Reviewerly/exHarmony huggingface dataset:
Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as “How can we measure their interpretability?” “How can we make an informed assessment of how HFSs should be designed to enhance interpretability?” The challenges consist of measuring the interpretability of HFSs include issues such as their topological structure, the number of layers, the meaning of intermediate variables, and so on. In this article, an initial framework to measure the interpretability of HFSs is proposed, combined with a participatory user design process to create a specific instance of the framework for an application context. This approach enables the subjective views of a range of practitioners, experts in the design and creation of FLSs, to be taken into account in shaping the design of a generic framework for measuring interpretability in HFSs. This design process and framework are demonstrated through two classification application examples, showing the ability of the resulting index to appropriately capture interpretability as perceived by system design experts.
The Determinants of Bitcoin Price Volatility: An Investigation With ARDL Model

The emergence of Bitcoin (BTC) has triggered intense discussions. Despite the particular interest of the public, the theoretical understanding of the value of this crypto currency is limited. This is why current research is trying to find better leads to evaluate a complex phenomenon: the BTC price. The volatility of its price presents a certain specificity compared to the traditional currencies. In order to understand the reasons for this volatility, we try to identify and to analyze the main determinants of the BTC price and to estimate their influence. We apply time series to daily data for the period from 19/12/2011 to 06/02/2018. We used several approaches, including the Auto Regressive Distributed Lag ARDL model, the cointegration test at Pesaran et al. (2001) and the Granger causality test in the sense of Toda and Yamamoto (1995). Our estimated results suggest that the number of addresses, the attractiveness indicator and the mining difficulty have a significant impact on the BTC price with variations over time. On the other hand, the transaction volume, the stock, the EUR/USD exchange rate and the macroeconomic and financial development do not determine the price of the BTC in the short term as well as in the long term.
Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis

Generative adversarial network (GAN) has been widely explored for cross-modality medical image synthesis. The existing GAN models usually adversarially learn a global sample space mapping from the source-modality to the target-modality and then indiscriminately apply this mapping to all samples in the whole space for prediction. However, due to the scarcity of training samples in contrast to the complicated nature of medical image synthesis, learning a single global sample space mapping that is “optimal” to all samples is very challenging, if not intractable. To address this issue, this paper proposes sample-adaptive GAN models, which not only cater for the global sample space mapping between the source- and the target-modalities but also explore the local space around each given sample to extract its unique characteristic. Specifically, the proposed sample-adaptive GANs decompose the entire learning model into two cooperative paths. The baseline path learns a common GAN model by fitting all the training samples as usual for the global sample space mapping. The new sample-adaptive path additionally models each sample by learning its relationship with its neighboring training samples and using the target-modality features of these training samples as auxiliary information for synthesis. Enhanced by this sample-adaptive path, the proposed sample-adaptive GANs are able to flexibly adjust themselves to different samples, and therefore optimize the synthesis performance. Our models have been verified on three cross-modality MR image synthesis tasks from two public datasets, and they significantly outperform the state-of-the-art methods in comparison. Moreover, the experiment also indicates that our sample-adaptive strategy could be utilized to improve various backbone GAN models. It complements the existing GANs models and can be readily integrated when needed.
Hyper-parameter optimization in classification: To-do or not-to-do

Hyper-parameter optimization is a process to find suitable hyper-parameters for predictive models. It typically incurs highly demanding computational costs due to the need of the time-consuming model training process to determine the effectiveness of each set of candidate hyper-parameter values. A priori, there is no guarantee that hyper-parameter optimization leads to improved performance. In this work, we propose a framework to address the problem of whether one should apply hyper-parameter optimization or use the default hyper-parameter settings for traditional classification algorithms. We implemented a prototype of the framework, which we use a basis for a three-fold evaluation with 486 datasets and 4 algorithms. The results indicate that our framework is effective at supporting modeling tasks in avoiding adverse effects of using ineffective optimizations. The results also demonstrate that incrementally adding training datasets improves the predictive performance of framework instantiations and hence enables “life-long learning.”
DLD: A Deep Learning Based Line Descriptor for Line Feature Matching

In this paper, we present an appearance based line descriptor which was developed with the help of machine learning. Our descriptor uses a ResNet which we modified in its size to improve the performance. We utilized the Unreal Engine and multiple scenes provided for it to create training data. The training was performed using a triplet loss, where the loss of the network is calculated with triplets each consisting of three lines including a matching pair and another non-matching line. During learning, the goal of the minimization function is to calculate descriptors with minimal descriptor distance to matching lines' descriptors and maximal descriptor distance to other lines' descriptors. We evaluate the performance of our descriptor on our synthetic datasets, on real-world stereo images from the Middlebury Stereo Dataset and on a benchmark for line segment matching. The results show that in comparison to state-of-the-art line descriptors our method achieves a greatly improved line matching accuracy.
Unifying Blockchian and IoT:Security Requirements, Challenges, Applications and Future Trends

