Reviewerly/exHarmony huggingface dataset:
Knowledge management capabilities and organizational outcomes: contemporary literature and future directions

Purpose The purpose of this systematic review is to summarize the possible organizational outcomes related to knowledge management capabilities (KMCs). It aims at offering the modern-day dynamic organizations a path, through which they can utilize KMC at hand for gaining competitive advantage and positive organizational outcomes. The review is based on previous studies in the field of knowledge management and explains how KMCs are associated with various organizational outcomes. Design/methodology/approach A systematic approach was utilized to collect, examine, interpret and synthesize researches regarding KMCs and their usefulness for various organizational outcomes. The meta-synthesis method was adopted to review 106 research papers after careful selection. Findings It was evident from this integrated review that KMCs help in promoting organizational effectiveness, innovative ability, organizational change, value creation, competitive advantage, organizational learning and performance. Furthermore, the current review suggests research avenues and knowledge gaps in current literature to provide future researchers unique research opportunities. Originality/value The review revealed a profound opinion that organizations have to develop an optimal mix of KMCs to achieve better organizational outcomes. The study identified how KMCs set a basis and build positive support to enhance the key organizational outcomes such as organizational effectiveness, innovative ability, organizational change, value creation, competitive advantage, organizational learning and performance. However, cooperation bias is one of the most considerable limitations in research studies included in this systematic literature review.
A Passive Target Recognition Method Based on LED Lighting for Industrial Internet of Things

We consider an industrial internet of things environment, where involves multiple production factors, such as automatic guided vehicles (AGVs), people, container, etc. A deep learning model is presented for multi-target recognition, where the training data is shadow image formed by the nonuniform illumination of LED lighting source. Three shadow models of typical shapes are constructed to describe the shadows at different positions. The performance of the optimal VGG-16-based Faster-RCNN model is analyzed in view of the recognition accuracy and speed, and it is proved that recognizing three, four, and five types of objects, the mean average precision is 93%, 94.8%, and 92.6%, respectively. To enhance the generalization performance, the optimal Faster-RCNN is combined with the motion state of objects and the corresponding threshold. Simulation results show that the proposed deep learning model obtains significant performance gains to reduce missed and false detection.
LDV-induced stroboscopic digital image correlation for high spatial resolution vibration measurement

Vibration measurement, particularly mode shape measurement, is an important aspect of structural dynamic analysis since it can validate finite element or analytical vibration models. Scanning laser Doppler vibrometry (LDV) and high-speed digital image correlation have become dominant methods for experimental mode shape measurement. However, these methods have high equipment costs and several disadvantages regarding spatial or temporal performance. This paper proposes a laser Doppler vibrometer induced stroboscopic digital image correction for non-contact mode shape and operational deflection shape measurement. Our results verify that single-point LDV and normal rate cameras can be used obtain high spatial resolution mode shape and operational deflection shape. Measurement frequency range is much higher than the camera capturing rate. We also show that the proposed approach coincides well with time-averaged electronic speckle pattern interferometry.
Optimizing Liver disease prediction with Random Forest by various Data balancing Techniques

Liver disease is a prominent disease other than heart attack, which is taking a lot of lives. Since most of the time liver disease is detected at a later stage leading to death. Number of liver patients is increasing because of several reasons like over consumption of alcohol, breathing in injurious gas, consuming polluted water and so on which can affect health parameters. Using a machine learning prediction models, liver diseases can be predicted using those health parameters in early stages. In this work to build the machine-learning model, Indian Liver Patient Dataset (ILPD) hosted at UCI.edu [1] is used, which is based on Indian patient and Random Forest (RF) algorithm is used to predict the disease with different preprocessing techniques. Data set is checked for skewness, outliers and imbalance using univariate and bivariate analysis and then suitable algorithms used to remove outliers and various oversampling and under sampling techniques are used to balance the data. Further refinement of model is done through hyper parameter tuning using grid search and feature selection. The final model provides 100% accuracy and also good score across different metrics.
Speech Encryption Technique using S - box based on Multi Chaotic Maps

