Reviewerly/exHarmony huggingface dataset:
A multi-perspective analysis of microclimate dynamics for air-based solar hydrogen production

Navigating the microclimatic environment for the optimal control of water-from-air devices could be a challenge. An example of such a device is an air-based solar hydrogen production device. Such a device promises the ability for off-grid, easily deployable and modular hydrogen production for on-site consumption. Novel analysis techniques, such as wavelet transform coherence analysis, could assist in better understanding the microclimate in which air-based hydrogen production devices might function. The analysis becomes complicated when a system is evaluated at the microclimatic level, especially when it is considered that the performance of air-based solar hydrogen devices are not only dependent on solar radiation, but also on humidity levels in the air. To get a grasp of the interactions that take place within a microclimatic system, a two-tiered approach is presented. It has been shown that relative humidity and temperature is stratified close to the ground, and that the stratification undergoes an inversion twice per day. A possible link between absolute humidity and wind direction is observed and humidity rallies are identified. Using microclimate monitoring and wavelet transform coherence analysis, an attempt is made to disentangle microclimatic variables by pointing out regions of high coherence and regions of low coherence between different variables. It is furthermore suggested that the propagation direction of a humidification process within the microclimate can be determined by considering the phase angle between relative humidity timeseries at different heights above the ground, using wavelet transform coherence analysis. It has been demonstrated that wavelet transform coherence analysis, in conjunction with a comprehensive microclimate monitoring process, could support the understanding of the complex processes that occur within the microclimatic environment and therefore support the management of water-from-air systems. In this regard a management framework is also presented.
Research on Prediction of Solar Power Considering the Methods of Statistical and Machine Learning – Based on the Data of Australian Solar Power Market

Abstract In this paper, we use the methods of machine learning and traditional time series to predict solar power generation, which is based on the Australia Market Data. In the paper, we analyze Ausgrid's Solar by using long and short-term memory (LSTM) methods and time series models (multiple regression models with related errors) to accurately estimate the parameters of photovoltaic (PV) array models, which is using the data of household electricity consumption from July 1, 2010, to June 30, 2013. The results show that the regression model with correlated errors is better than the machine learning-based LSTM algorithm, which is based on the differential MSE performance. The final prediction accuracy rate is as high as 98%, so the regression model can accurately predict solar power generation.
Conversational Recommendation via Hierarchical Information Modeling

Conversational recommendation system aims to recommend appropriate items to user by directly asking preference on attributes or recommending item list. However, most of existing methods only employ the flat item and attribute relationship, and ignore the hierarchical relationship connected by the similar user which can provide more comprehensive information. And these methods usually use the user accepted attributes to represent the conversational history and ignore the hierarchical information of sequential transition in the historical turns. In this paper, we propose Hierarchical Information-aware Conversational Recommender (HICR) to model the two types of hierarchical information to boost the performance of CRS. Experiments conducted on four benchmark datasets verify the effectiveness of our proposed model.
Diversity Vs Relevance: A Practical Multi-objective Study in Luxury Fashion Recommendations

Personalized algorithms focusing uniquely on accuracy might provide highly relevant recommendations, but the recommended items could be too similar to current users' preferences. Therefore, recommenders might prevent users from exploring new products and brands (filter bubbles). This is especially critical for luxury fashion recommendations because luxury shoppers expect to discover exclusive and rare items. Thus, recommender systems for fashion need to consider diversity and elevate the shopping experience by recommending new brands and products from the catalog. In this work, we explored a handful of diversification strategies to rerank the output of a relevance-focused recommender system. Subsequently, we conducted a multi-objective offline experiment optimizing for relevance and diversity simultaneously. We measured diversity with commonly used metrics such as coverage, serendipity, and neighborhood distance, whereas, for relevance, we selected ranking metrics such as recall. The best diversification strategy offline improved user engagement by 2% in click-through rate and presented an uplift of 46% in distinct brands recommended when AB tested against real users. These results reinforced the importance of considering accuracy and diversity metrics when developing a recommender system.
Sparse prior guided deep multi-view stereo

Recently, the learning-based multi-view stereo (MVS) methods have achieved notable progress. Despite this, the cost volumes of those methods are not robust enough for depth inference with the existence of noise, outlier and occlusion. In this work, we aim to reduce the matching ambiguities and increase the discrimination of the cost volume. For this purpose, we first propose a sparse prior guidance strategy which incorporates the sparse points of structure-from-motion (SfM) into cost volume construction. Further, we introduce a geometry-aware regularization module to enhance the representative power of cost volume regularization, which could adaptively fit the local geometric shapes. The two modules are straightforward yet effective, resulting in robust and accurate depth estimation. The detailed experiments are conducted on two public datasets (DTU and Tanks & Temples). The experimental results show that with the two components, the representative methods (e.g MVSNet and CasMVSNet) could make a significant gain in reconstruction quality.
Distilling Knowledge on Text Graph for Social Media Attribute Inference

