Reviewerly/exHarmony huggingface dataset:
DLBN: Group Storage Mechanism Based on Double-Layer Blockchain Network

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.
Research on application of article recommendation algorithm based on Word2Vec and Tfidf

With the rapid expansion of the amount of information in social media such as various news and information software, people urgently need to realize the automatic classification of this information to help users quickly find the information they need and filter spam. Aiming at the curse of feature dimension and non-semantic features in traditional text classification models, this paper studies the classification of article text based on the Word2vec model. One of the shortcomings of the Word2vec model is that the essentiality of words in diverse texts is not the same, so this paper introduces the TFIDF model, which can weight Word2vec word vectors to achieve a weighted Word2vec classification model. At last, the weighted Word2vec and TFIDF models are combined.
Benchmarking and Interpreting End-to-End Learning of MIMO and Multi-User Communication

End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. Our particular focus is on memoryless multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case studies are considered: two point-to-point (closed-loop and open-loop MIMO) and two MU scenarios (MIMO broadcast and interference channels). For the point-to-point scenarios, we explain some of the performance gains observed in prior work through the selection of improved baseline schemes that include geometric shaping as well as bit and power allocation. For the MIMO broadcast channel, we demonstrate the feasibility of a novel AE method with centralized learning and decentralized execution. Interestingly, the learned scheme performs close to nonlinear vector-perturbation precoding and significantly outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls when interpreting learned communication schemes. In particular, we show that the AE for the considered interference channel learns to avoid interference, albeit in a rotated reference frame. After de-rotating the learned signal constellation of each user, the resulting scheme corresponds to conventional time sharing with geometric shaping.
Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information

Quantify tradeoffs in performance, reproducibility, and resource demands across several strategies for developing clinically relevant word embeddings.We trained separate embeddings on all full-text manuscripts in the Pubmed Central (PMC) Open Access subset, case reports therein, the English Wikipedia corpus, the Medical Information Mart for Intensive Care (MIMIC) III dataset, and all notes in the University of Pennsylvania Health System (UPHS) electronic health record. We tested embeddings in six clinically relevant tasks including mortality prediction and de-identification, and assessed performance using the scaled Brier score (SBS) and the proportion of notes successfully de-identified, respectively.Embeddings from UPHS notes best predicted mortality (SBS 0.30, 95% CI 0.15 to 0.45) while Wikipedia embeddings performed worst (SBS 0.12, 95% CI -0.05 to 0.28). Wikipedia embeddings most consistently (78% of notes) and the full PMC corpus embeddings least consistently (48%) de-identified notes. Across all six tasks, the full PMC corpus demonstrated the most consistent performance, and the Wikipedia corpus the least. Corpus size ranged from 49 million tokens (PMC case reports) to 10 billion (UPHS).Embeddings trained on published case reports performed as least as well as embeddings trained on other corpora in most tasks, and clinical corpora consistently outperformed non-clinical corpora. No single corpus produced a strictly dominant set of embeddings across all tasks and so the optimal training corpus depends on intended use.Embeddings trained on published case reports performed comparably on most clinical tasks to embeddings trained on larger corpora. Open access corpora allow training of clinically relevant, effective, and reproducible embeddings.
An Improved Ant Colony Algorithm for Solving a Virtual Machine Placement Problem in a Cloud Computing Environment

The world is currently witnessing many successive changes in several fields, especially in the field of healthcare which forced countries to impose health policies to provide good services, which drew health sector strategies focusing on the digital transformation aspect.E-government healthcare applications are used by dozens of millions of users.This high usage of applications generates a huge network traffic and needs reliable cloud computing platforms and efficient virtual machine placement models.In this context, we proposed an improved Ant Colony algorithm to solve a virtual machine placement problem in a cloud computing environment in order to minimize the total network traffic and the maximum link utilization.The experimental results demonstrate the effectiveness and efficiency of our proposed algorithm.
Privacy-Preserving Color Image Feature Extraction by Quaternion Discrete Orthogonal Moments