Blockchain is a decentralized and distributed public ledgers which holds sensitive and invariant data in an encrypted and secured manner to ensure no mid-way alterations are possible in a transaction, proving user a secure and trustable facility. While cryptocurrency like Bitcoin are major and most popular faces, Blockchain technology has gained a huge momentum recently. Objective of this paper is to layout a detailed survey on blockchain technology, to explain some important terminologies related to Blockchain, to compare IoT and traditional network on the basis of security, compatibility and capacity and to provide an insight on blockchain based IoT and Industrial IoT. Later, the challenges faced while adopting blockchain in IoT and it's future scopes are discussed.
Online Trajectory Generation of a MAV for Chasing a Moving Target in 3D Dense Environments

This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment. In contrast to obstacle-free or sparse environments, the chaser should be able to handle collision and occlusion together with flight efficiency. In order to tackle these challenges in real-time, we introduce a metric for target visibility and propose a hierarchical chasing planner. In the first phase, we generate a sequence of waypoints and chasing corridors which ensure safety and optimize visibility. In the following phase, the corridors and waypoints are utilized as constraints and objective respectively in quadratic programming from which we complete a dynamically feasible trajectory for chasing. The proposed algorithm is tested in multiple dense environments. The simulator AutoChaser with full code implementation & GUI can be found in https://github.com/icsl-Jeon/traj_gen_vis and video is available at https://youtu.be/-2d3uDlYR_M.
Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model

With the availability of a broad range of applications for Big Data streaming, both the class imbalance and concept drift have become crucial learning issues. The concept of drift handling solutions is sensitive to class imbalance. The sampling techniques are widely applied to process the continuously arriving data streams with a sufficient number of instances. The selected instances have to build a statistical inference to support imbalanced class distribution. The stream data classification model without concept drift adaptation is not preferable to the imbalanced class distribution. To solve the issues, this article presents the dynamic sampling and an ensemble classification technique, named as Handling Imbalanced Data with Concept Drift (HIDC). To provide high statistical precision over imbalanced class distribution with concept drift, the HIDC decides an optimal reservoir size using the metrics regarding statistical properties of stream data and control parameter. The former refers to the inequality level in the values of instances arrived from a source, and the latter one controls over the selection of instances from multiple sources. The HIDC estimates the optimal reservoir size using such statistical and control parameters. To select the appropriate instances with an allocated optimal reservoir size, the HIDC applies random sampling over imbalanced classes and chooses a set of instances from multiple sources. The random sampling cannot solve the issues of imbalanced class distribution among the existing classes. To address such problems, the HIDC applies resampling techniques with respect to the imbalance factor. To identify and address the new concepts, the proposed HIDC sampling model trains the candidate classifier and replaces the worst ensemble member with the candidate classifier. Finally, the experimental results show that the HIDC performs better sampling and mining over imbalanced class distribution with concept drifts.
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, web-based GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.
Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition

As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multi-label image classification problem. An attention-based neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long short-term memory (ConvLSTM) is proposed (CNN-CAtt-ConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pre-trained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other state-of-the-art (SOTA) methods is achieved, which proves the superiority and validity of our method.
A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas

Several psychologists posit that performance is not only a function of personality but also of situational contexts, such as day-level activities. Yet in practice, since only personality assessments are used to infer job performance, they provide a limited perspective by ignoring activity. However, multi-modal sensing has the potential to characterize these daily activities. This paper illustrates how empirically measured activity data complements traditional effects of personality to explain a worker's performance. We leverage sensors in commodity devices to quantify the activity context of 603 information workers. By applying classical clustering methods on this multisensor data, we take a person-centered approach to describe workers in terms of both personality and activity. We encapsulate both these facets into an analytical framework that we call organizational personas. On interpreting these organizational personas we find empirical evidence to support that, independent of a worker's personality, their activity is associated with job performance. While the effects of personality are consistent with the literature, we find that the activity is equally effective in explaining organizational citizenship behavior and is less but significantly effective for task proficiency and deviant behaviors. Specifically, personas that exhibit a daily-activity pattern with fewer location visits, batched phone-use, shorter desk-sessions and longer sleep duration, tend to perform better on all three performance metrics. Organizational personas are a descriptive framework to identify the testable hypotheses that can disentangle the role of malleable aspects like activity in determining the performance of a worker population.
Employing Whole-Body Control in Assistive Robotics

