Reviewerly/exHarmony huggingface dataset:
Analyzing Success Factors of Digital Transformation in Aviation Industry Using Fuzzy Cognitive Map Approach

Rapid developments in technology have made widespread use of digital technologies in almost every sector. With the spread of these digital technologies, companies have found themselves in an inevitable process of digital transformation. Companies that have started to take part in a new digital ecosystem have tried to provide their competitiveness and sustainability to compete with the benefits of digital transformation. However, a successful digital transformation process has become a necessity to counter the disruptive power of digital transformation. In this study, critical factors necessary for successful digital transformation are analyzed. Fuzzy cognitive map (FCM) technique extension of cognitive maps (CM), which helps to analyze the relationship between these factors and allows scenario analysis for future situations, has been utilized with the thought that these factors are related to each other. These factors are discussed from the perspective of the aviation industry, one of the industries where the effects of digital transformation are intensely observed.
Transfer-Deep Learning Application for Ultrasonic Computed Tomographic Image Classification

Deep-learning techniques have led to a technological progress in several fields such as robotics, mechanics, and medicine specifically in the area of medical imaging. On the light of these recent developments in deep learning it will be recorded that medical imaging evolution needs a transfer deep learning application for the classification process. In this paper, our approach consists on a deep learning transfer models such us Inception V3, MobileNet, NasNet and Ameobanet on Ultrasonic Computed tomography images (USCT) to classify them automatically into three classes. In the beginning, USCT dataset augmentation has been done with pre-processing algorithms. Then, a Transfer Convolutional Neural Network Architecture has been applied with different models on our dataset. Finally, we have implemented our neural network application on GPU. As results we have overcoming previously works by a value of 100% for train accuracy and a value of 96% for test accuracy with a short time process.
Fundamental Limits of Data Utility: A Case Study for Data-Driven Identity Authentication

Big data can help with providing valuable perceptions into business activities and disclosing the potential benefits. Advances in machine learning and deep learning technologies make it easier to achieve significant performance in a wide range of domains from city planning and marketing analysis to credit evaluation and identity theft detection. However, it still requires great efforts in selecting efficient learning algorithms and precise model parameters that are deemed confidential in the light of experience. Also worth noting is that there is a fundamental gap between impracticable business requirements and the available value of data reflected. The data holder or data service provider may not have a clear understanding of data interference. The solution to these two problems depends on the capability of predicting the data utility in advance, which raises a fundamental question: to what degree is the data utility predictable? In this work, we present a primary analytical framework for information-theoretic bounds of data utility and utilize the current state-of-the-art and representative algorithms to obtain the achievable lower bounds on a real-world data set. The gap between theoretical upper bounds and achievable lower bounds indicates that the achievable lower bounds can still be optimized for performance.
Enhancing deep neural networks with morphological information

Abstract Deep learning approaches are superior in natural language processing due to their ability to extract informative features and patterns from languages. The two most successful neural architectures are LSTM and transformers, used in large pretrained language models such as BERT. While cross-lingual approaches are on the rise, most current natural language processing techniques are designed and applied to English, and less-resourced languages are lagging behind. In morphologically rich languages, information is conveyed through morphology, for example, through affixes modifying stems of words. The existing neural approaches do not explicitly use the information on word morphology. We analyse the effect of adding morphological features to LSTM and BERT models. As a testbed, we use three tasks available in many less-resourced languages: named entity recognition (NER), dependency parsing (DP) and comment filtering (CF). We construct baselines involving LSTM and BERT models, which we adjust by adding additional input in the form of part of speech (POS) tags and universal features. We compare the models across several languages from different language families. Our results suggest that adding morphological features has mixed effects depending on the quality of features and the task. The features improve the performance of LSTM-based models on the NER and DP tasks, while they do not benefit the performance on the CF task. For BERT-based models, the added morphological features only improve the performance on DP when they are of high quality (i.e., manually checked) while not showing any practical improvement when they are predicted. Even for high-quality features, the improvements are less pronounced in language-specific BERT variants compared to massively multilingual BERT models. As in NER and CF datasets manually checked features are not available, we only experiment with predicted features and find that they do not cause any practical improvement in performance.
Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection

Depth information available from an RGB-D camera can be useful in segmenting salient objects when figure/ground cues from RGB channels are weak. This has motivated the development of several RGB-D saliency datasets and algorithms that use all four channels of the RGB-D data for both training and inference. Unfortunately, existing RGB-D saliency datasets are small, which may lead to overfitting and limited generalization for diverse scenarios. Here we propose a semi-supervised system for RGB-D saliency detection that can be trained on smaller RGB-D saliency datasets without saliency ground truth, while also make effective joint use of a large RGB saliency dataset with saliency ground truth together. To generalize our method on RGB-D saliency datasets, a novel prediction-guided cross-refinement module which jointly estimates both saliency and depth by mutual refinement between two respective tasks, and an adversarial learning approach are employed. Critically, our system does not require saliency ground-truth for the RGB-D datasets, which saves the massive human labor for hand labeling, and does not require the depth data for inference, allowing the method to be used for the much broader range of applications where only RGB data are available. Evaluation on seven RGB-D datasets demonstrates that even without saliency ground truth for RGB-D datasets and using only the RGB data of RGB-D datasets at inference, our semi-supervised system performs favorable against state-of-the-art fully-supervised RGB-D saliency detection methods that use saliency ground truth for RGB-D datasets at training and depth data at inference on two largest testing datasets. Our approach also achieves comparable results on other popular RGB-D saliency benchmarks.
Object selection and scaling using multimodal interaction in mixed reality

Abstract Mixed Reality (MR) is the next evolution of human interacting with the computer as MR has the ability to combine the physical environment and digital environment and making them coexist with each other. Interaction is still a huge research area in Augmented Reality (AR) but very less in MR, this is due to current advanced MR display techniques still not robust and intuitive enough to let the user to naturally interact with 3D content. New techniques on user interaction have been widely studied, the advanced technique in interaction when the system able to invoke more than one input modalities. Multimodal interaction undertakes to deliver intuitive multiple objects manipulation with gestures. This paper discusses the multimodal interaction technique using gesture and speech which the proposed experimental setup to implement multimodal in the MR interface. The real hand gesture is combined with speech inputs in MR to perform spatial object manipulations. The paper explains the implementation stage that involves interaction using gesture and speech inputs to enhance user experience in MR workspace. After acquiring gesture input and speech commands, spatial manipulation for selection and scaling using multimodal interaction has been invoked, and this paper ends with a discussion.
Action Localization Through Continual Predictive Learning

The problem of action localization involves locating the action in the video, both over time and spatially in the image. The current dominant approaches use supervised learning to solve this problem. They require large amounts of annotated training data, in the form of frame-level bounding box annotations around the region of interest. In this paper, we present a new approach based on continual learning that uses feature-level predictions for self-supervision. It does not require any training annotations in terms of frame-level bounding boxes. The approach is inspired by cognitive models of visual event perception that propose a prediction-based approach to event understanding. We use a stack of LSTMs coupled with a CNN encoder, along with novel attention mechanisms, to model the events in the video and use this model to predict high-level features for the future frames. The prediction errors are used to learn the parameters of the models continuously. This self-supervised framework is not complicated as other approaches but is very effective in learning robust visual representations for both labeling and localization. It should be noted that the approach outputs in a streaming fashion, requiring only a single pass through the video, making it amenable for real-time processing. We demonstrate this on three datasets - UCF Sports, JHMDB, and THUMOS'13 and show that the proposed approach outperforms weakly-supervised and unsupervised baselines and obtains competitive performance compared to fully supervised baselines. Finally, we show that the proposed framework can generalize to egocentric videos and achieve state-of-the-art results on the unsupervised gaze prediction task. Code is available on the project page( https://saakur.github.io/Projects/ActionLocalization/ .).
Range-Lookup Approximate Computing Acceleration for Any Activation Functions in Low-Power Neural Network

Consumer electronics have become versatile for processing a lot of signals in any distribution. This results in that high-speed activating of neural network should fit for any distribution of error functions. In this paper, we propose a set of transistor-level magnitude-comparators. Then we apply them to develop range-addressable memory to design a lightweight-slope lookup table. We then develop an efficient algorithm for constructing a lightweight-slope piecewise line. The proposed techniques are suitable to design any sigmoidal activation functions in neural network. From experiments and comparisons, the proposed LUT can be more efficient effective than previous looking tables. Especially applied in the proposed range-addressable memory, the power-delay product can be reduced by more than 30 folds.
How Statisticians Should Grapple with Privacy in a Changing Data Landscape