To implement image storage and computation in cloud servers without violating users’ privacy, privacy-preserving feature extraction has been a new research interest. The existing works are mainly designed for grayscale images. For color images, they tend to convert them to grayscale images or obtain the results of the combination of single-channel processes. While the capabilities of features extracted from the encrypted color images will be affected if color information and inter-relationship between color channels are ignored. To fully preserve features of color images, we introduce quaternion theory to encode each color image and propose an improved vector homomorphic encryption scheme (IVHE) to encrypt quaternion-based color images. IVHE helps protect image content and keep vector characteristics of color images. Based on IVHE, the framework for feature extraction of privacy-preserving Quaternion Discrete Orthogonal Moments (PPQDOMs) is presented. Theoretical analyses prove that Quaternion Discrete Orthogonal Moments (QDOMs) can be extracted from the encrypted color images by PPQDOMs. Furthermore, we apply three common Discrete Orthogonal Moments to the proposed framework to evaluate its performance. Experimental results demonstrate that the proposed framework can protect color image content and perform well compared to QDOMs in image reconstruction and image recognition.
Morpheus II: A RISC-V Security Extension for Protecting Vulnerable Software and Hardware

Morpheus II is a secure processor designed to prevent control flow attacks. Morpheus II strengthens the defenses of the Morpheus [1] processor, by deploying always-on encryption to obfuscate code and pointers along with runtime churn to thwart side-channel attacks. Focusing on Remote Code Execution attacks, we modified the RISC-V Rocket core to support always-encrypted code and code pointers with negligible performance impact and less than 2% area overhead. Morpheus II was deployed running a web server interface to a mock medical database on AWS F1 instances, where it was red-teamed for three months by over 500 security researchers. No vulnerabilities were discovered in Morpheus II. In addition, we evaluated Morpheus II against a range of CWE attack classes including a Blind ROP attack on the web server. We show that Morpheus II defenses increase Blind ROP probe time for gadgets from weeks to likely thousands of years.
Towards the use of cybernetics for an enhanced cervical cancer care strategy

Cervical cancer is a prominent disease in women, with a high mortality rate worldwide. This cancer continues to be a challenge to concisely diagnose, especially in its early stages. The aim of this study was to propose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies. In this work, we applied machine learning (ML) models on 650 patients’ data collected from Hospital Universitario de Caracas in Caracas, Venezuela, where ethical approval and informed consent were granted. The data were hosted at the University of California at Irvine (UCI) database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer. Two contrasting methods are explored in the design of a ML-driven prediction machine in this study, namely, a probabilistic method using Gaussian mixture models (GMM), and fuzziness-based reasoning using the fuzzy c-means (FCM) clustering on the data from 650 patients. The models were validated using a K-Fold validation method, and the results show that both methods could be feasibly deployed in a clinical setting, with the probabilistic method (produced accuracies of 80+%/classifier dependent) allowing for more detail in the grading of a potential cervical cancer prediction, albeit at the cost of greater computation power; the FCM approach (produced accuracies around 90+%/classifier dependent) allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison. As part of the novelty of this work, a clinical cybernetic system is also proposed to host the prediction machine, which allows for a human-machine collaborative interaction and an enhanced decision support platform to augment overall care strategies. The present study showcased how the use of prediction machines can contribute towards early detection and prioritised care of patients with cervical cancer, while also allowing for cost-saving benefits when compared with routine cervical cancer screening. Further work in this area would now involve additional validation of the proposed clinical cybernetic loop and further improvement to the prediction machine by exploring non-linear dimensional embedding and clustering methods.
Lateral Vehicle Trajectory Planning Using a Model Predictive Control Scheme for an Automated Perpendicular Parking System

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions.
Micro-Service Placement Policies for Cost Optimization in Kubernetes

