Reviewerly/exHarmony huggingface dataset:
An Effective And Robust Computerized Library Management System

Earlier Librarian needs to keep the track of books manually which consumes more time. With the advancement of new technology, Library Management System which aims in developing a computerized system that manages and stores the books information electronically. In this paper we provide an efficient access over library system application that was developed through ServiceNow platform. In ServiceNow platform we can easily develop the applications and automate the things in very less time.[1]The main purpose of this paper is to provide an easy access to library. And also to provide the details of library books, issue of books and notify the user about due dates of book being issued. This application helps the user to access the information of the books which saves time and energy. Errors are also avoided by allowing the system to keep the track of information such as issue date, due date to return the book by sending notifications to the user. In this application we have used the concepts like Form Designer,Service portal, Client Scripts, Notifications, Events and Scheduled scripts.[2]Thus, this application reduce manual work to a great extent and allows smooth flow of library activities.
Students’ Career Decision-Making During Online Learning: The Mediating Roles of Self-Efficacy in Vocational Education

&lt;p style="text-align:justify"&gt;In the last decade, vocational education in Indonesia has experienced problems in making career decisions for students, which was exacerbated by the COVID-19 pandemic. Therefore, this research aims to examine the role of self-efficacy and mediate digital literacy, social environment, and counselling guidance in influencing career decision-making. This is an ex-post-facto research design with data collected from a sample of 566 vocational education students in Indonesia through a questionnaire method distributed online using Google Form. The collected data was then analyzed using structural equation modelling (SEM) with path analysis and bootstrap methods. The results revealed that self-efficacy plays a vital role in mediating digital literacy and guiding students in career decision-making. On the other hand, digital literacy, guidance, and counselling have a significant direct effect on self-efficacy and career decision-making. Meanwhile, the social environment only has a significant direct effect on students’ career decision-making. The real role of all elements of vocational education in strengthening self-efficacy, growing digital literacy, monitoring social environment interactions, and providing counselling guidance to students is needed to increase optimism and the quality of career decision-making in vocational education.&lt;/p&gt;
Long-tail session-based recommendation from calibration

Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias. Existing models for mitigating popularity bias have attempted to reduce the overconcentration of popular items by amplifying scores of less popular items. However, they normally ignore the users’ different preferences toward long-tail items. Thus, we incorporate calibration, where calibrated recommendations reflect the users’ interests in recommendation lists with appropriate proportions, to mitigate the popularity bias from the user’s perspective. Specifically, we propose a calibration module to predict the ratio of tail items in the recommendation list from the session representation, and align it to the ongoing session. Additionally, we utilize a two-stage curriculum training strategy to improve prediction in the calibration module. Experiments on benchmark datasets show that our model can both achieve the competitive accuracy of recommendation and provide more tail items.
Building trust with voice assistants for apparel shopping: The effects of social role and user autonomy

Voice shopping creates a novel shopping experience as it is prompted by human-AI interaction. It greatly reduces the time required in consumer decision-making, while it could impair consumers’ autonomy. This study thus investigates how the social role of voice assistants and user autonomy affect consumers’ relationships and buying decisions. Using media equation theory, two experimental studies examined the effects of social role and user autonomy on perceived human-likeness, trust, and purchase intention. Study 1 (86 participants) and Study 2 (112 participants) found that perceived human-likeness and trust serially mediated the relationship between user autonomy and purchase intention. This finding contributes to the media equation research by affirming the effectiveness of implicit anthropomorphism processing and highlights the importance of improving consumers’ confidence via voice channels.
Deep learning solution for medical image localization and orientation detection

