Reviewerly/exHarmony huggingface dataset:
Unleashing the power of 2D CNN with attention and pre-trained embeddings for enhanced online review analysis

Online reviews play a significant role in the success of a business. Deep learning models have emerged as crucial tools in this domain, with one-dimensional Convolutional Neural Network (1D CNN) being commonly used. However, this paper proposes a novel approach utilizing a Two-Dimensional Convolutional Neural Network (Att + 2D CNN) with attention mechanism, which effectively captures the dimensionality of the input text, resembling a 2D matrix. To further enhance the model’s performance, we employ pretrained word embeddings, specifically GloVe and Word2Vec. We thoroughly analyze the performance of these embeddings in conjunction with deep learning models. Remarkably, our proposed method, leveraging 2D CNN with attention, consistently achieves superior accuracy when compared to other models, specifically on Amazon Cell Phone reviews and Amazon Kindle reviews datasets, for both balanced and unbalanced natures. By employing this novel methodology, we demonstrate the ability to extract valuable insights from online reviews, enabling businesses to make informed decisions.
Maximum Acceptable Tilt Angle for Point Autofocus Microscopy

The complete and accurate acquisition of geometric information forms the bedrock of maintaining high-end instrument performance and monitoring product quality. It is also a prerequisite for achieving the 'precision' and 'intelligence' that the manufacturing industry aspires to achieve. Industrial microscopes, known for their high accuracy and resolution, have become invaluable tools in the precision measurement of small components. However, these industrial microscopes often struggle to demonstrate their advantages when dealing with complex shapes or large tilt angles. This paper introduces a ray-tracing model for point autofocus microscopy, and it provides the quantified relationship formula between the maximum acceptable tilt angle and the beam offset accepted in point autofocus microscopy, then analyzing the maximum acceptable tilt angle of the objects being measured. This novel approach uses the geometric features of a high-precision reference sphere to simulate the tilt angle and displacement of the surface under investigation. The research findings show that the maximum acceptable tilt angles of a point autofocus microscope vary across different measured directions. Additionally, the extent to which the maximum acceptable tilt angles are affected by the distances of the beam offset also varies. Finally, the difference between the experiment results and the theoretical results is less than 0.5°.
CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.
Upper bounds on key rates in device-independent quantum key distribution based on convex-combination attacks

The device-independent framework constitutes the most pragmatic approach to quantum protocols that does not put any trust in their implementations. It requires all claims, about e.g. security, to be made at the level of the final classical data in hands of the end-users. This imposes a great challenge for determining attainable key rates in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">device-independent quantum key distribution</mml:mtext></mml:mrow></mml:math> (DIQKD), but also opens the door for consideration of eavesdropping attacks that stem from the possibility of a given data being just generated by a malicious third-party. In this work, we explore this path and present the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">convex-combination attack</mml:mtext></mml:mrow></mml:math> as an efficient, easy-to-use technique for upper-bounding DIQKD key rates. It allows verifying the accuracy of lower bounds on key rates for state-of-the-art protocols, whether involving one-way or two-way communication. In particular, we demonstrate with its help that the currently predicted constraints on the robustness of DIQKD protocols to experimental imperfections, such as the finite visibility or detection efficiency, are already very close to the ultimate tolerable thresholds.
Novel method of building train and test sets for evaluation of machine learning models related to software bugs assignment

Nowadays many tools are in use in processes related to handling bug reports, feature requests, supporting questions or similar related issues which should be handled during software development or maintenance. Part of them use machine learning techniques. In introduction is presented a review of fundamental methods used for evaluation of machine learning models. This paper points out weak points of currently used metrics for evaluation in specific context of the cases related to software development especially bug reports. The disadvantages of state of the art are related to disregarding time dependencies which are important to be applied for creating train and test sets as they may have impact on results. Extensive research of the art has been conducted and has not been found any article with the use of time dependencies for evaluation of machine learning models in the context of works related to software development applications like machine learning solutions to supporting bug tracking systems. This paper introduces a novel solution which is devoid of these drawbacks. Experimental research showed the effectiveness of the introduced method and significantly different results obtained compared to the state-of-the-art methods.
On developing Block‐Chain based Secure Storage Model (<scp>BSSM</scp>) with auditing and integrity analysis in the cloud