Kubernetes enables deployment and orchestration of containerized applications across server infrastructures and in the cloud. Services running in separate containers are packed together in pods and are placed in Virtual Machines (VMs) or nodes. Application services must be placed in nodes in a way that minimizes the cost of operating an application. The problem often occurs when deploying applications comprising multiple communicating micro-services which may not fit all together in one node. This work suggests that an application is modeled by means of a graph and handles the problem of service placement as a graph clustering one. Service placement must minimize both, the node resources and the bandwidth consumed by the communicating nodes. Several graph clustering methods are deployed in the Kubernetes infrastructure of the Google Cloud Platform (GCP) to support service placement for two real use cases (an IoT architecture and an e-shop). The experimental results demonstrate that the costs of hosting applications on GCP can be significantly reduced compared to the costs of applications placed using the default Kubernetes method.
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer

Recent advances on prompt-tuning cast few-shot classification tasks as a masked language modeling problem. By wrapping input into a template and using a verbalizer which constructs a mapping between label space and label word space, prompt-tuning can achieve excellent results in few-shot scenarios. However, typical prompt-tuning needs a manually designed verbalizer which requires domain expertise and human efforts. And the insufficient label space may introduce considerable bias into the results. In this paper, we focus on eliciting knowledge from pretrained language models and propose a prototypical prompt verbalizer for prompt-tuning. Labels are represented by prototypical embeddings in the feature space rather than by discrete words. The distances between the embedding at the masked position of input and prototypical embeddings are used as classification criterion. To address the problem of random initialization of parameters in zero-shot settings, we elicit knowledge from pretrained language models to form initial prototypical embeddings. Our method optimizes models by contrastive learning. Extensive experimental results on several many-class text classification datasets with low-resource settings demonstrate the effectiveness of our approach compared with other verbalizer construction methods. Our implementation is at https://github.com/Ydongd/prototypical-prompt-verbalizer .
An Overview of Panoramic Video Projection Schemes in the IEEE 1857.9 Standard for Immersive Visual Content Coding

Panoramic video contains 360-degree video content and it is convenient to draw the content that the user wants to watch on the head-Mount display (HMD) of the virtual reality display device. However, few encoders code panoramic video directly. Panoramic video content needs to be projected to a compression-friendly rectangular plane first. Then the existing video coding standards such as AVC, HEVC, VVC, AVS are used to code the panoramic video content. In order to improve the panoramic video coding efficiency, IEEE 1857.9 immersive video content coding working group was established to develop efficient immersive video projection and coding methods. This paper presents an overview of the panoramic video projection schemes in Immersive Visual Content Coding Standard IEEE 1857.9.
Profiled Side-Channel Analysis in the Efficient Attacker Framework

Profiled side-channel attacks represent the most powerful category of side-channel attacks. There, the attacker has access to a clone device to profile its leaking behavior. Additionally, it is common to consider the attacker unbounded in power to allow the worst-case security analysis. This paper starts with a different premise where we are interested in the minimum power that the attacker requires to conduct a successful attack. We propose a new framework for profiled side-channel analysis that we call the Efficient Attacker Framework. With it, we require attacks to be as powerful as possible, but we also provide a setting that inherently allows a more objective analysis among attacks. To confirm our theoretical results, we provide an experimental evaluation of our framework in the context of deep learning-based side-channel analysis.
Validating markerless pose estimation with 3D X-ray radiography

ABSTRACT To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here, we evaluated the accuracy of DeepLabCut (DLC), a deep learning markerless motion capture approach, by comparing it with a 3D X-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed 3D kinematics in a common coordinate system. We used the toolkit Anipose to filter and triangulate DLC trajectories of 11 markers on the forelimb and torso and found a low median error (0.228 cm) between the two modalities corresponding to 2.0% of the range of motion. For studies allowing this relatively small error, DLC and similar markerless pose estimation tools enable the study of increasingly naturalistic behaviors in many fields including non-human primate motor control.
Design of remote monitored solar powered grasscutter robot with obstacle avoidance using IoT

Arduino UNO-based Solar powered Grasscutter designed to cut healthy grass in places like parks, hotels, public places, etc., The Grasscutter is designed through IoT (Internet of Things) technology, which is controlled remotely through Blynk application supported with Bluetooth module. The proposed model consists of hardware components like Arduino UNO, Solar panel, DC motor, motor driver, rechargeable batteries and Bluetooth module. The designed model is programmed through Arduino IDE to control the operation of the Grasscutter. The control mechanism and movements such as Forward movement, Backward movement, Right movement, Left movement, On mechanism, Off mechanism and Stop function for the Grasscutter prototype. An ultrasonic sensor connected to the head of the model avoids the system from colliding with obstacles while in movement.
3DVNet: Multi-View Depth Prediction and Volumetric Refinement

We present 3DVNet, a novel multi-view stereo (MVS) depth-prediction method that combines the advantages of previous depth-based and volumetric MVS approaches. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of coarse depth predictions, resulting in highly accurate predictions which agree on the underlying scene geometry. Unlike existing depth-prediction techniques, our method uses a volumetric 3D convolutional neural network (CNN) that operates in world space on all depth maps jointly. The network can therefore learn meaningful scene-level priors. Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on a feature-augmented point cloud, allowing for effective aggregation of multi-view information and flexible iterative refinement of depth maps. Experimental results show our method exceeds state-of-the-art accuracy in both depth prediction and 3D reconstruction metrics on the ScanNet dataset, as well as a selection of scenes from the TUM-RGBD and ICL-NUIM datasets. This shows that our method is both effective and generalizes to new settings.
Capability-Based Access Control With Trust for Effective Healthcare Systems

Because of the expanding fame of distributed storage, numerous medicinal services associations have begun moving electronic wellbeing records or EHR to cloud-based capacity frameworks. This change can diminish the expenses related with the administration of information sharing, correspondence overhead, and improve the Quality of Service. In this paper, we have projected an entrance regulator prototype which depends on the dependability of the mentioned client. This Access Control based Trust Model for Healthcare System structure made out of the trust component, trust model with access control approaches which upgrades exactness and effectiveness of the framework. This entrance control system will guarantee the main trusted and approved client can get to the information and assets. The detail structure and introduction of the working prototype show that the exactness and effectiveness of the social insurance framework identified with cloud are more when contrasted with other trust models.
Simple and Effective Multimodal Learning Based on Pre-Trained Transformer Models

Transformer-based models have garnered attention because of their success in natural language processing, and in several other fields, such as image and automatic speech recognition. In addition to them being trained on unimodal information, many transformer-based models have been proposed for multimodal information. In multimodal learning, a common problem encountered is the insufficiency of multimodal training data. In this study, to address this problem, a simple and effective method is proposed by using 1) unimodal pre-trained transformer models as encoders for each modal input and 2) a set of transformer layers to fuse their output representations. Further, the proposed method is evaluated by conducting several experiments on two common benchmarks: CMU multimodal opinion sentiment intensity dataset and multimodal internet movie database. The proposed model exhibits state-of-the-art performances on both benchmarks and is robust against the reduction in the amount of training data.
Quantum-like melody perception

I propose a quantum-like approach to the description of melody perception where classic intervals that constitute a melody are replaced by acoustical qubits, i.e. two-level acoustic systems, using Shepard tones for this purpose. Each of such qubits is considered to be a superposition of two intervals, ascending and descending, that form an octave when put together. Any melody perception can thus be treated analogously to a sequence of quantum measurements. Because of an acoustical collapse, analogous to the wave function reduction in quantum mechanics, just a single interval, ascending or descending, can be heard each time. Different melodies generated by the same sequence of acoustical qubits can be then perceived.