents of two SLs, and the temporal output of the two SLs are sampled and taken as the virtual node states for training and testing.Via Santa Fe time series prediction task and multi-waveform recognition task, the performances of the proposed RC system are investigated and compared with those of the system based on one reservoir.The results show that the system based on two parallel reservoirs behaves better performance and stronger parameter robustness than that based on one reservoir.Moreover, through analyzing the dependence of the system performances on the number of virtual node states actually used for readout, the potential data processing rate (DPR) of the system is evaluated.For processing a prediction task under guaranteeing the normalized mean square error below 0.1 and a recognition task under guaranteeing the signal error rate below 0.005, the potential DPR of the proposed RC system can achieve 200 MSa/s, which is twice the DPR of the system with only one reservoir.
Feature Rescaling and Fusion for Tiny Object Detection

Recent years have witnessed rapid developments on computer vision, however, there are still challenges in detecting tiny objects in a large-scale background. The tiny objects knowledge become sparse and weak due to their tiny size, which makes the tiny objects difficult to be detected with the common approaches. In this paper, a new network named Specific Characteristics based Feature Rescaling and Fusion (SFRF) is designed to detect tiny persons in a broad horizon and massive background. Different from the methods in general, a Nonparametric Adaptive Dense Perceiving Algorithm (NADPA) is designed to automatically select and generate a new resized feature map with the high density distribution of tiny objects. Then, a method called Many-For-One strategy is used for feature fusion of the feature pyramid network (FPN) layers to improve the feature representation and detection. Finally, an ensemble model method named hierarchical Coarse-to-fine mechanism is designed based on the proposed methods to further improve the performance. The experiments demonstrate that the proposed approach achieves an obvious performance improvement on tiny object detection than the existing approaches, and our approach has been awarded as the 1st-place in the first large-scale Tiny Object Detection (TOD) challenge.
Re-weighting Large Margin Label Distribution Learning for Classification

Label ambiguity has attracted quite some attention among the machine learning community. The latterly proposed Label Distribution Learning (LDL) can handle label ambiguity and has found wide applications in real classification problems. In the training phase, an LDL model is learned first. In the test phase, the top label(s) in the label distribution predicted by the learned LDL model is (are) then regarded as the predicted label(s). That is, LDL considers the whole label distribution in the training phase, but only the top label(s) in the test phase, which likely leads to objective inconsistency. To avoid such inconsistency, we propose a new LDL method Re-Weighting Large Margin Label Distribution Learning (RWLM-LDL). First, we prove that the expected <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$ L_1 $</tex></formula> -norm loss of LDL bounds the classification error probability, and thus apply <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$ L_1 $</tex></formula> -norm loss as the learning metric. Second, re-weighting schemes are put forward to alleviate the inconsistency. Third, large margin is introduced to further solve the inconsistency. The theoretical results are presented to showcase the generalization and discrimination of RWLM-LDL. Finally, experimental results show the statistically superior performance of RWLM-LDL against other comparing methods.
Quantum Fisher information of a qubit-qutrit system in Garfinkle–Horowitz–Strominger dilation space–time

We investigate the quantum Fisher information (QFI) of a qubit-qutrit system in the background of Garfinkle–Horowitz–Strominger dilation black hole. After deriving the analytical expression of the QFI, we examine its dynamics with respect to the dilation parameter D and the state parameter γ of the system. Our results show that the QFI for the estimation of γ is a fixed value, which is independent of the parameters D and γ. And the QFI for the estimation of D varies with the parameters D and γ. Additionally, we propose an effective strategy to steer the QFI by introducing weak measurement reversal. We find that the QFI can be remarkably enhanced by adjusting the appropriate reversing measurement strengths. Our findings might provide some useful insights for the study on parameter estimation of hybrid systems in the framework of relativity theory.
Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.
Detection and Classification of Leukocytes in Leukemia using YOLOv2 with CNN

The development of machine learning systems that used for diagnosis of chronic diseases is challenging mainly due to lack of data and difficulty of diagnosing. This paper compared between two proposed systems for computer-aided diagnosis (CAD) to detect and classify three types of white blood cells which are fundamental of an acute leukemia diagnosis. Both systems depend on the You Only Look Once (YOLOv2) algorithm based on Convolutional Neural Network (CNN). The first system detects and classifies leukocytes at the same time called computer-aided diagnosis with one model (CADM1). The second system separates detection and classification by using two models called computer-aided diagnosis with two models (CADM2). The main purpose of the paper is proving the high performance and accuracy by fragmentation of the main task into sub-tasks through comparing between CADM1 and CADM2. Also, the paper proved that can be depending only on deep learning without any traditional segmentation and preprocessing on the microscopic image. The (CADM1) achieved average precision for detection and classification class1=56%, class2=69% and class3 72% while (CADM2) achieved average precision up to 94% for detect leukocytes and accuracy 92.4% for classification. The result of the second system is very suitable for diagnosis leukocytes in leukemia.
Action Shuffling for Weakly Supervised Temporal Localization

