Reviewerly/exHarmony huggingface dataset:
Quantum parameter estimation on coherently superposed noisy channels

A generic qubit unitary operator affected by quantum noise is duplicated and inserted in a coherently superposed channel, superposing two paths offered to a probe qubit across the noisy unitary, and driven by a control qubit. A characterization is performed of the transformation realized by the superposed channel on the joint state of the probe-control qubit pair. The superposed channel is then specifically analyzed for the fundamental metrological task of phase estimation on the noisy unitary, with the performance assessed by the Fisher information, classical or quantum. A comparison is made with conventional estimation techniques and also with a quantum switched channel with indefinite causal order recently investigated for a similar task of phase estimation. In the analysis here, a first important observation is that the control qubit of the superposed channel, although it never directly interacts with the unitary being estimated, can nevertheless be measured alone for effective estimation, while discarding the probe qubit that interacts with the unitary. This property is also present with the switched channel but is inaccessible with conventional techniques. The optimal measurement of the control qubit here is characterized in general conditions. A second important observation is that the noise plays an essential role in coupling the control qubit to the unitary, and that the control qubit remains operative for phase estimation at very strong noise, even with a fully depolarizing noise, whereas conventional estimation and the switched channel become inoperative in these conditions. The results extend the analysis of the capabilities of coherently controlled channels which represent novel devices exploitable for quantum signal and information processing.
A Modified Version of Vigenere Cipher using 95 95 Table

Cryptography is a current field of research at the moment as it can be crucial to offer protection to extremely delicate and confidential records from criminal wrongdoings throughout transmission over the network. The vigenere cipher is one of the standard cryptographic algorithms that prevent attackers from understanding the raw data throughout the transmission. The traditional vigenere cipher encrypts only the alphabetic plaintext with a 26×26 vigenere table because it includes only 26 English alphabets A to Z. The researcher extended the original vigenere table into 95×95 which introduces all possible characters, mathematical symbols, digits, and punctuations that are available on an ordinary QWERTY keyboard layout that can be encrypted easily by this technique and conjointly introduced case sensitivity. The objective of this modification makes the cryptanalysis procedure more difficult and increases the information security.
An Artificial Intelligence Based Approach Towards Inclusive Healthcare Provisioning in Society 5.0: A Perspective on Brain Disorder

Face detection and sparse facial feature analysis is popular as a non-invasive approach to diagnosis special disease. In futuristic intelligent healthcare system, the confined way of preliminary computer aided diagnosis of diseases becoming more inclusive and faster than usual time. Therefore, face spacial feature analysis can be an elegant way of measuring attempt in tele-medicine industry. In this research paper, we investigate thorough review on disease diagnosis techniques, healthcare management and, data security features being used currently. Moreover, this work propose a i-health care monitoring and examining system of neuronal/brain disorder in layer base approach. Overall, this paper reviews about diseases which have already been detected by spacial feature of face using deep learning algorithm or feature based learning with a proposal of a monitoring system with its research area and challenges in smart intelligent healthcare system in society 5.0.
Research on the Role of Big Data Technology in the Reform of English Teaching in Universities

This paper provides an in-depth understanding and analysis of the reform of English teaching in colleges and universities by analyzing the role of big data technology for the reform through in-depth research and analysis. Based on the background of the era of education informatization, this study explores the transformative value of the integration of information technology and teaching activities and elaborates the relevant significance at the theoretical and practical levels. Based on this, the research method of this study is established. The realm of integration of information technology and teaching activities and its transformative value is taken as the focus of the study. The integration of information technology and teaching activities means that it is not only a simple superposition between information technology and teaching but also a process to explore the role and influence of information technology into teaching activities by deeply exploring the inner connection between the two, to complete the integration of information technology and teaching activities, and finally to realize the comprehensive development of student’s personality.
Dynamic Center Aggregation Loss With Mixed Modality for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval task which aims to match person images between the visible and infrared modality of the same identity. Existing methods usually adopt two-stream network to solve cross-modality gap, but they ignore the pixel-level discrepancy between the visible and infrared images. Some methods introduce auxiliary modalities in the network, but they lack powerful constraints on the feature distribution of multiple modalities. In this letter, we propose a Dynamic Center Aggregation (DCA) loss with mixed modality for VI-ReID. Concretely, we employ a mixed modality as a bridge between the visible and infrared modality, reducing the difference of the two modalities at the pixel-level. The mixed modality is generated by a Dual-modality Feature Mixer (DFM), which combines the features of visible and infrared images. Moreover, we dynamically adjust the relative distance across multi-modality through DCA loss, which is conducive to explore the modality-invariant feature. We evaluate the proposed method on two public available VI-ReID datasets (SYSU-MM01 and RegDB). Experimental results demonstrate that our method achieves competitive performance.
Factorized Neural Transducer for Efficient Language Model Adaptation

In recent years, end-to-end (E2E) based automatic speech recognition (ASR) systems have achieved great success due to their simplicity and promising performance. Neural Transducer based models are increasingly popular in streaming E2E based ASR systems and have been reported to outperform the traditional hybrid system in some scenarios. However, the joint optimization of acoustic model, lexicon and language model (LM) in neural Transducer also brings about challenges in adapting ASR using just adaptation text. This draw-back might prevent their potential applications in practice. In order to address this issue, we propose a novel model, factorized neural Transducer, by factorizing the blank and vocabulary prediction, and adopting a standalone language model for the vocabulary prediction. It is expected that this factorization can transfer the improvement of the standalone language model to the Transducer for speech recognition, which allows various language model adaptation techniques to be applied. We demonstrate that the proposed factorized neural Transducer yields 15.4% to 19.4% WER improvements when out-of-domain text data is used for language model adaptation, at the cost of a minor degradation in WER on a general test set.
Parameterized Hypercomplex Graph Neural Networks for Graph Classification

Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quaternion algebras, which have been found in several contexts to enable effective representation learning that inherently incorporates a weight-sharing mechanism, we develop graph neural networks that leverage the properties of hypercomplex feature transformation. In particular, in our proposed class of models, the multiplication rule specifying the algebra itself is inferred from the data during training. Given a fixed model architecture, we present empirical evidence that our proposed model incorporates a regularization effect, alleviating the risk of overfitting. We also show that for fixed model capacity, our proposed method outperforms its corresponding real-formulated GNN, providing additional confirmation for the enhanced expressivity of HC embeddings. Finally, we test our proposed hypercomplex GNN on several open graph benchmark datasets and show that our models reach state-of-the-art performance while consuming a much lower memory footprint with 70\(\%\) fewer parameters. Our implementations are available at https://github.com/bayer-science-for-a-better-life/phc-gnn.
Improved RRT-Connect Based Path Planning Algorithm for Mobile Robots

Path planning plays a key role in the application of mobile robots and it is an important way to achieve intelligent mobile robots.Traditional path planning algorithms need to model environmental obstacles in a deterministic space, which is complex and easily trapped in local minimal.The sampling-based path planning algorithm performs collision detection on the environment and it is able to quickly obtain a feasible path.In order to solve the problem of inefficient search of the sampling-based Rapidly Expanding Random Tree (RRT-Connect) path planning algorithm, an improved RRT-Connect mobile robot path planning algorithm (IRRT-Connect) is proposed in this paper.In order to continue to speed up the search of the algorithm, a simple and efficient third node is generated in the configuration space, allowing the algorithm to be greedily extended with a quadruple tree in the proposed algorithm.Further, the method of adding guidance is proposed to make the algorithm have the characteristics of biasing towards the target point when expanding, which improves the exploration efficiency of the algorithm.In order to verify the effectiveness of the proposed algorithm, this paper compares the execution performance of the four algorithms in six environments of different complexity.The results of the simulation experiments show that the proposed improved algorithm outperforms the RRT, RRT-Connect and RRT* algorithms in terms of the number of algorithm iterations, planning time and final path length in different environments.In addition, the improved algorithm was ported to the ROS mobile robot for experiments with real-world scenarios
The importance of adherence to international standards for depositing open data in public repositories

Abstract There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.
Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm

Ex vivo confocal microscopy (EVCM) generates digitally colored purple-pink images similar to H&E without time-consuming tissue processing. It can be used during Mohs surgery for rapid detection of basal cell carcinoma (BCC); however, reading EVCM images requires specialized training. An automated approach using a deep learning algorithm for BCC detection in EVCM images can aid in diagnosis. A total of 40 BCCs and 28 negative (not-BCC) samples were collected at Memorial Sloan Kettering Cancer Center to create three training datasets: (i) EVCM image dataset (663 images), (ii) H&E image dataset (516 images), and (iii) a combination of the two datasets. A total of seven BCCs and four negative samples were collected to create an EVCM test dataset (107 images). The model trained with the EVCM dataset achieved 92% diagnostic accuracy, similar to the H&E model (93%). The area under the receiver operator characteristic curve was 0.94, 0.95, and 0.94 for EVCM-, H&E-, and combination-trained models, respectively. We developed an algorithm for automatic BCC detection in EVCM images (comparable accuracy to dermatologists). This approach could be used to assist with BCC detection during Mohs surgery. Furthermore, we found that a model trained with only H&E images (which are more available than EVCM images) can accurately detect BCC in EVCM images.
Airborne/UAV Multisensor Surveys Enhance the Geological Mapping and 3D Model of a Pseudo-Skarn Deposit in Ploumanac’h, French Brittany

Taking advantage of a multi-sensor (multispectral and magnetic) drone survey, we address the detailed geological mapping and modeling of a mineralization in its geological environment. We stress that these high-resolution data allow us to bridge the gap between field observations and a regional aeromagnetic survey. On the one hand, the combination of multispectral imagery with field geological observations enhances detailed geological mapping. On the other hand, the combination of field magnetic susceptibility measurement and their use in detailed to regional magnetic modeling, constrained respectively by UAV-borne and airborne magnetic surveys, allows deriving a model of the mineralization consistent across the scales. This is demonstrated in a case study in a complex polyphased magmatic-metamorphic environment on the coast of French Brittany. The target area hosts a pseudo-skarn mineralization, exhibiting an outstanding magnetic anomaly. The combination of remotely sensed and field data allows deriving a realistic conceptual and geometrical model of the magnetic mineralization in its geological environment, tightly constrained by field observations and measurements.
Learning History Using Virtual and Augmented Reality

Master lectures of history are usually quite boring for the students, and to keep their attention requires a great effort from teachers. Virtual and Augmented Reality have a clear potential in education and can solve this problem. Serious games that use immersive technologies allow students to visit and interact with environments dated in different ages. Taking this in mind, this article presents a playful virtual reality experience set in Ancient Rome that allows the user to learn concepts from that age. The virtual experience reproduces as accurately as possible the different buildings and civil constructions of the time, making it possible for the player to create Roman cities in a simple way. Once built, the user can visit them, accessing the buildings and being able to interact with the objects and characters that appear. Moreover, in order to learn more information about every building, users can visualize them using Augmented Reality using marker-based techniques. Different information has been included related to every building, such as their main uses, characteristics, or even some images that represent them. In order to evaluate the effectiveness of the developed experience, several experiments have been carried out, taking as sample Secondary School students. Initially, the game’s quality and playability has been evaluated and, subsequently, the motivation of the virtual learning experience in history. The results obtained support on the one hand its gameplay and attractiveness, and on the other, the student’s increased interest in studying history, as well as the greater fixation of different concepts treated in a playful experience.
Progressive Enhancement of Label Distributions for Partial Multilabel Learning

Partial multi-label learning (PML) aims to learn a multilabel predictive model from the PML training examples, each of which is associated with a set of candidate labels where only a subset is valid. The common strategy to induce a predictive model is identifying the valid labels in each candidate label set. Nonetheless, this strategy ignores considering the essential label distribution corresponding to each instance as label distributions are not explicitly available in the training dataset. In this article, a novel partial multilabel learning method is proposed to recover the latent label distribution and progressively enhance it for predictive model induction. Specifically, the label distribution is recovered by considering the observation model for logical labels and the sharing topological structure from feature space to label distribution space. Besides, the latent label distribution is progressively enhanced by recovering latent labeling information and supervising predictive model training alternatively to make the label distribution appropriate for the induced predictive model. Experimental results on PML datasets clearly validate the effectiveness of the proposed method for solving partial multilabel learning problems. In addition, further experiments show the high quality of the recovered label distributions and the effectiveness of adopting label distributions for partial multilabel learning.
Evaluating Debiasing Techniques for Intersectional Biases

Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques.Evaluation of NLP debiasing methods has largely been limited to binary attributes in isolation, e.g., debiasing with respect to binary gender or race, however many corpora involve multiple such attributes, possibly with higher cardinality.In this paper we argue that a truly fair model must consider 'gerrymandering' groups which comprise not only single attributes, but also intersectional groups.We evaluate a form of bias-constrained model which is new to NLP, as well an extension of the iterative nullspace projection technique which can handle multiple protected attributes.
Provable Guarantees for Understanding Out-of-Distribution Detection

Out-of-distribution (OOD) detection is important for deploying machine learning models in the real world, where test data from shifted distributions can naturally arise. While a plethora of algorithmic approaches have recently emerged for OOD detection, a critical gap remains in theoretical understanding. In this work, we develop an analytical framework that characterizes and unifies the theoretical understanding for OOD detection. Our analytical framework motivates a novel OOD detection method for neural networks, GEM, which demonstrates both theoretical and empirical superiority. In particular, on CIFAR-100 as in-distribution data, our method outperforms a competitive baseline by 16.57% (FPR95). Lastly, we formally provide provable guarantees and comprehensive analysis of our method, underpinning how various properties of data distribution affect the performance of OOD detection.
Augmented Reality as a Tool to Guide PSI Placement in Pelvic Tumor Resections

Patient-specific instruments (PSIs) have become a valuable tool for osteotomy guidance in complex surgical scenarios such as pelvic tumor resection. They provide similar accuracy to surgical navigation systems but are generally more convenient and faster. However, their correct placement can become challenging in some anatomical regions, and it cannot be verified objectively during the intervention. Incorrect installations can result in high deviations from the planned osteotomy, increasing the risk of positive resection margins. In this work, we propose to use augmented reality (AR) to guide and verify PSIs placement. We designed an experiment to assess the accuracy provided by the system using a smartphone and the HoloLens 2 and compared the results with the conventional freehand method. The results showed significant differences, where AR guidance prevented high osteotomy deviations, reducing maximal deviation of 54.03 mm for freehand placements to less than 5 mm with AR guidance. The experiment was performed in two versions of a plastic three-dimensional (3D) printed phantom, one including a silicone layer to simulate tissue, providing more realism. We also studied how differences in shape and location of PSIs affect their accuracy, concluding that those with smaller sizes and a homogeneous target surface are more prone to errors. Our study presents promising results that prove AR’s potential to overcome the present limitations of PSIs conveniently and effectively.
An efficient sparse pruning method for human pose estimation

Human pose estimation (HPE) is crucial for computer vision (CV). Moreover, it’s a vital step for computers to understand human actions and behaviours. However, the huge number of parameters and calculations in the HPE model have brought big challenges to deploy to resource-constrained mobile devices. Aiming to overcome the challenge, we propose a sparse pruning method (SPM) for the HPE model. First, L1 regularisation is added in the training phase of the original model, and network parameters of the convolution layers (CLs) and batch normalisation layers (BNLs) are sparsely trained to obtain a network structure with sparse weights. We then combine the sparse weights of filters with the scaling parameters of the BNLs to determine their importance. Finally, the structured pruning method is used to prune the sparse filters and corresponding channels. SPM can reduce the number of model parameters and calculations without affecting precision. Promising results indicate that SPM outperforms other advanced pruning methods.
Real-Time LiDAR Point Cloud Semantic Segmentation for Autonomous Driving

LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning. LiDAR-based point cloud semantic segmentation is an important task with a critical real-time requirement. However, most of the existing convolutional neural network (CNN) models for 3D point cloud semantic segmentation are very complex and can hardly be processed at real-time on an embedded platform. In this study, a lightweight CNN structure was proposed for projection-based LiDAR point cloud semantic segmentation with only 1.9 M parameters that gave an 87% reduction comparing to the state-of-the-art networks. When evaluated on a GPU, the processing time was 38.5 ms per frame, and it achieved a 47.9% mIoU score on Semantic-KITTI dataset. In addition, the proposed CNN is targeted on an FPGA using an NVDLA architecture, which results in a 2.74x speedup over the GPU implementation with a 46 times improvement in terms of power efficiency.