Reviewerly/exHarmony huggingface dataset:
Retrievability Bias Estimation Using Synthetically Generated Queries

Ranking with pre-trained language models (PLMs) has shown to be highly effective for various Information Retrieval tasks. Previous studies investigated the performance of these models in terms of effectiveness and efficiency. However, there is no prior work on evaluating PLM-based rankers in terms of their retrievability bias. In this paper, we evaluate the retrievability bias of PLM-based rankers with the use of synthetically generated queries. We compare the retrievability bias in two of the most common PLM-based rankers, a Bi-Encoder BERT ranker and a Cross-Encoder BERT re-ranker against BM25, which was found to be one of the least biased models in prior work. We conduct a series of experiments with which we explore the plausibility of using synthetic queries generated with a generative model, docT5query, in the evaluation of retrievability bias. Our experiments show promising results on the use of synthetically generated queries for the purpose of retrievability bias estimation. Moreover, we find that the estimated bias values resulting from synthetically generated queries are lower than the ones estimated with user-generated queries on the MS MARCO evaluation benchmark. This indicates that synthetically generated queries might cause less bias than user-generated queries and therefore, by using such queries in training PLM-based rankers, we might be able to reduce the retrievability bias in these models.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnotehttps://github.com/sunxiaojie99/ATTEMPT.
BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation

Recently, Graph Convolutional Network (GCN) based methods have become novel state-of-the-arts for Collaborative Filtering (CF) based Recommender Systems. To obtain users' preferences over different items, it is a common practice to learn representations of users and items by performing embedding propagation on a user-item bipartite graph, and then calculate the preference scores based on the representations. However, in most existing algorithms, user/item representations are generated independently of target items/users. To address this problem, we propose a novel graph attention model named Bilateral Interactive GCN (BI-GCN), which introduces bilateral interactive guidance into each user-item pair and thus leads to target-aware representations for preference prediction. Specifically, to learn the user/item representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item/user. By this manner, we can obtain target-aware representations, i.e., the information of the target item/user is explicitly encoded in the corresponding user/item representation, for more precise matching. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of BI-GCN.
A Test Collection of Synthetic Documents for Training Rankers: ChatGPT vs. Human Experts

In this resource paper, we investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of strong models fine-tuned on both LLM-generated and human-generated data. We build ChatGPT-RetrievalQA based on an existing dataset, human ChatGPT Comparison Corpus (HC3), consisting of public question collections with human responses and answers from ChatGPT. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on LLM-generated responses are significantly more effective for out-of-domain re-ranking than those trained on human responses. For in-domain re-ranking, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models and can be used to augment training data, especially in domains with smaller amounts of labeled data. We believe that our dataset, ChatGPT-RetrievalQA, presents various opportunities for analyzing and improving rankers with human and synthetic data. We release our data, code, and model checkpoints for future work.
Bing Chat: The Future of Search Engines?

ABSTRACT Introduced by Microsoft in February 2023, Bing Chat is a feature of the Bing search engine that integrates an OpenAI large language model (LLM) customised for search (Mehdi, 2023a). This poster compares the outputs of Bing Chat and a standard existing search engine (DuckDuckGo) in response to identical keyword queries and corresponding natural language (NL) questions. Specifically, we examined: (1) the length of Bing Chat's responses and DuckDuckGo's first page of search results, by number of website links; and, (2) the length of Bing Chat's textual summaries, by number of website links. We found that, on average, significantly fewer websites were linked to in Bing Chat's responses compared to DuckDuckGo's search results. Our findings have important implications for website operators, who may receive less traffic and ad revenue if LLM‐enabled search engines are widely adopted in the future. Human‐Computer Interaction (HCI) will inevitably face the need for more research on human information behaviours adaptations in response to the changing search paradigm.
Solar power generation forecasting by a new hybrid cascaded extreme learning method with maximum relevance interaction gain feature selection

