Reviewerly/exHarmony huggingface dataset:
Adaptive Update Handling for Graph HTAP

Processing hybrid transactional/analytical workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which make inplace updates difficult. In this paper, we discuss an approach for adaptive handling of updates in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating updates from an update-friendly table storage to a GPU-optimized sparse matrix format.
Early Prediction of Sepsis for ICU Patients using Gradient Boosted Tree

Intensive care unit patients, especially those who have undergone surgeries or have severe health issues, tend to have a higher risk of developing sepsis due to a weaker immune system. Due to late detection of sepsis, no preventive actions can be taken to treat sepsis patients. Therefore, this research aims to identify, validate, and test suitable machine learning algorithms for the early prediction of sepsis using pre-processed data produced from the Medical Information Mart for Intensive Care III, MIMIC-III database. This research will be designing prediction models for 15 hours before sepsis onset using pre-processed data obtained from MIMIC-III database using Decision Tree, Random Forest, AdaBoost, Gradient Boosted Tree, and Multilayer Perceptron. A 10 cross-validation is used in validating the models. The performance of prediction models is evaluated mainly using ROC-AUC score. In model comparison, an extra set of prediction models using the same algorithms is developed for 10 hours before sepsis onset to compare its performance with the earlier prediction model developed. The result of model comparison shows that for the prediction model of 15 and 10 hours before sepsis onset, ROC-AUC score for Gradient Boosted Tree is the best with 0.777 for 15 hours and 0.769 respectively from 10 hours prediction model. The results can be optimized further using more data and using derived Boosted Trees algoritms.
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation From Depth Maps

Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes. In this letter, we propose a non-invasive and light-invariant framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera. The method can be applied to any robot without requiring hardware access to the internal states. We introduce a novel representation of the predicted pose, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semi-Perspective Decoupled Heatmaps</i> (SPDH), to accurately compute 3D joint locations in world coordinates adapting efficient deep networks designed for the 2D Human Pose Estimation. The proposed approach, which takes as input a depth representation based on XYZ coordinates, can be trained on synthetic depth data and applied to real-world settings without the need for domain adaptation techniques. To this end, we present the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SimBa</i> dataset, based on both synthetic and real depth images, and use it for the experimental evaluation. Results show that the proposed approach, made of a specific depth map representation and the SPDH, overcomes the current state of the art.
Semantic Super-Resolution of Text Images via Self-Distillation

This research develops an effective single-image super-resolution (SR) method that increases the resolution of scanned text or document images and improves their readability. To this end, we introduce a new semantic loss and propose a semantic SR method that guides an SR network to learn implicit text-specific semantic priors through self-distillation. Experiments on the enhanced deep SR (EDSR) model, one of the most popular SR networks, confirmed that semantic loss can contribute to further improving the quality of text SR images. Although the improvement varied depending on image resolution and dataset, the peak signal-to-noise ratio (PSNR) value was increased by up to 0.3 dB by introducing the semantic loss. The proposed method outperformed an existing semantic SR method.
Application of blockchain methodology in secure task scheduling in cloud environment

• Cloud services are greatly adapted to diverse problems of real-world computing. • By accessing the cloud services independent of user location, the service data faces variety of challenges in terms of security. • This article analyzes and performs detailed review on the methods of scheduling in cloud resources as well as services. • Also, the security constraints are greatly explored towards the methods of data security available. • Also, the article discusses the impact of blockchain methods in detail which support the maximization of QoS metrics. Cloud services are greatly adapted to diverse problems of real-world computing. The users access various cloud services to perform variety of tasks. Such tasks are handled through the services provided by different service providers. By accessing the cloud services independent of user location, the service data faces variety of challenges in terms of security. Also, it is necessary to consider different QoS metrics of services in the sense to improve the performance of services. However, there exist many scheduling algorithms exist, they differ with the constraints considered and the result produced. This article analyzes and performs detailed review on the methods of scheduling in cloud resources as well as services. Also, the security constraints are greatly explored towards the methods of data security available. Both the corners of cloud platform are analyzed and presented in detail. Finally, the methods of scheduling and security schemes are categorized in detail. Also, the article discusses the impact of blockchain methods in detail which support the maximization of QoS metrics.
Positive Maps and Entanglement in Real Hilbert Spaces

