Reviewerly/exHarmony huggingface dataset:
Quaternion-based local and global color image enhancement algorithm

Many images like medical images, satellite images, and real-life photographs may suffer poor contrast degradation. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a Hamiltonian quaternion framework-based model for color image enhancement. The basic idea is to apply α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The resulting image is a weighted mean of all processing blocks. The weights for every local and global enhanced image driven through optimization using a measure of enhancement (EME) as a cost function. We demonstrate our color image enhancement scheme and compare it with other state-of-the-art methods. The extensive computer simulations indicate that the proposed method outperforms the commonly used methods in both visual image quality and statistical information.
Particle Placement in Learner Language

Abstract This study presents the first multifactorial corpus‐based analysis of verb–particle constructions in a data sample comprising spoken and written productions by intermediate‐level learners of English as a second language from 17 language backgrounds. We annotated 4,911 attestations retrieved from native speaker and language learner corpora for 14 predictors, including syntactic complexity, rhythmic and segment alternation, and the verb framing of the speaker's native language. A multifactorial prediction and deviation analysis using regression (Gries &amp; Deshors, 2014), which stacks multiple regression analyses to compare native speaker and learner productions in identical contexts, revealed a complex picture in which processing demands, input effects, and native language typology jointly shape the degree to which learners’ choices of constructions are nativelike or not.
Double Refinement Network for Room Layout Estimation

Room layout estimation is a challenge of segmenting a cluttered room image into floor, walls and ceiling. We apply a Double Refinement Network (DRN) which has been successfully used to the monocular depth map estimation. Our method is the first not using encoder-decoder architecture for the room layout estimation. ResNet50 was utilized as a backbone for the network instead of VGG16 commonly used for the task, allowing the network to be more compact and faster. We introduced a special layout scoring function and layout ranking algorithm for key points and edges output. Our method achieved the lowest pixel and corner errors on the LSUN data set. The input image resolution is 224 * 224.
Deep Online Sequential Extreme Learning Machines and its Application in Pneumonia Detection

Deep neural networks have demonstrated high levels of accuracy in the fields of image classification. Deep learning is a multilayer perceptron artificial neural network algorithm, that uses a backpropagation based learning technique to approximate complicated functions and alleviating the difficulty associated with optimizing deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. Furthermore, Online Sequential Extreme Learning Machines (OSELM) is an adaptive algorithm based on ELM that does not require fresh training when faced with a new dataset, but can adapt to the new dataset by being trained on the new dataset alone. We combining MLELM and OSELM put forward Multilayer OSELM and apply it to the Pneumonia Chest X-Ray image dataset in this paper. By simulating and analysing the results of the experiments, effectiveness of the application of Multilayer OSELM in Pneumonia identification is confirmed.
Fishbone model and universal authentication framework for evaluation of multifactor authentication in mobile environment

The trend of rapid evolutionary development of mobile technologies and the existence of different user's priorities are creating new challenges with regard to selection of multifactor authentication (MFA) solutions. This becomes even more challenging by creating a universal authentication framework (UAF). In order to cope with these challenges, this paper has proposed a Fishbone model and developed in form of the UAF which is based on a larger number of linguistic variables and a wider set of user's priorities such as security, usability, accessibility, pricing, complexity, privacy and convenience (SUAPCPC). In comparison to all other papers available in the literature, the Fishbone model provides numerical evaluation of MFA with the possibility of changing weighted criteria for the selected user priorities. In addition, the contributions of this model are twofold. For user's, to enable easier choice of MFA solution, for developers, to identify spots where a method or solution could be improved. For development of the Fishbone model, fuzzy methodology is used in form of a Fuzzy Expert System (FES) tool. Also, the block diagram and the basic modules of the Fishbone model architecture are given. The results of implementation of the Fishbone model in form of the UAF have showed that this model is applicable and very efficient in practice. Finally, the Fishbone model gives an ideal template in UAF at which user's priorities satisfy the best individual users' solutions. The realization of this template presents challenge for all future developers of MFA solutions.
Algorithms and Data Structures for Sparse Polynomial Arithmetic

