Reviewerly/exHarmony huggingface dataset:
Fast and Robust Quantum State Transfer via Optimal Transitionless Quantum Driving

Abstract For the past few years, shortcuts to adiabaticity have attracted great attention since they can avoid the necessity of slow driving for adiabatic method while inheriting the partial robustness of the adiabatic dynamics. In this paper, a universal approach to implement quantum state transfer in a three‐state system via optimal transitionless quantum driving is presented. The fast path is optimized for great robustness against uncontrolled systematic error by selecting appropriate experimental parameters. The robustness of the protocol is discussed via numerical simulations. Compared with conventional transitionless quantum driving and adiabatic passage, the protocol via optimized transitionless quantum driving possesses more robustness against the systematic error. Furthermore, decoherence arising from atomic spontaneous emission and dephasing only has a slight impact on the fidelity of quantum state transfer. This work opens up a new avenue for high‐fidelity quantum state transfer in the case of the uncontrolled errors.
Semi-Complete Data Augmentation for Efficient State Space Model Fitting

We propose a novel efficient model-fitting algorithm for state space models. State space models are an intuitive and flexible class of models, frequently used due to the combination of their natural separation of the different mechanisms acting on the system of interest: the latent underlying system process; and the observation process. This flexibility, however, often comes at the price of more complicated model-fitting algorithms due to the associated analytically intractable likelihood. For the general case a Bayesian data augmentation approach is often employed, where the true unknown states are treated as auxiliary variables and imputed within the MCMC algorithm. However, standard “vanilla” MCMC algorithms may perform very poorly due to high correlation between the imputed states and/or parameters, often leading to model-specific bespoke algorithms being developed that are nontransferable to alternative models. The proposed method addresses the inefficiencies of traditional approaches by combining data augmentation with numerical integration in a Bayesian hybrid approach. This approach permits the use of standard “vanilla” updating algorithms that perform considerably better than the traditional approach in terms of improved mixing and lower autocorrelation, and has the potential to be incorporated into bespoke model-specific algorithms. To demonstrate the ideas, we apply our semi-complete data augmentation algorithm to different application areas and models, leading to distinct implementation schemes and improved mixing and demonstrating improved mixing of the model parameters. Supplementary materials for this article are available online.
Building Better Machine Learning Models for Rhetorical Analyses: The Use of Rhetorical Feature Sets for Training Artificial Neural Network Models

In this paper, we investigate two approaches to building artificial neural network models to compare their effectiveness for accurately classifying rhetorical structures across multiple (non-binary) classes in small textual datasets. We find that the most accurate type of model can be designed by using a custom rhetorical feature list coupled with general-language word vector representations, which outperforms models with more computing-intensive architectures.
Semantic Transfer Between Different Tasks in the Semantic Communication System

At present, the semantic communication system considered the case that one of the intelligent tasks needs to be completed, while a single Internet of Things (IoT) device is usually required to complete multiple different tasks in reality. Moreover, it is difficult to obtain a large number of labels for some complex tasks to achieve high precision, such as object detection. Existing methods for few-shot detection mainly focus on transfer across domains, but there is the following problem in application: the parameters of the encoder cannot be shared at transmitter among different tasks, which leads to the storage pressure of the IoT devices required to transmit semantic information for multiple different tasks. To address this problem, we propose a novel transfer learning approach, Semantic Transfer Across Tasks, in which we leverage the semantic information to guide the training process of object detection with fewer labels and share the encoder architecture between classification and detection in the semantic communication system. Inspired by Grad-CAM, we select the semantic information which is important to detection, and we propose a semantic distance to improve the performance of few-shot detection. Experimental results show that our approach improved the mean average precision of few-shot detection in the semantic communication system and reduced the storage pressure of IoT devices.
LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection

This paper proposes a learnable line encoding technique for bounding boxes commonly used in the object detection task. A bounding box is simply encoded using two main points: the top-left corner and the bottom-right corner of the bounding box; then, a lightweight convolutional neural network (CNN) is employed to learn the lines and propose high-resolution line masks for each category of classes using a pixel-shuffle operation. Post-processing is applied to the predicted line masks to filtrate them and estimate clear lines based on a progressive probabilistic Hough transform. The proposed method was trained and evaluated on two common object detection benchmarks: Pascal VOC2007 and MS-COCO2017. The proposed model attains high mean average precision (mAP) values (78.8% for VOC2007 and 48.1% for COCO2017) while processing each frame in a few milliseconds (37 ms for PASCAL VOC and 47 ms for COCO). The strength of the proposed method lies in its simplicity and ease of implementation unlike the recent state-of-the-art methods in object detection, which include complex processing pipelines.
Neural Unscented Kalman Filter for Submarine Active Target Tracking

