Reviewerly/exHarmony huggingface dataset:
Rethinking the Maturity of Artificial Intelligence in Safety‐Critical Settings

Artificial intelligence, in the form of machine learning, has the potential to transform many safety‐critical applications such as those in transportation and healthcare. However, despite significant investment and impressive demonstrations, such technologies have struggled to live up to their promises. To this end, this article illustrates that machine learning fundamentally lacks the ability to leverage top‐down reasoning, a critical element in safety‐critical systems. This is especially important in situations where uncertainty can grow very quickly, requiring adaption to unknowns. This fundamental lack of contextual reasoning, combined with a lack of understanding of what constitutes maturity in artificial intelligence‐embedded systems, has significantly contributed to the failures of these systems. Demonstrations where safety‐critical artificial intelligence‐enabled systems function as if they were almost operational should not be a substitute for testing. Instead, companies and regulatory agencies need to work together to develop clear criteria and certification protocols before such technologies are made publicly available.
Cloud resource mapping through crow search inspired metaheuristic load balancing technique

In the modern era of digitalization, many applications exist that consume a huge computational processing power. These applications due to massive costs involved in procurement and maintenance, looks at resource abundant cloud environments as effective solution. However, cloud environments often suffer from challenges that threaten the performance due to inefficient resource utilization. Load balancing across multiple virtual machines in cloud deployment is one of the major issue that leads to under-utilization of cloud resources. In this research work, the problem of task to resource mapping is addressed using crow search based load balancing algorithm for greater optimization as solution. The solution uses parameters such as average power consumption of data center, average cost of the data center and data center loading for allocating the best resource to the submitted tasks. The proposed solution is validated against standard ant colony optimization based load balancing algorithm. It has been found that the proposed algorithm has outperformed standard algorithm and emerges out as a most optimal load balancing algorithm.
Artificial Intelligence and Blockchain for Cybersecurity Applications

The convergence of Artificial Intelligence and Blockchain is growing very fast in everyday applications and industry. In centralized systems and applications like healthcare, data access and processing in real-time among various information systems is a bottleneck. Blockchain’s decentralized database architecture, secure storage, authentication, and data sharing would offer a solution to this problem. Besides, Artificial Intelligence can live at the top of Blockchain and generate insights from the generated shared data used to make predictions. Blockchain is a high-level cybersecurity technology that forms chains that connect the existing blocks stored in nodes and the new block chronologically by mutual agreements between nodes. Technology convergence accelerates various industries’ growth, such as banking, insurance, cybersecurity, forecasting, medical services, cryptocurrency, etc… The more digital systems are adopted and services provided by these industries, the greater the risk of hacking these systems. Combining blockchain power and artificial intelligence can provide a strong shield against these attacks and security threats. In this chapter, we will study the convergence of AI and Blockchain in cybersecurity. We will expand on their role in securing cyber-physical systems.
An Efficient and Generic Construction for Signal’s Handshake (X3DH): Post-Quantum, State Leakage Secure, and Deniable

The Signal protocol is a secure instant messaging protocol that underlies the security of numerous applications such as WhatsApp, Skype, Facebook Messenger among many others. The Signal protocol consists of two sub-protocols known as the X3DH protocol and the double ratchet protocol, where the latter has recently gained much attention. For instance, Alwen, Coretti, and Dodis (Eurocrypt’19) provided a concrete security model along with a generic construction based on simple building blocks that are instantiable from versatile assumptions, including post-quantum ones. In contrast, as far as we are aware, works focusing on the X3DH protocol seem limited.In this work, we cast the X3DH protocol as a specific type of authenticated key exchange (AKE) protocol, which we call a Signal-conforming AKE protocol, and formally define its security model based on the vast prior work on AKE protocols. We then provide the first efficient generic construction of a Signal-conforming AKE protocol based on standard cryptographic primitives such as key encapsulation mechanisms (KEM) and signature schemes. Specifically, this results in the first post-quantum secure replacement of the X3DH protocol on well-established assumptions. Similar to the X3DH protocol, our Signal-conforming AKE protocol offers a strong (or stronger) flavor of security, where the exchanged key remains secure even when all the non-trivial combinations of the long-term secrets and session-specific secrets are compromised. Moreover, our protocol has a weak flavor of deniability and we further show how to strengthen it using ring signatures. Finally, we provide a full-fledged, generic C implementation of our (weakly deniable) protocol. We instantiate it with several Round 3 candidates (finalists and alternates) to the NIST post-quantum standardization process and compare the resulting bandwidth and computation performances. Our implementation is publicly available.
Multi-Modal Chatbot in Intelligent Manufacturing