We provide a comprehensive presentation of algorithms, data structures, and implementation techniques for high-performance sparse multivariate polynomial arithmetic over the integers and rational numbers as implemented in the freely available Basic Polynomial Algebra Subprograms (BPAS) library. We report on an algorithm for sparse pseudo-division, based on the algorithms for division with remainder, multiplication, and addition, which are also examined herein. The pseudo-division and division with remainder operations are extended to multi-divisor pseudo-division and normal form algorithms, respectively, where the divisor set is assumed to form a triangular set. Our operations make use of two data structures for sparse distributed polynomials and sparse recursively viewed polynomials, with a keen focus on locality and memory usage for optimized performance on modern memory hierarchies. Experimentation shows that these new implementations compare favorably against competing implementations, performing between a factor of 3 better (for multiplication over the integers) to more than 4 orders of magnitude better (for pseudo-division with respect to a triangular set).
Towards Thwarting Template Side-channel Attacks in Secure Cloud Deduplications

As one of a few critical technologies to cloud storage service, deduplication allows cloud servers to save storage space by deleting redundant file copies. However, it often leaks side channel information regarding whether an uploading file gets deduplicated or not. Exploiting this information, adversaries can easily launch a template side-channel attack and severely harm cloud users' privacy. To thwart this kind of attack, we resort to the k-anonymity privacy concept to design secure threshold deduplication protocols. Specifically, we have devised a novel cryptographic primitive called “dispersed convergent encryption” (DCE) scheme, and proposed two different constructions of it. With these DCE schemes, we successfully construct secure threshold deduplication protocols that do not rely on any trusted third party. Our protocols not only support confidentiality protections and ownership verifications, but also enjoy formal security guarantee against template side-channel attacks even when the cloud server could be a “covert adversary” who may violate the predefined threshold and perform deduplication covertly. Experimental evaluations show our protocols enjoy very good performance in practice.
A hybrid-backward refinement model for salient object detection

The deep Convolutional Neural Networks (CNNs) have been investigated in many salient object detection works and have achieved state-of-the-art performance compared to the classic methods. However, most of the existing CNN-based methods still struggle in addressing the problem of incomplete contours of salient objects. To overcome this problem, this paper focuses on accurately capturing the fine details of salient objects by proposing a novel Hybrid-Backward Refinement Network (HBRNet), which combines the high-level and low-level features extracted from two different CNNs. Taking advantage of the access to the visual cues and semantic information of CNNs, our hybrid deep network helps in modeling the object’s context and preserving its boundaries as well. Specifically, we integrate effective hybrid refinement modules by merging feature maps of two consecutive layers from two deep networks. Also, our refinement model uses the residual convolutional unit in order to provide an effective end-to-end training. Furthermore, we apply the feature fusion technique to enable full exploitation of multi-scale features and progressively recover the resolution of the coarse prediction map. Through the experimental results, we demonstrate that the proposed framework achieves state-of-the-art performance on several popular benchmark datasets. The proposed hybrid refinement module, the residual convolutional unit as well as the fusion method, can appreciably improve the quality of the prediction maps.
Evolution of Data Management Systems and Their Security

The paper discusses the evolution of data management systems (up to Big data systems inclusive), the development of security tools, as well as the evolution of vulnerabilities and security features of advanced DBMS.
Quantification of Facial Traits

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability.
Enabling SQL-Query Processing for Ethereum-based Blockchain Systems

A blockchain is designed to make consistent and reliable agreement in an unreliable and decentralized environment. It also permits processing transactions, making smart contracts, which allows end users to perform the contracts without any intermediate entities. However, there are some challenges in retrieving the state in a smart contract on the blockchain. For example, an external database or user-defined data structures can be used to retrieve the data from a smart contract in a range, which can increase the management overhead and decrease the overall performance of the blockchain system. In this paper, we propose a scheme that enables SQL query operations in a blockchain system. In our proposed scheme, the register and query managers provide fast retrieval of range data without any user-defined data structure, and management at low cost without any external database, respectively. We have implemented our scheme on quorum which is an Ethereum-based blockchain system and evaluated it using a synthetic benchmark. The experimental results show that our system can improve the search performance up to about 22x compared with the existing system with low memory usage.
LSTM Guided Modulation Classification and Experimental Validation for Sub-Nyquist Rate Wideband Spectrum Sensing

