Reviewerly/exHarmony huggingface dataset:
Image-Based CLIP-Guided Essence Transfer

We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target. Crucially, the semantic attributes that constitute the essence of an image may differ from image to image. Our blending operator combines the powerful StyleGAN generator and the semantic encoder of CLIP in a novel way that is simultaneously additive in both latent spaces, resulting in a mechanism that guarantees both identity preservation and high-level feature transfer without relying on a facial recognition network. We present two variants of our method. The first is based on optimization, while the second fine-tunes an existing inversion encoder to perform essence extraction. Through extensive experiments, we demonstrate the superiority of our methods for essence transfer over existing methods for style transfer, domain adaptation, and text-based semantic editing. Our code is available at: https://github.com/hila-chefer/TargetCLIP .
Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features

Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology.The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures.Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications.A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs).This framework used the SS images, and it has been transformed into frequency subbands using Discrete Wavelet Transform (DWT).The sub-band wavelet coefficients of SSC and other Structure Features (SF) are extracted using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) techniques.Here, the maximum and minimum margin between hyperplane values of the kernel parameters are adjusted periodically as a result of storing the SF values of the SVs in the IoT devices.The performance characteristics of internal cross-validation and its statistical properties are evaluated by cross-entropy measures and compared by nonparametric Mann-Whitney U test.The significant differences in classification performance between the techniques are analyzed using the receiver operating characteristics (ROC) curve.The combination of QDA + SVM technique will be required for intelligent cancer diagnosis in the future, and it gives reduced statistic parameter feature set with greater classification accuracy.The IoT network based QDA + SVM classification technique has led to the improvement of SS cancer prognosis in medical industry applications using blockchain technology.
Research on Dynamic Path Planning of Mobile Robot Based on Improved DDPG Algorithm

Aiming at the problems of low success rate and slow learning speed of the DDPG algorithm in path planning of a mobile robot in a dynamic environment, an improved DDPG algorithm is designed. In this article, the RAdam algorithm is used to replace the neural network optimizer in DDPG, combined with the curiosity algorithm to improve the success rate and convergence speed. Based on the improved algorithm, priority experience replay is added, and transfer learning is introduced to improve the training effect. Through the ROS robot operating system and Gazebo simulation software, a dynamic simulation environment is established, and the improved DDPG algorithm and DDPG algorithm are compared. For the dynamic path planning task of the mobile robot, the simulation results show that the convergence speed of the improved DDPG algorithm is increased by 21%, and the success rate is increased to 90% compared with the original DDPG algorithm. It has a good effect on dynamic path planning for mobile robots with continuous action space.
Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks

Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional overhead in the signal. In this paper, we developed a universal software radio peripheral (USRP) based intelligent AMC system to detect and classify various digital modulation schemes in real-time. For each modulation scheme, we extracted different spectral features for different values of signal-to-noise ratio (SNR) values. Based on the extracted features, we train the neural network to classify the modulation schemes. Experimental results show that we achieve around 97% classification accuracy in real-time as compared to the existing offline classification schemes. Moreover, we also compare the performance of the proposed model with HisarMod2019.1 model in terms of various metrics such as cross-entropy and mean square error. Results clearly demonstrates the efficiency of the proposal for real-time implementation and classification.
LVTIA: A new method for keyphrase extraction from scientific video lectures

Due to the growth of technology, the expansion of communication infrastructure and crises of COVID-19 pandemic, e-learning and virtual education is expanding. One of the best ways to access and organize these information is indexing using automatic intelligent methods. Indexing requires assigning keywords or keyphrases to each video, to represent its content. The main focus of this research is to propose an approach by which appropriate keyphrases are assigned to scientific video lectures. For this purpose, a new algorithm called LVTIA, Lecture Video Text mining-base Indexing Algorithm, is proposed in which the textual content of video frames along with the text extracted from audio signal are merged together, and a new keyphrase extraction method is proposed. The proposed method considers new local and global features for each candidate phrases, along with a new feature reflecting the occurrence of each phrase in the audio signals or video frames. The method is implemented using five distinct data sets in English and Persian. The results are evaluated based on precision, recall, F1-measure and [email protected] metrics and compared with some of the well-known keyphrase extraction algorithms. Based on the results, the best [email protected] for English videos is related to LVTIA algorithm with the values of, 0.7912, 0.8069, 0.8069 for k=5,10,15, respectively. In addition, LVTIA is able to provide best [email protected] for Persian videos which are 0.6367, 0.6866, 0.6874 for k=5,10,15, respectively. According to Friedman nonparametric statistical test, the performance of different algorithms in precision, recall, F1-measure metrics, are statistically different from LVTIA as well.
Remembering for the right reasons: Explanations reduce catastrophic forgetting

Abstract The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization‐based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few‐shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons .
AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation

Image segmentation was significantly enhanced after the emergence of deep learning (DL) methods. In particular, deep convolutional neural networks (DCNNs) have assisted DL-based segmentation models to achieve state-of-the-art performance in fields critical to human beings, such as medicine. However, the existing state-of-the-art methods often use computationally expensive operations to achieve high accuracy and lightweight networks often lack a precise medical image segmentation. Therefore, this study proposes an accurate and efficient DCNN model (AEDCN-Net) based on an elaborate preprocessing step and a resourceful model architecture. The AEDCN-Net exploits bottleneck, atrous, and asymmetric convolution-based residual skip connections in the encoding path that reduce the number of trainable parameters and floating point operations (FLOPs) to learn feature representations with a larger receptive field. The decoding path employs the nearest-neighbor based upsampling method instead of a computationally resourceful transpose convolution operation that requires an extensive number of trainable parameters. The proposed method attains a superior performance in both computational time and accuracy compared to the existing state-of-the-art methods. The results of benchmarking using four real-life medical image datasets specifically illustrate that the AEDCN-Net has a faster convergence compared to the computationally expensive state-of-the-art models while using significantly fewer trainable parameters and FLOPs that result in a considerable speed-up during inference. Moreover, the proposed method obtains a better accuracy in several evaluation metrics compared with the existing lightweight and efficient methods.
Voluntary and instrumental information security policy compliance: an integrated view of prosocial motivation, self-regulation and deterrence

Understanding employees' motivations and behaviors toward compliance with information security policies (ISPs) remains a theoretical and practical challenge. Although previous information security researchers have investigated different motivational factors related to ISP compliance, most have not recognized different forms of ISP compliance behaviors characterized by their levels of willingness and persistence, nor have they noted the importance of adopting an other-oriented lens to examine such behaviors. In this paper, we propose and test an integrated model that investigates how various motivational factors affect different ISP compliance behaviors. Specifically, the model anchors on the prosocial motivational perspective in addition to the instrumental and self-regulatory motivational perspectives and investigates two types of compliance behaviors (voluntary ISP compliance and instrumental ISP compliance). We tested our model using survey data collected from 407 employee respondents. Our results show that the three sets of motivational factors have different effects on the two types of ISP compliance behaviors. Prosocial motivation and self-regulatory motivation positively affect voluntary ISP compliance behavior. Deterrence as an instrumental control leads to instrumental ISP compliance behavior but undermines voluntary ISP compliance behavior. Our study highlights that, to foster employees' voluntary ISP compliance, organizations need to take a more holistic approach by integrating the prosocial approach with the instrumental and self-regulatory approaches in managing voluntary compliance behaviors, while being mindful of the negative effects of instrumental controls (e.g., deterrence) on such behaviors.
SINS/GNSS integrated navigation system based on maximum versoria filter

In the missile-borne Strapdown Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) integrated navigation system, due to the factors such as the high dynamics, the signal blocking by obstacles, the signal intefereces, etc., there always exist pulse interferences or measurement information interruptions in the satellite receiver, which make nonstationary measurement process. The traditional Kalman Filter (KF) can tackle the state estimation problem under Gaussian white noise, but its performance will be significantly reduced under non-Gaussian noises. In order to deal with the non-Gaussian conditions in the actual missile-borne SINS/GNSS integrated navigation systems, a Maximum Versoria Criterion Extended Kalman Filter (MVC-EKF) algorithm is proposed based on the MVC and the idea of M-estimation, which assigns a smaller weight to the anomalous measurements so as to suppress the influence of anomalous measurements on the state estimation while maintaining a relatively low calculation cost. Finally, the integrated navigation simulation experiments prove the effectiveness and robustness of the proposed algorithm.
Toward Unaligned Guided Thermal Super-Resolution

Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images.
Design and Implementation of Smart Healthcare Monitoring System Using FPGA

With growing health awareness and the increasing cost of medical care, there is an impetus to new and advanced technologies for disease prevention and early diagnosis and treatment. The weakest link exposed by the COVID-19 pandemic in India is health care. Investment in critical health Infrastructure aided by modern technology is the need of the hour. So, this project aims to develop a comprehensive healthcare monitoring system by blending IoT and VLSI. It can monitor a patient’s basic health signs as well as the room condition where the patients are now in real time. We use Nexys4 Artix7 as a processor. In this system, six sensors are used to capture the data from the hospital environment named heartbeat sensor, body, and room temperature sensor, fall detection sensor, blood pressure sensor, air quality monitoring sensors, ECG sensor. The condition of the patients is conveyed via the ThingSpeak website and telephonic calls/SMS to relatives, medical staff.
Augmented Reality in the Secondary Education classroom: Teachers’ Visions

The advancement of emerging technologies in the classroom is a reality, however, the vision that teachers may have of them is essential in order for these to be incorporated. Using this as a foundation, this article's main objective is to know the vision that secondary education (SE) teachers have of the use of Augmented Reality (AR) in teaching at that educational level. Through a descriptive correlational quantitative study, an ex-post facto design, and an ad-hoc questionnaire of 39 items, (N=350 teachers), this objective has been corroborated. The main conclusion that has been reached is that SE teachers consider AR as an element that will cause a change in the student's learning methodology making it more autonomous and experiential. In addition, the teachers will need more training with AR, lower costs, and greater availability of resources to carry out the teaching process with greater ease.
Flexible Environments for Hybrid Collaboration: Redesigning Virtual Work Through the Four Orders of Design

Abstract The COVID-19 pandemic made visceral for many the fact that virtual forms of collaboration— simultaneously liberating and frustrating—are here to stay. Workers’ frustrations demonstrate that challenges remain for work and its design in increasingly “hybrid” collaboration— work in which some people, interacting face-to-face, are co-located while others with whom they work are remote. Using Buchanan's four orders of design, in conjunction with management and information systems scholarship, we present a framework for improving these virtual forms of collaboration. In this article, we review the latest knowledge from these disciplines on virtual collaboration through the lens of the four orders of design. In doing so, we demonstrate that conceiving of work in terms of flexible collaborative environments could increase the unity of purpose between work and workers by leveraging the capabilities of varying degrees of virtuality to engender experiences that benefit all those who interact with work systems.
Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.
Learning effectiveness of 3D virtual reality in hospitality training: a situated cognitive perspective

Purpose Training is one of the key dimensions of internal marketing. Virtual reality (VR), a computer technology that replicates an environment (real or imagined) and simulates a user’s physical presence in that environment to allow for user interaction, offers unique opportunities from a training perspective, such as allowing users to improve their skills without the consequence of failing real customers or the need to be in the real environment physically. This study aims to focus on comparing the effectiveness of VR hospitality training with that of real-world hospitality training. Design/methodology/approach This study adopts situated cognition theory to empirically test the effect of the awareness of contextual variables (social interaction, location and task) on learning and compare learning outcomes between tourism training in VR and real-world experimental settings. Findings Results indicate that location and task awareness enhance cognitive absorption, but social awareness does not influence cognitive absorption. There is no significant difference between training in real-world and VR environments. Finally, cognitive absorption has a positive effect on mental model change (the learning outcome). Originality/value This result advances the theoretical understanding on the significance of learning context by applying situated cognition theory in hospitality training and has significant implications for training that aims for rigor and efficiency within cost, location and time constraints.
Semi-supervised node classification via graph learning convolutional neural network

Graph convolutional neural networks (GCNs) have become increasingly popular in recent times due to the emerging graph data in scenes such as social networks and recommendation systems. However, engineering graph data are often noisy and incomplete or even unavailable, making it challenging or impossible to implement the de facto GCNs method directly on them. Current efforts for tackling this issue either require an overparameterized model that is hard to scale, or simply re-weight the existing edges for different downward tasks. In this work, we tackle this problem through introducing a graph learning convolutional neural network (GLCNN), which can be employed on both Euclidean space data and non-Euclidean space data. The similarity matrix is learned by a supervised method in the graph learning layer of the GLCNN. Moreover, graph pooling and distilling operations are utilized to reduce over-fitting. Comparative experiments are done on three different datasets: citation dataset, knowledge graph dataset, and image dataset. Results demonstrate that the GLCNN can improve the accuracy of the semi-supervised node classification by mining useful relationships among nodes. The performance is more obvious especially on datasets of Euclidean space. Specifically, GLCNN outperforms the best baseline by 3.1% and 1.1% on MNIST and SVHN datasets. Moreover, the robustness is explored by adding noises on the edge of the graph. Sensitive analysis and visualizations are performed to demonstrate effects of some key parameters.