Light-weight robotic manipulators in combination with power wheelchairs can help to restore the mobility of people with disabilities. While such systems are available on the market, they typically are limited to fully manual control modes. In research, shared control methods are employed, to increase the usability of these systems. Here, we present an additional extension, by introducing a whole-body control concept to the assistive robotic system EDAN. Combined with shared control, the whole-body controller allows the realization of complex tasks which necessitate the coordination of arm and platform, while ensuring compliant behavior resulting from the impedance control law. The implemented approach is analyzed and validated in an exemplary task of opening a door, passing through it and closing it afterwards. While this task would exceed the reachability of the arm in a classical approach, the combination of whole-body control with a shared control scheme allows for quick and efficient execution.
Semi-Supervised Attention-Guided CycleGAN for Data Augmentation on Medical Images

Recently, deep learning methods, in particular, convolutional neural networks (CNNs), have made a massive breakthrough in computer vision. And a big amount of annotated data is the essential cornerstone to reach this success. However, in the medical domain, it is usually difficult (and sometimes even impossible) to get sufficient data for some specific learning tasks. Consequently, in this work, a novel data augmentation solution, called semi-supervised attention-guided CycleGAN (SSA-CycleGAN) is proposed to resolve this problem. Specifically, a cycle-consistency GANs-based model is first proposed to generate synthetic tumor (resp., normal) images from normal (resp., tumor) images. Then, a semi-supervised attention module is further proposed to enhance the model's capability in learning the important details of the training images, which in turns help the generated synthetic images become more realistic. To verify its effectiveness, experimental studies are conducted on three medical image datasets with limited amounts of MRI images, and the proposed SSA-CycleGAN is applied to generate synthetic tumor and normal MRI images for data augmentation. Experimental results show that i) SSA-CycleGAN can add (resp., remove) tumor lesions on (resp., from) the original normal (resp., tumor) images and generate very realistic synthetic tumor (resp. normal) images; and ii) in the ResNet18-based MRI image classification tasks based on these datasets, data augmentation using SSA-CycleGAN achieves much better classification performances than the classic data augmentation methods.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Dataset and Benchmark for Facial Pose Analysis

Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-the-art methods demonstrate the challenge offered by this new database.
Repetitive readout enhanced by machine learning

Abstract Single-shot readout is a key component for scalable quantum information processing. However, many solid-state qubits with favorable properties lack the single-shot readout capability. One solution is to use the repetitive quantum-non-demolition readout technique, where the qubit is correlated with an ancilla, which is subsequently read out. The readout fidelity is therefore limited by the back-action on the qubit from the measurement. Traditionally, a threshold method is taken, where only the total photon count is used to discriminate qubit state, discarding all the information of the back-action hidden in the time trace of repetitive readout measurement. Here we show by using machine learning (ML), one obtains higher readout fidelity by taking advantage of the time trace data. ML is able to identify when back-action happened, and correctly read out the original state. Since the information is already recorded (but usually discarded), this improvement in fidelity does not consume additional experimental time, and could be directly applied to preparation-by-measurement and quantum metrology applications involving repetitive readout.
Modelling of optimised neural network for classification and prediction of benchmark datasets

Along with the reason of optimization of the Spread factor of a generalized regression neural network (Grnn) which is a variant of radial basis function network (Rbfn), the projected paper presents a novel algorithm which incorporates two of the soft-computing methods explicitly particle swarm optimization (Pso) as well as genetic algorithm (Ga). As in case of a normal Grnn for simulation of the network, instead of taking any random value for Spread factor, the Spread factor value has been optimized by using Pso then Ga as a result with the purpose of the network converges more rapidly with greater accuracy and minimum mean square error (MSE). Initial some random values of the Spread factor are calculated according to the positions in PSO algorithm and then the more unrealistic values of the spread are converted into realistic values using improved Ga. First on top of a few part of the UCI dataset Grnn is trained and this is used for testing on the remaining datasets. Projected algorithms classify and predict databases in the lesser amount of mean square error and with high accuracy also, the performance of the designed algorithm is tested by cross fold validation methods.
MFENet: Multi-level feature enhancement network for real-time semantic segmentation

Accuracy is of vital importance for real-time semantic segmentation. However, modern methods often weaken high-level or low-level feature extraction to promote inference speed, thereby resulting in poor accuracy. In this paper, we present a Multi-level Feature Enhancement Network (MFENet) to enhance the feature extraction of each level in backbone. This approach can achieve high performance while maintaining high inference speed. We first rely on a Spatial and Edge Extraction Module with the Laplace Operator to improve the edge information extraction of low-level features. Next, we design a Context Boost Module to increase the context information inside each object of high-level features. Finally, we introduce the Selective Refinement Module to selectively combine the information from these two modules. Our network attained precise real-time segmentation results on Cityscapes, CamVid and COCO-Stuff datasets. More specifically, the architecture achieved 76.7% Mean IoU on the Cityscapes test dataset with 12.5 GFLOPS and a speed of 47 FPS on one NVIDIA Titan Xp card, which is more accurate than existing real-time methods.