Reviewerly/exHarmony huggingface dataset:
AFrame: Extending DataFrames for Large-Scale Modern Data Analysis

Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity issues for “normal” data scientists. This paper introduces AFrame, a new scalable data analysis package powered by a Big Data management system that extends the data scientists' familiar DataFrame operations to efficiently operate on managed data at scale. AFrame is implemented as a layer on top of Apache AsterixDB, transparently scaling out the execution of DataFrame operations and machine learning model invocation through a parallel, shared-nothing big data management system. AFrame incrementally constructs SQL++ queries and leverages AsterixDB's semistructured data management facilities, user-defined function support, and live data ingestion support. In order to evaluate the proposed approach, this paper also introduces an extensible micro-benchmark for use in evaluating DataFrame performance in both single-node and distributed settings via a collection of representative analytic operations. This paper presents the architecture of AFrame, describes the underlying capabilities of AsterixDB that efficiently support modern data analytic operations, and utilizes the proposed benchmark to evaluate and compare the performance and support for largescale data analyses provided by alternative DataFrame libraries.
Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance.
Building a Multi-aspect Ontology for Semantic Interoperability in PLM

Interoperability support is a key task to enable seamless integration between various information systems. Today, in the era of Internet of Things and cyber-physical systems more and more systems have to collaborate. Product lifecycle management is not an exception. It covers multiple processes related to all stages of the product lifecycle and usually aimed at solving various tasks using different apparatus. As a result, a dilemma arises: on the one hand, there is a need of common information models enabling seamless information exchange, and on the other hand, the existing information models need to be preserved in order not to lose the already achieved efficiency in solving various tasks. In the present research, the problem of developing a single ontology for PLM support is investigated taking into account differences between terminologies (multi-aspect ontology) used at various stages of the PLM cycle. In this paper, the process of the multi-aspect PLM ontology building is presented based on the case study of PLM support at the automation equipment producer, Festo AG & Co KG.
EyeTAP: Introducing a multimodal gaze-based technique using voice inputs with a comparative analysis of selection techniques

One of the main challenges of gaze-based interactions is the ability to distinguish normal eye function from a deliberate interaction with the computer system, commonly referred to as 'Midas touch'. In this paper we propose, EyeTAP (Eye tracking point-and-select by Targeted Acoustic Pulse) a hands-free interaction method for point-and-select tasks. We evaluated the prototype in two separate user studies, each containing two experiments with 33 participants and found that EyeTAP is robust even in presence of ambient noise in the audio input signal with tolerance of up to 70 dB, results in a faster movement time, and faster task completion time, and has a lower cognitive workload than voice recognition. In addition, EyeTAP has a lower error rate than the dwell-time method in a ribbon-shaped experiment. These characteristics make it applicable for users for whom physical movements are restricted or not possible due to a disability. Furthermore, EyeTAP has no specific requirements in terms of user interface design and therefore it can be easily integrated into existing systems with minimal modifications. EyeTAP can be regarded as an acceptable alternative to address the Midas touch.
An RNN-Ensemble approach for Real Time Human Pose Estimation from Sparse IMUs

With recent advances in various hardware technologies, human motion capturing (MoCap) has gained importance in the fields such as computer vision, computer animation, gesture recognition in gaming, and most importantly in bio-mechanical analysis. In this direction, human motion is being captured using various kinds of sensors. Correspondingly, many model-based and data-based techniques have been developed in order to decode sensor readings into information understandable by a person. Given that the current technologies still lack applicability in real-world scenarios considering cost and ease of information gathering, leaves substantial room for improvement. This article focuses on the development of a novel machine learning based proof of concept for real-time human pose estimation using data collected from sparse inertial measurement units (IMU) system which is cost-effective and least intrusive in the scope of skilled crafts domain. Training diverse bi-directional recurrent neural networks (bi-RNN) with variable window size and building an ensemble of these models to estimate human pose in terms of human-joints' angles more accurately and robustly is discussed.
A pedestrian extraction algorithm based on single infrared image

Infrared image can show the state of objects at night, which is an important way to obtain the information of objects at night. To solve the extraction of pedestrians completely from single infrared image, we analyze the features of infrared image and propose a pedestrian extraction algorithm based on single infrared image. Firstly, neighborhood and multi-projection models are constructed to locate suspected pedestrian areas. Then, the head in the infrared imaging centripetally is used to build the template. Finally, the weighted fusion of global template and head template is used to extract pedestrians. Experiments show that the algorithm proposed in this paper can extract pedestrians in various motion modes under complex conditions, and has strong robustness.
Camera 3D positioning mixed reality-based interface to improve worker safety, ergonomics and productivity

This research develops a new mixed reality-based worker interface for industrial camera 3D positioning, which is intuitive and easy to manage, in order to enhance the worker safety, ergonomics and productivity. An experimental prototype to be used in the car body quality control is developed in the paper. The benefits and drawbacks of the proposed interface are discussed along the paper and sustained through several usability tests conducted with users familiar and not-familiar with mixed reality devices. Furthermore, the feasibility of the proposed approach is demonstrated by tests made in an industrial environment with skilled workers from Alfatec Sistemas company.
Blockchain e-voting system with the use of intelligent agent approach