Magnetic Resonance (MR) imaging plays an important role in medical diagnosis and biomedical research. Due to the high in-slice resolution and low through-slice resolution nature of MR imaging, the usefulness of the reconstruction highly depends on the positioning of the slice group. Traditional clinical workflow relies on time-consuming manual adjustment that cannot be easily reproduced. Automation of this task can therefore bring important benefits in terms of accuracy, speed and reproducibility. Current auto-slice-positioning methods rely on automatically detected landmarks to derive the positioning, and previous studies suggest that a large, redundant set of landmarks are required to achieve robust results. However, a costly data curation procedure is needed to generate training labels for those landmarks, and the results can still be highly sensitive to landmark detection errors. More importantly, a set of anatomical landmark locations are not naturally produced during the standard clinical workflow, which makes online learning impossible. To address these limitations, we propose a novel framework for auto-slice-positioning that focuses on localizing the canonical planes within a 3D volume. The proposed framework consists of two major steps. A multi-resolution region proposal network is first used to extract a volume-of-interest, after which a V-net-like segmentation network is applied to segment the orientation planes. Importantly, our algorithm also includes a Performance Measurement Index as an indication of the algorithm’s confidence. We evaluate the proposed framework on both knee and shoulder MR scans. Our method outperforms state-of-the-art automatic positioning algorithms in terms of accuracy and robustness.
Automatic Modulation Recognition: An FPGA Implementation

Very recently, convolution neural network (CNN) based deep-learning (DL) models have been used in automatic modulation classification (AMC) systems and achieved superior performance. However, the huge CNN model size and floating-point weights and activation make deployment of such systems very complex with limited hardware resources such as FPGAs. In this work, we have designed CNN-based AMC schemes for the complex-valued temporal radio signal domain and implemented them on an FPGA platform. This work focuses on quantized CNN and low precision mathematics to solve the problem of bigger model size and floating-point weights and activations. However, the low precision weights, activation, and quantized CNN significantly impact the model’s accuracy. To overcome this, we proposed an RU-based AMC scheme and used an iterative pruning-based training mechanism to maintain the overall accuracy above a certain threshold while decreasing the model size on hardware. The proposed scheme achieves at least 1.4% higher accuracy and takes only 40% hardware resources than the baseline. The RFSoc implemented model achieves a real-time throughput of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$527k$ </tex-math></inline-formula> classification per second with a latency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\mu\text{s}$ </tex-math></inline-formula> .
A PVT-Tolerant Oscillation-Collapse-Based True Random Number Generator With an Odd Number of Inverter Stages

This brief proposes a PVT-tolerant true random number generator (TRNG) based on a ring oscillator (RO) with an odd number of inverter stages. By introducing a Muller C-element in each racing path of the RO, the TRNG successfully eliminated the need of collapse detection circuits. The proposed topology enables a bidirectional controllability in the number of cycles to collapse (CTC) and provides flexibility for the number of random bits per cycle. Implemented TRNG in 40nm CMOS shows intrinsic robustness against PVT variations across a supply range of 0.5V-to-1.4V and a temperature range of −40°C-to-125°C without calibration. It consumes 0.0107 nJ/bit at 0.9V and occupies 305.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {\mathrm{ m}}^{2}$ </tex-math></inline-formula> .
A Secured Healthcare Model for Sensor Data Sharing With Integrated Emotional Intelligence

Integration of healthcare and Internet of Things (IoT) has a potential to revolutionize the medical treatments, diagnosis and predict medical issues thereby enabling patients, families, doctors and medical insurers stay connected for a proactive delivery of services. However, IoT devices operate in infrastructure-less environments, and hence data security and privacy is always a concern. Counterfeit IoT devices create huge menace in sensitive applications. Moreover, conventional healthcare systems are not patient-centric in the sense that they do not include patient’s emotions during treatments. EI helps the clinicians better understand and mange medical procedures. This paper presents a human-centered approach in healthcare domain by the integration of emotional intelligence (EI) and sensor network built around IoT devices. A Raspberry Pi connected with sensors and a camera acts as an IoT device. These sensors collect body vital parameters and Facial expression recognition (FER) based EI. The system is hosted on an Ethereum permissioned blockchain for reliability, security and tamper-proof data sharing and storage. Devices in the network are authenticated using physical unclonable functions (PUFs). Comparative analysis confirms that the PUF-based authentication is 330% faster than conventional methods. The system offers latency of as low as 20 ms. Using smart contracts, the proposed model provides role-based access control and helps in building scalable and harmonious digital healthcare platforms.
Black-Box Accountable Authority CP-ABE Scheme for Cloud-Assisted E-Health System

