Reviewerly/exHarmony huggingface dataset:
CORSICA: A Framework for Conducting Real-World Side-Channel Analysis

With the abundance of computing devices in our everyday life, such as IoT devices, improving their security has become a number one priority. While the major focus lies on software security, hardware vulnerabilities are often not considered. Here, particularly side-channel attacks pose a realistic threat to such systems. However, conducting Side-Channel Analysis (SCA) to evaluate those threats currently requires deep expert knowledge, a lab environment, and numerous manual steps. Therefore, it is often ignored in security considerations.In this paper, we analyze the challenges when conducting SCA on consumer-grade devices using template-matching based triggering techniques. By introducing a three-staged framework called CORSICA, we elaborate the obstacles and deficiencies of current state-of-the-art techniques and provide potential solutions for them. Moreover, we validate our claims by introducing a method for semi-automatic extraction of a waveform template of an AES-128 encryption that can be used in combination with a template-matching triggering system. This extraction is based on generic meta information and is demonstrated on a consumer-grade ARM processor board.
Planar-Equirectangular Image Stitching

The 360 cameras have served as a convenient tool for people to record their special moments or everyday lives. The supported panoramic view allowed for an immersive experience with a virtual reality (VR) headset, thus adding viewer enjoyment. Nevertheless, they cannot deliver the best angular resolution images that a perspective camera may support. We put forward a solution by placing the perspective camera planar image onto the pertinent 360 camera equirectangular image region of interest (ROI) through planar-equirectangular image stitching. The proposed method includes (1) tangent image-based stitching pipeline to solve the equirectangular image spherical distortion, (2) feature matching scheme to increase correct feature match count, (3) ROI detection to find the relevant ROI on the equirectangular image, and (4) human visual system (HVS)-based image alignment to tackle the parallax error. The qualitative and quantitative experiments showed improvement of the proposed planar-equirectangular image stitching over existing approaches on a collected dataset: (1) less distortion on the stitching result, (2) 29.0% increased on correct matches, (3) 5.72 ROI position error from the ground truth and (4) lower aggregated alignment-distortion error over existing alignment approaches. We discuss possible improvement points and future research directions.
Evaluation of Unsupervised Entity and Event Salience Estimation

Salience Estimation aims to predict term importance in documents.Due to few existing human-annotated datasets and the subjective notion of salience, previous studies typically generate pseudo-ground truth for evaluation. However, our investigation reveals that the evaluation protocol proposed by prior work is difficult to replicate, thus leading to few follow-up studies existing. Moreover, the evaluation process is problematic: the entity linking tool used for entity matching is very noisy, while the ignorance of event argument for event evaluation leads to boosted performance. In this work, we propose a light yet practical entity and event salience estimation evaluation protocol, which incorporates the more reliable syntactic dependency parser. Furthermore, we conduct a comprehensive analysis among popular entity and event definition standards, and present our own definition for the Salience Estimation task to reduce noise during the pseudo-ground truth generation process. Furthermore, we construct dependency-based heterogeneous graphs to capture the interactions of entities and events. The empirical results show that both baseline methods and the novel GNN method utilizing the heterogeneous graph consistently outperform the previous SOTA model in all proposed metrics.
The problem with feedback

Feedback can be a powerful tool for promoting healthcare safety.However, 'feedback' as a concept tends to embody a number of implicit assumptions arising from behaviourist educational traditions, such as power differentials between feedback providers and receivers, unidirectionality of transmission, and simple notions of 'error'.Such assumptions can present challenges when designing and implementing feedback initiatives to address complex problems within healthcare such as improving prescribing.Sociocultural conceptions of feedback may be helpful in such cases, so that correspondingly complex solutions can be formulated.The purpose of this article therefore, is to step back and reconsider the nature and value of the term 'feedback' when applied to complex healthcare problems.We consider a number of different conceptions, definitions and understandings of feedback, using our own research on optimising prescribing as a case study and drawing upon our joint reflexive discussions as a team.In doing so, we examine the differences between simple, complicated and complex problems and how these might relate to feedback in medical education.We also explore alternative but related terms to feedback, considering their similarities, differences, and the appropriateness of each when applied to complex activities such as prescribing.Finally, we reflect on the dilemma of whether the medical education community should persevere with using a term which eludes embedded shared conceptualisation, perhaps redefining it to better reflect contemporary usage, or embrace a different term altogether.By sharing our experiences as a research team grappling with a challenging topic, we hope to engage with the wider community and their insights, in order to benefit our own research and the medical education research field.
Tomographic Description of a Quantum Wave Packet in an Accelerated Frame

The tomography of a single quantum particle (i.e., a quantum wave packet) in an accelerated frame is studied. We write the Schrödinger equation in a moving reference frame in which acceleration is uniform in space and an arbitrary function of time. Then, we reduce such a problem to the study of spatiotemporal evolution of the wave packet in an inertial frame in the presence of a homogeneous force field but with an arbitrary time dependence. We demonstrate the existence of a Gaussian wave packet solution, for which the position and momentum uncertainties are unaffected by the uniform force field. This implies that, similar to in the case of a force-free motion, the uncertainty product is unaffected by acceleration. In addition, according to the Ehrenfest theorem, the wave packet centroid moves according to classic Newton's law of a particle experiencing the effects of uniform acceleration. Furthermore, as in free motion, the wave packet exhibits a diffraction spread in the configuration space but not in momentum space. Then, using Radon transform, we determine the quantum tomogram of the Gaussian state evolution in the accelerated frame. Finally, we characterize the wave packet evolution in the accelerated frame in terms of optical and simplectic tomogram evolution in the related tomographic space.
A Fusion-Denoising Attack on InstaHide with Data Augmentation

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye. In recent work, however, Carlini et al. show that it is possible to reconstruct private images from the encrypted dataset generated by InstaHide. Nevertheless, we demonstrate that Carlini et al.'s attack can be easily defeated by incorporating data augmentation into InstaHide. This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. The basic idea is to use a comparative network to identify encrypted images that are likely to correspond to the same private image, and then employ a fusion-denoising network for restoring the private image from the encrypted ones, taking into account the effects of data augmentation. Extensive experiments demonstrate the effectiveness of the proposed attack in comparison to Carlini et al.'s attack.
A Graphical Calculus for Lagrangian Relations

Symplectic vector spaces are the phase spaces of linear mechanical systems.The symplectic form describes, for example, the relation between position and momentum as well as current and voltage.The category of linear Lagrangian relations between symplectic vector spaces is a symmetric monoidal subcategory of relations which gives a semantics for the evolution -and more generally linear constraints on the evolution -of various physical systems.We give a new presentation of the category of Lagrangian relations over an arbitrary field as a 'doubled' category of linear relations.More precisely, we show that it arises as a variation of Selinger's CPM construction applied to linear relations, where the covariant orthogonal complement functor plays the role of conjugation.Furthermore, for linear relations over prime fields, this corresponds exactly to the CPM construction for a suitable choice of dagger.We can furthermore extend this construction by a single affine shift operator to obtain a category of affine Lagrangian relations.Using this new presentation, we prove the equivalence of the prop of affine Lagrangian relations with the prop of qudit stabilizer theory in odd prime dimensions.We hence obtain a unified graphical language for several disparate process theories, including electrical circuits, Spekkens' toy theory, and odd-prime-dimensional stabilizer quantum circuits.
Three-Dimensional Target Detection Based on RGB-D Data

The current three-dimensional (3D) target detection model has a low accuracy, because the surface information of the target can only be partially represented by its two-dimensional (2D) image detector. To solve the problem, this paper studies the 3D target detection in the RGB-D data of indoor scenes, and modifies the frustum PointNet (F-PointNet), a model superior in point cloud data processing, to detect indoor targets like sofa, chair, and bed. The 2D image detector of F-PointNet was replaced with you only look once (YOLO) v3 and faster region-based convolutional neural network (R-CNN) respectively. Then, the F-PointNet models with the two 2D image detectors were compared on SUN RGB-D dataset. The results show that the model with YOLO v3 did better in target detection, with a clear advantage in mean average precision (&gt;6.27).
Using Multi-Attribute Decision-Making Approach to Evaluate Learning Management Systems

E-learning is one of the fastest growing areas of the high technology development, especially in the academic environments. However, the instructor is a very important factor in the learning process, but the advantages of e-learning change the role which the instructor plays in this process. E-learning gives an opportunity to anyone to learn in a rapid and customised way. Nowadays, many learning management systems (LMSs) available in the marketplace offer electronic teaching and learning tools. Choosing the most appropriate LMS that fits the needs and requirements of instructor and the learner is one of the most confusing and difficult decisions to any educational institution. Accordingly, the need to a computer-based tool for getting help in taking such a decision is rising on. This paper offers a solution to this problem. It provides a description about a web-based decision support system named Easy Way to Evaluate LMS (EW-LMS). It has been developed by adopting multi-attribute decision-making algorithm in order to select the best LMS depending on the user needs.
AlexResNet+: A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification

The exponential rise in cancer diseases, primarily the breast cancer has alarmed academia-industry to achieve more efficient and reliable breast cancer tissue identification and classification. Unlike classical machine learning approaches which merely focus on enhancing classification efficiency, in this paper the emphasis was made on extracting multiple deep features towards breast cancer diagnosis. To achieve it, in this paper A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification named, AlexResNet+ was developed. We used two well known and most efficient deep learning models, AlexNet and shorted ResNet50 deep learning concepts for deep feature extraction. To retain high dimensional deep features while retaining optimal computational efficiency, we applied AlexNet with five convolutional layers, and three fully connected layers, while ResNet50 was applied with modified layered architectures. Retrieving the distinct deep features from AlexNet and ResNet deep learning models, we obtained the amalgamated feature set which were applied as input for support vector machine with radial basis function (SVM-RBF) for two-class classification. To assess efficacy of the different feature set, performances were obtained for AlexNet, shorted ResNet50 and hybrid features distinctly. The simulation results over DDMS mammogram breast cancer tissue images revealed that the proposed hybrid deep features (AlexResNet+) based model exhibits the highest classification accuracy of 95.87%, precision 0.9760, sensitivity 1.0, specificity 0.9621, F-Measure 0.9878 and AUC of 0.960.
Ways of processing semantic information during different change detection tasks

Recent research on change blindness phenomenon revealed contradictory findings about scene-object relationship. These discrepant results might be stemming from procedure and task constraints. Therefore, we aimed to investigate the effect of the type of paradigm on change blindness phenomenon during the natural scene viewing in the frame of high-level scene perception. For this purpose, we compared two frequently used change detection tasks; flicker and one-shot paradigms. Additionally, eye movements were recorded to investigate the active attention mechanisms during the change detection performance. Our results suggested that change detection performance and eye movements varied across the different paradigms. We interpreted this result as the influence of different stimuli exposures and different interruptions on processing of visual stimuli during the detection of change. We explained the inconsistent results revealed by the previous research in terms of attention mechanisms, namely attention attraction and attention disengagement that might differ while performing the different change detection tasks.
Selection Strategies for Inductive Reasoning From Conditional Belief Bases and for Belief Change Respecting the Principle of Conditional Preservation

Given a belief base ∆ consisting of a set of conditionals,there are many different ways an agent may inductivelycomplete the knowledge represented by ∆ to a completeepistemic state; two well-known approaches are given by systemP and system Z, and also each ranking model of ∆ induces afull inference relation. C-representations are special rankingmodels that obey the principle of conditional indifference.Inductive reasoning using c-representations can be done withrespect to all c-representations, with respect to a subclass of,e.g., minimal c-representations, or with respect to singlec-representations. In this paper, we present and investigateselection strategies for determining single c-representations tobe used for inductive reasoning from belief bases. We developaxioms for specifying characteristics of selection strategies.We illustrate which desirable properties, like syntaxsplitting, are ensured by the axioms, and develop constructionsfor obtaining selection strategies satisfying the axioms.Furthermore, we also present and study the extension of selectionstrategies to c-revisions that follow the principle ofconditional preservation and that have been employed successfullyin various belief change settings.
Statistics and machine learning methods for EHR data – from data extraction to data analytics

Electronic health records (EHR) carry a rich source of data for real-world evidence (RWE) generation. As more and more healthcare providers use EHR in practice and due to the increasing accessibili...
Effects of the Production-Oriented Approach on College English Writing Instruction in Yangtze University

English writing ability is one of non-English-majored undergraduate students' important abilities in comprehensive applying English.This paper reviewed a four-week experiment on production-oriented approach (POA) in teaching the
A ROADMAP FOR SUCCESSFUL IOT IMPLEMENTATION: EMPIRICAL EVIDENCE FROM THE ENERGY INDUSTRY

The Internet of Things has become a spotlight in contemporary literature in the domain of industry 4.0 and emerging technologies.Majority of research studies focuses on technicalities and theoretical implications of implementing IoT in the energy sector.However, in practice, organisations face a combination of technical and non-technical challenges.There is a clear need for the studies to empirically evaluate IoT use cases, challenges, and a roadmap for successfully implementing emerging technologies.To fulfil the research gap and provide insights for practitioners, we conducted seven semi-structured interviews with professionals working in Finland's energy sector.Based on results retrieved through content analysis of transcribed interviews, we developed a roadmap with managerial implications for successfully implementing IoT and other emerging technologies.Furthermore, study results present practical insights on IoT applications, benefits, and major IoT utilisation challenges in the Finnish energy sector industrial ecosystem.In the end, conclusion and future research directions are discussed.
Immersive and brand journalism in educational and cultural information. The case of RTVE Lab

The aim of this research is to analyze the use of immersive and brand journalism in the information published by RTVE Lab. The case study method has been used, which combines both qualitative and quantitative techniques. The results reveal that virtual reality (VR) is an attractive format for disseminating cultural news because it enhances the immersive experience of the user; yet approximately only 20% of VR is used to provide information about branded content.
Currency Detection for Visually Impaired Iraqi Banknote as a Study Case

It is relatively simple for a normal human to interpret and understand every banknote, but one of the major problems for visually impaired people are money recognition, especially for paper currency.&#x0D; Since money plays such an important role in our everyday lives and is required for every business transaction, real-time detection and recognition of banknotes become a necessity for blind or visually impaired people For that purpose, we propose a real-time object detection system to help visually impaired people in their daily business transactions.&#x0D; Dataset Images of the Iraqi banknote category are collected in different conditions initially and then, these images are augmented with different geometric transformations, to make the system strong. These augmented images are then annotated manually using the "LabelImg" program, from which training sets and validation image sets are prepared.&#x0D; We will use YOLOv3 real-time Object Detection algorithm trained on custom Iraqi banknote dataset for detection and recognition of banknotes. Then the label of the banknotes is identified and then converted into audio by using Google Text to Speech (gTTS), which will be the expected output.&#x0D; The performance of the trained model is evaluated on a test dataset and real-time live video. The test results demonstrate that the proposed method can detect and recognize Iraqi paper money with high mAP reaches 97.405% and a short time.
An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection

Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at scale and in very high fidelity. Many works focus on exploring various network architectures or various artifacts produced by deep generative models. Instead, in this work, we focus on the loss functions which have been shown to play a significant role in the context of face recognition. We perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation. With extensive experiments on the challenging FaceForensic++ and Celeb-DF datasets, the evaluation results provide a clear overview of the performance comparisons of different loss functions and generalization capability across different deepfake data.
IInfoGAN: Improved Information Maximizing Generative Adversarial Networks

Generative Adversarial Networks (GANs) have achieved huge success in some unsupervised learning fields. There is no doubt that clustering takes a lot of weight in unsupervised algorithm. And in this paper, we raise the Improved Information Maximizing Generative Adversarial Networks (IInfoGAN) algorithm for learning discriminative classifiers from unlabeled data. The basis of our method is an math function that contains the Mutual Information (MI) and Cross Entropy of the observed examples and their predicted classification category distribution, thus enhancing the robustness of the classifier to adversarial generative models. Experiments show that the interpretable representation learned by IInfoGAN is competitive with the representation learned by existing unsupervised methods.
Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning

The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.
Police Efforts in Tackling Theft Crimes

Crime often occurs with various backgrounds, which are frequently encountered, for example, due to economic factors.A person can act recklessly to steal for reasons that there is no cost to life, bullying, or even stem from bad habits or diseases such as gambling, drunkenness, and prostitution, even narcotics.The method of approach in this study uses an empirical juridical legal system.Legal Research on the application or implementation of normative legal provisions in action on any particular legal event in the community.