The popularization of social media generates a large amount of user-oriented data, where text data especially attracts researchers and speculators to infer user attributes (e.g., age, gender) for fulfilling their intents. Generally, this line of work casts attribute inference as a text classification problem, and starts to leverage graph neural networks for higher-level text representations. However, these text graphs are constructed on words, suffering from high memory consumption and ineffectiveness on few labeled texts. To address this challenge, we design a text-graph-based few-shot learning model for social media attribute inferences. Our model builds a text graph with texts as nodes and edges learned from current text representations via manifold learning and message passing. To further use unlabeled texts to improve few-shot performance, a knowledge distillation is devised to optimize the problem. This offers a trade-off between expressiveness and complexity. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on attribute inferences with considerably fewer labeled texts.
Axiomatically Regularized Pre-training for Ad hoc Search

Recently, pre-training methods tailored for IR tasks have achieved great success. However, as the mechanisms behind the performance improvement remain under-investigated, the interpretability and robustness of these pre-trained models still need to be improved. Axiomatic IR aims to identify a set of desirable properties expressed mathematically as formal constraints to guide the design of ranking models. Existing studies have already shown that considering certain axioms may help improve the effectiveness and interpretability of IR models. However, there still lack efforts of incorporating these IR axioms into pre-training methodologies. To shed light on this research question, we propose a novel pre-training method with \underlineA xiomatic \underlineRe gularization for ad hoc \underlineS earch (ARES). In the ARES framework, a number of existing IR axioms are re-organized to generate training samples to be fitted in the pre-training process. These training samples then guide neural rankers to learn the desirable ranking properties. Compared to existing pre-training approaches, ARES is more intuitive and explainable. Experimental results on multiple publicly available benchmark datasets have shown the effectiveness of ARES in both full-resource and low-resource (e.g., zero-shot and few-shot) settings. An intuitive case study also indicates that ARES has learned useful knowledge that existing pre-trained models (e.g., BERT and PROP) fail to possess. This work provides insights into improving the interpretability of pre-trained models and the guidance of incorporating IR axioms or human heuristics into pre-training methods.
Image Coding for Machines with Omnipotent Feature Learning

Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success. In this paper, we attempt to develop an ICM framework by learning universal features while also considering compression. We name such features as omnipotent features and the corresponding framework as Omni-ICM. Considering self-supervised learning (SSL) improves feature generalization, we integrate it with the compression task into the Omni-ICM framework to learn omnipotent features. However, it is non-trivial to coordinate semantics modeling in SSL and redundancy removing in compression, so we design a novel information filtering (IF) module between them by co-optimization of instance distinguishment and entropy minimization to adaptively drop information that is weakly related to AI tasks (e.g., some texture redundancy). Different from previous task-specific solutions, Omni-ICM could directly support AI tasks analysis based on the learned omnipotent features without joint training or extra transformation. Albeit simple and intuitive, Omni-ICM significantly outperforms existing traditional and learning-based codecs on multiple fundamental vision tasks.
IPR

Explicit feedback---user input regarding their interest in an item---is the most helpful information for recommendation as it comes directly from the user and shows their direct interest in the item. Most approaches either treat the recommendation given such feedback as a typical regression problem or regard such data as implicit and then directly adopt approaches for implicit feedback; both methods, however,tend to yield unsatisfactory performance in top-k recommendation. In this paper, we propose interaction-level preference ranking(IPR), a novel pairwise ranking embedding learning approach to better utilize explicit feedback for recommendation. Experiments conducted on three real-world datasets show that IPR yields the best results compared to six strong baselines.
Learning probabilistic models for static analysis alarms

We present BayeSmith, a general framework for automatically learning probabilistic models of static analysis alarms. Several probabilistic reasoning techniques have recently been proposed which incorporate external feedback on semantic facts and thereby reduce the user's alarm inspection burden. However, these approaches are fundamentally limited to models with pre-defined structure, and are therefore unable to learn or transfer knowledge regarding an analysis from one program to another. Furthermore, these probabilistic models often aggressively generalize from external feedback and falsely suppress real bugs. To address these problems, we propose BayeSmith that learns the structure and weights of the probabilistic model. Starting from an initial model and a set of training programs with bug labels, BayeSmith refines the model to effectively prioritize real bugs based on feedback. We evaluate the approach with two static analyses on a suite of C programs. We demonstrate that the learned models significantly improve the performance of three state-of-the-art probabilistic reasoning systems.
Advancing construction of conditional probability tables of Bayesian networks with ranked nodes method

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Research on cross-chain method based on distributed Digital Identity

