Reviewerly/exHarmony huggingface dataset:
Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training

Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domainspecific benchmark datasets.In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, largescale verbalization.We further show that verbalizing a comprehensive, encyclopedic KG like Wikidata can be used to integrate structured KGs and natural language corpora.In contrast to the many architectures that have been developed to integrate these two sources, our approach converts the KG into natural text, allowing it to be seamlessly integrated into existing language models.It carries the further advantages of improved factual accuracy and reduced toxicity in the resulting language model.We evaluate this approach by augmenting the retrieval corpus in a retrieval language model and showing significant improvements on the knowledge intensive tasks of open domain QA and the LAMA knowledge probe.
Anomaly detection using edge computing in video surveillance system: review

The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.
Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.
Quantum Error Mitigation as a Universal Error Reduction Technique: Applications from the NISQ to the Fault-Tolerant Quantum Computing Eras

In the early years of fault-tolerant quantum computing (FTQC), it is expected that the available code distance and the number of magic states will be restricted due to the limited scalability of quantum devices and the insufficient computational power of classical decoding units. Here, we integrate quantum error correction and quantum error mitigation into an efficient FTQC architecture that effectively increases the code distance and $T$-gate count at the cost of constant sampling overheads in a wide range of quantum computing regimes. For example, while we need $10^4$ to $10^{10}$ logical operations for demonstrating quantum advantages from optimistic and pessimistic points of view, we show that we can reduce the required number of physical qubits by $80\%$ and $45\%$ in each regime. From another perspective, when the achievable code distance is up to about 11, our scheme allows executing $10^3$ times more logical operations. This scheme will dramatically alleviate the required computational overheads and hasten the arrival of the FTQC era.
Intrusion Detection in the IoT Under Data and Concept Drifts: Online Deep Learning Approach

Although the existing machine learning-based intrusion detection systems in the Internet of Things (IoT) usually perform well in static environments, they struggle to preserve their performance over time, in dynamic environments. Yet, the IoT is a highly dynamic and heterogeneous environment, leading to what is known as data drift and concept drift. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To detect data and concept drifts, we first propose a drift detection technique that capitalizes on the principal component analysis (PCA) method to study the change in the variance of the features across the intrusion detection data streams. We also discuss an online outlier detection technique that identifies the outliers that diverge both from historical and temporally close data points. To counter these drifts, we discuss an online deep neural network (DNN) that dynamically adjusts the sizes of the hidden layers based on the Hedge weighting mechanism, thus enabling the model to steadily learn and adapt as new intrusion data come. Experiments conducted on an IoT-based intrusion detection data set suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static DNN model, which is widely used for intrusion detection.
Are we really making much progress?

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements. In this work, we present a systematical reproduction of 12 recent HGNNs by using their official codes, datasets, settings, and hyperparameters, revealing surprising findings about the progress of HGNNs. We find that the simple homogeneous GNNs, e.g., GCN and GAT, are largely underestimated due to improper settings. GAT with proper inputs can generally match or outperform all existing HGNNs across various scenarios. To facilitate robust and reproducible HGNN research, we construct the Heterogeneous Graph Benchmark (HGB) , consisting of 11 diverse datasets with three tasks. HGB standardizes the process of heterogeneous graph data splits, feature processing, and performance evaluation. Finally, we introduce a simple but very strong baseline Simple-HGN-which significantly outperforms all previous models on HGB-to accelerate the advancement of HGNNs in the future.
Experimentally realized in situ backpropagation for deep learning in photonic neural networks

Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using “in situ backpropagation,” a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>&gt;</mml:mo> </mml:math> 94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.
DPSNet: End-to-end Deep Plane Sweep Stereo

Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion. Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions. In this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches for dense depth reconstruction. Rather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the dense depth map from the cost volume. The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network. Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.
Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning

Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques, analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions. While this principle has been implemented either for unsupervised, semi-supervised, or supervised machine learning tasks, the combination of all three methodologies remains challenging. In this paper, a visual analytics approach is presented, combining a variety of machine learning capabilities with four linked visualisation views, all integrated within the mVis (multivariate Visualiser) system. The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions, from which a classifier can be built. In the workflow, the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning. Once a dataset has been interactively labelled, the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset. Using a novel technique called automatic dimension selection, interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms. A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks, from initial labelling through iterations of data exploration, clustering, classification, and active learning to refine the named partitions, to finally producing a high-quality labelled training dataset suitable for training a classifier. The tool empowers the analyst with interactive visualisations including scatterplots, parallel coordinates, similarity maps for records, and a new similarity map for partitions.
Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme

Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task. We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations. We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.
Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection

The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, namely an upper branch to perform oral cancer detection, and a lower branch to perform semantic segmentation and ROI marking. With the upper branch the network model extracts the cancerous regions, and the lower branch makes the cancerous regions more precision. To make the features in the cancerous more regular, the network model extracts the texture images from the input image. A sliding window is then applied to compute the standard deviation values of the texture image. Finally, the standard deviation values are used to construct a texture map, which is partitioned into multiple patches and used as the input data to the deep convolutional network model. The method proposed by this paper is called texture-map-based branch-collaborative network. In the experimental result, the average sensitivity and specificity of detection are up to 0.9687 and 0.7129, respectively based on wavelet transform. And the average sensitivity and specificity of detection are up to 0.9314 and 0.9475, respectively based on Gabor filter.
Modeling Document-level Causal Structures for Event Causal Relation Identification

Lei Gao, Prafulla Kumar Choubey, Ruihong Huang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
The Blockchain Consensus Algorithm for Viable Management of New and Renewable Energies

Efficient information flow in an intelligent system is vital for effectively controlling the entire system. Currently, intelligent systems are used in many industries related to energy production, sustainable agriculture/transport, and intelligent building/cities. Information technology (IT) and information and communication technologies (ICT) play vital roles in introducing technical or technological innovation in these industries as well as establishing a collaborative network. Also, the digitization of existing systems has been quite effective at creating a sustainable global environment as it allows more efficient and well-balanced control of socio-economic factors. However, it has become clear that adopting an intelligent system to achieve innovation, sustainability, and safety may well depend on the quality of the algorithms to be used for that very system. Despite recent controversies, new and renewable energies are considered as a realistic alternative to fossil fuels, which have been integral to modern industries but are regarded as a cause of environmental or economic problems, not to mention their limited deposits. Therefore, since renewable energies will gradually replace existing energy sources but require more time to be fully available, it is essential to find a method of managing them in a fair and transparent way. The United States, Japan, and some European countries are attempting to achieve such a goal by utilizing a blockchain system, but the issues pertaining to its functionality, security, or efficiency have yet to be addressed. This study introduces a viable consensus algorithm (Hyper Delegation Proof of Randomness, or HDPoR algorithm) for blockchain and attempts to validate its parallel computing capability through simulations. This study also attempts to design an efficient but secure peer-to-peer (P2P) transaction service model for these energies for the future where blockchain-based systems will hold a key position in the digitalized world. As its main contribution, this study introduces an effective method of applying blockchain to a new and renewable energy transaction system by presenting a consensus algorithm that can improve its infrastructure and performance.
Improving Neural Conversational Models with Entropy-Based Data Filtering

Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances.Priors such as persona, emotion, or topic provide additional information to dialog models to aid response generation, but annotating a dataset with priors is expensive and such annotations are rarely available.While previous methods for improving the quality of open-domain response generation focused on either the underlying model or the training objective, we present a method of filtering dialog datasets by removing generic utterances from training data using a simple entropy-based approach that does not require human supervision.We conduct extensive experiments with different variations of our method, and compare dialog models across 17 evaluation metrics to show that training on datasets filtered this way results in better conversational quality as chatbots learn to output more diverse responses.
A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.
Recurrent Stacking of Layers for Compact Neural Machine Translation Models

In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in the encoder and decoder. While the addition of each new layer improves the sequence generation quality, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model. We report on an extensive case study on neural machine translation (NMT) using our proposed method, experimenting with a variety of datasets. We empirically show that the translation quality of a model that recurrently stacks a single-layer 6 times, despite its significantly fewer parameters, approaches that of a model that stacks 6 different layers. We also show how our method can benefit from a prevalent way for improving NMT, i.e., extending training data with pseudo-parallel corpora generated by back-translation. We then analyze the effects of recurrently stacked layers by visualizing the attentions of models that use recurrently stacked layers and models that do not. Finally, we explore the limits of parameter sharing where we share even the parameters between the encoder and decoder in addition to recurrent stacking of layers.