There exist many voting solutions, which have different benefits and issues. The most significant difficulties faced by such systems are lack of transparency and auditability. Recently developed blockchain technology may be a solution to these issues. The paper describes the use of intelligent agents and multi-agent system concept for Auditable Blockchain Voting System (ABVS), which integrates e-voting process with blockchain technology into one supervised non-remote internet voting system which is end-to-end verifiable.
Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data.
Improved Color Image Watermarking Using Logistic Maps and Quaternion Legendre-Fourier Moments

In this chapter, an improved color image watermarking method is presented where the logistic maps were utilized with the quaternion Legendre-Fourier moments (Logistic maps–QLFMs). An exact, highly accurate, fast and numerically stable method is used to compute the Logistic maps–QLFMs in polar coordinates. In the Logistic maps–QLFMs method, the radial and angular kernels are computed over circular pixels by using analytical integration. Based on the geometric invariance of QLFMs, the robustness of the Logistic maps–QLFMs method is improved against geometric attacks, and the logistic mapping is used to randomly select QLFMs coefficients. Therefore, selected QLFMs are quantized to embed the binary digital watermark in the host color image. Moreover, the Logistic maps–QLFMs watermarking method is compared with quaternion Legendre-Fourier moments and existing quaternion methods. The QLFMs method is computed by using the same computation of the Logistic maps–QLFMs method. Utilizing an efficient computational method, the radial kernels of existing quaternion are computed using the accurate Gaussian quadrature method while the angular kernel is computed using analytical integration with the same as Logistic maps–QLFMs and QLFMs methods. Numerical experiments are performed where the performance of Logistic maps–QLFMs method is compared with the existing quaternion moment-based watermarking methods in terms of visual imperceptibility and robustness against different attacks. The comparison clearly shows that the Logistic maps–QLFMs is outperformed than existing quaternion moment-based watermarking methods.
GANs for Children: A Generative Data Augmentation Strategy for Children Speech Recognition

Due to the high acoustic variability, children speech recognition suffers significant performance reduction on most ASR systems which are optimized mainly using adults speech with limited or even none children speech. One of the most straight ideas to solve this problem is to increase the children's speech data during training, however, it is restricted by the more difficult process and higher cost when collecting children's speech compared to adults'. In this work, we develop a generative adversarial network (GANs) based data augmentation method to increase the size of children's training data to improve speech recognition performance for children's speech. Two different types of GANs are explored under WGAN-GP training framework, including the unconditional GANs with an unsupervised learning framework and the conditional GANs using acoustic states as conditions. The proposed data augmentation approaches are evaluated on a Mandarin speech recognition task, with only 40-hour children speech or further including 100-hour adult speech in the training. The results show that more than relative 20% WER reduction can be obtained on children speech testset with the proposed method, and the generated children speech with GAN even can improve the adults' speech within our experimental setups.
CAD4EM-P: Security-Driven Placement Tools for Electromagnetic Side Channel Protection

Side-Channel Analysis (SCA) attacks are major threats to hardware security. Upon this security threat, various countermeasures at different design layers have been proposed against SCA attacks. These approaches often introduce significant performance overheads and impose high requirements of side-channel security backgrounds to IC designers. In this paper, we propose an automatic computer-aided design (CAD) tool that can enhance the circuit resistance against electromagnetic (EM) SCA attacks. This new tool will guide a security-driven placement process and can be seamlessly integrated into the modern IC design flow. The protected IC design will be resilient to SCA attacks with negligible area and power overheads. In order to develop this tool, we first investigate the root-cause of EM leakage at layout level and mathematically demonstrate the feasibility of security-driven placement through the EM leakage modeling. We then identify that the correlation between the data under protection and the EM leakage can be significantly reduced through data-dependent registers reallocation. Simulation results on cryptographic circuits prove the effectiveness of both the constructed EM leakage model and the EM model based CAD tool for EM security.
Filtering Strategies for Inexact Subgraph Matching on Noisy Multiplex Networks

We study the problem of detecting matching subgraphs in a large multiplex background network based on predefined subgraph templates. Our approach extends existing filtering-based subgraph matching algorithms and proposes a new set of filters leveraging the monotone function properties in the multiplex setting. This enables effective pruning of irrelevant subgraph regions and expedites the overall matching process. In addition, our approach proposes a new strategy based on maximum likelihood estimate to identify “closely matched” subgraphs that are not isomorphic to the given templates from a noisy background network. This allows us to generalize this approach to real-world networks, which are often noisy, incomplete and ambiguous. We demonstrate the effectiveness of the proposed method on a real-world multiplex network provided by the DARPA Modeling Adversarial Activity (MAA) program. Our approach obtains highly accurate subgraph matching results for both the clean and noisy versions of the network, which significantly outperforms the baseline filtering methods. Furthermore, our proposed approach is parallelizable such that it can scale up to handle large input networks.
Neural Machine Translation with Target-Attention Model

