Reviewerly/exHarmony huggingface dataset:
Low-light Enhancement Using Retinex-Decomposition Convolutional Neural Networks

This paper proposes a new retinex-decomposition convolutional network (DC-Net) to enhance low-light images based on retinex theory. The proposed method estimates the reflectance and illumination components using Dc-Net. Bright-Net and Smooth-Net are used for the refined illumination, and Denoise-Net returns the noise-removed reflectance. Finally, A resultant image can be estimated by multiplying the noise-removed reflectance map and brightness-improved illumination. The experimental results show that the proposed scheme can provide high-quality images without saturation.
Hybrid Playfair: a modified Playfair cipher combining 2D and 3D Playfair

Existing modifications to the original (2D) Playfair cipher have made significant progress on improving its security, but little attention was given to optimize or maintain the original time and space efficiency. To close the gap, this paper proposes Hybrid Playfair, a modified Playfair cipher using a three-dimensional key and 2-character message pairs for encryption and decryption. With a schema made of five different substitution policies covering seven distinct cases, as well as an implementation guideline with sorted entry map and cross-layered key storage, Hybrid Playfair can offer not only better protection against frequency analysis attacks than 2D Playfair but also lower space and time usage than both 2D and 3D Playfair cipher. Hybrid Playfair can be used for applications on less powerful platforms with high throughput demands, such as live streaming and audio filtering devices, with its high performance-cost ratio and unimpacted security.
Proof of Digitally Owned Content

Blockchain has been instrumental in changing the way we interact on the Internet. The scope of Blockchain doesn&#x0027;t limit itself to financial and medical fields, but also finds applications in ownership of digital content. Because of its features like transparency, immutability, timestamping and decentralization, trust is built among the users and protection of personal data is ensured. A decentralized system, devoid of a third party is prone to a number of attacks and security risks. In this paper, we discuss the introduction of this emerging technology, research in Blockchain related to protecting digital content, implementation of smart contracts, various consensus mechanisms used in different Blockchain applications, and the potential vulnerabilities in Smart Contracts.
Mobile Learning

Mobile learning is learning across multiple contexts using smartphones and tablets, digital watches and fitness bands, wearable tags, and other more specialized devices. In educational applications, these devices are often linked together through the Internet or Bluetooth wireless technology, supporting collaboration and interaction. Mobile devices support seamless learning by extending learning beyond the classroom into everyday real-world experience. Mobile devices support the blending of the “formal learning” of the classroom with “informal learning” that takes place at home, in museums, or with peers. Mobile devices support personalized learning, and yet when networked together they can at the same time support collaboration and group learning. Many mobile devices generate large volumes of data from each student’s device, supporting learning analytics applications.
What Idea of Information Do ICT Practitioners Have in Mind?

We mean to reflect on the divide extant between theoretical studies on information and the knowledge of information shared by ICT (information and communication technologies) professionals. For example, many believe that Shannon&rsquo;s work provides significant support to ICT practitioners while this myth is fading. We put forward a new vein of research more adherent to the perspectives of practitioners.
Developing a data-driven ocean forecast system

&amp;lt;p&amp;gt;The recent boom in machine learning and data science has led to a number of new opportunities in the environmental sciences. In particular, process-based weather and climate models (simulators) represent the best tools we have to predict, understand and potentially mitigate the impacts of climate change and extreme weather. However, these models are incredibly complex and require huge amounts of High Performance Computing resources. Machine learning offers opportunities to greatly improve the computational efficiency of these models by developing data-driven emulators.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here I discuss recent work to develop a data-driven model of the ocean, an integral part of the weather and climate system. Much recent progress has been made with developing data-driven forecast systems of atmospheric weather, highlighting the promise of these systems. These techniques can also be applied to the ocean, however modelling of the ocean poses some fundamental differences and challenges in comparison to modelling the atmosphere, for example, oceanic flow is bathymetrically constrained across a wide range of spatial and temporal scales.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We train a neural network on the output from an expensive process-based simulator of an idealised channel configuration of oceanic flow. We show the model is able to learn well the complex dynamics of the system, replicating the mean flow and details within the flow over single prediction steps. We also see that when iterating the model, predictions remain stable, and continue to match the &amp;amp;#8216;truth&amp;amp;#8217; over a short-term forecast period, here around a week.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Sound Synthesis, Propagation, and Rendering