Artificial intelligence (AI) has been widely used in various industries. In this work, we concentrate on what AI is capable of doing in manufacturing, in the form of a chatbot. We designed a chatbot that helps users complete an assembly task that simulates those in manufacturing settings. In order to recreate this setting, we have users assemble a Meccanoid robot through multiple stages, with the help of an interactive dialogue system. Based on classifying users' intent, the chatbot is able to provide answers or instructions to the user when the user encounters problems during the assembly process. Our goal is to improve our system so that it can capture users' needs by detecting their intent and therefore provide relevant and helpful information to the user. However, in a multiple-step task, we cannot rely on intent classification with user question utterance as the only input, as user questions raised from different steps may share the same intent but require different responses. In this paper, we proposed two methods to address this problem. One is that we capture not only textual features but also visual features through the YOLO-based Masker with CNN (YMC) model. Another is the usage of an Autoencoder to encode multi-modal features for user intent classification. By incorporating visual information, we have significantly improved the chatbot's performance from the experiments conducted on different dataset.
Effective Training of Convolutional Neural Networks With Low-Bitwidth Weights and Activations

This paper tackles the problem of training a deep convolutional neural network of both low-bitwidth weights and activations. Optimizing a low-precision network is very challenging due to the non-differentiability of the quantizer, which may result in substantial accuracy loss. To address this, we propose three practical approaches, including (i) progressive quantization; (ii) stochastic precision; and (iii) joint knowledge distillation to improve the network training. First, for progressive quantization, we propose two schemes to progressively find good local minima. Specifically, we propose to first optimize a network with quantized weights and subsequently quantize activations. This is in contrast to the traditional methods which optimize them simultaneously. Furthermore, we propose a second progressive quantization scheme which gradually decreases the bitwidth from high-precision to low-precision during training. Second, to alleviate the excessive training burden due to the multi-round training stages, we further propose a one-stage stochastic precision strategy to randomly sample and quantize sub-networks while keeping other parts in full-precision. Finally, we adopt a novel learning scheme to jointly train a full-precision model alongside the low-precision one. By doing so, the full-precision model provides hints to guide the low-precision model training and significantly improves the performance of the low-precision network. Extensive experiments on various datasets (e.g., CIFAR-100, ImageNet) show the effectiveness of the proposed methods.
Optimization of AI-driven communication systems for green hospitals in sustainable cities

• System optimization algorithms based on AI technologies are proposed. • The architecture and functional module were designed for green hospital. • The accuracy of the PSO algorithm is compared with a single BP algorithm. The green hospital is an important part for constructing Sustainable Cities. In this paper, system optimization algorithms based on artificial intelligence technologies are proposed by studying the communication system of the green hospital in the smart sustainable city, and the overall architecture design and detailed functional module design were carried out. The functions in the system can be divided into basic information management function, patient monitoring function, and remote self-test, etc. The medical equipment management system is characterized by the combination of Internet of Things technology, so that equipment managers can control the operating status of the equipment at any time, and can remotely upgrade and control the equipment through the system, and medical staff can view the measurement data of the equipment at the system interface and grasp the real-time information of treating patients to improve work efficiency. The accuracy of the improved particle swarm optimization (PSO) algorithm was compared with that of a single BP neural network algorithm. According to the requirement analysis of the health monitoring software and the overall design scheme of the system, each functional module of the health monitoring software is designed and the specific implementation of each functional module is carried out.
Pre-training on Large-Scale Heterogeneous Graph

Graph neural networks (GNNs) emerge as the state-of-the-art representation learning methods on graphs and often rely on a large amount of labeled data to achieve satisfactory performance. Recently, in order to relieve the label scarcity issues, some works propose to pre-train GNNs in a self-supervised manner by distilling transferable knowledge from the unlabeled graph structures. Unfortunately, these pre-training frameworks mainly target at homogeneous graphs, while real interaction systems usually constitute large-scale heterogeneous graphs, containing different types of nodes and edges, which leads to new challenges on structure heterogeneity and scalability for graph pre-training. In this paper, we first study the problem of pre-training on large-scale heterogeneous graph and propose a novel pre-training GNN framework, named PT-HGNN. The proposed PT-HGNN designs both the node- and schema-level pre-training tasks to contrastively preserve heterogeneous semantic and structural properties as a form of transferable knowledge for various downstream tasks. In addition, a relationbased personalized PageRank is proposed to sparsify large-scale heterogeneous graph for efficient pre-training. Extensive experiments on one of the largest public heterogeneous graphs (OAG) demonstrate that our PT-HGNN significantly outperforms various state-of-the-art baselines.
Optimizing One-time and Continuous Subgraph Queries using Worst-case Optimal Joins

