Reviewerly/exHarmony huggingface dataset:
A Survey on Quantum Computing for Recommendation Systems

Recommendation systems play a key role in everyday life; they are used to suggest items that are selected among many candidates that usually belong to huge datasets. The recommendations require a good performance both in terms of speed and the effectiveness of the provided suggestions. At the same time, one of the most challenging approaches in computer science is quantum computing. This computational paradigm can provide significant acceleration for resource-demanding and time-consuming algorithms. It has become very popular in recent years, thanks to the different tools available to the scientific and technical communities. Since performance has great relevance in recommendation systems, many researchers in the scientific community have recently proposed different improvements that exploit quantum approaches to provide better performance in recommendation systems. This paper gives an overview of the current state of the art in the literature, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain.
Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this study added a feature extraction structure to the data valuation algorithm, and it can receive raw-level inputs and achieve excellent performance. As IMU-based HAR data are multivariate time-series data, the proposed algorithm incorporates an architecture capable of extracting both local and global features by inserting a transformer encoder after the one-dimensional convolutional neural network (1D-CNN) backbone in the data value estimator. In addition, the 1D-CNN-based stacking ensemble structure, which exhibits excellent efficiency and performance on IMU-based HAR data, is used as a predictor to supervise model training. The Berg balance scale (BBS) IMU-based HAR dataset and the public datasets, UCI-HAR, WISDM, and PAMAP2, are used for performance evaluation in this study. The valuation performance of the proposed algorithm is observed to be excellent on IMU-based HAR data. The rate of discovering corrupted data is higher than 96% on all datasets. In addition, classification performance is confirmed to be improved by the suppression of discovery of low-value data.
A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

There are numerous internet-connected devices attached to the industrial process through recent communication technologies, which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things (IIoTs).Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services, external trusted authorities, and centralized architectures; they have high computation and communication costs, low performance, and are exposed to a single authority of failure and bottleneck.Blockchain technology (BC) is widely adopted in the industrial sector for its valuable features in terms of decentralization, security, and scalability.In our work, we propose a decentralized, scalable, lightweight, trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application.The proposed scheme offers a double layer of data encryption, device authentication, scalability, high performance, low power consumption, and improves the industry's operations; provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage.We also address data aggregation operations, which are considered challenging in electric power smart grids.We utilize a multi-chain proof of rapid authentication (McPoRA) as a consensus mechanism, which helps to enhance the computational performance and effectively improve the latency.The advanced reduced instruction set computer (RISC) machines ARM Cortex-M33 microcontroller adopted in our work, is characterized by ultra-low power consumption and high performance, as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm (ECDSA).This improves the computational execution, increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer.Our experimental results show that the proposed scheme achieves excellent performance, data security, real-time data processing, low power consumption (70.880 mW ), and very CMC, 2023, vol.74, no.3 low memory utilization (2.03% read-only memory (RAM) and 0.9% flash memory) and execution time (0.7424 s) for the cryptographic algorithm.This enables autonomous network reconfiguration on-demand and real-time data processing.
A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.
AEGuard: Image Feature-Based Independent Adversarial Example Detection Model

With the rapid development of image processing technology, image recognition systems based on massive image data are being developed and deployed. The wrong decision regarding an image recognition system for security-sensitive systems can cause serious problems such as personal accidents and property damage. Furthermore, adversarial attacks, which are security attacks that cause malfunctions in image recognition systems by inserting adversarial noise, have emerged and evolved. Several studies have been conducted to prevent adversarial attacks. However, existing mechanisms have low classification accuracy and low detection accuracy for adversarial examples with small adversarial noise. This paper proposes an adversarial example detection mechanism based on image feature extraction and a deep neural network (DNN) model. The proposed system achieves versatility and independence by detecting adversarial examples based on image features, such as edge noise and discrete cosine transform (DCT) bias, which adversarial examples have in common. The proposed system shows relatively higher detection accuracy than existing mechanisms for various types and amounts of adversarial noise and different sharpness of adversarial examples because the proposed system detects them depending on the characteristics of each type of adversarial example.
Fundamentals of All the Realities: Virtual, Augmented, Mediated, Multimediated, and Beyond