Suppose you had a data set that contained records of individuals, including demographics such as their age, sex, and race. Suppose also that these data contained additional in-depth personal inform...
Study on the Influence Factors of Rod Plate Gap Corona Discharge Environment Based on <scp>UV</scp> Imaging

To study the influence of environmental factors such as temperature, humidity, wind speed, and air pressure on the detection of corona discharge using ultraviolet imaging, the rod‐plate gap model was used, and the ultraviolet video signals of AC discharge under different environmental conditions were recorded by CoroCAM504 ultraviolet imager, and the environmental factors affecting UV discharge from electrical equipment were analyzed. The experimental results show that the area of the discharge spot increases nonlinearly with temperature. The area of the discharge spot decreases with increasing humidity, and a power‐law relationship governs this behavior. At a humidity of 40 to 90%, the power index varies from 1.6 to 2.6. Wind speed weakens coronal discharge: when the wind speed is 12 m/s and the voltage is 50 kV the discharge spot area is reduced by 76.7% compared with that in still conditions. Air pressure has a significant effect on ultraviolet imaging, and field tests also show that discharge at high altitudes is stronger than that at lower altitude. Based on the above experimental research, normalization of spot area by correction coefficient method is proposed. The correction error is less than 20%, which offers guidance to engineering practice. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
On linear sets of minimum size

An Fq-linear set of rank k, k≤h, on a projective line PG(1,qh), containing at least one point of weight one, has size at least qk−1+1 (see De Beule and Van De Voorde (2019)). The classical example of such a set is given by a club. In this paper, we construct a broad family of linear sets meeting this lower bound, where we are able to prescribe the weight of the heaviest point to any value between k∕2 and k−1. Our construction extends the known examples of linear sets of size qk−1+1 in PG(1,qh) constructed for k=h=4 (Bonoli and Polverino, 2005) and k=h in Lunardon and Polverino (2000). We determine the weight distribution of the constructed linear sets and describe them as the projection of a subgeometry. For small k, we investigate whether all linear sets of size qk−1+1 arise from our construction. Finally, we modify our construction to define rank k linear sets of size qk−1+qk−2+…+qk−l+1 in PG(l,qh). This leads to new infinite families of small minimal blocking sets which are not of Rédei type.
An Effective Approach to Cloud Migration for Small and Medium Enterprises (SMEs)

Given the recent technological boom, companies have begun adopting cloud computing to host their IT services. Cloud computing is scalable, secure, and economical. Many software and larger enterprises who employ IT experts or outsource IT contracts have already migrated, or in the process of migrating, to the cloud. However, Small to Medium Enterprises (SMEs) are falling behind due to concerns regarding migration complexities, data privacy, cost, and more. As SMEs play an imperative role in any country’s economy, it is very important to address these issues and allow them to harness the benefits of the cloud computing. The motivation for writing this paper is to provide an effective approach to cloud migration for SMEs by proposing a comprehensive and detailed framework. The goal is to help SMEs understand the basic terminology of cloud computing and how they can map out a migration to on-premises cloud services.
On Katz’s (<i>A,B</i>)-Exponential Sums

Abstract We deduce Katz’s theorems for (A, B)-exponential sums over finite fields using $\ell$-adic cohomology and a theorem of Denef–Loeser, removing the hypothesis that A + B is relatively prime to the characteristic p. In some degenerate cases, the Betti number estimate is improved using toric decomposition and Adolphson–Sperber’s bounds for degrees of L-functions. Applying the facial decomposition theorem, we prove that the universal family of (A, B)-polynomials is generically ordinary for its L-function when p is in certain arithmetic progression.
Research on Double Watermarking Algorithm Based on PDF Document Structure

The transmission of PDF files is becoming more and more popular on the Internet. In order to ensure that electronic publications are published and distributed on the Internet, copyright transactions occur, or the original watermarked documents are still fully extracted when the watermark information is extracted, a double watermarking algorithm based on PDF documents is designed: robustness against attacks watermarking algorithm and watermarking algorithm to verify the integrity of content. The experimental results demonstrate that the algorithm has good invisibility and robustness.
Pitch and noise normalized acoustic feature for children's ASR

In this work, we have analyzed the pitch robustness of the recently reported power normalized cepstral coefficient (PNCC) feature for noise robust children's speech recognition. The PNCC feature is intended to suppress various types of common additive noise present in speech data. The PNCC feature is noted to be susceptible to pitch variations. In order to normalize the pitch effect on PNCC, a pitch base spectral normalization step is incorporated in the PNCC feature extraction process. By the inclusion of pitch normalization stage, the pitch-robustness of PNCC is enhanced without loss of noise suppression capability. The efficacy of the proposed pitch normalized PNCC (PN-PNCC) feature is investigated using acoustic modeling based on recurrent structures, such as gated recurrent units (GRU), and light gated recurrent units (LiGRU). The PN-PNCC feature is noted to yield performance improvement over PNCC for adults' and children's speech recognition under clean as well as the noisy test condition.
Development of a Cyber-Situational Awareness Model of Risk Maturity Using Fuzzy FMEA

This paper uses Endsley's situational awareness model as a starting point for creating a new cyber-security awareness model for risk maturity. This is used to model the relationship between risk management-based situational awareness and levels of maturity in making decisions to deal with potential cyber-attacks. The risk maturity related to cyber situational awareness using the fuzzy failure mode effect analysis (FMEA) method is needed as a basis for effective risk-based decision making and to measure the level of maturity in decision making using the Software Engineering Institute Capability Maturity Model Integration (SEI CMMI) approach. The novelty of this research is that it builds a model of the relationship between the level of maturity and the level of risk in cyber-situational awareness. Based on the data during the COVID-19 pandemic, there was a decrease in the number of incidents, including the following decreases: from 15-29 cases of malware attacks to 8-12 incidents, from 20-35 phishing cases to 12-15 cases and from 5-10 ransomware cases to 5-6 cases.
Research on the Application of Blockchain in Copyright Protection

In recent years, the total number of digital media works issued has continued to increase. Due to the easy copying and dissemination of digital copyright works, digital copyright infringement has become increasingly serious. Because of the security, traceability, distribution, and programmable features of blockchain technology fully meet the needs of copyright protection, research on copyright protection based on blockchain technology has gradually become a popular research direction for many scholars. This article introduces the principles, characteristics and classification of blockchain technology in detail. It also deeply analyzes the research status, key technologies and research difficulties of copyright protection technology. In addition, this article has conducted a detailed study on the copyright protection technology based on the blockchain, and implemented a safe, efficient, and lightweight copyright protection system based on the alliance chain.
WebCloud: Web-Based Cloud Storage for Secure Data Sharing Across Platforms

With more and more data moving to the cloud, privacy of user data have raised great concerns. Client-side encryption/decryption seems to be an attractive solution to protect data security, however, the existing solutions encountered three major challenges: low security due to encryption with low-entropy PIN, inconvenient data sharing with traditional encryption algorithms, and poor usability with dedicated software/plugins that require certain types of terminals. This work designs and implements WebCloud, a practical browser-side encryption solution, leveraging modern Web technologies. It solves all the above three problems while achieves several additional remarkable features: robust and immediate user revocation, fast data processing with offline encryption and outsourced decryption. Notably, our solution works on any device equipped with a Web user agent, including Web browsers, mobile and PC applications. We implement WebCloud based on ownCloud for basic file management utility, and utilize WebAssembly and Web Cryptography API for complex cryptographic operations integration. Finally, comprehensive experiments are conducted with many well-known browsers, Android and PC applications, which indicates that WebCloud is cross-platform and efficient. As an interesting by-product, the design of WebCloud naturally embodies a dedicated and practical ciphertext-policy attribute-based key encapsulation mechanism (CP-AB-KEM) scheme, which can be useful in other applications.
Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.
Robust Deep Graph Based Learning for Binary Classification

Convolutional neural network (CNN)-based feature learning has become the state-of-the-art for many applications since, given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning is more challenging if training labels are noisy as CNN tends to overfit to the noisy training labels, resulting in sub-par classification performance. In this paper, we propose a robust binary classifier by learning CNN-based deep metric functions, to construct a graph, used to clean the noisy labels via graph Laplacian regularization (GLR). The denoised labels are then used in two proposed loss correction functions to regularize the deep metric functions. As a result, the node-to-node correlations in the graph are better reflected, leading to improved predictive performance. The experiments on three datasets, varying in number and type of features and under different levels of noise, demonstrate that given a noisy training dataset for the semi-supervised classification task, our proposed networks outperform several state-of-the-art classifiers, including label-noise robust support vector machine, CNNs with three different robust loss functions, model-based GLR, and dynamic graph CNN classifiers.