The motive of the work is to investigate the use of active sonar measurements by submarines to track a ship's trajectory underwater. An unscented Kalman filter (UKF) with neural network (NN) is applied to estimate the motion parameters of target. The model is approximated by the nonlinear state space NN, and its weights are then trained online through the UKF. As the process is stochastic, Monte-Carlo simulation is used and the outcomes are compared with that of UKF algorithm. As expected, the results with UKF are better than that of UKF with NN. The purpose of NN is to be used when the process uncertainties cannot be modeled exactly. NN need not be used when the analytical solution is possible, and solution is tractable.
Cross-modal propagation network for generalized zero-shot learning

Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic knowledge from seen classes to unseen ones. Since only seen classes are available during training, the domain bias issue, i.e., the trained model is biased toward seen classes, is the key issue for ZSL. To alleviate the bias problem, generation-based approaches are proposed to build generative models that can generate fake visual features of unseen classes by utilizing semantic vectors. However, most of the existing generative methods still suffer some degree of domain bias caused by the ambiguous generation of fake features. In this paper, we propose a cross-modal propagation network (CMPN), which adopts an episode-based meta-learning strategy. CMPN incorporates the adaptive graph construction and label propagation into the generative ZSL framework for guaranteeing an unambiguous and discriminative fake feature generating. By further leveraging the manifold structure of different modalities in the latent space, CMPN can implicitly ensure intra-class compactness and inter-class separation through label propagation classification in latent space. Extensive experiments on four datasets validate the effectiveness of CMPN under both ZSL and generalized ZSL (GZSL) settings.
Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA

Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have banned the use of facial recognition by law enforcement and governments, but FPT are still being developed and used in a wide variety of contexts where they primarily are regulated by state biometric information privacy laws. Among these laws, the 2008 Illinois Biometric Information Privacy Act (BIPA) has generated a significant amount of litigation. Yet, with most BIPA lawsuits reaching settlements before there have been meaningful clarifications of relevant technical intricacies and legal definitions, there remains a great degree of uncertainty as to how exactly this law applies to FPT. What we have found through applications of BIPA in FPT litigation so far, however, points to potential disconnects between technical and legal communities. This paper analyzes what we know based on BIPA court proceedings and highlights these points of tension: areas where the technical operationalization of BIPA may create unintended and undesirable incentives for FPT development, as well as areas where BIPA litigation can bring to light the limitations of solely technical methods in achieving legal privacy values. These factors are relevant for (i) reasoning about biometric information privacy laws as a governing mechanism for FPT, (ii) assessing the potential harms of FPT, and (iii) providing incentives for the mitigation of these harms. By illuminating these considerations, we hope to empower courts and lawmakers to take a more nuanced approach to regulating FPT and developers to better understand privacy values in the current U.S. legal landscape.
Efforts and Suggestions for Improving Cybersecurity Education

In this growing technology epoch, one of the main concerns is about the cyber threats. To tackle this issue, highly skilled and motivated cybersecurity professionals are needed, who can prevent, detect, respond, or even mitigate the effect of such threats. However, the world faces workforce shortage of qualified cybersecurity professionals and practitioners. To solve this dilemma several cybersecurity educational programs have arisen. Before it was just a couple of courses in a computer science graduate program. Now a day&#x2019;s different cybersecurity courses are introduced at the high school level, undergraduate computer science and information systems programs, even in the government level. Due to some peculiar nature of cybersecurity, educational institutions face many issues when designing a cybersecurity curriculum or cybersecurity activities.
A Fine-Grained Secure Service Provisioning Platform for Hypervisor Systems

As computing technology has been recently widely adopted, most computing devices provide security-related services as basic requirements, which is an important research issue for sustainability of computing devices. The rapid increase of software components makes it difficult to detect or prevent vulnerabilities in the large-size software. One of the prominent approaches for ensuring secure service is the isolation of service which allows the related code and data to be executed only in a particular area. In this paper, we provide a secure service provisioning platform for hypervisor systems. The main contribution of the proposed framework is to enhance the previous secure service provisioning platform in order to solve the non-preemption problem of secure services. Thus, the proposed framework improves the security isolation service in hypervisors and can be used for fine-grained secure service in secure embedded systems.
Proof-of-Enough-Work Consensus Algorithm for Enhanced Transaction Processing in Blockchain