We study the problem of optimizing one-time and continuous subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We make two main contributions: 1. A cost-based dynamic programming optimizer for one-time queries that (i) picks efficient query vertex orderings for worst-case optimal plans and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. In addition to our optimizer, we describe an adaptive technique that changes the query vertex orderings of the worst-case optimal subplans during query execution for more efficient query evaluation. The plan space of our one-time optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. 2. A cost-based greedy optimizer for continuous queries that builds on the delta subgraph query framework. Given a set of continuous queries, our optimizer decomposes these queries into multiple delta subgraph queries, picks a plan for each delta query, and generates a single combined plan that evaluates all of the queries. Our combined plans share computations across operators of the plans for the delta queries if the operators perform the same intersections. To increase the amount of computation shared, we describe an additional optimization that shares partial intersections across operators. Our optimizers use a new cost metric for worst-case optimal plans called intersection-cost . When generating hybrid plans, our dynamic programming optimizer for one-time queries combines intersection-cost with the cost of binary joins. We demonstrate the effectiveness of our plans, adaptive technique, and partial intersection sharing optimization through extensive experiments. Our optimizers are integrated into GraphflowDB.
Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points.
Leveraging Latent Features for Local Explanations

As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level. Locally, most explanation methods have focused on identifying relevance of features, limiting the types of explanations possible. In this paper, we investigate a new direction by leveraging latent features to generate contrastive explanations; predictions are explained not only by highlighting aspects that are in themselves sufficient to justify the classification, but also by new aspects which if added will change the classification. The key contribution of this paper lies in how we add features to rich data in a formal yet humanly interpretable way that leads to meaningful results. Our new definition of "addition" uses latent features to move beyond the limitations of previous explanations and resolve an open question laid out in Dhurandhar, et. al. (2018), which creates local contrastive explanations but is limited to simple datasets such as grayscale images. The strength of our approach in creating intuitive explanations that are also quantitatively superior to other methods is demonstrated on three diverse image datasets (skin lesions, faces, and fashion apparel). A user study with 200 participants further exemplifies the benefits of contrastive information, which can be viewed as complementary to other state-of-the-art interpretability methods.
Estimation of Seismic Moment Tensors Using Variational Inference Machine Learning

Abstract We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment‐tensor parameters. A monitoring volume has been predefined to form a three‐dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment‐tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down‐stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes.
AQD: Towards Accurate Quantized Object Detection

Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices. However, designing aggressively low-bit (e.g., 2-bit) quantization schemes on complex tasks, such as object detection, still remains challenging in terms of severe performance degradation and unverifiable efficiency on common hardware. In this paper, we propose an Accurate Quantized object Detection solution, termed AQD, to fully get rid of floating-point computation. To this end, we target using fixed-point operations in all kinds of layers, including the convolutional layers, normalization layers, and skip connections, allowing the inference to be executed using integer-only arithmetic. To demonstrate the improved latency-vs-accuracy trade-off, we apply the proposed methods on RetinaNet and FCOS. In particular, experimental results on MS-COCO dataset show that our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes, which is of great practical value. Source code and models are available at: https://github.com/aim-uofa/model-quantization
The PLUM Earthquake Early Warning Algorithm: A Retrospective Case Study of West Coast, USA, Data

Abstract The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source‐based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity ( I MMI ) using velocity and acceleration data, leveraging co‐located sensors to avoid problematic signals. An event detection is issued when the observed I MMI exceeds a given threshold(s). We find a two‐station detection method using I MMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes ( N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify I MMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.
Deep Clustering based Fair Outlier Detection

In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitly encode and propagate statistical bias in data and raise societal concerns. To correct such unfairness and deliver a fair set of potential outlier candidates, we propose Deep Clustering based Fair Outlier Detection (DCFOD) that learns a good representation for utility maximization while enforcing the learnable representation to be subgroup-invariant on the sensitive attribute. Considering the coupled and reciprocal nature between clustering and outlier detection, we leverage deep clustering to discover the intrinsic cluster structure and out-of-structure instances. Meanwhile, an adversarial training erases the sensitive pattern for instances for fairness adaptation. Technically, we propose an instance-level weighted representation learning strategy to enhance the joint deep clustering and outlier detection, where the dynamic weight module re-emphasizes contributions of likely-inliers while mitigating the negative impact from outliers. Demonstrated by experiments on eight datasets comparing to 17 outlier detection algorithms, our DCFOD method consistently achieves superior performance on both the outlier detection validity and two types of fairness notions in outlier detection.