Weakly supervised action localization is a challenging task with extensive applications, which aims to identify actions and the corresponding temporal intervals with only video-level annotations available. This paper analyzes the order-sensitive and location-insensitive properties of actions, and embodies them into a self-augmented learning framework to improve the weakly supervised action localization performance. To be specific, we propose a novel two-branch network architecture with intra/inter-action shuffling, referred to as ActShufNet. The intra-action shuffling branch lays out a self-supervised order prediction task to augment the video representation with inner-video relevance, whereas the inter-action shuffling branch imposes a reorganizing strategy on the existing action contents to augment the training set without resorting to any external resources. Furthermore, the global-local adversarial training is presented to enhance the model's robustness to irrelevant noises. Extensive experiments are conducted on three benchmark datasets, and the results clearly demonstrate the efficacy of the proposed method.
Two-Level Multimodal Fusion for Sentiment Analysis in Public Security

Large amounts of data are widely stored in cyberspace. Not only can they bring much convenience to people’s lives and work, but they can also assist the work in the information security field, such as microexpression recognition and sentiment analysis in the criminal investigation. Thus, it is of great significance to recognize and analyze the sentiment information, which is usually described by different modalities. Due to the correlation among different modalities data, multimodal can provide more comprehensive and robust information than unimodal in data analysis tasks. The complementary information from different modalities can be obtained by multimodal fusion methods. These approaches can process multimodal data through fusion algorithms and ensure the accuracy of the information used for subsequent classification or prediction tasks. In this study, a two-level multimodal fusion (TlMF) method with both data-level and decision-level fusion is proposed to achieve the sentiment analysis task. In the data-level fusion stage, a tensor fusion network is utilized to obtain the text-audio and text-video embeddings by fusing the text with audio and video features, respectively. During the decision-level fusion stage, the soft fusion method is adopted to fuse the classification or prediction results of the upstream classifiers, so that the final classification or prediction results can be as accurate as possible. The proposed method is tested on the CMU-MOSI, CMU-MOSEI, and IEMOCAP datasets, and the empirical results and ablation studies confirm the effectiveness of TlMF in capturing useful information from all the test modalities.
Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding

Every natural text is written in some style.Style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc.One cannot form a complete understanding of a text without considering these factors.The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called crossstyle language understanding.This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation.The benchmark contains text in 15 different styles under the proposed four theoretical groupings: figurative, personal, affective, and interpersonal groups.For valid evaluation, we collect an additional diagnostic set by annotating all 15 styles on the same text.Using XSLUE, we propose three interesting crossstyle applications in classification, correlation, and generation.First, our proposed crossstyle classifier trained with multiple styles together helps improve overall classification performance against individually-trained style classifiers.Second, our study shows that some styles are highly dependent on each other in human-written text.Finally, we find that combinations of some contradictive styles likely generate stylistically less appropriate text.We believe our benchmark and case studies help explore interesting future directions for crossstyle research.The preprocessed datasets and code are publicly available.
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning

Reactive motion generation problems are usually solved by computing actions as a sum of policies. However, these policies are independent of each other and thus, they can have conflicting behaviors when summing their contributions together. We introduce Composable Energy Policies (CEP), a novel framework for modular reactive motion generation. CEP computes the control action by optimization over the product of a set of stochastic policies. This product of policies will provide a high probability to those actions that satisfy all the components and low probability to the others. Optimizing over the product of the policies avoids the detrimental effect of conflicting behaviors between policies choosing an action that satisfies all the objectives. Besides, we show that CEP naturally adapts to the Reinforcement Learning problem allowing us to integrate, in a hierarchical fashion, any distribution as prior, from multimodal distributions to non-smooth distributions and learn a new policy given them
CamouFinder: Finding Camouflaged Instances in Images

In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the CAMO-instance data. Last but not least, we develop an interactive user interface which demonstrates the performance of different state-of-the-art instance segmentation methods on the task of camouflaged instance segmentation. The users are able to compare the results of different methods on the given input images. Our work is expected to push the envelope of the camouflage analysis problem.
BatchMixup: Improving Training by Interpolating Hidden States of the Entire Mini-batch

Usually, we train a neural system on a sequence of mini-batches of labeled instances.Each mini-batch is composed of k samples, and each sample will learn a representation vector.MIXUP implicitly generates synthetic samples through linearly interpolating inputs and their corresponding labels of random sample pairs in the same mini-batch.This means that MIXUP only generates new points on the edges connecting every two original points in the representation space.We observed that the new points by the standard MIXUP cover pretty limited regions in the entire space of the mini-batch.In this work, we propose BATCHMIXUP-improving the model learning by interpolating hidden states of the entire mini-batch.BATCHMIXUP can generate new points scattered throughout the space corresponding to the mini-batch.In experiments, BATCHMIXUP shows superior performance than competitive baselines in improving the performance of NLP tasks while using different ratios of training data.