One of the biggest criticisms of the Bitcoin Core blockchain technology lies in its consensus algorithm, known as Proof-of-Work (PoW). This compute-intensive consensus algorithm is resulting in an ever-increasing electrical cost per transaction, this is compounded by its data storage limitations. Consequently, this phenomenon determines the maximum limit in the transaction processing rate. This limitation is causing many industries to refrain from using Blockchain technology. This paper proposes a Proof-of-Enough-Work (PoEW) consensus algorithm for a BC blockchain configuration to get rid of this obstacle. We present parallel block processing through the reallocation of computing resources, specifically used to add blocks to the blockchain, also called mining. This is explicitly done with an extension to the key BC mining parameters, called the block difficulty or the amount of computing effort needed to process a block. The introduction of an efficient use of computing resources or effort significantly increases the transaction processing rate.Through simulations we demonstrate a significant increase in transaction processing rate compared to conventional PoW. It produces transaction processing comparable to centralised transaction networks or a high transaction throughput solution. Moreover, the proposed consensus algorithm reduces the cost per transaction in terms of electricity usage.
PRE-SERVICE TEACHERS’ EXPERIENCES OF THE TRANSITION FROM ANALOGUE TO DIGITAL LEARNING DURING THE COVID-19 PANDEMIC

The transition from analogue campus-based learning to digital distance learning during the Covid-19 pandemic affected our society in many ways. This study set out to explore the experiences of 30 pre-service teachers when transitioning to digital distance learning. The pre-service teachers (PSTs) participated in a series of focus-group interviews that were subsequently analysed using qualitative content analysis. The results indicate that the transition to digital distance learning worked well and that the technology that was used performed satisfactorily. However, the PSTs experienced both opportunities and obstacles with respect to distance learning. The opportunities associated with digital distance learning were reported to be instantiated by teacher-led ‘flipped classroom’ teaching sessions and the additional freedom that was enjoyed by the PSTs (in both space and time). An obstacle associated with digital learning was the feeling that important aspects of interpersonal interaction disappeared. The PSTs remained sedentary and isolated at home, and the learning experience became somewhat dysfunctional when the teaching was not clearly structured and teacher-lead. The study concludes that it is not possible to replace IRL (‘in real life’) teaching entirely with digital distance learning. However, the results suggest that a hybrid form of teaching or elements of digital teaching can work well as a complement to future campus-based courses. Keywords: Covid-19, digital learning, digital teaching, distance learning, hybrid learning
Investigating the geometric structure of neural activation spaces with convex hull approximations

Neural networks have achieved great success in many tasks, including data classification and pattern recognition. However, how neural networks work and what representations they learn are still not fully understood. For any data sample fed into a neural network, we wondered how its corresponding vectors expanded by activated neurons change throughout the layers and why the final output vector could be classified or clustered. To formally answer these questions, we define the data sample outputs of each layer as activation vectors and the space expanded by them as the activation space. Then, we investigate the geometric structure of the high-dimensional activation spaces of neural networks by studying the geometric characters of the massive activation vectors through approximated convex hulls. We find that the different layers of neural networks have different roles, where the former and latter layers can disperse and gather data points, respectively. Moreover, we also propose a novel classification method based on the geometric structures of activation spaces, called nearest convex hull (NCH) classification, for the activation vectors in each layer of a neural network. The empirical results show that the geometric structure can indeed be utilized for classification and often outperforms original neural networks. Finally, we demonstrate that the relationship among the convex hulls of different classes could be a good metric to help us optimize neural networks in terms of over-fitting detection and network structure simplification.
Spectral clustering based on the local similarity measure of shared neighbors

Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement exploits the local density information between data points based on the weight of the shared neighbors in a directed k $$ k $$ -nearest neighbor graph with only one parameter k $$ k $$ , that is, the number of nearest neighbors. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering performance measured via the normalized mutual information, clustering accuracy, and F $$ F $$ -measure. As an example, the proposed method can provide an improvement of 15.82% in the clustering performance for the Soybean dataset.
How field experience shapes pre-service primary teachers’ technology integration knowledge and practice

