Reviewerly/exHarmony huggingface dataset:
NVIDIA’s Cloud Native Supercomputing

NVIDIA is defining a High-Performance Computing system architecture called Cloud Native Supercomputing to provide bare-metal system performance with security isolation and functional offload capabilities. Cloud Native Supercomputing delivers a cloud-based user experience in a way that maintains the performance and scalability that is uniquely delivered with supercomputing facilities. This new set of capabilities is being driven by the need to accommodate new scientific workflows that combine traditional simulation with experimental data from the edge and combine it with AI, data analytics and visualization frameworks in an integrated and even real-time fashion. These new workflows stress the system management, security and non-computational functions of traditional cloud or supercomputing facilities. Specifically, workflows that include data from untrusted (or non-local) sources, user experiences that range from Jupyter notebooks and interactive jobs to Gordon Bell-class capacity batch runs and I/O patterns that are unique to the emerging mix of in silico and live data sources. To achieve these objectives, we introduce a new architectural component called the Data Processing Unit (DPU), which in early embodiments is a system-on-a-chip (SoC) that includes an InfiniBand (IB) and Ethernet network adapter, programmable Arm cores, memory, PCI switches, and custom accelerators. The BlueField-1 and BlueField-2 devices are NVIDIA's first DPU instances. This paper describes the architecture of cloud native supercomputing systems that use DPUs for isolation and acceleration, along with system services provided by that DPU. These services provide enhanced security through isolation, file-system management capabilities, monitoring, and the offloaded support for communication libraries.
Unsupervised Domain Adaptation With Global and Local Graph Neural Networks Under Limited Supervision and Its Application to Disaster Response

Identification and categorization of social media posts generated during disasters are crucial to reduce the suffering of the affected people. However, the lack of labeled data is a significant bottleneck in learning an effective categorization system for a disaster. This motivates us to study the problem as unsupervised domain adaptation (UDA) between a previous disaster with labeled data (source) and a current disaster (target). However, if the amount of labeled data available is limited, it restricts the learning capabilities of the model. To handle this challenge, we use limited labeled data along with abundantly available unlabeled data, generated during a source disaster to propose a novel two-part graph neural network (GNN). The first part extracts domain-agnostic global information by constructing a token-level graph across domains and the second part preserves local instance-level semantics. In our experiments, we show that the proposed method outperforms state-of-the-art techniques by 2.74% weighted F1 score on average on two standard public datasets in the area of disaster management. We also report experimental results for granular actionable multilabel classification datasets in disaster domain for the first time, on which we outperform BERT by 3.00% on average w.r.t. weighted F1. Additionally, we show that our approach can retain performance when minimal labeled data are available.
Rydberg Wire Gates for Universal Quantum Computation

Rydberg atom arrays offer flexible geometries of strongly interacting neutral atoms, which are useful for many quantum applications such as quantum simulation and quantum computation. Here, we consider an all-optical gate-based quantum computing scheme for the Rydberg atom arrays, in which auxiliary atoms (wire atoms) are used as a mean of quantum-mechanical remote-couplings among data-qubit atoms, and optical individual-atom addressing of the data and wire atoms is used to construct universal quantum gates of the data atoms. The working principle of our gates is to use the wire atoms for coupling mediation only, while leaving them in noncoupling ground states before and after each gate operation, which allows the double-excited states of data qubits to be accessible by a sequence of π or π /2 pulses addressing the data and wire atoms. Optical pulse sequences are constructed for standard one-, two-, and multi-qubit gates, and the arbitrary two-qubit state preparation is considered for universal computation prospects. We further provide a detailed resource estimate for an experimental implementation of this scheme in a Rydberg quantum simulator.
DICOM SIVR: A web architecture and platform for seamless DICOM image and volume rendering

Quick access to radiological images is important for timely diagnosis and effective patient treatment. In this paper, we present a web-based client–server system for seamless image and volume rendering of DICOM images that provides fast access to the data needed for diagnosis without placing a heavy load on computer resources on the client side. DICOM images are rendered on the server, and the resulting 2D images are sent to physicians who can view and analyze them via web browser. Security of patient medical data is ensured by encryption during storage and transfer. The system’s communication model hides the latency of remote rendering to ensure a seamless experience for the user.
A Framework for Collaborative All-Source Navigation With Fault Detection and Exclusion