This book gives a broad overview of research on sound simulation driven by a variety of applications. Vibrating objects produce sound, which then propagates through a medium such as air or water befor
Context-aware Frameworks and Architectures-A Comprehensive Survey

Context-aware framework offers and provide completely new open doors for application designers and for the common users collecting context information and adjusting frameworks operations in understandable manner. Particularly in relation with the smart phones, these instruments are of higher integration and utilized to increment ease of use enormously. In this area of paper, we present regular design standards of Context-aware systems and determine a layer reasonable plan structure to clarify the unique basic components of most context-aware models. In view of these plan standards, we present different current context-aware frameworks concentrating on context-aware middleware and systems, which also facilitate the advancement of context-aware applications. We examine different methods and examine essential parts of context-aware figuring based on the exhibited frameworks.
ZKAttest: Ring and Group Signatures for Existing ECDSA Keys

Cryptographic keys are increasingly stored in dedicated hardware or behind software interfaces. Doing so limits access, such as permitting only signing via ECDSA. This makes using them in existing ring and group signature schemes impossible as these schemes assume the ability to access the private key for other operations. We present a $$\varSigma $$ -protocol that uses a committed public key to verify an ECDSA or Schnorr signature on a message, without revealing the public key. We then discuss how this protocol may be used to derive ring signatures in combination with Groth–Kohlweiss membership proofs and other applications. This scheme has been implemented and source code is freely available.
Orthogonal Single-Target Tracking

In this study, we propose a novel Wasserstein distributional tracking method that can balance approximation with accuracy in terms of Monte Carlo estimation.To achieve this goal, we present three different systems: sliced Wasserstein-based (SWT), projected Wasserstein-based (PWT), and orthogonal coupled Wasserstein-based (OCWT) visual tracking systems.Sliced Wasserstein-based visual trackers can find accurate target configurations using the optimal transport plan, which minimizes the discrepancy between appearance distributions described by the estimated and ground truth configurations.Because this plan involves a finite number of probability distributions, the computation costs can be considerably reduced.Projected Wasserstein-based and orthogonal coupled Wasserstein-based visual trackers further enhance the accuracy of visual trackers using bijective mapping functions and orthogonal Monte Carlo, respectively.Experimental results demonstrate that our approach can balance computational efficiency with accuracy, and the proposed visual trackers outperform other state-of-the-art visual trackers on several benchmark visual tracking datasets.
Intelligent Input and Analysis System of Pre-Qin Literature Based on Intelligent Text Extraction and Analysis Algorithm

Intelligent input and analysis system of pre-Qin literature based on intelligent text extraction and the analysis algorithm is studied in the paper. Compared with the traditional computing model, the active storage computing model distributes the computing tasks on the host node and the device node. If the computing task is allocated properly, the computing efficiency can be increased by about 65%. The character segmentation of Chinese characters is not suitable for the above method due to the complexity of the Chinese characters. The trough projection method is used to segment Chinese characters, that is, column scanning. Considering this, the novel intelligent text extraction and the analysis algorithm is designed considering SVM and PCA. The test is applied to the recognition tasks. Through the verification analysis on the results, the performance is shown.
A Systematic BPCLSTM Algorithm for Concept Drift Detection Incorporated Sentiment Mining

Concept drift is a problem where context behind the word changes in different times as different. This happens ahead of the minimum stability period. Many applications are there in which the problem of the concept drift needs to be addressed, including online shopping preferences and review of the consumer, spam detection, climate change predictions and social stream data. In E-Commerce applications the concept drift plays a vital role in deciding the review and sentiment analysis. This research focus on the concept drift which impacts the sentiment mining. This research will provide a novel algorithm based on the deep learning recurrent neural network variant.
Design and Implementation of a Real-time Target Detection and Tracking System

Target detection is to quickly and accurately locate the object to be measured in the video image and correctly classify it. Target tracking is to obtain the motion parameters of the object in the image sequence, and to detect, extract, identify and track the specific object. Combining target detection and tracking to follow specific objects is one of the research hotspots in the realization of intelligent embedded terminals. This paper designs and implements a system that combines target detection and tracking, which can realize real-time tracking of specific objects. Finally, the experiments prove the reliability of the proposed system, which can be used in intelligent systems such as robots.
Light Face: A Light Face Detector for Edge Devices