This chapter introduces the different “realities”: virtual, augmented, (deliberately) diminished, mixed, mediated, multimediated, and phenomenological. The contributions of this chapter include (1) a taxonomy, framework, conceptualization, etc., of all of the “realities”; (2) a new kind of “reality” that comes from nature itself, which expands our notion beyond synthetic realities to include also phenomenological realities; and (3) methods of using phenomenological reality as a means of visualizing as well as understanding hidden phenomena present in the world around us. VR (virtual reality) replaces the real world with a simulated experience (a “virtual” world). AR (augmented reality) allows the real world to be experienced while at the same time, adding to it, a virtual world. Mixed reality provides blends that interpolate between real and virtual worlds in various proportions, along a “virtuality” axis, and extrapolate to an “X-axis” defined by “X-Reality” (eXtended reality). Mediated reality goes a step further by mixing/blending and also modifying reality, including, for example, deliberate diminishing of reality (e.g., a computerized welding helmet that darkens bright subject matter while lightening dark subject matter). This modifying of reality introduces a second axis called “mediality.” Mediated reality is useful as a seeing aid (e.g., modifying reality to make it easier to understand) and for psychology experiments like George Stratton’s 1896 upside-down eyeglasses experiment. Multimediated reality (“all reality” or “All R”) is a multidimensional multisensory mediated reality that includes not just interactive multimedia-based “reality” for our five senses but also includes additional senses (like sensory sonar, sensory radar, etc.), as well as our human actions/actuators. These extra senses are mapped to our human senses using synthetic synesthesia. This allows us to directly experience real (but otherwise invisible) phenomena, such as wave propagationWave propagation and wave interference patterns, so that we can see radio waves and sound waves and how they interact with objects and each other, i.e., phenomenological reality. Moreover, multimediated reality considers not just multiple axes in addition to the X-Reality axis but also that the origin of the axes exists at zero sensory stimuli. In this way, we can account for various (virtual, augmented, etc.) realities in a sensory deprivation float tank. Consider, for example, wearing an underwater VR headset while floating in a sensory deprivation tank. Then consider an augmented reality headset while floating in an immersive multimedia pool. Consider Internet-connected immersive multimedia water therapy and music therapy pools that allow multiple users to play a hydraulophone (underwater pipe organ) while meditating together in a hydraulically multimediated collective. These are examples of what we call “fluid (user) interfaces,” and they fall outside the range of any previously existing “reality taxonomy” or conceptualization.
Augmented Reality in Education: Exploring Greek Teachers’ Views and Perceptions

This study investigated how teachers, who were familiar with AR and implemented AR-based instructional interventions, identify and perceive the factors that affect the integration of AR in educational practice. The research data were collected through online semi-structured interviews from teachers who had previously participated in a professional development program about AR in education. The findings indicated that the participant teachers considered that the use of AR in their instruction can provide many benefits for the students by harnessing a range of affordances that promote active learning. In addition, the findings revealed several factors that affect the integration of AR in primary and secondary classrooms.
Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach

Sentiment analysis aims to empower automated methods with the capacity to recognize sentiments, opinions and emotions in text. This recognition capacity is now highly demanded to process and extract proper knowledge from the exponentially-growing volume of user-generated data. Applications such as analyzing online products reviews on e-commerce marketplaces, opinion mining from social networks and support chat-bots optimization are putting into practice various methods to perform this complex natural language processing task. In this paper, we apply a hybrid generative-discriminative approach using Fisher kernels with generalized inverted Dirichlet-based hidden Markov models to improve the recognition performance in the context of textual analysis. We propose a method that combines HMMs as a generative approach, with the discriminative approach of Support Vector Machine. This strategy allows us to deal with sequential information of the text, and at the same time use the special focus on the classification task that SVM could provide us. Experiments on two challenging user reviews datasets i.e. Amazon for products reviews and IMDb movies reviews, demonstrate an effective improvement of the recognition performance compared to the standard generative and Gaussian-based HMM approaches.
Breast Cancer Classification from Mammogram Images Using Extreme Learning Machine-Based DenseNet121 Model

Breast cancer is characterized by abnormal discontinuities in the lining cells of a woman’s milk duct. Large numbers of women die from breast cancer as a result of developing symptoms in the milk ducts. If the diagnosis is made early, the death rates can be decreased. For radiologists and physicians, manually analyzing mammography images for breast cancer become time-consuming. To prevent manual analysis and simplify the work of classification, this paper introduces a novel hybrid DenseNet121-based Extreme Learning Machine Model (ELM) for classifying breast cancer from mammogram images. The mammogram images were processed through preprocessing and data augmentation phase. The features were collected separately after the pooling and flatten layer at the first stage of the classification. Further, the features are fed as input to the proposed DenseNet121-ELM model’s fully connected layer as input. An extreme learning machine model has replaced the fully connected layer. The weights of the extreme learning machine have been updated by the AdaGrad optimization algorithm to increase the model’s robustness and performance. Due to its faster convergence speed than other optimization techniques, the AdaGrad algorithm optimization was chosen. In this research, the Digital Database for Screening Mammography (DDSM) dataset mammogram images were utilized, and the results are presented. We have considered the batch size of 32, 64, and 128 for the performance measure, accuracy, sensitivity, specificity, and computational time. The proposed DenseNet121+ELM model achieves 99.47% and 99.14% as training accuracy and testing accuracy for batch size 128. Also, it achieves specificity, sensitivity, and computational time of 99.37%, 99.94%, and 159.7731 minutes, respectively. Further, the comparison result of performance measures is presented for batch sizes 32, 64, and 128 to show the robustness of the proposed DenseNet121+ELM model. The automatic classification performance of the DenseNet121+ELM model has much potential to be applied to the clinical diagnosis of breast cancer.
Intrinsic decoherence dynamics and dense coding in dipolar spin system