This paper proposes the establishment of a secure encryption system for data transmission that includes three stages: first stage is a scrambling stage that is divided into scrambling of different sizes blocks and scrambling bits, the second stage is the use of DNA code to flip the bits and the third stage is the encryption using the Substitution box by 256 keys. The scheme is analysed using a variety of metrics. The findings demonstrate that the proposed system is significantly more reliable and robust against various forms of attacks than several recent related speech signal encryption systems.
Earnings-21: A Practical Benchmark for ASR in the Wild

Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR systems in the wild with special attention towards named entity recognition. We benchmark four commercial ASR models, two internal models built with open-source tools, and an open-source LibriSpeech model and discuss their differences in performance on Earnings-21. Using our recently released fstalign tool, we provide a candid analysis of each model's recognition capabilities under different partitions. Our analysis finds that ASR accuracy for certain NER categories is poor, presenting a significant impediment to transcript comprehension and usage. Earnings-21 bridges academic and commercial ASR system evaluation and enables further research on entity modeling and WER on real world audio.
Large-Scale Self- and Semi-Supervised Learning for Speech Translation

In this paper, we improve speech translation (ST) through effectively leveraging large quantities of unlabeled speech and text data in different and complementary ways. We explore both pretraining and self-training by using the large Libri-Light speech audio corpus and language modeling with CommonCrawl. Our experiments improve over the previous state of the art by 2.6 BLEU on average on all four considered CoVoST 2 language pairs via a simple recipe of combining wav2vec 2.0 pretraining, a single iteration of self-training and decoding with a language model. Different to existing work, our approach does not leverage any other supervision than ST data. Code and models will be publicly released.
BiFNet: Bidirectional Fusion Network for Road Segmentation

Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird’s eye views (BEVs) of the LiDAR remains the space structure in the horizontal plane, this article proposes a bidirectional fusion network (BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) the dense space transformation (DST) module, which solves the mutual conversion between the camera image space and BEV space and 2) the context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features. This method has achieved competitive results on the KITTI dataset.
SetMargin loss applied to deep keystroke biometrics with circle packing interpretation

This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation space that reveals a “semantic” structure based on distances. In this work, we propose a novel DML method specifically designed to address the challenges associated to free-text keystroke identification where the classes used in learning and inference are disjoint. The proposed SetMargin Loss (SM-L) extends traditional DML approaches with a learning process guided by pairs of sets instead of pairs of samples, as done traditionally. The proposed learning strategy allows to enlarge inter-class distances while maintaining the intra-class structure of keystroke dynamics. We analyze the resulting representation space using the mathematical problem known as Circle Packing, which provides neighbourhood structures with a theoretical maximum inter-class distance. We finally prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,000 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approaches.
Interpreting Adversarial Examples and Robustness for Deep Learning-Based Auto-Driving Systems

Deep learning-based auto-driving systems are vulnerable to adversarial examples attacks which may result in wrong decision making and accidents. An adversarial example can fool the well trained neural networks by adding barely imperceptible perturbations to clean data. In this paper, we explore the mechanism of adversarial examples and adversarial robustness from the perspective of statistical mechanics, and propose an statistical mechanics-based interpretation model of adversarial robustness. The state transition caused by adversarial training based on the theory of fluctuation dissipation disequilibrium in statistical mechanics is formally constructed. Besides, we fully study the adversarial example attacks and training process on system robustness, including the influence of different training processes on network robustness. Our work is helpful to understand and explain the adversarial examples problems and improve the robustness of deep learning-based auto-driving systems.
Exploiting in-Hub Temporal Locality in SpMV-based Graph Processing

The skewed degree distribution of real-world graphs is the main source of poor locality in traversing all edges of the graph, known as Sparse Matrix-Vector (SpMV) Multiplication. Conventional graph traversal methods, such as push and pull, traverse all vertices in the same manner, and we show applying a uniform traversal direction for all edges leads to sub-optimal memory locality, hence poor efficiency. This paper argues that different vertices in power-law graphs have different locality characteristics and the traversal method should be adapted to these characteristics.
Likelihood Estimation and Wavelet Transformation Based Optimization for Minimization of Noisy Pixels