Face detection is one of the most important and basic steps in the recognition and verification of human identity. Using models based on convolutional networks such as face detection models is very difficult and challenging due to a large number of parameters, computational complexity, and high power consumption in environments such as edge devices, mobiles with limited memory storage resources, and low computing power. In this paper, a light and fast face detection model is proposed to predict the face boxes with real-time speed and high accuracy. The proposed model is structured based on the YOLO algorithm and CSPDarknet53 tiny backbone. Some tricks such as calculating custom anchor boxes aimed to solve the detection problem of varying face scales and some optimization techniques such as pruning and quantization have also been used to optimize and reduce the number of parameters and improve the speed to make the final model strong and suitable for use in environments with low computational power. One of our best models with a MAP of 67.52% on the WIDER FACE dataset and a volume of 1.7 Mb and a speed of 1.43 FPS on a mobile phone with ordinary hardware has shown significant performance
Differential Privacy Analysis using Convolutional Neural Networks in COVID-19 X-ray Images Classification Model

This paper proposed that the artificial intelligence (AD methods be used in conjunction with medical to detect COVID-19 affected individuals. But the investigations needs to be performance with maximum caution so as not to jeopardise the privacy of the person’s data that are required for these beneficial applications to be successful. As a result, the differential privacy (DP) application, is deemed to be an intriguing research topic, has been utilized in this study. For the development of a diagnostic concept, XRay pictures obtained from COVID-19-infected public data sources were utilised. This model has been trained using Convolutional neural networks (CNN), an effective deep learning model, and it proposes the probability of infection with an accuracy of 94.7 percent using CNN model. Among the most significant findings of the research was the recommendation of differentiated privacy practise PATE for such apps in order for them to be trustworthy in real-world use scenarios. According to this point of view, tests were redone with DP-applied pictures, and the findings obtained were then discussed. An method called Private Aggregation of Teacher Ensemble (PATE) was utilized to assure that the students’ personal information was protected.
Evaluation of enterprise financial investment environment based on block‐chain and cloud computing

Abstract Improving and strengthening the various factors of the financial investment system to improve the entire investment system has become an important part of modern economic development. This research is based on block‐chain and cloud computing on the evaluation of corporate financial investment environment. It aims to fully apply the advantages of block‐chain and computing technology to conduct a thorough analysis of corporate financial investment environment and increase the stability of corporate financial investment decisions. Provide economic benefits to a certain extent. The article mainly analyzes the status quo, influencing factors, and evaluation principles of the financial investment environment, and combines the advantages of cloud computing and block‐chain in data acquisition and processing applications to conduct experiments; this research constructs a structural correlation model between the enterprise and the external environment, and investment business data process and cloud computing and financial investment information organization structure model, which can visually present specific investment operation details, and so on; formulate comprehensive evaluation indicators for regional investment, and focus on the role of the institutional environment based on the analysis of the role of regional factors on the regional development of the enterprise. The research results show that cloud computing technology and block‐chain technology have greatly improved the construction of financial investment data whether it is intertemporal, predictive or follow‐up. From the data inspection results, the maximum value of this performance has been expanded from the original 5.98 to 9.27; from the perspective of computational performance, it has been increased from 5.98 to 9.27.
Optimized Deep Representation Learning Based Healthcare Diagnosis and Classification Model

In recent times, machine learning (ML) and deep learning (DL) based classification models have been significantly employed in the healthcare sector to determine diseases. Since manual disease diagnosis process is difficult and laborious, automated tools for disease diagnosis have been developed. The ML and DL models can be employed for various disease diagnosis using healthcare data and medical images. Therefore, this article presents an optimal deep representation extreme learning machine (ODR-ELM) technique for medical data classification. The presented ODR-ELM technique mainly intends to identify the occurrence of the disease using the patient medical data. The ODR-ELM technique primarily applies data preprocessing to enhance data quality. Secondly, the DR-ELM classification model is utilized to classify the existence or nonexistence of the disease. To boost the classifier efficacy of the DR-ELM technique, the glowworm swarm optimization (GSO) technique is utilized. The result analysis of the ODR-ELM model take place using three benchmark medical dataset and the results are inspected under several aspects. The simulation results reported the better performance of the ODR-ELM technique interms of different measures.
Healthcare Solutions Extraction Based on ANN-GOA and TDES