Ciphertext-policy attribute-based encryption (CP-ABE) for the cloud-assisted E-health system can effectively serve patients and healthcare professionals to achieve fine-grained sharing of health records. However, the problem of private key abuse could pose a security threat to patient data, while most of traceable CP-ABE schemes only can implement white-box traceability, which means that the traitor tracing problem cannot be solved thoroughly. Considering that malicious users or authority may forge the decryption device without getting caught, in this article, a black-box accountable CP-ABE is provided, which can identify the owner of the faked decryption device and the malicious activity of the authority. The authority will submit the tuple related with the user to the auditor for tracing the user’s identity. To avoid users from being framed by authority, the full decryption key should be generated by the interaction between the user and the authority. Namely, the user embeds a secret value in the key, which is used to generate an auditing ciphertext by the auditor for monitoring the malicious behavior of the authority. Furthermore, the indistinguishability under chosen-plaintext attack (IND-CPA) security of the proposed scheme is proven based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> -type assumption, and the performance analysis indicates that the proposed scheme has more secure functionalities and no significant loss of efficiency.
Defending Batch-Level Label Inference and Replacement Attacks in Vertical Federated Learning

In a vertical federated learning (VFL) scenario where features and models are split into different parties, it has been shown that sample-level gradient information can be exploited to deduce crucial label information that should be kept secret. An immediate defense strategy is to protect sample-level messages communicated with Homomorphic Encryption (HE), exposing only batch-averaged local gradients to each party. In this paper, we show that even with HE-protected communication, private labels can still be reconstructed with high accuracy by gradient inversion attack, contrary to the common belief that batch-averaged information is safe to share under encryption. We then show that backdoor attack can also be conducted by directly replacing encrypted communicated messages without decryption. To tackle these attacks, we propose a novel defense method, Confusional AutoEncoder (termed <b>CAE</b>), which is based on autoencoder and entropy regularization to disguise true labels. To further defend attackers with sufficient prior label knowledge, we introduce DiscreteSGD-enhanced CAE (termed <b>DCAE</b>), and show that DCAE significantly boosts the main task accuracy than other known methods when defending various label inference attacks.
Athena: Probabilistic Verification of Machine Unlearning

The right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The status of this long held notion was legally solidified recently by the General Data Protection Regulation (GDPR) in the European Union. As a consequence, there is a need for mechanisms whereby users can verify if service providers comply with their deletion requests. In this work, we take the first step in proposing a formal framework, called Athena, to study the design of such verification mechanisms for data deletion requests – also known as machine unlearning – in the context of systems that provide machine learning as a service (MLaaS). Athena allows the rigorous quantification of any verification mechanism based on hypothesis testing. Furthermore, we propose a novel verification mechanism that leverages backdoors and demonstrate its effectiveness in certifying data deletion with high confidence, thus providing a basis for quantitatively inferring machine unlearning. We evaluate our approach over a range of network architectures such as multi-layer perceptrons (MLP), convolutional neural networks (CNN), residual networks (ResNet), and long short-term memory (LSTM) and over 6 different datasets. We demonstrate that: (1) our approach has minimal effect on the accuracy of the ML service but provides high confidence verification of unlearning, even if multiple users employ our system to ascertain compliance with data deletion requests, and (2) our mechanism is robust against servers deploying state-of-the-art backdoor defense methods. Overall, our approach provides a foundation for a quantitative analysis of verifying machine unlearning, which can provide support for legal and regulatory frameworks pertaining to users’ data deletion requests.
Spatially Oriented Format for Acoustics 2.1: Introduction and Recent Advances

Spatially oriented acoustic data can range from a simple set of impulse responses, such as head-related transfer functions, to a large set of multiple-input multiple-output spatial room impulse responses obtained in complex measurements with a microphone array excited by a loudspeaker array at various conditions.The spatially oriented format for acoustics (SOFA), which was standardized by AES Standard 69, provides a format to store and share such data.SOFA takes into account geometric representations of many acoustic scenarios, data compression, network transfer, and a link to complex room geometries and aims at simplifying the development of interfaces for many programming languages.With the recent advancement of SOFA, the format offers new continuous-direction representation of data by means of spherical harmonics and novel conventions representing many measurement scenarios, such as source directivity and multiple-input multiple-output spatial room impulse responses.This article reviews SOFA by first providing an introduction to SOFA and then describing examples that demonstrate the most recent features of the SOFA 2.1 (AES Standard 69-2022).
Cold start aware hybrid recommender system approach for E-commerce users

