Reviewerly/exHarmony huggingface dataset:
Going beyond the Limits of SFI: Flexible and Secure Hardware-Assisted In-Process Isolation with HFI

We introduce Hardware-assisted Fault Isolation (HFI), a simple extension to existing processors to support secure, flexible, and efficient in-process isolation. HFI addresses the limitations of existing software-based isolation (SFI) systems including: runtime overheads, limited scalability, vulnerability to Spectre attacks, and limited compatibility with existing code. HFI can seamlessly integrate with current SFI systems (e.g., WebAssembly), or directly sandbox unmodified native binaries. To ease adoption, HFI relies only on incremental changes to the data and control path of existing high-performance processors. We evaluate HFI for x86-64 using the gem5 simulator and compiler-based emulation on a mix of real and synthetic workloads.
VEHICLE DETECTION AND COUNTING

In an intelligent transportation system, a vehicle detection and counting system is critical, especially for traffic management.Using computer vision technology, proposes a video-based method for vehicle detection and counting.To find foreground objects in a video sequence, the proposed method employs background subtraction.Several computer vision techniques, such as thresholding, hole filling, and adaptive morphology operations, are then used to detect moving vehicles more accurately.Finally, a virtual detection zone is used to count the number of vehicles. I.
BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space

The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common. This paper reports a Python package called BiometricBlender, which is an ultra-high dimensional, multi-class synthetic data generator to benchmark a wide range of feature screening methods. During the data generation process, the overall usefulness and the intercorrelations of blended features can be controlled by the user, thus the synthetic feature space is able to imitate the key properties of a real biometric dataset.
Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach

Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email problem from a different and novel perspective. Focusing on the needs of cybersecurity units, we follow a topic-based approach for addressing the classification of spam email into multiple categories. We propose SPEMC-15K-E and SPEMC-15K-S, two novel datasets with approximately 15K emails each in English and Spanish, respectively, and we label them using agglomerative hierarchical clustering into 11 classes. We evaluate 16 pipelines, combining four text representation techniques -Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words, Word2Vec and BERT- and four classifiers: Support Vector Machine, Näive Bayes, Random Forest and Logistic Regression. Experimental results show that the highest performance is achieved with TF-IDF and LR for the English dataset, with a F1 score of 0.953 and an accuracy of 94.6%, and while for the Spanish dataset, TF-IDF with NB yields a F1 score of 0.945 and 98.5% accuracy. Regarding the processing time, TF-IDF with LR leads to the fastest classification, processing an English and Spanish spam email in 2ms and 2.2ms on average, respectively.
Pure Dephasing of Light-Matter Systems in the Ultrastrong and Deep-Strong Coupling Regimes

Pure dephasing originates from the nondissipative information exchange between quantum systems and environments, and plays a key role in both spectroscopy and quantum information technology. Often pure dephasing constitutes the main mechanism of decay of quantum correlations. Here we investigate how pure dephasing of one of the components of a hybrid quantum system affects the dephasing rate of the system transitions. We find that, in turn, the interaction, in the case of a light-matter system, can significantly affect the form of the stochastic perturbation describing the dephasing of a subsystem, depending on the adopted gauge. Neglecting this issue can lead to wrong and unphysical results when the interaction becomes comparable to the bare resonance frequencies of subsystems, which correspond to the ultrastrong and deep-strong coupling regimes. We present results for two prototypical models of cavity quantun electrodynamics: the quantum Rabi and the Hopfield model.
A Common Error Analysis in Students’ English Narrative Writing

This research aims to explore error analysis in students’ academic writing. The study was conducted at the University of HKBP Nommensen Medan. The method used is a mixed-method. The research participants were 26 English department students in the third semester. Thus, the research data consist of 26 narrative stories. The results showed that around 252 errors were found in all categories. The most dominant error category is omission 92 (36.51%) then is respectively followed by addition 64 (25.40%), misinformation 56 (22.22%), and disordering 40 (15.87%). The most dominant factor as the cause of the error is first language interference, and then translation and carelessness. In conclusion, all types of errors (omission, addition, misinformation, and disordering) were identified in the students’ narrative writing.&#x0D; Keywords: Error Analysis, Factor of Errors, Narrative Writing, University Students
Chiral-coupling-assisted refrigeration in trapped ions

Abstract Trapped ions can be cooled close to their motional ground state, which is imperative in implementing quantum computation and quantum simulation. Here, we theoretically investigate the capability of light-mediated chiral couplings between ions to enable a superior cooling scheme exceeding the single-ion limit of sideband cooling. Under asymmetric driving, the target ion manifests the chiral-coupling-assisted refrigeration at the price of heating others, where its steady-state phonon occupation outperforms the lower bound set by a single ion. We further explore the optimal operation conditions for the refrigeration where a faster rate of cooling can still be sustained. Under an additional nonguided decay channel, a broader parameter regime emerges to support the superior cooling and carries over into the reciprocal coupling, suppressing the heating effect instead. Our results present a tunable resource of collective chiral couplings which can help surpass the bottleneck of the cooling procedure and open up new possibilities in applications of trapped-ion-based quantum computation and simulation.
Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation

Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).
A Pulse Generation Framework with Augmented Program-aware Basis Gates and Criticality Analysis

Near-term intermediate-scale quantum (NISQ) devices are subject to considerable noise and short coherence time. Consequently, it is critical to minimize circuit execution latency and improve fidelity. Traditionally, each basis gate of a transpiled circuit is decoded into a fixed episode of the device control pulses. Recent studies investigate the merged pulse generation method for customized gates through quantum optimal control (QOC). In this work, we propose PAQOC, a novel QOC framework that can (i) exploit an augmented program-aware (APA) basis gate set for the tradeoff between compilation time and circuit performance, (ii) prune the search space based on a criticality-centric analytical model and experiment observations we learned from 150 benchmarks. Evaluations using seventeen applications show that PAQOC can achieve an average 54% reduction of the circuit latency, on average 43% reduction in compilation overhead, and a 1.27× improvement in fidelity. PAQOC is available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Encoding Hierarchical Information in Neural Networks Helps in Subpopulation Shift

Over the past decade, deep neural networks have proven to be adept in image classification tasks, often surpassing humans in terms of accuracy. However, standard neural networks often fail to understand the concept of hierarchical structures and dependencies among different classes for vision related tasks. Humans on the other hand, seem to intuitively learn categories conceptually, progressively growing from understanding high-level concepts down to granular levels of categories. One of the issues arising from the inability of neural networks to encode such dependencies within its learned structure is that of subpopulation shift – where models are queried with novel unseen classes taken from a shifted population of the training set categories. Since the neural network treats each class as independent from all others, it struggles to categorize shifting populations that are dependent at higher levels of the hierarchy. In this work, we study the aforementioned problems through the lens of a novel conditional supervised training framework. We tackle subpopulation shift by a structured learning procedure that incorporates hierarchical information conditionally through labels. Furthermore, we introduce a notion of hierarchical distance to model the catastrophic effect of mispredictions. We show that learning in this structured hierarchical manner results in networks that are more robust against subpopulation shifts, with an improvement up to 3% in terms of accuracy and up to 11% in terms of hierarchical distance over standard models on subpopulation shift benchmarks.
A Literature Review on RSA, DES and AES Encryption Algorithms

Now a days, as more and more information is communicated via computers, the need is to ensure that this information is secure and information can be secured by the means of encryption algorithms. Securing information refers to the methodologies that are implemented to protect sensitive information from misuse or disclosure as it causes risk. Encryption is the process of conglomerating or scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information security is becoming much more important in data storage and transmission. With the evolution of human intelligence, the art of cryptography has become more complex in order to make information more secure. Various encryption algorithms are deployed to make information more secure. In this paper, a survey of RSA, DES and AES encryption algorithms are presented.
Research on Recognition for Subway Track Based on Canny Edge Detection and Hough Transformation

In recent years, line foreign bodies have led to many major safety incidents on urban rail transit lines, posing a huge potential risk to line operation safety. In this paper, a fast and accurate Hough transform technique is applied to identify two rails, followed by establishing a detection window of suitable size with two rails as the reference. Before detection, the original image also needs to be grayed out and filtered, and finally the improved Canny operator is used to extract the edge feature information of the image to make it easy for the next step of training recognition. In the process of implementation, this paper adopts a multi-frame Hough transform method to detect foreign objects, first extracting the feature information of steel rails with the original image, and then using the improved Canny operator to extract the image edge features. Through experiments, it is proved that the method can quickly and accurately detect foreign objects of steel rails, and it has achieved good results in engineering applications.
Compressed Screen Content Image Super Resolution

Screen content has become one of the prominent mediums in the increasingly connected world. With the prevalence of remote collaboration and communication such as virtual conferences and online education, recent years have witnessed a dramatic increase in the data volume of the screen content. Screen content compression serves as the fundamental technology in fostering the storage, transmission, and exhibition of screen content. In this article, we target the super-resolution of the compressed screen content images, intending to tackle the real-world challenge problems. A dataset is proposed for the super-resolution of the screen contents contaminated with different compression distortion levels. Subsequently, we introduce the principle of the multi-hypothesis into the super resolution and propose a new paradigm for the restoration of the compressed screen content images. The luminance and sharpness similarity metric is adopted in the network learning to better adapt to the screen content characteristic and ensure perceptual fidelity. Experimental results verify the superiority and effectiveness of the proposed method.
A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes

Dear Editor, This letter provides a simple framework to generalized zero-shot learning for fault diagnosis. For industrial process monitoring, supervised learning and zero-shot learning (ZSL) can only deal with seen and unseen faults, respectively. However, in the online monitoring stage of the actual industrial process, both seen and unseen faults may occur. This makes supervised learning and zero-shot learning impractical in industrial process monitoring. Generalized zero-shot learning (GZSL) can handle this problem, but its implementation process is too complicated. This letter introduces GZSL into industrial process fault diagnosis, and a simple end-to-end framework is provided to implement GZSL-based fault diagnosis. In this framework, GZSL-based fault diagnosis can be realized by using only a binary classification algorithm. Experimental results show that the proposed framework can accomplish this challenging task of GZSL for fault diagnosis.
Survival Prediction via Hierarchical Multimodal Co-Attention Transformer: A Computational Histology-Radiology Solution

The rapid advances in deep learning-based computational pathology and radiology have demonstrated the promise of using whole slide images (WSIs) and radiology images for survival prediction in cancer patients. However, most image-based survival prediction methods are limited to using either histology or radiology alone, leaving integrated approaches across histology and radiology relatively underdeveloped. There are two main challenges in integrating WSIs and radiology images: (1) the gigapixel nature of WSIs and (2) the vast difference in spatial scales between WSIs and radiology images. To address these challenges, in this work, we propose an interpretable, weakly-supervised, multimodal learning framework, called Hierarchical Multimodal Co-Attention Transformer (HMCAT), to integrate WSIs and radiology images for survival prediction. Our approach first uses hierarchical feature extractors to capture various information including cellular features, cellular organization, and tissue phenotypes in WSIs. Then the hierarchical radiology-guided co- attention (HRCA) in HMCAT characterizes the multimodal interactions between hierarchical histology-based visual concepts and radiology features and learns hierarchical co- attention mappings for two modalities. Finally, HMCAT combines their complementary information into a multimodal risk score and discovers prognostic features from two modalities by multimodal interpretability. We apply our approach to two cancer datasets (365 WSIs with matched magnetic resonance [MR] images and 213 WSIs with matched computed tomography [CT] images). Our results demonstrate that the proposed HMCAT consistently achieves superior performance over the unimodal approaches trained on either histology or radiology data alone, as well as other state-of-the-art methods.
What is the Metaverse?—In the view of philosophical perspective

What is the Metaverse actually? It is an important issue in the terms of theory and practice with huge controversy. Regarding the Metaverse from a philosophical point of view, it could better highlight the inner essence behind the controversy of representation. From the connotation view, it is a digital mapping and mirror world of universe. Extensively, it is a comprehensive integration of digital information technologies. In the view of philosophy, it is an artificial universe created by human beings with digital technology, which is digital universe corresponding to the real universe. In the view of spiritual philosophy, it is a cosmic brain created by human beings with wisdom of natural universe, which is a cosmic heart for the body of natural universe. In the view of cognitive philosophy, it is a digital universe that recognizes everything, and a digital laboratory for human innovation and creation.
Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience

Abstract Objective The purpose of this study is to assess the “real-world” impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center. Methods Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations. Results Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9–1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8–1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97–1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8–1.5). Conclusion Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.
Evaluation of Urban Mobility Alternatives For Blockchain Use In Metaverse

Blockchain is the driving technology behind the metaverse, which is a new and promising technology. Integration of metaverse with many sectors and implementations is possible. This integration has the potential to increase the efficiency and effectiveness of the implementation due to the provided aspects of blockchain technology such as the ease of trusted data transactions, payment, and data privacy. The aim of this study is to present a framework where blockchain and metaverse co-operate for micromobility applications using one of the fuzzy decision making models. In this study, the aim is to propose three metaverse integration alternatives considering urban mobility and to prioritize these alternatives according to their advantages by using a fuzzy Dombi Bonferroni (DOBI) based multi-critera decision-making (MCDM) model. The alternatives are using blockchain in the metaverse for electric toll collection, using blockchain in the metaverse for public transportation fare collection, and using blockchain for micromobility and car sharing implementation. These alternatives are prioritized based on 9 different criteria, which are categorized under 4 main aspects, namely transportation, user, technology, and regulation.
An efficient activity recognition for homecare robots from multi-modal communication dataset

Human environments are designed and managed by humans for humans. Thus, adding robots to interact with humans and perform specific tasks appropriately is an essential topic in robotics research. In recent decades, object recognition, human skeletal, and face recognition frameworks have been implemented to support the tasks of robots. However, recognition of activities and interactions between humans and surrounding objects is an ongoing and more challenging problem. Therefore, this study proposed a graph neural network (GNN) approach to directly recognize human activity at home using vision and speech teaching data. Focus was given to the problem of classifying three activities, namely, eating, working, and reading, where these activities were conducted in the same environment. From the experiments, observations, and analyses, this proved to be quite a challenging problem to solve using only traditional convolutional neural networks (CNN) and video datasets. In the proposed method, an activity classification was learned from a 3D detected object corresponding to the human position. Next, human utterances were used to label the activity from the collected human and object 3D positions. The experiment, involving data collection and learning, was demonstrated by using human-robot communication. It was shown that the proposed method had the shortest training time of 100.346 seconds with 6000 positions from the dataset and was able to recognize the three activities more accurately than the deep layer aggregation (DLA) and X3D networks with video datasets.