The theory of positive maps plays a central role in operator algebras and functional analysis and has countless applications in quantum information science. The theory was originally developed for operators acting on complex Hilbert spaces and less is known about its variant on real Hilbert spaces. In this article, we study positive maps acting on a full matrix algebra over the reals, pointing out a number of fundamental differences with the complex case and discussing their implications in quantum information. We provide a necessary and sufficient condition for a real map to admit a positive complexification and connect the existence of positive maps with non-positive complexification with the existence of mixed states that are entangled in real Hilbert space quantum mechanics, but separable in the complex version, providing explicit examples both for the maps and for the states. Finally, we discuss entanglement breaking and PPT maps, and we show that a straightforward real version of the PPT-squared conjecture is false even in dimension 2. Nevertheless, we show that the original PPT-squared conjecture implies a different conjecture for real maps, in which the PPT property is replaced by a stronger property of invariance under partial transposition (IPT). When the IPT property is assumed, we prove an asymptotic version of the conjecture.
H-FHAUI: Hiding frequent high average utility itemsets

High average-utility itemset mining consists of analyzing a quantitative customer transactional database to identify high average-utility itemsets (HAUIs), that is sets of items that have a high average utility (e.g. profit). Although important information about customers’ habits can be revealed by HAUIs, they can expose sensitive information. To address this concern, the problem of hiding frequent HAUIs (FHAUIs) is studied, which is to modify a transaction database to ensure that sensitive FHAUIs cannot be discovered. An algorithm is designed, named H-FHAUI, which relies on an extended border approach based on the support (occurrence frequency) and the average-utility of itemsets. Moreover, to hide all FHAUIs, H-FHAUI utilizes a novel extended lower border named BdE- based on weak upper bounds on the average-utility to only hide a small number of FHAUIs. Then, all remaining FHAUIs are also hidden. Besides, H-FHAUI utilizes a novel weight-based strategy named ICS to choose items and transactions to be modified, a novel TIU-VIU structure to quickly update weak upper bounds, and a strategy named DUSWUB to quickly hide FHAUIs while ensuring that the total utility of D is preserved as much as possible. Experimental results show that H-FHAUI outperforms a baseline in terms of runtime, memory usage, and quality of the sanitized database.
A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection

The copyright problem of digital products is becoming more and more prominent. In this case, digital watermarking technology has attracted the attention of many experts and scholars in the field of information security. Among the proposed technologies, zero-watermarking technology has been favored greatly with its excellent imperceptibility. In this paper, a novel robust audio zero-watermarking scheme is designed by applying non-negative matrix decomposition algorithm to zero-watermarking technology. Firstly, the proposed scheme divides the input audio signal into fixed frames, then applies fast Fourier transform(FFT) and non-negative matrix factorization(NMF) algorithm to extract the feature vector of the original audio signal. Finally, XOR the feature vector and the digital watermark sequence to achieve the embedding of zero-watermarking. The experimental results show that the proposed scheme performs more effectively in resisting common and frame-desynchronization attacks than the existing zero-watermarking schemes.
A Segmented PageRank-Based Value Compensation Method for Personal Data in Alliance Blockchains

Alliance blockchains provide a multi-party trusted data trading environment, promoting the development of the data trading market in which the value compensation for personal data is still a key issue. However, limited by the data format and content, traditional attempts on data value compensation cannot form a widely applicable solution. Therefore, we propose a universal value compensation method for personal data in alliance blockchains. The basic idea of this method is to evaluate the value weight of data based on the collaborative relationship of data value. First, we construct a Data Collaboration Markov Model (DCMM) to formalize the collaboration network of data value. Then, aiming at data collaboration networks with different structures, the corresponding Segmented PageRank (SPR) algorithm is proposed. SPR can universally evaluate the value weight of each data account without being subjected to the data format or content. Finally, we theoretically deduce that the time complexity and space complexity of SPR algorithm are respectively 1 / K and 1 / K 2 taken by PageRank algorithm. Experiments show the feasibility and superior performance of SPR.
Alt-tech social forensics: Forensic analysis of alternative social networking applications