The ARMAS-SOM framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a GNSS user. This framework uses a multifilter residual monitoring approach for fault detection and exclusion, which is augmented with an additional “observability” extended Kalman filter sublayer for resilience. We monitor the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> state covariances in this sublayer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collaborative</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proprioceptive</i> instances and employing the “stingy collaboration” technique, we are able to maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Last, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.
Research on Epidemic Data Sharing Model Based on Cross-Chain Mechanism

Blockchain technology is an important means for joint epidemic prevention and control to realize epidemic data sharing. Analyzed the shortcomings of the current application of blockchain technology in the field of epidemic data sharing, and introduced a cross-chain mechanism. A model of epidemic data sharing between multiple consortium blockchains based on a cross-chain mechanism is constructed, and the specific cross-chain design architecture and safety guarantee mechanism of the model are elaborated. This model deploys multiple consortium blockchains and introduces the cross-chain mechanism of blockchain for data interaction, which improves the scalability and security of the blockchain, breaks information islands, realizes data sharing, and improves the efficiency of epidemic prevention and control.
The automation of the detection of large class bad smell by using genetic algorithm and deep learning

In Software Engineering (SE), metrics are used for detecting software design problems (bad smells) like the large-class bad smell, where a lot of different metrics were defined to find out the existence of this problem in the design of a class. Examples of these metrics are size metrics, cohesion metrics, and coupling metrics. Selecting the right metrics to detect the large-class bad smell is a common problem, and it is usually accomplished manually. The questions remain: Can a module with the best combination of two metrics, for detecting the problem of large-class bad smell, be formed automatically rather than manually? And how is this double valued threshold determined to be used to infer the existence of this problem? This paper proposes the Hybrid Approach to detect Large Class Bad Smell (HA-LCBS). This approach utilizes the Genetic Algorithm (GA) to automate the composing of a detecting module that consists of a cohesion metric type and a coupling metric type and passes its resulting paired value to a deep learning approach to automate the detection of the large class bad smell. The accuracy that has been gained from using this approach reached 94.21%.
A Taxonomy on Strategic Viewpoint and Insight Towards Multi-Cloud Environments

Distributed computing is one of the standard modern technologies. Distributed computing provides easy access and elite registering by the data. One more important factor is the face of the organization programming could be stored at the data capacity for best experiences. This paper studies various aspects of multi-cloud environments towards modern to current day cloud computing technologies. This article gives a speedy prologue to multi-cloud distributed storage. It covers the vital innovations in “cloud computing and multi-cloud storage”. As a significant innovation zone and exploration bearing, multi-cloud distributed storage is turning into a hot examination for both scholarly world and industry. The future significant examination works were summed up towards the end.
Personalized Federated Learning System Based on Permissioned Blockchain

Federated learning ensures the privacy of data generated by large-scale IoT devices. Existing federated learning frameworks, based on centralized model coordinators, still face serious security challenges such as single point of failure and lack of privacy. In this paper, we propose a personalized federated learning system based on permissioned blockchain, which is divided into four layers of architecture are IoT device layer, network layer, edge computing layer, blockchain layer, and application layer, using permissioned blockchain as a federated learning server. And a permission blockchain-based personalized federation learning algorithm is proposed, which can achieve privacy protection and resistance to poisoning attacks with high accuracy. The experimental results revealed that the system has high privacy protection and anti-poisoning attack capability and can be deployed in edge computing situations.
Homomorphic Decryption in Blockchains via Compressed Discrete-Log Lookup Tables

Many privacy preserving blockchain and e-voting systems are based on the modified ElGamal scheme that supports homomorphic addition of encrypted values. For practicality reasons though, decryption requires the use of precomputed discrete-log (dlog) lookup tables along with algorithms like Shanks’s baby-step giant-step and Pollard’s kangaroo. We extend the Shanks approach as it is the most commonly used method in practice due to its determinism and simplicity, by proposing a truncated lookup table strategy to speed up decryption and reduce memory requirements. While there is significant overhead at the precomputation phase, these costs can be parallelized and only paid once and for all. As a starting point, we evaluated our solution against the widely-used secp family of elliptic curves and show that we can achieve storage reduction by 7x–14x, depending on the group size. Our algorithm can be immediately imported to existing works, especially when the range of encrypted values is known, such as in Zether, PGC and Solidus protocols.
Balancing Utility Versus Privacy in the 2020 Census: Sentiments from Data Users

When designing disclosure avoidance systems, federal statistical agencies must understand how data users balance data utility and privacy in their assessments of privacy protection frameworks, such as differential privacy. Through one-on-one semi-structured interviews with 33 participants across government, research institutions, and community organizations, we collected data usersâ€™ opinions on the new privacy safeguards implemented by the U.S. Census Bureau for the 2020 Census. We identified several messaging themes, which were organized into seven groups. We utilized this feedback to develop a series of recommendations, including creating a separate data file without post-processing, developing disclosure avoidance system criteria transparently, and opening more avenues for accessing unprotected microdata.
Computer Vision Positioning and Local Obstacle Avoidance Optimization Based on Neural Network Algorithm

Due to the rapid development of social computerization and smart devices, there is an increasing demand for indoor positioning of mobile robots in the robotics field, so it is very important to realize the autonomous navigation of mobile robots. However, in indoor scenes, due to factors such as dark walls, the global positioning system cannot effectively locate, and the broadband and wired positioning technologies used indoors have problems such as base station laying and delay. Computer vision positioning technology has greatly improved the camera hardware due to its simple equipment and low cost. Compared with other sensor cameras, it is less affected by environmental changes, so visual positioning has received extensive attention. Image matching has become the most critical link in visual positioning. The accuracy, speed, and robustness of image matching directly determine the results of visual positioning, so image matching has become the main topic of this study. In this study, the neural network algorithm is systematically optimized, especially for the robot's local obstacle avoidance, and an obstacle data acquisition method based on VGG16 and fast RCNN is proposed. In order to solve the problem that the semantic image segmentation algorithm based on AlexNet and ResNet is difficult to accurately obtain the information of multiple objects, and an image semantic segmentation algorithm combined with VGG16 is designed to classify the background and road in the image at the pixel level and capture the path boundary line. The collection of robot obstacle path information improves the speed and accuracy of highly automated local obstacle avoidance. This study uses neural network algorithms to systematically optimize computer vision positioning and also studies the accuracy optimization of local obstacle avoidance, aiming to promote its better development.
Event Causal Relationship Retrieval

Analyzing history has numerous benefits, including understanding what the people in the past did for events and what results they obtained and using historical knowledge to the present. Several past studies have analyzed historical events based on the assumption that each event is described in texts. Most of them analyze how similar the words and their categories used in the descriptions are instead of taking care of event-causal relationships.
Robust Data-Aware Sensing Based on Half Quadratic Hashing for the Visual Internet of Things

In the Visual Internet of Things (VIoT) such as drones over flying ad-hoc networks (FANETs), terminal nodes need to preliminarily identify sensing data. Thus, data can be rapidly dispatched to dedicated edge nodes for further processing. Nonetheless, terminals may collect partially observed visual data, which cause biased labeling. In view of such, this study proposes half quadratic supervised discrete hashing (HQSDH) that can conquer the biased similarity estimation for partially observed images, especially when large continuous occlusion, missing values, or sample-specific outliers exist. The proposed method can automatically and adaptively adjust itself via HQ auxiliary variables to avoid oversensitivity to large errors caused by partially observed data. In this study, to solve HQ discrete optimization problems in HQSDH, HQ discrete cyclic coordinate descent (HQDCCD) is developed. Second, partially observed images may cause inconsistent binary codes and increase the Hamming distances. Thus, this study devises HQ balancing decorrelation constraints along with normalization regularization to accommodate the problems. Third, a systematic and generic solution that allows various HQ functions to jointly work in the same framework is modeled to provide versatility. Experiments on open image data sets were carried out for evaluation. The results showed that the proposed method provided more resilience against continuous occlusion, missing values, and sample-specific outliers than the baselines when dealing with partially observed images.
Breaking the Expression Bottleneck of Graph Neural Networks

Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures. There were also improvements proposed in analogy to k-WL test ( <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$k&gt;1$</tex></formula> ). However, the aggregations in these GNNs are far from injective as required by the WL test, and suffer from weak distinguishing strength, making it become the expression bottleneck. In this paper, we improve the expressiveness by exploring powerful aggregations. We reformulate an aggregation with the corresponding aggregation coefficient matrix, and then systematically analyze the requirements on this matrix for building more powerful and even injective aggregations. We also show the necessity of applying nonlinear units ahead of aggregations, which is different from most existing GNNs. Based on our theoretical analysis, we develop ExpandingConv. Experimental results show that our model significantly boosts performance, especially for large and densely connected graphs.
Applying an Enhanced Elliptic Curve Integrated Encryption Scheme ECIES to Enhance Smart Energy IoT Platform Security Based on Constrained Protocol