The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when the interaction is made among a limited number of items. These issues not only pose a negative impact on the recommendation but also significantly condense the diversity of choices available on the particular platform. To tackle these issues, a novel methodological approach called sparsity and cold start aware hybrid recommended system (SCSHRS) has been designed to suppress data sparsity and CSP in RS. The performance of the proposed SCSHRS method is tested on MovieLens-20 M, Last.FM and Book-Crossing data sets and compared with the prevailing techniques. Based on the evaluation reports with the standards, the proposed SCSHRS system gives Mean Absolute Percentage Error of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain of 0.65. This study completely describes the SCSHRS mechanism and its comparison with other pre-proposed historic and traditional processes based on collaborative filtering.
Intelligent Process Monitoring of Laser-Induced Graphene Production With Deep Transfer Learning

Three-dimensional graphene has been increasingly used in many applications due to its superior properties. The laser-induced graphene (LIG) technique is an effective way to produce 3-D graphene by combining graphene preparation and patterning into a single step using direct laser writing. However, the variation in process parameters and environment could largely affect the formation and crystallization quality of 3-D graphene. This article develops a vision and deep transfer learning-based processing monitoring system for LIG production. To solve the problem of limited labeled data, novel convolutional de-noising auto-encoder (CDAE)-based unsupervised learning is developed to utilize the available unlabeled images. The learned weights from CDAE are then transferred to a Gaussian convolutional deep belief network (GCDBN) model for further fine-tuning with a very small amount of labeled images. The experimental results show that the proposed method can achieve the state-of-art performance of precise and robust monitoring for the quality of the LIG formation.
Blockchain-Based Lightweight Authentication for Resilient UAV Communications: Architecture, Scheme, and Future Directions

Representing a new stage in the industrial value chain, Industry 4.0 paves the way to the future eco-systems of industrial innovation, where cyber-physical systems (e.g., smart machines) form its basis by bridging the physical and digital worlds. As one of the most popular groups in intelligent devices, unmanned aerial vehicles (UAVs) have shown great potential for facilitating various industrial sectors. With research on new applications flourishing, the accompanying communication security issue between UAVs is also of concern because of the vital role of communication in enabling the reliable performance of UAV networks. Authentication is recognized as the first defense line for guaranteeing communication security, but traditional authentication schemes are either highly dependent on the central authority or too costly for UAV networks, making them less feasible in practical applications. In this article, we propose a novel authentication scheme for UAV networks using blockchain technology, where blockchain edge nodes are introduced to maintain the ledger, while drones can just be clients to use the distributed blockchain services by calling smart contract APIs. Experimental results illustrate its efficiency, and future research directions are also summarized to guide explorations for realizing more general authentication systems for UAV-assisted industrial applications.
CSformer: Cross-Scale Features Fusion Based Transformer for Image Denoising

Window self-attention based Transformer receives the advanced results in image denoising. However, the current methods still have some limitations in capturing the global dependencies and local responses. To tackle these problems, this paper proposes a novel Transformer based image denoising method, called as CSformer, which is equipped with two key blocks, including the cross-scale features fusion (CS2F) block and mixed global-local Swin (M-Swin) Transformer block. The CSformer has a specific multi-scale framework, in which the multi-scale features, extracted by M-Swin Transformer, are fused using CS2F block. Such cross-scale fusion not only enriches the features, but also yields the multi-scale self-attention. In addition, the M-Swin Transformer block consists of the Swin Transformer block and the separable convolution based convolutional local-extraction (CLE) block, which can boost the ability of Transformer in local representation. We demonstrate the superiority of CSformer on some well-known datasets at different noise levels with comparisons to several state-of-the-art methods.
Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Aiming at the problems of slow convergence and easy fall into local optimal solution of the classic ant colony algorithm in path planning, an improved ant colony algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and increase the pheromone content on the guiding path. Through the difference in initial pheromone, the ant colony is guided to quickly find the target node. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap to increase the probability of ants finding the target node. Thirdly, the gravity concept in the artificial potential field method and the concept of distance from the optional node to the target node are introduced to improve the heuristic function to make up for the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption and combines the dynamic optimization idea to optimize the pheromone update method, to avoid the algorithm falling into the local optimal solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively.