Reviewerly/exHarmony huggingface dataset:
Development of Stochastic Distribution Model of Contaminated Water Treatment Complex

The object of the research is the treatment facilities of the city of Nukus.Purpose of the study. The aim of the study is to develop and analyze mathematical models and algorithms for solving deterministic problems of polluted water purification, establishing the ratio of duality and optimality conditions, as well as establishing a marginal ratio for these tasks and determining the optimal size of costs for the purification of polluted water with various technological treatment schemes, development of a software package for solving deterministic problems of the complex for the purification of polluted waters.In general, on the research topic under study, theoretical studies were carried out to determine the optimality conditions for solving optimization problems and the stability of the deterministic model of the complex for the purification of polluted waters.The results of the study allow, determine the optimal size of the cost of treating polluted water with various technological treatment schemes.
Demo Abstract: Pixel Similarity-Based Content Reuse in Edge-Assisted Virtual Reality

Offloading the computation-intensive virtual reality (VR) frame rendering to the edge server is a promising approach to providing immersive VR experiences in mobile VR devices with limited computational capability and battery lifetime. However, edge-assisted VR systems require the data delivery at a high data rate, which poses challenges to the wireless communication between the edge and the device. In this demo, to reduce the communication resource consumption, we present PixSimVR, a pixel similarity-based content reuse framework for edge-assisted VR. PixSimVR analyzes the similarity of the pixels across different VR frames that correspond to different viewport poses, i.e., users’ points of view in the virtual world. Based on the pixel similarity level, PixSimVR adaptively splits the VR content into the foreground and the background, reusing the background that has a higher similarity level across frames. Our demo showcases how PixSimVR reduces bandwidth requirements by adaptive VR content reuse. Demo participants will develop an intuition for the potential of exploiting the correlation between VR frames corresponding to similar viewport poses specifically, and for the promises and the challenges of edge-assisted VR as a whole. This demonstration accompanies [1].
A Study on Integrating Multimodal English and American Literature Resources Using Data Mining

The study of English and American literature is a significant part of curriculum for foreign language majors. In classrooms, multimodal theory teaching is often utilized, in which the single-modal interactive manner of textual or teacher’s vocal transmission is complemented and augmented by other multimodal teaching techniques, i.e., visual and auditory techniques. The information resources of English and American literature have dramatically expanded as a result of today’s information explosion and mature network technology. The lack of resources is not the bottleneck that prevents English and American literature from becoming studied. On the other hand, there are too many resources that depend on data mining technologies. This paper develops an integration analysis platform based on data mining for integrating the content of English and American literature resources and uses the optimized Apriori algorithm to process multichannel data. Using web technologies provides a single data management system that optimizes and queries the target data. We measure the quality of the target data of English and American literature resources and at the same time obtain the grey correlation degree of the data by calculation and assign a value function to find. The proposed method can optimize the target data by calculating the grey correlation degree of the acquired data and assigning a value function to it. The contribution value of the target data and the similarity discrimination threshold are obtained, which are used for calculating the total value. The simulation outcomes demonstrate that the proposed method can effectively simplify the data structure, enhance the data accuracy and quality, improve the query efficiency, and has high applicability and high-quality robustness.
Curriculum Development for Teaching Cybersecurity of Industrial Control Systems &amp; Critical Infrastructure

It is well-established in academic and professional circles that there currently exists a knowledge/skill gap between engineers and IT/Cybersecurity professionals with respect to protecting the nation’s Industrial Control Systems (ICS) and Critical Infrastructure (CI) from cyber threats and attacks. This paper documents relevant work and outlines a roadmap for developing a curriculum for a Graduate Certificate as part of the effort to bridge this gap. Given the complex, cross-sector dependencies and interdependencies of ICS and CI, this paper adopts a multidisciplinary approach to developing the proposed Curriculum. To this end, the curriculum augments existing courses in Cybersecurity and Engineering Technology and creates new ones for meeting sector-specific learning outcomes & objectives. Overall, the paper and the proposed curriculum hold the promise of contributing to the ongoing effort to bridge the knowledge/skill gap by educating the future engineering and security workforce on protecting the ICS and CI from cybersecurity threats and attacks.
On counting Z-convex polyominoes

Abstract We show a decomposition that allows to compute the number of convex polyominoes of area n and degree of convexity at most 2 (the so-called Z-convex polyominoes) in polynomial time.
Communication without Connection

Port knocking emerged as method of authentication in the Early 2000s. Repurposing the technology has created a new communication medium. Instead of forming a client-server connection, we utilize port knocking to send Morse code between two computers. The idea is still in testing, but it shows promise with discreet communication. (Abstract)
Authorship Verification via Linear Correlation Methods of n-gram and Syntax Metrics

This research evaluates the accuracy of two methods of authorship prediction: syntactical analysis and n-gram, and explores its potential usage. The proposed algorithm measures n-gram, and counts adjectives, adverbs, verbs, nouns, punctuation, and sentence length from the training data, and normalizes each metric. The proposed algorithm compares the metrics of training samples to testing samples and predicts authorship based on the correlation they share for each metric. The severity of correlation between the testing and training data produces significant weight in the decision-making process. For example, if analysis of one metric approximates 100% positive correlation, the weight in the decision is assigned a maximum value for that metric. Conversely, a 100% negative correlation receives the minimum value. This new method of authorship validation holds promise for future innovation in fraud protection, the study of historical documents, and maintaining integrity within academia.
Design and Implementation of a Quadcopter Drone Control System for Photography Applications

Quadcopter drones equipped with onboard cameras are increasingly being utilized in photography to obtain pictures that would otherwise be impossible or too expensive to shoot. The portability and flexibility of quadcopter drones is often utilized by amateur photographers. An important aspect of designing a quadcopter drone for photography applications is ensuring that the drone will automatically stabilize and remain steady to ensure the perfect shot, without needing input on part of the user. To achieve this purpose, a quadcopter drone uses a control system with tuned parameters to remain as steady as possible. This paper describes an implementation of a quadcopter control system using a PID controller and Kalman filter to improve the inputs from the onboard gyroscope, to make the drone as steady as possible for a perfect shot.
Condenser Vacuum Degree Prediction Model with Multi-View Information Fusion

Abstract Vacuum degree is a crucial factor for the operation of thermoelectric generating set. Existing approaches typically use machine learning algorithms to link the relationship between unit operating data and condenser vacuum degree by focusing on the temporal information within the data, while ignoring the frequency information implied in the historical condenser vacuum degree. To fully use of frequency information and further improve prediction accuracy, we propose a novel condenser vacuum degree prediction model with multi-view information fusion. In specific, the implicit frequency information in the historical vacuum degree sequence is explored via a combination of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). Furthermore, Transformer encoder is used to extract the temporal information from the unit operating data. Finally, the two views information are fused for condenser vacuum degree prediction. Extensive experiments conducted on the real data collected from a power plant demonstrate the superiority of the proposed method over several state-of-the-art methods.
Enhancing SARS-CoV-2 variants Research with Blockchain Architecture

The emergence of the novel SARS-CoV-2 (Covid-19) virus in 2019 has led to continuous monitoring of the outbreak attempting to generate accurate reports of people's health information to understand the pandemic's impact. It is likely that more variants will emerge since not all countries and populations have been vaccinated. Thus, with SARS-CoV-2's constant mutation, researchers need to collect individuals' health data to study these variants and vaccine efficacy, especially those who show symptoms. However, researchers have difficulties building comprehensive datasets because people are unwilling to release their health information or have no way to report their health statuses (i.e., at-home testing). This problem stems from a lack of complete control over who assesses their health data. Hence, they cannot guarantee the security, privacy, and integrity of the disclosed health information. As the problem of building secure databases persists, researchers find it challenging to accurately report any evolving variants within a short period. This problem has resulted in several new outbreaks of the pandemic. In this work, we propose a blockchain architecture that can guarantee patients' health data integrity, privacy, and security, encouraging individuals to disclose their health information freely. This solution gives patients complete control over who assesses their health information. The framework proposed access management to patients' health data for researchers and contact tracers. This solution classifies patient health information to different sensitivity levels and manages access based on this sensitivity. In case of unauthorized access, the proposed solution detects and prevents such access, thereby ensuring the patient's health information's security, integrity, and privacy. Based on the classification, contact tracers can quickly assess the information needed from patients while the patients will be confident that no sensitive information is disclosed, reducing the burden on contact tracers.
Performance and Scaling of Parallel Systems with Blocking Start and/or Departure Barriers

Parallel systems divide jobs into smaller tasks that can be serviced by many workers at the same time. Some parallel systems have blocking barriers that require all of their tasks to start and/or depart in unison. This is true of many parallelized machine learning workloads, and the popular Apache Spark processing engine has recently added support for Barrier Execution Mode, which allows users to add such barriers to their jobs. The drawback of these barriers is reduced performance and stability compared to equivalent non-blocking systems.We derive analytical expressions for the stability regions for parallel systems with blocking start and/or departure barriers. We extend results from queueing theory to derive waiting and sojourn time bounds for systems with blocking start barriers. Our results show that for a given system utilization and number of servers, there is an optimal degree of parallelism that balances waiting time and job execution time. This observation leads us to propose and implement a class of self-adaptive schedulers, we call "Take-Half", that modulate the allowed degree of parallelism based on the instantaneous system load, improving mean performance and eliminating stability issues.
Image Captioning- Bangladesh’s Heritage Perspective Using Deep Learning

Image captioning aims to make a textual short explanation of a given image. Despite the fact that it looks to be a straightforward task for human being, it is difficult for computers since it involves the ability to analyze the image and provide a human-like description. Encoder-decoder architectures have recently reached advanced outcomes in the form of picture captioning. With some existing datasets, e.g., Flickr_data, Flickr8k_token.txt, and heritage dataset, we build our model that can create captions from the images related to Bangladeshi culture, tradition and historical places. Bangladesh is enriched with great culture; many heritage places and cultural programs that attract travelers to visit our country. We try to relate our culture, place, and food, together with machine learning techniques by appropriate captioning and spread over our cultural strengths through proper captioning. Our image captioning tool can be very helpful for travel lovers who want to know more about Bangladesh.
User Intent Driven Path Switching in Video Delivery - An Edge Computing Based Approach

In emerging on-demand and live surveillance video applications, end users may actively change content resolutions which may trigger sudden and potentially substantial change of data rate requirements. Traditional IP based static paths may not be able to seamlessly handle such change of user intent in video applications, and hence may lead to potential user QoE deterioration. In this paper, we propose an SRv6 enabled SDN framework that can allow on the fly change of video delivery paths (when necessary) upon the detection of dynamic user intent on different video resolutions. This is typically achieved through offline definition of possible user intent scenarios on specific video resolutions which can be captured by edge computing based intent framework before the path switching action is triggered. We demonstrate a use case of a 4K video quality switch on an implemented framework, and the results show substantially reduced resolution switching delay upon user intent during ongoing video sessions.
Ldasip: A Lightweight Dynamic Audit Approach for Sensitive Information Protection in Cloud Storage

How to audit the integrity of data stored on the cloud with incomplete trust is an important problem that restricts the development of cloud storage. Although there are several data integrity audit schemes in cloud storage, the increased need to protect sensitive information and support large-scale data storage and dynamic update will result in a significant increase in audit cost, which seriously affects the efficiency of existing cloud audit systems. To solve this problem, we propose Ldasip, a lightweight dynamic auditing method that supports sensitive information protection in cloud storage. Exploiting identity-based data integrity audit, a data masking technology is introduced into to protect user’s sensitive information. At the same time, an improved multibranch tree structure is proposed to realize dynamic audit and reduce communication overhead in the verification process. Theoretical analysis and comprehensive experiments have been conducted, which demonstrate the effectiveness of Ldasip. The results show that Ldasip can ensure the correctness of the audit, protect the sensitive information in the user's stored content, and support the dynamic update of data with less audit time and communication overhead.
A Novel System for Automated Coloring of Neat Sketches

The process of coloring neat sketches is a significant activity while making animated movies or for better visualization in computer modelling. The color filling tools are widely available in almost all the image/video editing software, which will help us to pick a color from a color palette, and it can be filled on a selected region. This process is known as flat coloring. The flat coloring process is having a number of challenges. One of the major challenges is that the color may leak from the selected regions to other neighbouring regions if there are some small openings on the contours. The second concern while using flat coloring is that the whole selected regions will be completely filled the same color and hence the drawing will not have an artistic look. In this paper, we propose a new software application that will take a neat sketch as the input and the system will generate a colored drawing as the output. In the proposed scheme, we have converted the given sketch as grayscale or binary image, and we applied image dilation operation to fill the small open spaces in the contours (if any). Further, the closed regions are identified and colored with a predefined set of colors or with random color combinations. While coloring the regions, the proposed system will ensure that the adjacent regions will not colored with the same colors. A number of sketches have been considered during the experimental study and the results are validated manually.
Trusted Storage Architecture for Machine Reasoning based on Blockchain

With the urgent demand for interpretability and generalization of artificial intelligence decision-making process, machine reasoning based on production rules provides an auditable scheme for knowledge construction process. Aiming at the problems of traditional machine reasoning, such as lack of trusted storage, reasoning and difficulty in tracing results, this paper proposes a trusted machine reasoning process storage scheme based on blockchain. In this scheme, the fact and rule storage and trusted machine reasoning algorithm are encapsulated in the alliance blockchain smart contract, to realize the trusted reasoning and reliable storage in the whole reasoning process and ensure the transparency and traceability of rule logic update. Index Terms–Blockchain, Machine Reasoning, Smart Contract, Production rule, Trusted Reasoning
Bangla Handwritten Character Recognition Method

Recognizing and extracting handwritten character information is still a challenge in the scanning process. This research describes a method for OCR applications where Bengali handwritten characters can be recognized effectively. This method mainly focuses on post processing steps like feature extraction and building classification model to find a favorable accuracy rate. For feature extraction LBP method is used. Local Binary Pattern is a coming of age feature extracting method which is applied very few times in Bengali Language. This work is also an experimental approach of confirming what occurs when LBP is used in Bengali Characters. To classify Random Forest algorithm is applied, which is also a unique classification method. The datasets are gathered by collecting Bengali characters written in various fashions. Initially, scanned images of Bengali characters are given as input and by applying LBP required features are extracted. Principal Component Analysis (PCA) is applied on the collected feature vectors to reduce the dimension. Finally, RF algorithm is implied on the output to generate a recognition rate. Support vector machine (SVM) is also used as a classifier to evaluate and compare.
A Discussion of the Design and Development of an Intrinsically Safe Drone for Underground Mining Applications

Drone usage in underground coal mines is restricted due to a lack of permitted platforms. The Mine Safety and Health Administration (MSHA) sets standards for all underground coal mine equipment. These constraints make developing a legal drone design extremely difficult. As a result, this article describes the endeavor to create an intrinsically safe drone. First, MSHA's standards for designing an inherently safe, acceptable, or explosion-rated drone were established. The basic size and design of the drone were then carried out based on these specifications. As the drone propulsion system faces several challenges in this project, a test rig was built to assess its performance. The motor shall be enclosed in accordance with MSHA regulations. The presence of this enclosure necessitated the addition of a motor propeller coupler system, and the effect of the coupler on the propulsion system was studied. In addition, the thermal effect of the motor casing and thermal studies were provided.
The Quality Attibutes and Architectural Tactics of Amazon Web Services (AWS)

Software architecture plays a critical role in ensuring the quality attributes of a software system (i.e. security, performance, availability, usability, and maintainability). With the advent of cloud computing, popular software architectures are changing. An important change is that some aspects of some quality attributes are now implemented in the cloud software. Architecture elements called tactics (i.e. design decisions that affect the achievement of quality attributes) are built to address the quality attributes of a software architecture. The implementation of a software application using components from a commercial cloud computing platform inherits the quality aspects of the tactics used as part of the implementation of the cloud components, freeing application developers from worrying as much about the quality attributes. We investigated components of the cloud service Amazon Web Services (AWS) and found implementations of key elements of several quality attributes. Furthermore, we identified existing and new tactics used in AWS components to address these quality attributes. In particular, we identified five common quality attributes implemented in AWS components, along with associated tactics: Reliability, Scalability, Performance, Availability, and Security. Most of the tactics are modifications of existing tactics; but several tactics in Security are new. This illustrates the rapid change in the area of security. opportunities for additional research include expanded studies of tactics in cloud services, and organizing and cataloguing of new tactics.
Optical Flow Velocimetry using a Quasi-Optimal Basis with Implicit Regularization

A set of quasi-optimal bases of orthonormal functions to represent fluid flow fields are formed using the singular value decomposition of a diverse set of direct numerical simulations (DNS) of various turbulent flows. It is found that the bases are capable of sparsely representing a wide variety of fluid flow fields with minimal accuracy loss, and hence can be effectively used to solve the ill-posed optical flow problem to perform optical flow velocimetry (OFV), by reducing the dimensionality of the problem to resolve the ill-posedness. A novel OFV method is developed using the new quasi-optimal basis with implicit regularization. The bases can impose implicit regularization on the solution via truncation of coefficients, which acts as a physics-informed smoothing. The effect of the user defined parameters, including the patch size, on the bases themselves and on the resulting OFV solutions, and it is found that at small enough patch size, the quality of the reconstructions with the quasi-optimal bases are dataset independent, indicating universality in the flow structures at small scales.