Summary In present scenario, Cloud Computing is developing vast, as the resources are utilized by many people and provided on‐demand. Recent works on cloud reports the success rates of cyber activities using cloud models for deceptive actions, because of its significant features and lack of apposite forensic models for cloud. For developing a cloud model for forensic support is an important and intricate issue like, directorial and technical challenges due to system distribution, virtualization, and dynamic characteristic of nodes. Considering the issues, this article develops a Block‐chain based Secure Storage Model (BSSM) incorporating Integrity and Auditing Examination Model for evaluating reliability of forensic data and auditing for validation. Moreover, this model effectively works on the domain of Forensic as a Service (FaaS) in cloud. The forensic data are formed into block‐chain for ensuring security. The model also aids in reducing the storage space effectively and the auditing and integrity analysis model incorporated in this work minimizes the dependency of trusts on Cloud Service Provider (CSP). And, the evaluation results show that the proposed model effectively achieves the objective in better way than compared works.
Focus on Physics: Physics Activities With Metersticks

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of the new fifty-year celebration 13th edition of Conceptual Physics; the 6th edition of Conceptual Physical Science, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
Focus on Physics: Unnoticed Sunballs All Around Us

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, new 13th edition; Conceptual Physical Science, 6th edition, coauthored with Leslie Hewitt and John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
Right to the Source: The Tuskegee Airmen: Teaching the 4Cs

"Right to the Source: The Tuskegee Airmen: Teaching the 4Cs." The Science Teacher, 87(6), p. 64 Additional informationNotes on contributorsAmara AlexanderAmara Alexander (aalexander@loc.gov) is the 2019-20 Library of Congress Albert Einstein Distinguished Educator Fellow.
Immersive Learning Through Experiential Inquiry of a Virtual Epidemic

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsTyler HansenTyler Hansen (tyler.hansen@ccsdut.org) is a PhD Student of Teacher Education and Leadership, Utah State University, Logan, UT.Deborah FieldsDeborah Fields (deborah.fields@usu.edu) is an Associate Professor of Instructional Technologies and Learning Sciences, Utah State University, Logan, UT.Amanda StrawhackerAmanda Strawhacker is Associate Director of the Early Childhood Technology at Eliot-Pearson Department of Child Study and Human Development, Tufts University, Boston, MA.Yasmin KafaiYasmin Kafai (kafai@upenn.edu) is Lori and Michael Milken President’s Distinguished Professor at the Graduate School of Education, University of Pennsylvania, Philadelphia, PA.
Large Range Dynamic Phase Extraction of Extrinsic Fabry-Pérot Interferometric Sensors Using Phase-step Insensitive FM-PSI

In this paper, we propose a phase-step insensitive frequency modulated phase shifting interferometry (FM-PSI) for large range dynamic phase extraction of extrinsic Fabry-Pérot (F-P) interferometric sensors. A modulated grating Y-branch (MG-Y) laser is employed to achieve fast and programmable phase shifting. In order to extend the dynamic range of the conventional quadrature phase demodulation scheme, it is necessary to consider the real-time dynamic changes of the phase shift step, reflected light intensity, and interference contrast when the cavity length changes over a wide range. Herein, we introduce equal phase difference by fast switching of four optical frequencies with fixed frequency intervals, and calibrate the laser injection current to keep the output power at different optical frequencies consistent, thereby mitigating the influence of light intensity modulation effect. Combined with the 4-step Carré algorithm, fast and large dynamic range phase demodulation is realized while the initial cavity length and phase shift step are unknown. Furthermore, the proposed FM-PSI serves as an ideal choice for the simultaneous measurement of multi-channel dynamic interferometric sensors. Simulation and experimental results show that the technique can detect large-amplitude vibrations and monitor multi-dimensional dynamic displacements.
Focus on Physics: Tips for Teaching the Physics of Falling

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of the new 13th edition of Conceptual Physics; Conceptual Physical Science, 6th edition, coauthored with Leslie Hewitt and John Suchocki; and Conceptual Integrated Science, 3rd edition, with Suzanne Lyons, John Suchocki, and Jennifer Yeh.
A comprehensive review of video watermarking technique in deep learning environments

In recent years, the advent of the Internet and the rapid growth of digital media applications have made video a primary medium for information transmission. However, this progress has also brought about new challenges, including concerns related to unauthorized copying, digital plagiarism, and the distribution and utilization of copyrighted digital content. In order to address these issues, watermarking has emerged as a solution. It involves embedding a watermark into a digital cover and subsequently extracting it to resolve ownership disputes and copyright infringements related to media content. Numerous conventional video watermarking techniques have been introduced, demonstrating their effectiveness in achieving both invisibility and robustness against various types of attacks. Recently, the application of deep learning principles in embedding signatures into video content has gained significant attention. This approach offers considerable advantages in the field of watermarking due to its accuracy, superior outcomes, and exceptional learning capabilities. This paper provides an overview of recent advancements in deep learning-based video watermarking. It categorizes the proposed approaches according to the employed network architecture, offering a comprehensive summary of the field’s latest developments. The study concludes by examining potential research avenues in the domain of deep learning-based video watermarking.
An Efficient Deep Learning Approach to Deal with Cyberbullying

Now a days Internet become more Integral part of World particularly social media platforms, such as Instagram, Facebook & What’s app etc. which led to emergence of cybercrime. Cyberbullying is a special way of bullying which emerged with development of social networks. People are bullyingother people by commenting or by posting toxic commentor embarrassing picture which lead to cause harm to Victim reputation. It is very common among teenagers. Stating to the above challenge we have pitched a deep learning based modelto identify bullied text in meme data, our model performed well in categorizing the text in to toxic comment 10 %, obscene in between 5-10 %, insult is about 5 %, severe toxic & identity hate is almost between 2 % & threat is 0-5 %.
Garbage Classification using Faster R-CNN

Inefficiency in waste management contributes to the rising amount of pollution in the community, causing the public demand for more proper waste management and classification. Sorting or classifying waste is the beginning of the waste recycling process, which can help reduce the amount of garbage in the environment. However, compounded with the lack of waste sorting awareness due to minimum public education on waste management, the waste sorting system is still conducted manually using human power. Thus, it is necessary to have a waste sorting or classification system to encourage people to manage their waste properly. This study aims to design a tool to detect the waste types and classify them into three categories; metal, paper, and plastic waste. The system can recognize the shape of the waste image using deep learning methods developed using the Faster R-CNN with Resnet50 as the network architecture. This research began with collecting 250 metal, paper, and plastic waste datasets used for training data and test data in the testing process. The test consisted of 6 trials. In each trial, the training process ran up to 3,000, 4,000, 5,000, and 8,000 steps, where each had a loss function parameter. The test results show that increasing the number of steps during the exercise increases the F1 score, but the maximum number of steps with an increase in the F1 score is 5,000 by 91%.
Enhancing Security of Color Image Exchange using Authentication and Encryption

The real risk in the privacy of a plain image transmitted over an unsecured connection is content alteration or illegal eavesdropping. The architecture proposed in this paper provides Encryption and Authentication (EA) in their comprehensively for protecting a transmitted image from unauthorized with an average execution time of one second for each 256*256 color image. At the origin point (sender side), it encrypts and signs the image’s content. On the receiver side, it decrypts and verifies the cipher image, achieving two stages of confidentiality and two stages of verification for the image content without depending upon any third party and reinforced with two key chaos equal size color images. Every image has a unique hash value (signature or identity) with QR Code watermarking used to detect forgeries, even if the change is slight (even by one bit). EA has features that make it more resistant to security attacks, as shown by the experiments in this paper.
VisualIE: Receipt-Based Information Extraction with a Novel Visual and Textual Approach

Information extraction (IE) from a receipt-based document requires understanding the contextual and visual semantics of texts. However, prior studies have predominantly utilized pre-trained language models and Optical Character Recognition (OCR) engines to extract textual features from the document and conduct entity extraction. Besides, this paper presents a novel approach for information extraction from receipt-based documents by leveraging both visual and textual features. The proposed approach employs a convolutional neural network to capture visual features and a word embedding model to capture textual features. These features are then combined and fed into a fully connected layer for entity classification. The experimental results demonstrate that the proposed approach outperforms the baseline and achieves state-of-the-art results in information extraction from receipts.
GF2PReID: A Novel Framework for Person Re-IDentification Using Generative Networks

Person Re-Identification is a critical component in modern video surveillance systems for locating individuals across cameras from various viewpoints. However, one of the significant challenges in person ReID arises when facial information is unavailable. To address this issue, we propose GF2PReID, a novel framework that leverages state-of-the-art deep learning-based ReID approaches to generate prior knowledge of the face region and provide detailed information about human body images. Our approach utilizes a deep residual network model trained with transfer learning to extract discriminative features from images with low discrimination, including low illumination and occlusion, collected from diverse datasets. Experimental results, on 2 challenging datasets: Market-1501 and CUHK03, demonstrate that our GF2PReID framework improves the datasets and significantly improves the performance of the Resnet-50 model, reaching the highest performance.
Cryptojacking: Detection and Prevention Techniques

Cryptojacking has emerged as a significant cyber security threat in recent years, fuelled by the rise in popularity and value of cryptocurrencies. It refers to the unauthorized use of computing resources to mine cryptocurrencies, such as Bitcoin or Monero, without the knowledge or consent of the device owner. This illicit activity is typically facilitated by injecting malicious code into websites, ads, or software applications, allowing the code to silently run in the background and exploit the device’s processing power and energy resources for mining purposes. Cryptojacking can have detrimental effects, including decreased device performance, increased power consumption, and potential financial losses for victims. As a result, researchers and security experts have focused on developing effective detection and prevention techniques to mitigate the risks associated with cryptojacking. These techniques encompass various approaches, such as machine learning algorithms, behavior analysis, network monitoring, and user education. Collaboration among stakeholders, including industry, academia, and policymakers, is also crucial in addressing this evolving threat. Inclusive, understanding, and combating cryptojacking is essential to safeguard computing resources, protect individuals and organizations from financial losses, and ensure a secure digital environment in the face of an ever-changing cyber security landscape.
Knowledge-based adaptation of XR user interfaces for stock market data visualization

Adaptation of Extended Reality (XR) interfaces to fit user needs and preferences presents a significant challenge. The available XR application development tools often require advanced programming skills, forming a barrier for non-specialists in XR technology and 3D modeling. The main contribution of this paper is a knowledge-based method for the adaptation of XR user interfaces, with a focus on the usage of semantic representation of the users’ profiles to tailor individual experiences within an XR environment to the users’ requirements. The primary aim is to optimize user satisfaction and performance by enabling the adaptation of XR content by non-IT specialists using common or domain knowledge. As a case study for utilizing user profile-based adaptation, the paper presents an adaptive XR stock exchange data visualization system.
Chaotic Model-Based Blind Watermarking with LSB Technique for Digital Fundus Image Authentication

Telemedicine, particularly when conducted through unprotected connections, necessitates secure delivery of medical data. In the context of medical image transmission, watermarking techniques are frequently employed to enhance the security and privacy of digital content. This paper proposes a secure and blind watermarking algorithm based on spatial domain Least Significant Bit (LSB), chaotic sequences and key point detection using BRISK (Binary Robust Invariant Scalable Keypoints) and GFTT (Good Feature To Track) algorithms, combined with K-means clustering. Chaotic sequences are used for watermark encryption and embedding, ensuring robust security. The algorithm converts the message to binary format, then divides the cover medical image into three bands (red, green, blue) and hides the encrypted watermark within the green and blue bands. The merged bands (red, modified green, modified blue) create the final image with hidden watermark. The proposed approach has undergone experimental investigation within the field of fundus images, which are a specialized form of medical images capturing the back portion of the eye, known as the fundus. In this particular application, fundus images incorporate a watermark encrypting patient-related data and ensuring its integrity during transmission and storage. In addition, integrity check data and other important data are delivered to the recipient to verify the watermark. Through the experimental study, the proposed approach showcases its effectiveness and ability to withstand different attacks, making it highly suitable for secure telemedicine applications.