Nowadays, the online networking mechanism interacts with patients otherwise called clients, their close relations and curative experts so as to gather health-related data with the intention of sharing fruitful data on diagnosis of the disease, prescription, medicines and treatment to offer a helping hand to the clients with diverse ailments. With an eye on regaining the anchor site based on positioning, an innovative encryption based online social medicine recommendation system is envisaged. The user query is effectively explored in the MedHelp website. The contents in the website are retrieved together with responses and follows. In this regards the websites are assigned ranking by the Adaptive Artificial Neural Network (AANN) depending on the scores produced by the sentiwordnet. Further, in AANN, the weights are effectively optimized by Grasshopper Optimization Algorithm (GOA). Finally, the website with the maximum rank is encrypted with the help of the Triple Data Encryption Standard (TDES) algorithm for the safe recovery of the health-related content.
Towards WAN-aware join sampling over geo-distributed data

Large scale data analytics over geographically distributed data sources is challenging primarily due to the constrained and heterogeneous resource availability such as the wide area network (WAN) bandwidth. In this work, we look at the problem of generating random samples over joins for geo-distributed data sources. Joins are one of the most fundamental yet expensive operations in data analytics. To reduce the cost of computing joins, existing techniques have looked at efficiently generating a random sample over the join result for centralized environments, where all the data is available in one location. These techniques fail to address the unique challenges posed by geo-distributed environments. To address these challenges, we propose a sampling technique which aims to reduce the WAN traffic and latency, thereby reducing the overall latency for generating samples over joins for geo-distributed data sources. We implement our geo-distributed sampling technique on top of Apache Spark and compare it with existing state-of-the-art sampling techniques to identify scenarios where the proposed approach gives significant benefits. Based on this exploration, we provide a detailed outline of additional factors which should be considered when designing a WAN-aware join sampling technique for geo-distributed environments.
Graph-Powered Interpretable Machine Learning Models for Abnormality Detection in Ego-Things Network

In recent days, it is becoming essential to ensure that the outcomes of signal processing methods based on machine learning (ML) data-driven models can provide interpretable predictions. The interpretability of ML models can be defined as the capability to understand the reasons that contributed to generating a given outcome in a complex autonomous or semi-autonomous system. The necessity of interpretability is often related to the evaluation of performances in complex systems and the acceptance of agents' automatization processes where critical high-risk decisions have to be taken. This paper concentrates on one of the core functionality of such systems, i.e., abnormality detection, and on choosing a model representation modality based on a data-driven machine learning (ML) technique such that the outcomes become interpretable. The interpretability in this work is achieved through graph matching of semantic level vocabulary generated from the data and their relationships. The proposed approach assumes that the data-driven models to be chosen should support emergent self-awareness (SA) of the agents at multiple abstraction levels. It is demonstrated that the capability of incrementally updating learned representation models based on progressive experiences of the agent is shown to be strictly related to interpretability capability. As a case study, abnormality detection is analyzed as a primary feature of the collective awareness (CA) of a network of vehicles performing cooperative behaviors. Each vehicle is considered an example of an Internet of Things (IoT) node, therefore providing results that can be generalized to an IoT framework where agents have different sensors, actuators, and tasks to be accomplished. The capability of a model to allow evaluation of abnormalities at different levels of abstraction in the learned models is addressed as a key aspect for interpretability.
Intelligent Image Captioning Approach with Novel Ensembled Recurrent Neural Network Model

This paper presents an unique approach for extracting the features of an image using a ResNet50 Convolutional Neural Network architecture and to predict the caption of the image using a novel ensemble of GRU (Gated Recurrent Units) and Recurrent Neural Network algorithms namely Long Short-Term Memory (LSTM). This approach aims to combine the faster training and performance from GRU and greater volatility throughout its gradient descent from LSTM. This technique has been implemented assuring the major factors being capacity, robustness, and visual quality. The proposed method for image captioning has been evaluated using metrics such as BLEU, CIDEr, METEOR and it outperforms when compared to conventional systems.
A Framework to Automate Data Science Tasks Through Personalized Chatbots

Several solutions have been proposed for automating implementations of conversational interfaces. However, these solutions do not reach the desired levels of automation in the data science scenario. We identify the need to automate data science procedures up to different levels of automation. Our research focuses on helping data scientists during the automation of these procedures by using conversational interfaces. We propose a framework for creating a chatbot system to facilitate the automation of data science common scenarios. To test our solution, we generate a bot to detect outliers. We conducted a study with 28 participants to demonstrate that data scientists can use the proposed framework and reach the desired level of automation in some stages of the DS/ML process. All participants completed the activities correctly, and 75 to 80% found the framework relatively easy to extend and use. Our analysis suggests that the use of conversational interfaces can facilitate the automation of data science tasks. Keywords: