Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D Medical Image Classification

Multi-modality 3D medical images play an important role in the clinical practice. Due to the effectiveness of exploring the complementary information among different modalities, multi-modality learning has attracted increased attention recently, which can be realized by Deep Learning (DL) models. However, it remains a challenging task for two reasons. First, the prediction confidence of multi-modality learning network cannot be guaranteed when the model is trained with weakly-supervised volume-level labels. Second, it is difficult to effectively exploit the complementary information across modalities and also preserve the modality-specific properties when fusion. In this paper, we present a novel Reinforcement Learning (RL) driven approach to comprehensively address these challenges, where two Recurrent Neural Networks (RNN) based agents are utilized to choose reliable and informative features within modality (intra-learning) and explore complementary representations across modalities (inter-learning) with the guidance of dynamic weights. These agents are trained via Proximal Policy Optimization (PPO) with the confidence increment of the prediction as the reward. We take the 3D image classification as an example and conduct experiments on a multi-modality brain tumor MRI data. Our approach outperforms other methods when employing the proposed RL-based multi-modality representation learning.
Geodesic Learning With Uniform Interpolation on Data Manifold

Recently with the development of deep learning on data representation and generation, how to sampling on a data manifold becomes a crucial problem for research. In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method is able to generate high-quality uniform interpolations with the shortest path between two given data samples. Specifically, we use an autoencoder network to map data samples into the latent space and perform interpolation in the latent space via an interpolation network. We add prior geometric information to regularize our autoencoder for a flat latent embedding. The Riemannian metric on the data manifold is induced by the canonical metric in the Euclidean space in which the data manifold is isometrically immersed. Based on this defined Riemannian metric, we introduce a constant-speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolations along the learned geodesic on the manifold. We provide a theoretical analysis of our model and use image interpolation as an example to demonstrate the effectiveness of our method.
DIGITAL TRANSFORMATION OF HIGHER EDUCATION THROUGH DISRUPTIVE PEDAGOGIES: WRAPPING A COURSE AROUND A COURSE TO PROMOTE LEARNER AGENCY

Today's professionals are increasingly seeking learning opportunities to augment their skills and strengthen their resumes.As a result, there has been an increase in the demand for, and acceptance of, digital credentials by employers across all industries.This creates an opportunity for colleges and universities to create innovative pathways for granting academic credit to external digital credentials.In addition to embracing disruptive technologies, the digital transformation of higher education also necessitates the investigation of disruptive pedagogies-teaching methods and practices that challenge the status quo-to prepare their graduates to enter the workforce ready and equipped to hit the job running.This article describes a potentially disruptive pedagogy whereby students in an online graduate course are able to engage in a self-directed learning opportunity that promotes learner agency and enhances professional knowledge through the pursuit of an external digital credential in a topic or subject of their choice.An overview of the status of digital credentialing in professional and higher education settings, as well as the results of a survey where students weighed in on the perceived value of their personalized, selfdirected learning experience are also presented.
Region-Guided CycleGANs for Stain Transfer in Whole Slide Images

In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H &E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue landscape. In the case of detecting metastases, IHC provides a distinct readout that is readily interpretable by pathologists. IHC, however, is a more expensive approach and not available at all medical centers. Virtually generating IHC images from H &E using deep neural networks thus becomes an attractive alternative. Deep generative models such as CycleGANs learn a semantically-consistent mapping between two image domains, while emulating the textural properties of each domain. They are therefore a suitable choice for stain transfer applications. However, they remain fully unsupervised, and possess no mechanism for enforcing biological consistency in stain transfer. In this paper, we propose an extension to CycleGANs in the form of a region of interest discriminator. This allows the CycleGAN to learn from unpaired datasets where, in addition, there is a partial annotation of objects for which one wishes to enforce consistency. We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells. We demonstrate the superiority of our approach over prior art in stain transfer on histopathology tiles over two datasets. Our code and model are available at https://github.com/jcboyd/miccai2022-roigan .
A Research on the Sharing Platform of Wild Bird Data in Yunnan Province Based on Blockchain and Interstellar File System

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain's decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.
Early Experiments on Automatic Annotation of Portuguese Medieval Texts

This paper presents the challenges and solutions adopted to the lemmatization and part-of-speech (PoS) tagging of a corpus of Old Portuguese texts (up to 1525), to pave the way to the implementation of an automatic annotation of these Medieval texts. A highly granular tagset, previously devised for Modern Portuguese, was adapted to this end. A large text ( $$\sim $$ 155 thousand words) was manually annotated for PoS and lemmata and used to train an initial PoS-tagger model. When applied to two other texts, the resulting model attained 91.2% precision with a textual variant of the same text, and 67.4% with a new, unseen text. A second model was then trained with the data provided by the previous three texts and applied to two other unseen texts. The new model achieved a precision of 77.3% and 82.4%, respectively.
Object Detection Algorithm Based on Improved Feature Pyramid

Feature pyramid network is widely used in advanced object detection. By simply changing the network connection, the performance of small object detection can be greatly improved without increasing the amount of calculation of the original model. However, the algorithm still has some shortcomings. Therefore, a new attention feature pyramid network (attention PFN) is proposed. Firstly, an improved receptive field module is added, which can make full use of global and local context information. Secondly, the connection mode of the pyramid is further optimized. Deconvolution is used to replace the nearest neighbor interpolation in top-down up-sampling and a channel attention module is added to the horizontal connection to highlight important context information. Finally, adaptive fusion and spatial feature balance are used for each feature pyramid, so that the network can learn the weights of different feature layers. Each pyramid layer contains more discrimination information. Attention PFN is tested on Pascal VOC and MS COCO datasets, respectively. The experiment results revealed that the proposed method has better performance than the original algorithm. Therefore, the attention PFN is an effective algorithm.
HUMAN MOTION RECOGNITION IN SMALL SAMPLE SCENARIOS BASED ON GAN AND CNN MODELS

In the research of radar-based human motion classification and recognition, the traditional manual feature extraction is more complicated, and the echo data set is generally smaller.In view of this problem, a method of human motion recognition in small sample scenarios based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) models is proposed.First, a 77 GHz millimeter wave radar data acquisition system is built to obtain echo data.Secondly, the collected human motion echo data is preprocessed, the micro-Doppler features are extracted, and the range Doppler map (RDM) is used to project the velocity dimension and accumulate the two-dimensional micro-Doppler time-frequency map data set of the human motion frame by frame.Finally, the deep convolution generative adversarial network (DCGAN) is constructed to achieve data augmentation of the sample set, and the CNN is constructed to realize automatic feature extraction to complete the classification recognition of different human motions.Experimental studies have shown that the combination of GAN and CNN can achieve effective recognition of daily human motions, and the recognition accuracy can reach 96.5%.Compared with the manual feature extraction, the recognition accuracy of CNNs is improved by 7.3%.Compared with the original data set, the system recognition accuracy based on the sample augmentation data set is improved by 2.17%, which shows that the GAN has an excellent performance in human motion recognition in small sample scenarios.
Development Of Android-Based Edutainment Game Learning Media On Phytagoras Theorem For Junior High School Students

Distance learning during the COVID-19 pandemic caused students' learning motivation to decrease. For this reason, an interesting learning process is needed, one of which is by applying game-based learning.This study aims to develop and determine the feasibility of an Android-based edutainment game learning media on the Pythagorean theorem material for junior high school students. This type of research is Research and Development (RD) using the ADDIE development model, namely Analyze, Design, Development, Implementation, and Evaluation. This research was conducted at MTs Muttaqien PSM Takeran in June 2021. The results of the research on the development of android-based edutainment game learning media include 1) Android-based edutainment game learning media meets the validity criteria with the combined validation average score by the media validator of 84.10% and is included in the fairly valid category, 2) Edutainment game learning media android-based meet the practicality criteria obtained from the average results of filling out student response questionnaires of 87.50% and is included in the very practical category, 3) Android-based edutainment game learning media meets the effectiveness criteria obtained from student learning outcomes tests with a score of N- The gain is 62.32% and is included in the medium category. Thus, it can be concluded that the Android-based edutainment game learning media is feasible to use in learning mathematics on the Pythagorean theorem material. The results of this study provide a contribution in the form of an Android-based edutainment game learning media that is used as an alternative in the learning process.
Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning

Contrastive learning enables learning useful audio and speech representations without ground-truth labels by maximizing the similarity between latent representations of similar signal segments.In this framework various data augmentation techniques are usually exploited to help enforce desired invariances within the learned representations, improving performance on various audio tasks thanks to more robust embeddings.Now, selecting the most relevant augmentations has proven crucial for better downstream performances.Thus, this work introduces a conditional independance-based method which allows for automatically selecting a suitable distribution on the choice of augmentations and their parametrization from a set of predefined ones, for contrastive self-supervised pre-training.This is performed with respect to a downstream task of interest, hence saving a costly hyper-parameter search.Experiments performed on two different downstream tasks validate the proposed approach showing better results than experimenting without augmentation or with baseline augmentations.We furthermore conduct a qualitative analysis of the automatically selected augmentations and their variation according to the considered final downstream dataset.
Multioutput Regression Neural Network Training via Gradient Boosting

A novel sequential procedure to train the final layers of a multi-output regression neural network (NN) based on Gradient Boosting is proposed, where the NN is an additive expansion of the Gradient Boosting.The method works by training portions of the network in an iterative manner in such a way that each new portion of the NN is learnt to compensate for the errors of the already trained portions, and the final result of the network forms by provided weight and the last hidden layer output.This is in contrast to the standard training of NNs in which the whole network is trained to learn the concept at hand.Extensive experiments show the good performance of the proposed method with respect to NN.
Revisiting Edge Pooling in Graph Neural Networks

Sparse pooling methods for graph neural networks typically perform graph reduction by keeping only the top-k vertices according to an adaptive scoring function.Although fast and scalable, these methods destroy the relational information of the graph and possibly make it disconnected.EdgePool is one of the few sparse alternatives that preserve the connectivity of the input graph by performing a series of edge contractions according to an adaptive scoring of the edges, but it has the drawback of being sequential and not scalable on large scale graphs.In this paper we show that EdgePool can be efficiently computed adapting a well-known parallel algorithm from literature, and we also propose a novel, parallel alternative that leverages on an adaptive scoring function of the nodes.We test both methods on standard benchmark datasets, showing that they generally outperform other sparse pooling methods from the literature.
Tamper detection and localization in color images using secure block-based watermarking

Nowadays, the facilities that offers Internet in conjunction with the capabilities of the infrastructure of communication and information technologies, allowing that the multimedia data can be shared and transmitted in easy and efficient way. As consequence, the multimedia data can be copied or edited in an easy form, and this can be affecting its original content. In a context of digital images, this paper presents a tamper detection and localization algorithm in color images using secure block-based watermarking. This proposal is based on the frequency domain using the lifting wavelet transform, together with checksum criteria that make up the hash key codes, which they are hidden in the least significant bits of each low frequency component. The proposed method is evaluated in terms of imperceptibility and false positive, false negative and tamper detection rates, respectively. Watermark imperceptibility is measured using the peak signal to noise ratio and structural similarity index, respectively. The tamper detection ability has been tested on color and grayscale images using a wide variety of manipulations such as copy-paste, copymove, constant averaging, impulsive noise, and random manipulations. Comparative with the current state-of-the-art is provided.
Post-Quantum Cryptography for Embedded Systems

Over the years, important breakthroughs have been made in protecting data at rest or in-transit. The emergence of the Internet of Things (IoT) and Cyber Physical Systems (CPS) has pose new challenges for protecting data in systems and devices tightly close to humans. Embedded systems with constrained computing capabilities are a central part in IoT and CPS, susceptible to different security attacks and vulnerabilities. Lightweight cryptography is the term used for grouping the different cryptographic algorithms and techniques tailored to observe the requirements for data security in such constrained applications. Nowadays, the potential emergence of quantum computing threatens these security solutions widely in use. Thus, novel cryptographic algorithms deemed to resist quantum attack models are being standardized by the National Institute of Standards and Technology (NIST) in the Post-Quantum Cryptography (PQC) Standardization process. In this paper, we present a study on how the advent of quantum computing might have a deep impact on the security of constrained applications in the IoT and CPS domains. We focused on the NIST PQC finalists, from the perspective of their use for securing embedded systems.
Related-Key Differential Attacks on Reduced-Round LBlock

LBlock, as one of the typical lightweight encryption schemes, is a 32-round block cipher with 64 bit block and 80 bit master key. It can be widely applied in the IoT environment because of its friendly software and hardware implementations. Since it came out, it has encountered many attacks. In this paper, we evaluate LBlock’s ability against related-key differential attack more accurately based on SMT method. On the one hand, we propose tighter lower bounds on the minimal number of active S-boxes for up to 19 rounds of LBlock, which are 8 more rounds than previous ones. Then, we propose the upper bounds of total probabilities for up to 19 rounds of LBlock for the first time. On the other hand, with a suitable 17-round related-key differential distinguisher, we propose attacks on 22- and 23-round LBlock. Each of these attacks has lower time complexity and data complexity than previous ones for the same rounds of LBlock.
Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks

Quantum entanglement is a unique phenomenon of quantum mechanics, which has no classical counterpart and gives quantum systems their advantage in computing, communication, sensing, and metrology. In quantum sensing and metrology, utilizing an entangled probe state enhances the achievable precision more than its classical counterpart. Noise in the probe state preparation step can cause the system to output unentangled states, which might not be resourceful. Hence, an effective method for the detection and classification of tripartite entanglement is required at that step. However, current mathematical methods cannot robustly classify multiclass entanglement in tripartite quantum systems, especially in the case of mixed states. In this paper, we explore the utility of artificial neural networks for classifying the entanglement of tripartite quantum states into fully separable, biseparable, and fully entangled states. We employed Bell's inequality for the dataset of tripartite quantum states and train the deep neural network for multiclass classification. This entanglement classification method is computationally efficient due to using a small number of measurements. At the same time, it also maintains generalization by covering a large Hilbert space of tripartite quantum states.
Real-Time Multisignal Detection and Identification in Known and Unknown HF Channels: A Deep Learning Method

The high-frequency (HF) signal detection and identification plays an import role in HF communications, and it is challenging since the HF environment randomly varies. Due to the success of deep learning (DL) methods in the fields of computer vision and natural language processing, some researchers adopt DL-based object detection methods to detect and identify signals in wideband spectrograms and achieve the good performance. However, the existing DL-based methods are not suitable for real-time HF signal detection, and their performance will be significantly degraded when these methods are applied to an unknown HF environment. In this paper, we design a novel multiresolution signal detection and identification network for real-time HF signal detection and identification and propose a domain adaptation method to adapt the network to unknown environments. The experimental results show that the running speed and accuracy of our designed network are superior to ones of the existing DL-based networks in different HF environments, and the proposed domain adaptation method can achieve obvious performance improvement in unknown environments.
Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.