With the rapid development of blockchain technology and increasingly rich application scenarios, it is very important to use cross-chain technology to promote the interconnection between blockchain. Cross-chain data transmission and cross-chain asset transfer are important cross-chain scenarios, which have high requirements for cross-chain transaction efficiency. However, the existing high-performance cross-chain mode side chain and notary mode cannot meet the trust requirements in these scenarios. DID (distributed digital Identity) does not depend on the central identity provider, in distributed scenarios, each user is given the ability to control and use digital identity independently, improve the credibility of side chain and notary mode, and then use high-performance cross-chain mode to meet the requirements of cross-chain transaction efficiency. In this paper, we discuss the feasibility of combining distributed digital identity and cross-chain technology, design a cross-chain model based on distributed digital identity, and explain how to introduce distributed digital identity in the key steps of cross-chain interaction, access control, cross-chain transactions and so on. A cross-chain transaction process based on distributed digital identity is formed, including transaction algorithm process, example and cross-chain transaction voucher format based on distributed digital identity. In the experiment, in the case of cross-chain data transmission and cross-chain asset transaction, compared with the current mainstream relay chain mode with high credibility, it is concluded that the cross-chain method based on distributed digital identity has obvious performance advantages under the premise of ensuring trust.
Offline Retrieval Evaluation Without Evaluation Metrics

Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or normalized discounted cumulative gain. We can use this metric to compare the performance of multiple systems for the same request. Although evaluation metrics provide a convenient summary of system performance, they also collapse subtle differences across users into a single number and can carry assumptions about user behavior and utility not supported across retrieval scenarios. We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. RPP simulates multiple user subpopulations per query and compares systems across these pseudo-populations. Our results across multiple search and recommendation tasks demonstrate that RPP substantially improves discriminative power while correlating well with existing metrics and being equally robust to incomplete data.
PERD: Personalized Emoji Recommendation with Dynamic User Preference

Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.
LoL

Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models.
LRP-based Policy Pruning and Distillation of Reinforcement Learning Agents for Embedded Systems

Reinforcement Learning (RL) is an effective approach to developing control policies by maximizing the agent’s reward. Deep Reinforcement Learning (DRL) uses Deep Neural Networks (DNNs) for function approximation in RL, and has achieved tremendous success in recent years. Large DNNs often incur significant memory size and computational overheads, which greatly impedes their deployment into resource-constrained embedded systems. For deployment of a trained RL agent on embedded systems, it is necessary to compress the Policy Network of the RL agent to improve its memory and computation efficiency. In this paper, we perform model compression of the Policy Network of an RL agent by leveraging the relevance scores computed by Layer-wise Relevance Propagation (LRP), a technique for Explainable AI (XAI), to rank and prune the convolutional filters in the Policy Network, combined with fine-tuning with Policy Distillation. Performance evaluation based on several Atari games indicates that our proposed approach is effective in reducing model size and inference time of RL agents.
Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching

In this work, we propose an effective multi-stage neural ranking system for the clinical trial matching problem. First, we introduce NQS, a neural query synthesis method that leverages a zero-shot document expansion model to generate multiple sentence-long queries from lengthy patient descriptions. These queries are independently issued to a search engine and the results are fused. We find that on the TREC 2021 Clinical Trials Track, this method outperforms strong traditional baselines like BM25 and BM25 + RM3 by about 12 points in [email protected], a relative improvement of 34%. This simple method is so effective that even a state-of-the-art neural relevance ranking method trained on the medical subset of MS MARCO passage, when reranking the results of NQS, fails to improve on the ranked list. Second, we introduce a two-stage neural reranking pipeline trained on clinical trial matching data using tailored ranking templates. In this setting, we can train a pointwise reranker using just 1.1k positive examples and obtain effectiveness improvements over NQS by 24 points. This end-to-end multi-stage system demonstrates a 20% relative effectiveness gain compared to the second-best submission at TREC 2021, making it an important step towards better automated clinical trial matching.
MGPolicy

Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training. However, off-policy learning from logged data suffers biases caused by the policy shift between the target policy and the logging policy. Consequently, most off-policy learning resorts to inverse propensity scoring (IPS) which however tends to be over-fitted over exposed (or recommended) items and thus fails to explore unexposed items.
QSG Transformer

Query-Focused Summarization (QFS) is a task that aims to extract essential information from a long document and organize it into a summary that can answer a query. Recently, Transformer-based summarization models have been widely used in QFS. However, the simple Transformer architecture cannot utilize the relationships between distant words and information from a query directly. In this study, we propose the QSG Transformer, a novel QFS model that leverages structure information on Query-attentive Semantic Graph (QSG) to address these issues. Specifically, in the QSG Transformer, QSG node representation is improved by a proposed query-attentive graph attention network, which spreads the information of the query node into QSG using Personalized PageRank, and it is used to generate a summary that better reflects the information from the relationships of a query and document. The proposed method is evaluated on two QFS datasets, and it achieves superior performances over the state-of-the-art models.