The quantum decoherence is an ineluctable process when a quantum system interacts with its surrounding environment. Milburn's dynamical master equation, as a modified shape of the Schrödinger equation, is typically used to study the effects decoherence even without intervention of environment. We studied the temporal evolution of non-local correlations in a dipolar-coupled spin system under the influence of intrinsic decoherence. Primarily, the generation in correlations of Einstein–Podolsky–Rosen steering, measurement-induced non-locality and entanglement in an uncorrelated initial system state were studied. Also, the effect of the intrinsic decoherence on the quality of the dense coding capacity dynamics for the dipolar-coupled spin system was examined. Our results highlight that the initial system's role in defining the quantum correlations' robustness, and we also brought out the impacts of system's parameters on the non-local correlations and the efficiency of dense coding process.
Innovative Teaching Skills: A Necessity in the Era of Globalization

The present paper aims at analyzing the role of ‘educational institutions’ in the era of globalization. The boon of globalization, i.e., the rapid invention of modern scientific techniques as well as the bane, i.e., emotional detachment in the inter-personal relationships resulting in the lack of respect especially, in teacher-taught relationship that is creating a vacuum in the society are also discussed. The paper encompasses some of the innovative pedagogical skills from a teacher’s perspective as well as that of a student’s too. The aim of the enlisted methods is to make classroom teaching more effective and interesting. The paper is therefore, divided into two sections, the first comprising teacher’s ideology regarding effective teaching and the second section incorporates the expectations of students from the teacher regarding the same, as teaching is a two-way process which remains incomplete until or unless the receiver or student is present.
Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.
“Can I have the scan on Tuesday?” User repair in interaction with a task-oriented chatbot and the question of communication skills for AI

Repair describes the process through which participants in conversation address problems in speaking, understanding, and hearing. In interactions with AI-driven chatbots, user repair addresses chatbots' lack of understanding or misunderstanding of the user's intent. This paper represents a user-centred description of user repair strategies in interactions with a task-oriented chatbot. It is based on the analysis of simulated user interactions with a chatbot facilitating health appointment bookings. The analysis shows that the repair strategies which users draw on most frequently (e.g., rephrasing) are not necessarily the ones which prompt the bot to correctly recognise intent and provide relevant responses, whereas the less frequently used self-repair strategies (e.g. restating the intent) are more successful in achieving intent recognition. This suggest that the rules of interaction with conversational AI need to be made explicit to users as they lack familiarity with the context, limitations and patterning of interactions facilitated through AI.
Influence mechanism of icon semantics on visual search performance: Evidence from an eye-tracking study

Explicit icon semantics can reduce the difficulty of understanding complex visual information. Optimizing the icon semantics and text semantics of icons can effectively improve the cognitive performance of digital interfaces. This paper adopts visual search tasks to study the effects of different combinations of icon semantic familiarity and the presence or absence of text on icon search performance under horizontal and vertical layouts. The behavioral experiment results show that under two layouts: 1. The main effect of icon semantics is significant, and the search performance increases with the increase of semantic familiarity. 2. The main effect of text is significant, and the search performance is negatively correlated with the addition of text. The eye movement experiment found that the semantic familiarity of icons had a significant impact on average fixation time. Furthermore, the number of fixation points changed significantly after the text variable was added. Therefore, there was no significant difference in the number of fixation points in the horizontal layout, and icon semantics was the main influencing factor in visual search. In the vertical layout, there was no significant difference in average fixation time, and text was the main influencing factor of visual search. The results show that the semantic familiarity of icons and different combinations with or without text significantly affect visual search performance in horizontal and vertical layouts. This paper provides a theoretical reference for the combination of icons and text in interface design.
Lossless Recompression of JPEG Images Using Transform Domain Intra Prediction

JPEG, which was developed 30 years ago, is the most widely used image coding format, especially favored by the resource-deficient devices, due to its simplicity and efficiency. With the evolution of the Internet and the popularity of mobile devices, a huge amount of user-generated JPEG images are uploaded to social media sites like Facebook and Flickr or stored in personal computers or notebooks, which leads to an increase in storage cost. However, the performance of JPEG is far from the state-of-art coding methods. Therefore, the lossless recompression of JPEG images is urgent to be studied, which will further reduce the storage cost while maintaining the image fidelity. In this paper, a hybrid coding framework for the lossless recompression of JPEG images (LLJPEG) using transform domain intra prediction is proposed, including block partition and intra prediction, transform and quantization, and entropy coding. Specifically, in LLJPEG, intra prediction is first used to obtain a predicted block. Then the predicted block is transformed by DCT and then quantized to obtain the predicted coefficients. After that, the predicted coefficients are subtracted from the original coefficients to get the DCT coefficient residuals. Finally, the DCT residuals are entropy coded. In LLJPEG, some new coding tools are proposed for intra prediction and the entropy coding is redesigned. The experiments show that LLJPEG can reduce the storage space by 29.43% and 26.40% on the Kodak and DIV2K datasets respectively without any loss for JPEG images, while maintaining low decoding complexity.