The expectation for teachers to integrate technology within their classroom practice is growing. However, few studies examine the influences on pre-service teachers' technology integration knowledge and practice, and the role of field experience in shaping these. Narratives from semi-structured pre-COVID interviews with 35 pre-service primary school teachers in one teacher education programme in Ireland, some of whom used a digital camera, were analysed using an inductive thematic approach. Opportunities for pre-service teachers to develop their knowledge and practice were shaped by their interaction with the primary school context, the people within it and the digital resources provided, the Zone of Proximal Development (ZPD). While the initial teacher education programme provided opportunities and digital resources to support the primary teachers' learning, the Zone of Promoted Action, these were ultimately shaped by the ZPD. The results have implications for professional development programmes striving to support teachers in developing innovative practice in a post-COVID era.
IR-ORAM: Path Access Type Based Memory Intensity Reduction for Path-ORAM

Path ORAM is an effective ORAM (Oblivious RAM) primitive for protecting memory access patterns. Path ORAM converts each off-chip memory request from user program to tens to hundreds of memory accesses. While several schemes have been proposed to mitigate the total number of memory accesses, Path ORAM remains a highly memory intensive primitive that leads to large memory bandwidth occupation and performance degradation.In this paper, we propose IR-ORAM to reduce the memory intensity based on path access types in Path ORAM. Path accesses in Path ORAM, while being kept oblivious to ensure privacy protection, can be categorized to three types: paths for requested data blocks, paths for position map blocks, and dummy paths. We develop a set of techniques to reduce the memory intensity of each type while ensuring the obliviousness at the same time &#x2014; we reduce the number of data blocks to access for each tree path, reduce the number of path accesses for position maps, and convert many dummy path accesses to early write-backs of dirty data in LLC. Our experimental results show that IR-ORAM achieves on average 42% performance improvement over the state-of-the-art while effectively enforcing the memory access obliviousness and the same level of security protection.
Robustness of Modified Non-Separable HaarWavelet Transform and Singular Value Decomposition for Non-blind Digital Image Watermarking

Security is a matter of significant concern for image media. An effective way to protect images is by digital watermarking. This paper introduces a non-blind digital watermarking scheme using modified non-separable Haar wavelet transform (NSHWT), singular value decomposition (SVD), Arnold's cat map, and Rabin-p cryptosystem to embed a binary watermark image into a color cover image. Aside from robustness, security is also prioritized in the scheme. High robustness is achieved using two transform domain techniques, discrete wavelet transform (DWT) and SVD, while security is heightened with the double encryption by Arnold's cat map scrambling and Rabin-p cryptosystem. A disadvantage of the discrete wavelet transform in digital image watermarking is the calculation complexity and the high cost of changing memory and time. The proposed algorithm uses a modified NSHWT instead of the traditional method of DWT. Therefore, the load of the process on the hardware can significantly be reduced while still maintaining the advantages of DWT. The algorithm is objectively evaluated in terms of imperceptibility, robustness, and embedding capacity for both binary and color watermarks, as well as efficiency. It has been concluded that the proposed scheme performs competently compared with other recent watermarking techniques based on DWT and SVD.
Lightweight Security Wear Detection Method Based on YOLOv5

Given a large number of network parameters of the existing security wear detection, it is difficult to run on the embedded platform. Based on the idea of deep separable convolution, a lightweight security wearable target detection network based on improved YOLOv5 is proposed. Specifically, the feature extraction network structure of YOLOv5 is lightweight improved to reduce computation of the proposed model, including the increase of the number of network layers and decrease of the number of parameters. In addition, an attention mechanism is introduced to weigh different channels of the feature map to improve detection accuracy. The model has been tested on PASCAL VOC dataset and security wear dataset. The experimental results show that the size of the proposed model is 8.0 MB, the number of parameters is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>7.5</mn> </mrow> <mrow> <mo>∗</mo> </mrow> </msup> <mn>105</mn> </math> , and the number of FLOPs is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mn>7.5</mn> </mrow> <mrow> <mo>∗</mo> </mrow> </msup> <mn>105</mn> </math> . Compared with the YOLOv5 model, the required memory is reduced by 44.8%, and the number of parameters decreased by 45.58%, FLOPs decreased by 54.54%. Accordingly, the results have demonstrated that the proposed method can significantly improve the detection speed while maintain the accuracy. Especially, we have successfully deployed the proposed model in the high-speed detection of security wear.
Call-by-push-value

Call-by-push-value (CBPV) is a form of typed λ-calculus that plays a fundamental role in the study of computational effects. This article is intended to be an accessible introduction. I thank Mike Mislove and SIGLOG for inviting me to expound one of my favourite subjects.