Mainstream social platforms boast billions of users worldwide. In recent years, popular social platforms have seen a decline in their users that are choosing to migrate to alternative-tech social applications reinforced by frustrations of mainstream social platforms over alleged censorship of free speech and banning of predominant public figures such as the former president of the United States (U.S.). As such, group effect of similar minded users on alternative-tech social platforms may lead to fostering events such as the U.S. Capitol attack on January 6th, 2021, where the spreading of false information and extremist ideologies through alt-tech applications such as Parler and MeWe took place. These cases demonstrate the immense forensic need to understand how alternative-tech social applications operate and what they store about their users' personal information and activities. We present the primary account for the digital forensic study of (n = 9) alternative-tech social applications used on Android and iOS devices. Our analysis includes Parler, MeWe, CloutHub, Wimkin, Minds (Minds Mobile and Minds Chat), SafeChat, 2nd1st, and GETTR. Results revealed that some applications do store unencrypted user information on the devices, such as usernames, phone numbers, email addresses, posts and comments, and private chat messages. Furthermore, some security vulnerabilities were discovered that allow users to download data that should have been private (such as sent private images) without authentication and authorization by other users. Finally, to aid in the analysis and automatic extraction of relevant evidence, we share Alternative Social Networking Applications Analysis Tool (ASNAAT), that automatically aggregates forensically relevant data from the alt-tech social networking applications when presented with a mobile device's forensic image.
Design and Innovation of Piano Impromptu Accompaniment for College Music Performance Majors under the Information Environment

The development of information technology has a profound impact on the development of education. The integration of information technology into the teaching of professional courses has changed not only teaching methods and means, but more importantly, the teaching philosophy, teaching organization, and teaching evaluation are supported by information technology. Changes and progress have also been made. Piano improvisational accompaniment is an indispensable and important course in the teaching of music majors in colleges and universities. Relying on the information-based teaching environment and means, systematically designing piano impromptu accompaniment teaching for music performance majors to improve the effectiveness of teaching is a new exploration in the education and teaching reform of the current higher vocational music performance talent training courses. This study analyzes and studies the problems existing in the current teaching of piano improvisation accompaniment in colleges and universities and proposes the feasible strategies of piano improvisation accompaniment teaching in a targeted manner, to provide reference and guidance for the majority of educators.
Breaking CAPTCHA with Capsule Networks

Convolutional Neural Networks have achieved state-of-the-art performance in image classification. Their lack of ability to recognise the spatial relationship between features, however, leads to misclassification of the variants of the same image. Capsule Networks were introduced to address this issue by incorporating the spatial information of image features into neural networks. In this paper, we are interested in showcasing the digit recognition task on CAPTCHA images, widely considered a challenge for computers in relation to human capabilities. Our intention is to provide a rigorous empirical regime in which we can compare the competitive performance of Capsule Networks against the Convolutional Neural Networks. Indeed since CAPTCHA distorts images, by adjusting the spatial positioning of features, we aim to demonstrate the advantages and limitations of Capsule Networks architecture. We train the Capsule Networks with Dynamic Routing version and the convolutional-neural-network-based deep-CAPTCHA baseline model to predict the digit sequences on numerical CAPTCHAs, investigate the performance results and propose two improvements to the Capsule Networks model.
An adaptive differential evolution algorithm with elite gaussian mutation and bare-bones strategy

Both differential evolution algorithm (DE) and Bare-bones algorithm (BB) are simple and efficient, but their performance in dealing with complex multimodal problems still has room for improvement. DE algorithm has great advantages in global search and BB algorithm has great advantages in local search. Therefore, how to combine these two algorithms' advantages remains open for further research. An adaptive differential evolution algorithm based on elite Gaussian mutation strategy and bare-bones operations (EGBDE) is proposed in this paper. Some elite individuals are selected and then the mean and the variance of the bare-bones operation are adjusted with the information from the selected elite individuals. This new mutation strategy enhances the global search ability and search accuracy of differential evolution with parameters free. It also helps algorithm get a better search direction and effectively balance the exploration and exploitation. An adaptive adjustment factor is adopted to dynamically balance between differential mutation strategy and the elite Gaussian mutation. Twenty test functions are chosen to verify the performance of EGBDE algorithm. The results show that EGBDE has excellent performance when comparing with other competitors.
Embedding Guided End-to-End Framework for Robust Image Watermarking

In recent years, deep learning-based watermarking algorithms have received extensive attention. However, the existing algorithms mainly use the autoencoder to insert watermark automatically and ignore using the prior knowledge to guide the watermark embedding. In this paper, an end-to-end framework based on embedding guidance is proposed for robust image watermarking. It contains four modules, i.e., prior knowledge extractor, encoder, attacking simulator, and decoder. To guide the watermark embedding, the prior knowledge extractor providing chrominance and edge information of cover images is used to modify cover images before inserting the watermark by the encoder. To enhance the robustness of watermark extraction, the attacking simulator applying various differentiable attacks on the encoded images is introduced before extracting the watermark by the decoder. Experimental results show that the proposed algorithm achieves a good balance between invisibility and robustness and is superior to state-of-the-art algorithms.
Effect of quality of opto-mechanical components on fringes of diffraction Lloyd mirror interferometer

Diffraction Lloyd mirror interferometer (DLMI) is a relatively new interferometer where interference fringes are generated by superposition of two diffracted beams instead of two geometrical beams of light. The interferometer is suitable for performing basic investigations on diffraction and interference of light. In this work, a number of set-up parameters determining the performance of DLMI including characteristics of knife-edge, quality of mirror, quality of lenses, and effect of algorithm, are experimentally studied and reported for the first time. The effect of these parameters on interference fringes of DLMI is evaluated in terms of change in average intensity, signal to noise ratio (SNR) and contrast of the fringes. By optimizing above parameters, a DLMI set-up is realized and used for characterization of silicon optics fabricated using the single point diamond turning process. The validation results by a commercial optical profiler confirms the effectiveness of DLMI system. This study would provide useful reference for setting up the DLMI system for different applications such as optical profilometry, optical metrology, and to study the diffraction of light etc.
Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

Abstract Seeing through dense occlusions and reconstructing scene images is an important but challenging task. Traditional frame-based image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames. Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution. However, synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream, and the initial brightness is unknown. In this paper, we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion, which uses event streams to provide complete scene information and frames to provide color and texture information. An event stream encoder based on the spiking neural network (SNN) is proposed to encode and denoise the event stream efficiently. A comparison loss is proposed to generate clearer results. Experimental results on a large-scale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.
Effect of Organizational Justice on Organizational Performance with Mediating Variables of Job Satisfaction and Organizational Commitment- Systematic Article Review Study

The main aim of this systematic review study is to assess the effect of organizational justice on organizational performance with mediating variables of organizational commitment and job satisfaction. This study used 25 reputable research articles which are more related to the title “organizational justice on organizational performance with mediating variables of organizational commitment and job satisfaction”. Positive perception of employees in relation to organizational justice is a significant antecedent to employees’ job satisfaction, which in turn mediated the relationship between justice perceptions and organizational performance. Intrinsic and extrinsic job satisfaction has a positive and significant effect on organizational commitment. But some findings show that procedural and interactional justice has no significant impact on employee performance and the relationship of organizational justice to work performance was mostly indirect, mediated by organizational commitment. Organizational justice and organizational commitment have generally confirmed a positive relationship, and organizational commitment mediates the relationship between organizational justice and organizational performance significantly.
A CBR for integrating sentiment and stress analysis for guiding users on social network sites

This work presents a Case-Based Reasoning (CBR) module that integrates sentiment and stress analysis on text and keystroke dynamics data with context information of users interacting on Social Network Sites (SNSs). The context information used in this work is the history of positive or negative messages of the user, and the topics being discussed on the SNSs. The CBR module uses this data to generate useful feedback for users, providing them with warnings if it detects potential future negative repercussions caused by the interaction of the users in the system. We aim to help create a safer and more satisfactory experience for users on SNSs or in other social environments. In a set of experiments, we compare the effectiveness of the CBR module to the effectiveness of different affective state detection methods. We compare the capacity to detect cases of messages that would generate future problems or negative repercussions on the SNS. For this purpose, we use messages generated in a private SNS, called Pesedia. In the experiments in the laboratory, the CBR module managed to outperform the other proposed analyzers in almost every case. The CBR module was fine-tuned to explore its performance when populating the case base with different configurations.