Future generation wireless radios (WRs) are expected to dynamically tune their transmission parameters like transmitting frequency and modulation scheme to enhance the spectrum utilization and data rate. To perform this, the base station needs to identify the suitable spectrum resources and determine the modulation schemes of legacy users which are transmitting in the neighboring vacant spectral bands. Since the determination of modulation scheme indicates the presence of a signal, in this paper, we perform automatic modulation classification (AMC) based wideband spectrum sensing (WSS) to reduce the additional overhead caused by WSS. To avoid the need of power and area hungry Nyquist rate analog to digital converter (ADC), we exploit the sparse nature of the wideband spectrum to perform digitization at the sub-Nyquist rate. The proposed sub-Nyquist sampling based AMC (SNS-AMC) employs a long short-term memory neural network to classify various modulation schemes. The performance of the proposed SNSAMC is analyzed for different tapped delay line (TDL) channel models and is also validated on USRP based hardware testbed. It has been shown that the classification accuracy of the proposed SNS-AMC outperforms machine learning SNS-AMC and the accuracy of SNS-AMC approaches to the accuracy of Nyquist sampling based AMC with an increase in signal to noise ratio.
SSA-SDA: Subjectivity and Sentiment Analysis of Sudanese Dialect Arabic

In the past few years, the Subjectivity and Sentiment Analysis (SSA) has garnered a lot of attention, and many unsuccessful attempts were made for developing an SSA model for the morphologically-rich languages like Arabic. In this study, we aimed to fill the gap by designing a new manual and auto-annotated corpus of the Sudanese-Dialect Arabic (SDA) along with a novel polarity lexicon. The corpus is a collection of political tweets, which were annotated by Twitter on a sentence-level. We have also described some automated, online SSA-tagging tools, which could explore all annotated data. Furthermore, we have investigated the effect of various pre-processing techniques on the Sudanese SSA process. Here, we presented a novel SSA technique for the Sudanese dialect, which displayed a good subjectivity classification result and showed an 83.5% accuracy for the Decision Tree performance.
Application Performance Prediction and Optimization Under Cache Allocation Technology

Many applications running on high-performance computing systems share limited resources such as the last-level cache, often resulting in lower performance. Intel recently introduced a new control mechanism, called cache allocation technology (CAT), which controls the cache size used by each application. To intelligently utilize this technology for automated management, it is essential to accurately identify application performance behavior for different cache allocation scenarios. In this work, we show a novel approach which automatically builds a prediction model for application performance changes with CAT. We profile the workload characteristics based on Intel Top-down Microarchitecture Analysis Method (TMAM), and train the model using machine learning. The model predicts instructions per cycle (IPC) across available cache sizes allocated for the applications. We also design a dynamic cache management technique which utilizes the prediction model and intelligently partitions the cache resource to improve application throughput. We implemented and evaluated the proposed framework in Intel PMU profiling tool running on Xeon Platinum 8186 Skylake processor. In our evaluation, we show that the proposed model accurately predicts the IPC changes of applications with 4.7% error on average for different cache allocation scenarios. Our predictive online cache managements achieves improvements on application performance of up to 25% as compared to a prediction-agnostic policy.
Compression Improves Image Classification Accuracy

We study the relationship between the accuracy of image classification and the level of image compression. Specifically, we look at how various levels of JPEG and SVD compression affect the score of the correct answer in Inception-v3, a TensorFlow-based image classifier trained on the ImageNet database. Surprisingly, the compression seems to improve the ability of Inception-v3 to recognize images, with the best performance seen at fairly high degrees of compression for most images tested (with half achieving maximal score at JPEG quality under 15, corresponding to more than tenfold reduction in file size). The same behaviour holds for images compressed using the singular value decomposition (SVD) method. This phenomenon suggests that even significant compression can be beneficial rather than detrimental to image classification accuracy, in particular for convolutional neural networks. Understanding when and why compression helps, and which compression algorithm and compression ratio are optimal for any given image remains an open problem.
Augmented Reality in Intralogistics Planning of the Automotive Industry : State of the Art and Practical Recommendations for Applications

This article investigates Augmented Reality (AR) as a potential tool to support intralogistics planning in the automotive industry. Starting with a literature review and an investigation of the dissemination of AR usage in logistics in general, we analyze potential reasons for the apparent lack of AR applications in intralogistics planning. From this, we derive requirements for a successful application of AR in intralogistics planning and demonstrate a prototypical solution implemented within the Daimler AG. Based on this example, we further discuss the advantages of applying AR to intralogistics planning.
A Multimodal Approach for Automatic Cricket Video Summarization

Summarizing cricket matches is a labor intensive task that demands a certain level of proficiency. The paper proposes a novel method for automatically detecting and summarizing important events in a cricket match. Our model takes into input the video recording of the entire cricket match and returns the most important clips of the game as the output. Techniques like optical character recognition, sound detection and replay detection have been used to extract important events such as boundaries, wickets and other playfield scenarios in a cricket match. These events are then clipped together to form the entire highlight for the cricket match. We performed several qualitative and quantitative experiments to evaluate our model. Our model achieves an accuracy of 89.45% to detect events like wickets, fours and sixes which indicates the usefulness of the model in real life scenarios.
Adaptive Kalman Filter for Detectable Linear Time-Invariant Systems

A novel covariance matching technique is proposed for estimating the states and unknown entries of the process and measurement noise covariance matrices for additive white Gaussian noise elements in a linear Kalman filter. Under this assumption of detectability (that is, unobservable modes remain stable), the stability and convergence properties of the covariance matching Kalman filter are established. It is shown that the measurement covariance matrix cannot be unambiguously estimated if the measurement model contains linearly dependent measurements. Monte Carlo simulations evaluate the numerical properties of the proposed algorithm.
Exploring training modes for industrial augmented reality learning

In this paper, we present a conceptual approach and the first prototype of a mobile training system to provide non-expert users with helpful information about the functionality of complex automated industrial systems. The system uses an augmented reality (AR) tablet application to visualize information about internal processes, sensor states, settings and hidden parts of a production system directly in the field of view of a user. The available information can be accessed via four different methods which combine elements of step-by-step tutorials and open exploration. Our prototype aims to support users to better understand automated systems. While such systems will become more complex in future, we believe that augmented reality is a key concept that could help humans to better understand and experience automated systems and its consequences in general.
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline

This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Zhedong Zheng, Liang Zheng and Yi Yang, published by IEEE Xplore and entitled "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro" 2017 IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2017.405.
Portable TOR Router with Raspberry Pi

Nowadays, internet has become an important part of everyone's lives. The internet has turned out to be a reliable tool for communication, data transfer and many more applications for people from all over the world. An individual's internet activities are being tracked and traced without their knowledge by various means. Thus, online security is a genuine worry for web clients who intend to visit interpersonal interaction locales, make an online transaction, or take an interest in internet diversions, as they need their protection to be ensured. Internet browsing is not anonymized as many Internet Service Providers (ISP's) could legally sell users information without informing users. People's internet activities are being tracked and traced by ISPs. Moreover, network spies and snooping attempts are very hard to be defeated as an ample network snooping tools are available in the market. These issues can be curbed by using a router that has anonymous browsing infrastructure. However, most of the routers that gives anonymous browsing are not portable. From this project, people will be able to browse anonymously without being traced. Besides that, network snooping and spying attempts can be defeated. Finally, a portable The Onion Routing (TOR) router can be created with a Raspberry Pi..