Today, renewable energies have a key role in sustainable and clean power generation. On the other hand, solar energy is available in most regions in developing economies. Therefore, photovoltaic power forecasting is crucial for low-carbon power operation, planning, and trading. In this paper, the proposed hybrid cascaded forecasters network model combines LSTM, NARX, ELM, MRIG feature selection, and WT techniques, enabling accurate Solar Power Generation Forecasting (SPGF) for the next 24 h. By integrating these diverse models, the hybrid approach capitalizes on the strengths of each model and compensates for their weaknesses. This synergy significantly enhances the efficiency and accuracy of the forecasting method, leading to improved SPGF performance. The results of day-ahead SPGF for this case study demonstrate the hybrid model's impressive accuracy. For instance, the average prediction error for SPGF of the Mahan PV power plant resulted in a MAPE of 4.7213%, MAE of 0.0100 MW, and RMSE of 0.0198 MW.
Power generation enhancement analysis of a 400 kWp grid-connected rooftop photovoltaic power plant in a hilly terrain of India

Photovoltaic (PV) technology projects operate for 25–30 years under different climatic conditions. However, for consistent economic returns, the system performance has to be assessed regularly. As per international standards, indicators and tests are prescribed to ensure reliable PV plant performance. In this study, performance analysis of a 400 kWp grid-connected solar plant with 10 subsystems is carried out, in a western Himalayan location of India. The annual solar power generation is found to be 431,088.539 kWh which is significantly low due to non-optimized installation and other factors. The minimum and maximum performance ratio of PV subsystems, are found to be 37 % and 92 % respectively. The total performance ratio of the plant is found to be 68 % which is relatively low as compared to similar PV plants installed in India. The average maximum power generated by a subsystem installed at near optimum tilt angle is found to be 4kWh/day/kWp, signifying the relevance of optimized system installation. The results show that the optimized PV panel tilt and orientation correction will lead to enhance energy production by 7.22 % and all corrective measures to identified factors will enhance the solar power generation by 121,833 kWh/year and reduction of 113 tons CO2 emissions.
FDSFL: Filtering Defense Strategies toward Targeted Poisoning Attacks in IIoT-Based Federated Learning Networking System

As a novel distributed machine learning scheme, federated learning (FL) efficiently realizes the collaborative training of models by global participants while also protecting their data privacy. Due to the independence of participants' local data and the inability of the FL server to access the local data, many IIoT applications with strong data sensitivity are increasingly incorporating FL technology. However, it also exposes a great security vulnerability. Malicious adversaries manipulate local data to perform covert targeted poisoning attacks or other harmful behaviors to affect the global model. In addition, due to the diversity of IIoT data in actual scenarios, different data distribution scenarios can also cause different attack effects In this work, we devise a defense technique called FDSFL against multiple malicious adversaries and various targeted poisoning attacks involving both IID and non-IID data distribution scenarios. It runs on the server-side and mainly includes three execution modules: pairwise cosine similarity, clustering mechanism, and filtering strategy, which can dynamically filter malicious updates during the iterative training process. We demonstrate that our designed FDSFL outperforms the state-of-the-art in maintaining global model accuracy and reducing attack success rates through extensive experiments on three general datasets.
Spatiotemporal Data Processing with Memristor Crossbar‐Array‐Based Graph Reservoir

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.
Large language models for chemistry robotics

Abstract This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, using large language models together with task and motion planning. Adding natural language interfaces to autonomous chemistry experiment systems lowers the barrier to using complicated robotics systems and increases utility for non-expert users, but translating natural language experiment descriptions from users into low-level robotics languages is nontrivial. Furthermore, while recent advances have used large language models to generate task plans, reliably executing those plans in the real world by an embodied agent remains challenging. To enable autonomous chemistry experiments and alleviate the workload of chemists, robots must interpret natural language commands, perceive the workspace, autonomously plan multi-step actions and motions, consider safety precautions, and interact with various laboratory equipment. Our approach, CLAIRify , combines automatic iterative prompting with program verification to ensure syntactically valid programs in a data-scarce domain-specific language that incorporates environmental constraints. The generated plan is executed through solving a constrained task and motion planning problem using PDDLStream solvers to prevent spillages of liquids as well as collisions in chemistry labs. We demonstrate the effectiveness of our approach in planning chemistry experiments, with plans successfully executed on a real robot using a repertoire of robot skills and lab tools. Specifically, we showcase the utility of our framework in pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization. Further details about CLAIRify can be found at https://ac-rad.github.io/clairify/ .
Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

This research presents a novel approach for cervical cancer detection and segmentation using tissue images with multiple cells. The study employs a novel deep learning architecture based on Mask Region-Based Convolutional Neural Network (RCNN) and statistical analysis. This new architecture enables us to achieve a high percentage of detection and pix-to-pix area segmentation. A mean Average Precision (mAP) higher than 60% for 3-class and 5-class was achieved. In addition, higher F1-scores of 70% for 3-class and 5-class were obtained. This investigation is a collaborative work, where a medical consultant collected the samples from the Papanicolaou (Pap) Smear examination and labeled the cells presented to the liquid-based cytology (LBC). Furthermore, the online available benchmark data set, SIPaKMeD, was also utilized. Additionally, sample images from the Mendeley data set were also labeled by the trained medical consultant for comparison. The proposed scheme automatically generates a full report for a medical consultant to identify the location of the malicious cells in the given images and expedite the diagnosis and treatment process.
Adaptive fuzzy multi-neighborhood feature selection with hybrid sampling and its application for class-imbalanced data

For imbalanced data, classification efficiency degrades significantly due to the missing information for the positive class, and existing sampling schemes do not consider the distributions of samples. Additionally, the global parameters of fuzzy neighborhoods are set manually. These defects affect the effectiveness of classifier. To address these problems, we offer an adaptive fuzzy multi-neighborhood feature selection methodology with intercluster distance-based hybrid sampling for class-imbalanced data. First, the number of clusters can be defined in terms of the number of samples in the negative or positive class. The initial centers of the clusters are determined according to the number of clusters, and the dissimilarity and similarity measures are calculated by using the intercluster distances between samples. Then, the cluster center, fuzzy membership matrix, and intercluster distance are studied, and then the optimization objective function is designed. The hybrid sampling scheme can be used to combine the generated positive class samples and negative class samples and obtain a class-balanced system. Second, according to the sample distribution, the standard deviation and a set of adaptive fuzzy multi-neighborhood radii are designed. A fuzzy multi-neighborhood similarity relation is defined by introducing a Gaussian kernel model to obtain a fuzzy multi-neighborhood granule, and an improved fuzzy multi-neighborhood rough set model is provided. Uncertain measures of fuzzy neighborhood systems are evaluated by the positive region and dependency. Third, by integrating fuzzy dependence with fuzzy complementary condition entropy, fuzzy multi-neighborhood complementary mutual information is provided on two viewpoints of algebra and information. Finally, a heuristic feature subset selection methodology for imbalanced classification with hybrid sampling using fuzzy c-means clustering is studied to obtain this excellent set of features. Experiments on 26 imbalanced datasets show the effectiveness of our designed algorithm.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer

Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blur, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may lead to unsatisfactory restoration results. In this paper, we propose an end-to-end transformer-based framework for nighttime haze removal, called NightHazeFormer. Our proposed approach consists of two stages: supervised pre-training and semi-supervised fine-tuning. During the pre-training stage, we introduce two powerful priors into the transformer decoder to generate the non-learnable prior queries, which guide the model to extract specific degradations. For the fine-tuning, we combine the generated pseudo ground truths with input real-world nighttime hazy images as paired images and feed into the synthetic domain to fine-tune the pre-trained model. This semi-supervised fine-tuning paradigm helps improve the generalization to real domain. In addition, we also propose a large-scale synthetic dataset called UNREAL-NH, to simulate the real-world nighttime haze scenarios comprehensively. Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
Pedestrian-specific Bipartite-aware Similarity Learning for Text-based Person Retrieval

Text-based person retrieval is a challenging task that aims to search pedestrian images with the same identity according to language descriptions. Current methods usually indiscriminately measure the similarity between text and image by matching global visual-textual features and matched local region-word features. However, these methods underestimate the key cue role of mismatched region-word pairs and ignore the problem of low similarity between matched region-word pairs. To alleviate these issues, we propose a novel Pedestrian-specific Bipartite-aware Similarity Learning (PBSL) framework that efficiently reveals the plausible and credible levels of contribution of pedestrian-specific mismatched and matched region-word pairs towards overall similarity. Specifically, to focus on mismatched region-word pairs, we first develop a new co-interactive attention that utilizes cross-modal information to guide the extraction of pedestrian-specific information in a single modality. We then design a negative similarity regularization mechanism to use the negative similarity score as a bias to correct the overall similarity. Additionally, to enhance the contribution of matched region-word pairs, we introduce graph networks to aggregate and propagate local information of pedestrian-specific, using overall visual-textual similarity to evaluate locally matched region-word pairs for weight refinement. Finally, extensive experiments are conducted on the CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets to demonstrate the competitive performance of the proposed PBSL in the text-based person retrieval task.
FedCE: Personalized Federated Learning Method based on Clustering Ensembles

Federated learning (FL) is a privacy-aware computing framework that enables multiple clients to collaborate in solving machine learning problems. In real scenarios, non-IID data held by different edge devices will degrade the performance of global FL models. To address this issue, most FL methods utilize cluster algorithms to group clients with similar distributions. However, these methods do not fully utilize the distribution features of client data, resulting in a lack of generalization in the cluster model. In order to make the cluster more suitable for the distribution features of user data, we propose a clustering-ensemble based federated learning method (FedCE) that sets each client associated with multiple clusters. We extract the features of client distributions to quantify the relationship between clients and clusters, and optimize the local model of clients through the historical performance of the cluster model. Furthermore, we dynamically estimate the number of clusters each client belongs to through the diversity of client performance. We conduct experiments on scenarios with mixture two-distributions, three-distributions and Dirichlet-distributions. The results show that the FedCE algorithm has better performance than the state-of-the-art clustered FL methods in both cluster and client models under different data distributions.
Suspected Objects Matter: Rethinking Model's Prediction for One-stage Visual Grounding

Recently, one-stage visual grounders attract high attention due to their comparable accuracy but significantly higher efficiency than two-stage grounders. However, inter-object relation modeling has not been well studied for one-stage grounders. Inter-object relationship modeling, though important, is not necessarily performed among all objects, as only part of them are related to the text query and may confuse the model. We call these objects "suspected objects". However, exploring their relationships in the one-stage paradigm is non-trivial because: (1) no object proposals are available as the basis on which to select suspected objects and perform relationship modeling; (2) suspected objects are more confusing than others, as they may share similar semantics, be entangled with certain relationships, etc, and thereby more easily mislead the model's prediction. Toward this end, we propose a Suspected Object Transformation mechanism (SOT), which can be seamlessly integrated into existing CNN and Transformer-based one-stage visual grounders to encourage the target object selection among the suspected ones. Suspected objects are dynamically discovered from a learned activation map adapted to the model's current discrimination ability during training. Afterward, on top of suspected objects, a Keyword-Aware Discrimination module (KAD) and an Exploration by Random Connection strategy (ERC) are concurrently proposed to help the model rethink its initial prediction. On the one hand, KAD leverages keywords contributing high to suspected object discrimination. On the other hand, ERC allows the model to seek the correct object instead of being trapped in a situation that always exploits the current false prediction. Extensive experiments demonstrate the effectiveness of our proposed method.