In recent times the statistical computation techniques are gaining a lot of interest for analyzing the behavior of various mathematical distributions. This paper derives the likelihood distribution of image priors which has been further used to denoise the image. This paper aims to maximize the likelihood estimation of the parameters of interest i.e. prior and posterior estimation. We have proposed a prior-based distribution model which has been applied to additive, multiplicative and mixed noise cases. The various estimation parameters such as statistical variance and mean parameters have been used to evaluate the maximum likelihood of image priors for these noise models. Later, we have used an optimization technique based on the likelihood to reconstruct noise-free images efficiently. This paper uses conditional likelihood and wavelet transformation-based minimization techniques to minimize the noise in the pixels and a final denoised image is recovered. The conditional likelihood of the image has been optimized using pixel-based minimization w.r.t. the wavelet transformation coefficients. The simulation and analytical results have also been presented for the different noise cases.
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires

Quality-Diversity (QD) algorithms are powerful exploration algorithms that allow robots to discover large repertoires of diverse and high-performing skills. However, QD algorithms are sample inefficient and require millions of evaluations. In this paper, we propose Dynamics-Aware Quality-Diversity (DA-QD), a framework to improve the sample efficiency of QD algorithms through the use of dynamics models. We also show how DA-QD can then be used for continual acquisition of new skill repertoires. To do so, we incrementally train a deep dynamics model from experience obtained when performing skill discovery using QD. We can then perform QD exploration in imagination with an imagined skill repertoire. We evaluate our approach on three robotic experiments. First, our experiments show DA-QD is 20 times more sample efficient than existing QD approaches for skill discovery. Second, we demonstrate learning an entirely new skill repertoire in imagination to perform zero-shot learning. Finally, we show how DA-QD is useful and effective for solving a long horizon navigation task and for damage adaptation in the real world. Videos and source code are available at: https://sites.google.com/view/da-qd.
Quantifying Nonlocality: How Outperforming Local Quantum Codes Is Expensive

Quantum low-density parity-check (LDPC) codes are a promising avenue to reduce the cost of constructing scalable quantum circuits. However, it is unclear how to implement these codes in practice. Seminal results of Bravyi et al. [Phys. Rev. Lett. 104, 050503 (2010)] have shown that quantum LDPC codes implemented through local interactions obey restrictions on their dimension $k$ and distance $d$. Here we address the complementary question of how many long-range interactions are required to implement a quantum LDPC code with parameters $k$ and $d$. In particular, in 2D we show that a quantum LDPC code with distance $d\ensuremath{\propto}{n}^{1/2+ϵ}$ requires $\mathrm{\ensuremath{\Omega}}({n}^{1/2+ϵ})$ interactions of length $\stackrel{\texttildelow{}}{\mathrm{\ensuremath{\Omega}}}({n}^{ϵ})$. Further, a code satisfying $k\ensuremath{\propto}n$ with distance $d\ensuremath{\propto}{n}^{\ensuremath{\alpha}}$ requires $\stackrel{\texttildelow{}}{\mathrm{\ensuremath{\Omega}}}(n)$ interactions of length $\stackrel{\texttildelow{}}{\mathrm{\ensuremath{\Omega}}}({n}^{\ensuremath{\alpha}/2})$. As an application of these results, we consider a model called a stacked architecture, which has previously been considered as a potential way to implement quantum LDPC codes. In this model, although most interactions are local, a few of them are allowed to be very long. We prove that limited long-range connectivity implies quantitative bounds on the distance and code dimension.
Sugeno Fuzzy Logic Analysis: Navigation of Multiple Humanoids in Complex Environments

This paper presents sugeno fuzzy system with hybridized membership functions for the determination of exact turning angles for the humanoid robots during their path traversal from starting point to goal point. This helps the robot to reach the destination effectively by avoiding the hurdles in the path and obtains the global minimal path. The hybridized membership functions used in the sugeno fuzzy approach plays a key role in providing the exact turning angles to avoid hurdles. Image sensors and ultrasonic sensors are used for mapping the environment and, also provide the hurdle distances to the fuzzy controller as input parameter. The hurdle distances from the robot in front, left and right direction are the input parameter to the fuzzy controller and exact turning angle is the output parameter from the controller. The developed controller is carried out in both simulation and experimental environment having complex hurdles. A comparison has been made between the results of both simulation and experimental scenariosand, a good agreement is found among those with minimal deviations.
Image Generation Using GAN and Its Classification Using SVM and CNN

In the computer vision world, generative adversarial networks have acquired huge recognition because of their data generation potential without modeling the function of probability density. Generative adversarial networks (GANs) have capability to generate new samples similar to data they were trained on. A smart means of integrating samples without label into preparation and applying higher-order accuracy is adversarial loss generated by discriminator. In this paper, GAN is implemented on MNIST dataset and recognizes latent representation of the feature for digit generation. The model also consists of support vector machine (SVM) which is initially trained on the same MNIST dataset that is being used by GAN for generating new data similar to that of MNIST. The data generated by GAN are then passed through the pre-trained SVM classifier for predicting its equivalent label. The support vector machine model which was used with the MNIST dataset obtains the accuracy of 96.67%. Moreover, the proposed model with convolution neural network (CNN) on the same MNIST dataset produces an accuracy of 99.22%.
A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games

The automatic detection of events in complex sports games like soccer and handball using positional or video data is of large interest in research and industry. One requirement is a fundamental understanding of underlying concepts, i.e., events that occur on the pitch. Previous work often deals only with so-called low-level events based on well-defined rules such as free kicks, free throws, or goals. High-level events, such as passes, are less frequently approached due to a lack of consistent definitions. This introduces a level of ambiguity that necessities careful validation when regarding event annotations. Yet, this validation step is usually neglected as the majority of studies adopt annotations from commercial providers on private datasets of unknown quality and focuses on soccer only. To address these issues, we present (1) a universal taxonomy that covers a wide range of low and high-level events for invasion games and is exemplarily refined to soccer and handball, and (2) release two multi-modal datasets comprising video and positional data with gold-standard annotations to foster research in fine-grained and ball-centered event spotting. Experiments on human performance demonstrate the robustness of the proposed taxonomy, and that disagreements and ambiguities in the annotation increase with the complexity of the event. Datasets are available at https://github.com/mm4spa/eigd
Multi-Scale Spatial Attention-Guided Monocular Depth Estimation With Semantic Enhancement

Depth estimation from single monocular image is a vital but challenging task in 3D vision and scene understanding. Previous unsupervised methods have yielded impressive results, but the predicted depth maps still have several disadvantages such as missing small objects and object edge blurring. To address these problems, a multi-scale spatial attention guided monocular depth estimation method with semantic enhancement is proposed. Specifically, we first construct a multi-scale spatial attention-guided block based on atrous spatial pyramid pooling and spatial attention. Then, the correlation between the left and right views is fully explored by mutual information to obtain a more robust feature representation. Finally, we design a double-path prediction network to simultaneously generate depth maps and semantic labels. The proposed multi-scale spatial attention-guided block can focus more on the objects, especially on small objects. Moreover, the additional semantic information also enables the objects edge in the predicted depth maps more sharper. We conduct comprehensive evaluations on public benchmark datasets, such as KITTI and Make3D. The experiment results well demonstrate the effectiveness of the proposed method and achieve better performance than other self-supervised methods.
Online Multiple Object Tracking Using a Novel Discriminative Module for Autonomous Driving

Multi object tracking (MOT) is a key research technology in the environment sensing system of automatic driving, which is very important to driving safety. Online multi object tracking needs to accurately extend the trajectory of multiple objects without using future frame information, so it will face greater challenges. Most of the existing online MOT methods are anchor-based detectors, which have many misdetections and missed detection problems, and have a poor effect on the trajectory extension of adjacent object objects when they are occluded and overlapped. In this paper, we propose a discrimination learning online tracker that can effectively solve the occlusion problem based on an anchor-free detector. This method uses the different weight characteristics of the object when the occlusion occurs and realizes the extension of the competition trajectory through the discrimination module to prevent the ID-switch problem. In the experimental part, we compared the algorithm with other trackers on two public benchmark datasets, MOT16 and MOT17, and proved that our algorithm has achieved state-of-the-art performance, and conducted a qualitative analysis on the convincing autonomous driving dataset KITTI.
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve the segmentation accuracy with a new two-stage clustering algorithm. Our method demonstrates that non-photorealistic synthetic RGB and depth images can be used to learn feature embeddings that transfer well to real-world images for unseen object instance segmentation.