The smart city is a new urban development idea whose purpose is to improve people’s quality of life while also protecting the environment by utilizing new technologies that rely on an ecosystem of objects and services to make cities more adaptable and efficient. And the Internet of Things (IoT) is at the heart of practically all smart city gadgets and solutions. Data cannot be collected and presented in the different ways required by the city without it. It allows smart city sensors to regulate lighting, water and waste management, sound and air quality sensors, parking management, etc. Real-time data from buildings, streets, and infrastructure can now be actionable and valuable to all parties involved due to the IoT. In our work, we developed an intelligent and secure IoT Smart energy platform to monitor and control energy use in various parts of a smart city, utilizing the CoAP communication protocol to transport data from smart meters to a web server. We also used different topologies such as STAR, TREE, MESH, and CLUSTER to optimize the performance of our platform in terms of execution time and memory consumption, and we evaluated the impact of the ECIES security protocol compared to other security protocols such as AES-SHA256, RSA, and EEECC.
EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events

Clinical events such as clinic visits, hospital admissions, ECG readings and lab tests are recorded in modern healthcare systems. While these offer a great wealth of information about the state of health for a patient, modeling is challenging because (a) the events are sparse and irregular; (b) reading types vary greatly between episodes; and (c) the readings do not directly tell about the underlying continuous biological and mental processes that give rise to these readings. To tackle these challenges, we propose EvSys, a deep recurrent system that disentangles the observed measurement processes from latent health processes. With this design, the model is native to arbitrarily sparse and irregular clinical measurement events, and it captures the interacting underlying health processes that are not directly observed. We validate EvSys on two public datasets, namely PhysioNet 2012 and MIMIC-III, and demonstrate that the proposed model performs favorably against state-of-the-art methods.
ErrHunter: Detecting Error-Handling Bugs in the Linux Kernel Through Systematic Static Analysis

Error handling is essential for operating systems, thus, there are many bugs in error-handling code, which could result in serious consequences. In this paper, we revisit the problem of error miss-handling bugs and analyze the root cause of the most common ones in the Linux kernel. Based on the analysis, we propose a systematic static taint-analysis-based approach, ErrHunter, to detect multiple kinds of error miss-handling bugs in the Linux kernel. An automated critical variable identification approach is proposed to identify critical variables in the error-handling paths. A static cross-control-flow taint analysis approach is proposed to construct critical-variable control flow graphs (CCFGs), which describe the processing of critical variables in separate control flows. Based on the CCFGs, ErrHunter can target the root cause of the most common error miss-handling bugs and detect the bugs in a systematic way. ErrHunter is designed for kernel bug detection, so it can handle many specific features of the Linux kernel, such as memory management mechanisms, etc.
Computational Linguistics

This chapter first summarizes the contributions of Systemic Functional Linguistics (SFL) to computational linguistics. It elaborates on Martin Kay’s Functional Unification Grammar, highlights the achievements of the Penman Project on text generation directed by William C. Mann and comments on the influences from computational linguistics on SFL. The connections between Cardiff Grammar and Nigel Grammar are also discussed.
Research on Image Defogging Algorithms Based on Color Attenuation Prior

Abstract As for the defogging algorithm of color attenuation prior, an optimization algorithm based on color attenuation prior is proposed to solve the problem of image blurring in high frequency area of close-up view. Firstly, by improving the scene depth estimation information, the nonlinear model about scene depth is re-established to find the appropriate scene depth. Second, binary tree search algorithm is used to find the optimal atmospheric light value, and finally, a certain range of parameters are adjusted to achieve model optimization. The experimental results show that the algorithm solves the problem of blurring the close-up area and improves the speed of the algorithm.
Improving Blocking Bug Pair Prediction via Hybrid Deep Learning

Blocking bug pair (BBP) is a critical relationship between bugs that indicate one bug prevent the other bug from being fixed in time and cost more effort to repair itself in software maintenance. We propose a novel blocking bug pair prediction approach based on the combination of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neu-ral Network (CNN). Specifically, our approach first extracts summaries and descriptions from bug reports to construct two classifiers, respectively. Second, our approach combines two classifiers into a hybrid model to predict the blocking relationship of each blocking bug pair. Finally, our approach generates a report of identified blocking bugs for developers. We conduct an empirical study on five large-scale open-source projects. The final experimental results show that our approach can achieve better performance than the recent state-of-the-art baseline techniques.