Reviewerly/exHarmony huggingface dataset:
Data-Efficient Reinforcement Learning with Self-Predictive Representations

While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment. Our method, SPR, trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent's parameters and we make predictions using a learned transition model. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari in a setting limited to 100k steps of environment interaction, which represents a 55% relative improvement over the previous state-of-the-art. Notably, even in this limited data regime, SPR exceeds expert human scores on 7 out of 26 games. We've made the code associated with this work available at https://anonymous.4open.science/r/b4b93ec6-6e5d-4f43-9b53-54bdf73bea95/.
Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications

The article presents issues related to hybrid nature-inspired population-based algorithms of global optimization and their industrial applications. To this end, the article presents a general concept of nature-inspired population-based optimization methods and the ways in which those methods can be hybridized with other techniques. Concrete literature-based examples have been used to illustrate each type of hybridization. This article also demonstrates the publication popularity of selected hybrid nature-inspired population-based optimization algorithms and indicates their most common application areas. Moreover, this article refers to the computational and implementation complexity of the algorithms in question. Next, the focus shifts to industrial applications of hybrid nature-inspired population-based optimization methods. References have been made to numerous works that present different versions of hybrid optimization algorithms, showing the areas of their practical application. Moreover, this article discusses the problems inherent in hybrid optimization methods as well as indicates open research points in this field.
WITHDRAWN: Implementing blockchain technologies for health insurance claim processing in hospitals

Blockchain is getting expanding consideration from academy and industry, since it is viewed as an achievement innovation that could bring advantages to a wide range of divisions. Insurance is one of the sectors that started to investigate the possibilities of blockchain. It can be utilized over the protection esteem bind because of its capacity to offer longvital advantages, for example, lower operational expenses as decreased duplication of procedures, diminished counter party dangers, expanded robotization of procedures, and secure and decentralized exchanges. Medical coverage: A blockchain that interfaces clinics, doctors, lab merchants and safety net providers could empower a consistent stream of well being data for improved endorsing and approving of cases. The protection business is vigorously reliant on numerous procedures between executing parties for starting, keeping up and shutting differing sort of arrangements. The major concerns involved in executing the claims are the time involved for processing and settlement and also security. The objective of our paper is to develop a blockchain based on health insurance claim processing system which will help insurance companies build a secure network which is free from tampering. It also aims to build the structure of an effective methodology for handling protection relevant exchanges dependent on a blockchain-empowered stage.
Informative and Controllable Opinion Summarization

Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product).Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset.However, the extractive model leads to loss of information which may be useful depending on user needs.In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries.The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model.We showcase an effective instantiation of our framework which produces more informative summaries and also allows to take user preferences into account using our zero-shot customization technique.Experimental results demonstrate that our model improves the state of the art on the Rotten Tomatoes dataset and generates customized summaries effectively."Coach Carter" Reviews • Samuel L. Jackson plays the real-life coach of a high school basketball team in this solid sports drama ... • Great performance by Samuel Jackson but predictable as a slam dunk ...
A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

In this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of today’s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth quantum circuit (HELD) is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD training and 3) A systematical analysis on QTSA’s performance under various quantum factors is performed. QTSA underpins a foundation of quantum machine learning-enabled power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.
Portfolio Optimisation Using the D-Wave Quantum Annealer

The first quantum computers are expected to perform well on quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S&P500 index, using the state-of-the-art implementation of D-Wave’s quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state-of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog

Visual dialog, which aims to hold a meaningful conversation with humans about a given image, is a challenging task that requires models to reason the complex dependencies among visual content, dialog history, and current questions.Graph neural networks are recently applied to model the implicit relations between objects in an image or dialog.However, they neglect the importance of 1) coreference relations among dialog history and dependency relations between words for the question representation; and 2) the representation of the image based on the fully represented question.Therefore, we propose a novel relation-aware graph-over-graph network (GoG) for visual dialog.Specifically, GoG consists of three sequential graphs: 1) H-Graph, which aims to capture coreference relations among dialog history; 2) History-aware Q-Graph, which aims to fully understand the question through capturing dependency relations between words based on coreference resolution on the dialog history; and 3) Questionaware I-Graph, which aims to capture the relations between objects in an image based on fully question representation.As an additional feature representation module, we add GoG to the existing visual dialogue model.Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin.
Improving the phishing website detection using empirical analysis of Function Tree and its variants

The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist anti-phishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based meta-learning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its meta-learner variants for phishing website detection and applicable cybersecurity attacks are recommended.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models, and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
Towards Multi-Scale Style Control for Expressive Speech Synthesis

This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis. The proposed method employs a multi-scale reference encoder to extract both the global-scale utterance-level and the local-scale quasi-phoneme-level style features of the target speech, which are then fed into the speech synthesis model as an extension to the input phoneme sequence. During training time, the multi-scale style model could be jointly trained with the speech synthesis model in an end-to-end fashion. By applying the proposed method to style transfer task, experimental results indicate that the controllability of the multi-scale speech style model and the expressiveness of the synthesized speech are greatly improved. Moreover, by assigning different reference speeches to extraction of style on each scale, the flexibility of the proposed method is further revealed.
Relation-aware Video Reading Comprehension for Temporal Language Grounding

Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence.Previous methods treat it either as a boundary regression task or a span extraction task.This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it.This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction.A choicequery interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction.Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection.Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution.
Text mining methodologies with R: An application to central bank texts

We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented.
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

Contemporary deep-learning object detection methods for autonomous driving usually presume fixed categories of common traffic participants, such as pedestrians and cars. Most existing detectors are unable to detect uncommon objects and corner cases (e.g., a dog crossing a street), which may lead to severe accidents in some situations, making the timeline for the real-world application of reliable autonomous driving uncertain. One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases. Hence, we introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The dataset consists of 1500 carefully selected real-world driving scenes, each containing four object-level corner cases (on average), spanning more than 30 object categories. On CODA, the performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. Moreover, we experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA, suggesting that a robust perception system for autonomous driving is probably still far from reach. We expect our CODA dataset to facilitate further research in reliable detection for real-world autonomous driving. Our dataset is available at https://coda-dataset.github.io .
Cross-Collaborative Fusion-Encoder Network for Robust RGB-Thermal Salient Object Detection

With the prevalence of thermal cameras, RGB-T multi-modal data have become more available for salient object detection (SOD) in complex scenes. Most RGB-T SOD works first individually extract RGB and thermal features from two separate encoders and directly integrate them, which pay less attention to the issue of defective modalities. However, such an indiscriminate feature extraction strategy may produce contaminated features and thus lead to poor SOD performance. To address this issue, we propose a novel CCFENet for a perspective to perform robust and accurate multi-modal expression encoding. First, we propose an essential cross-collaboration enhancement strategy (CCE), which concentrates on facilitating the interactions across the encoders and encouraging different modalities to complement each other during encoding. Such a cross-collaborative-encoder paradigm induces our network to collaboratively suppress the negative feature responses of defective modality data and effectively exploit modality-informative features. Moreover, as the network goes deeper, we embed several CCEs into the encoder, further enabling more representative and robust feature generation. Second, benefiting from the proposed robust encoding paradigm, a simple yet effective cross-scale cross-modal decoder (CCD) is designed to aggregate multi-level complementary multi-modal features, and thus encourages efficient and accurate RGB-T SOD. Extensive experiments reveal that our CCFENet outperforms the state-of-the-art models on three RGB-T datasets with a fast inference speed of 62 FPS. In addition, the advantages of our approach in complex scenarios (e.g., bad weather, motion blur, etc.) and RGB-D SOD further verify its robustness and generality. The source code will be publicly available via our project page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.openi.org.cn/OpenVision/CCFENet</uri> .
On Transferability of Prompt Tuning for Natural Language Processing

Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, PT requires much more training time than fine-tuning. Intuitively, knowledge transfer can help to improve the efficiency. To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work. We find that (1) in zero-shot setting, trained soft prompts can effectively transfer to similar tasks on the same PLM and also to other PLMs with a cross-model projector trained on similar tasks; (2) when used as initialization, trained soft prompts of similar tasks and projected prompts of other PLMs can significantly accelerate training and also improve the performance of PT. Moreover, to explore what decides prompt transferability, we investigate various transferability indicators and find that the overlapping rate of activated neurons strongly reflects the transferability, which suggests how the prompts stimulate PLMs is essential. Our findings show that prompt transfer is promising for improving PT, and further research shall focus more on prompts' stimulation to PLMs. The source code can be obtained from https://github.com/thunlp/Prompt-Transferability.
Interoperability approaches of blockchain technology for supply chain systems

Purpose This work focuses on the interoperability of the blockchain from the viewpoint of its technological evolution in the wider context of supply chain systems. Interoperability concerns the most since it is among the most persevering problems in the process of blockchain adaptation. The study aims to trace and pinpoint all the different methods that affect the interoperability of blockchains, gather all the obtainable evidence and recognize gaps in between the applicable approaches mentioned across the contemporary academic literature. Design/methodology/approach An analysis is done of seven interoperability approaches based on their relevance, practical usefulness and adoption processes concerning the blockchain. Mainly, this work examines interoperability from several different perspectives. An interoperability modular decomposition via a layered model has been defined through the level of conceptual interoperability (LCIM) and level of information system interoperability (LISI), which represent the most popular metrics with regard to assessing the level of interoperability from a qualitative level. Findings Generally, the findings of this work are directed in two major aspects: Firstly, it expands the research around blockchain technology interoperability by providing crucial background information and pinpointing important connotations for both industrial and academic environments. It also defines and analyzes several different approaches toward the blockchain platform's interoperability. Secondly, it also identifies and proposes several scenarios that take advantage of multiple blockchain application approaches, highlights various issues and challenges to the evolution of interoperability standards and solutions and indicates the desired areas of further research. Originality/value An essential aspect of the originality of this paper is that, contrary to other work contributions, this work summarized the different interoperability approaches for blockchain in supply chain systems. Mainly, seven approaches were discussed for practitioners and researchers, and the open issues and future research direction were considered.