Attention mechanism, which selectively focuses on source-side information to learn a context vector for generating target words, has been shown to be an effective method for neural machine translation (NMT). In fact, generating target words depends on not only the source-side information but also the target-side information. Although the vanilla NMT can acquire target-side information implicitly by recurrent neural networks (RNN), RNN cannot adequately capture the global relationship between target-side words. To solve this problem, this paper proposes a novel target-attention approach to capture this information, thus enhancing target word predictions in NMT. Specifically, we propose three variants of target-attention model to directly obtain the global relationship among target words: 1) a forward target-attention model that uses a target attention mechanism to incorporate previous historical target words into the prediction of the current target word; 2) a reverse target-attention model that adopts a reverse RNN model to obtain the entire reverse target words information, and then to combine with source context information to generate target sequence; 3) a bidirectional target-attention model that combines the forward target-attention model and reverse target-attention model together, which can make full use of target words to further improve the performance of NMT. Our methods can be integrated into both RNN based NMT and self-attention based NMT, and help NMT get global target-side information to improve translation performance. Experiments on the NIST Chinese-to-English and the WMT English-to-German translation tasks show that the proposed models achieve significant improvements over state-of-the-art baselines.
Permissionless reputation‐based consensus algorithm for blockchain

Reputation‐based consensus algorithms are one of the most promising, but least investigated types of consensus algorithms. Its limitation to persmissioned node identity management shades its potential and causes many applications to find blockchain unsuitable. In this paper, we upgrade PoRX, an existing consensus algorithm, into becoming the first permissionless pure reputation‐based consensus algorithm. This upgrade is highly potential since it only affects the admission process and does not intervene with the consensus process, thus can be extended into other types of consensus algorithms.
Exploring Model Units and Training Strategies for End-to-End Speech Recognition

In this work, we explore end-to-end speech recognition models (CTC, RNN-Transducer and attention-based models) with different model units (character, wordpiece and word) and various training strategies. We show that wordpiece unit outperforms character unit for all end-to-end systems on the Switchboard Hub5'00 benchmark. To improve the performance of end-to-end systems, we propose a multi-stage pretraining strategy, which gives 25.0% and 18.0% relative improvements over training from scratch for attention and RNN-T models respectively with wordpiece units. We achieve state-of-the-art performance on the Switchboard+Fisher-2000h task, outperforming all prior work. Together with other training strategies such as label smoothing and data augmentation, we achieve 5.9%/12.1% WER on the Switch-board/CallHome test set without using any external language models. This is a new performance milestone for a single end-to-end system, and it is also much better than the previous published best hybrid system, which is 6.7%/12.5% on each set individually.
Between sentential and model-based abductions: a dialogical approach

Abstract Most of the standard approaches consider abduction in terms of a backward reasoning and miss some of its fundamental features. Overall, they neglect its pragmatic dimension and the conjectural aspect of the conclusion. In this paper, we approach abduction in terms of strategic adjustment process in the context of dialogical logic. This sheds light on the use of conjectures in argumentative interactions. Although abductive dialogues are sometimes based upon sentential conjectures, they can also involve hypotheses about the context of argumentation itself. Indeed, the underlying logic of an argumentative interaction is not always settled since the beginning. In this context, abduction is not only concerned with the introduction of sentential hypotheses, but also with hypotheses concerning the structural rules governing the dialogue itself. We thus emphasize the instrumental dimension of abduction in dialogues.
VEDL: A Novel Video Event Searching Technique Using Deep Learning

Saha, Suprativ Bhattacharya, TanmayEvent searching in videos is important task for video genre classification, event detection, content-based video searching, etc. Being able to search for a particular event without going through the whole video saves lots of time for the users. It also helps in indexing the videos based on an event in it, so that users can retrieve videos with same event efficiently. The proposed approach is three-step approach. The approach uses the map-reduce concept to decrease the overall processing time for a video using deep learning. As the number of frames in a video can be large but events are limited so to solve this issue, we have used Eratosthenes sieve based key-frame extraction technique for extracting key-frames that describe the video efficiently, then from the key-frames, we have extracted the events in them, and then indexing of the events is done after finding event boundaries.
Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets

Abstract Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.
Outdoor RGBD Instance Segmentation With Residual Regretting Learning

Indoor semantic segmentation with RGBD input has received decent progress recently, but studies on instance-level objects in outdoor scenarios meet challenges due to the ambiguity in the acquired outdoor depth map. To tackle this problem, we proposed a residual regretting mechanism, incorporated into current flexible, general and solid instance segmentation framework Mask R-CNN in an end-to-end manner. Specifically, regretting cascade is designed to gradually refine and fully unearth useful information in depth maps, acting in a filtering and backup way. Additionally, embedded by a novel residual connection structure, the regretting module combines RGB and depth branches with pixel-level mask robustly. Extensive experiments on the challenging Cityscapes and KITTI dataset manifest the effectiveness of our residual regretting scheme for handling outdoor depth map. Our approach achieves state-of-the-art performance on RGBD instance segmentation, with 13.4% relative improvement over Mask R-CNN on Cityscapes by depth cue.