Reviewerly/exHarmony huggingface dataset:
Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification

Few-shot relation classification (RC) aims to determine the labeled relation between two entities in a given sentence using only a few training instances. Previous studies integrate models with explicit triple knowledge, using the inherent concepts of entities to improve the instance representation. However, these studies neglect the implicit structural knowledge present in the knowledge graph (KG). In this paper, we present SKProto, a knowledge-enhanced prototypical network that leverages deep structured semantic knowledge from the multi-hop neighbors of entity-linked concepts. Specifically, we propose a concept-guided hybrid attention mechanism to learn implicit structural semantic knowledge for enhancing the context-aware instance representation. To further distinguish subtle semantic differences among the concepts, the multi-granularity semantic distinction approach is proposed to construct the negative samples with various difficulties (i.e. hard, medium, and easy) based on the conceptual hierarchical structure. Experimental results on the FewRel 2.0 benchmark show that SKProto outperforms state-of-the-art models. We also demonstrate that SKProto has better robustness than other competitive models in low-shot scenarios.
Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities

Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the city. However, the scarcity of city-level user check-ins brings a significant challenge to user preference learning. Although prior studies attempt to mitigate this challenge by exploiting various context information, e.g., spatio-temporal information, they ignore to transfer the knowledge (i.e., common behavioral pattern) from other relevant cities (i.e., auxiliary cities). In this paper, we investigate the effect of knowledge distilled from auxiliary cities and thus propose a novel Meta-learning Enhanced next POI Recommendation framework (MERec). The MERec leverages the correlation of check-in behaviors among various cities into the meta-learning paradigm to help infer user preference in the target city, by holding the principle of "paying more attention to more correlated knowledge". Particularly, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities. Extensive experiments verify the superiority of the proposed MERec against state-of-the-art algorithms.
Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases

An uncertain geo-referenced transactional database represents the probabilistic data produced by stationary spatial objects observing a particular phenomenon over time. Useful patterns that can empower the users to achieve socio-economic development lie hidden in this database. Finding these patterns is challenging as the existing frequent pattern mining studies ignore the spatial information of the items in a database. This paper proposes a generic model of Geo-referenced Frequent Patterns (GFPs) that may exist in an uncertain geo-referenced transactional database. This paper also introduces two new upper-bound constraints, namely "neighborhood-aware prefix item camp" and "neighborhood-aware expected support", to effectively reduce the search space and the computational cost of finding the desired patterns. An efficient neighborhood-aware pattern-growth algorithm has also been presented in this paper to find all GFPs in a database. Experimental results demonstrate that our algorithm is efficient.
Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation

Reinforcement learning has been widely used in recommender systems in order to optimize users' long-term utilities. An accurate and explainable user simulator is crucial for reinforcement learning based recommendation, as an online interactive environment is often unavailable. On short video platforms, it is very important to keep users on the platform as long as possible in each session. Thus, session-based user utilities depend on two factors: how much users like every single video (video preference) and the number of videos watched (video views) in each session. To this end, the simulator should simultaneously model the user's degree of liking for each video and video views. However, most previous studies on the short video recommendation only paid attention to the former. In this work, we propose KESWA, a Knowledge-Enhanced Session-Wide Attention method for short video user simulation. KESWA fuses information foraging theory with a deep learning model for both video preference and video views modeling, providing an explainable prediction for users' staying and leaving behavior. Comparative experiments demonstrate that KESWA provides a better simulation of video views compared with existing models. Meanwhile, reinforcement learning agents can achieve higher session-based user utilities trained by KESWA than by other user simulators.
Global-Aware External Attention Deep Model for Sequential Recommendation

The sequential recommender plays a major role in contemporary recommendation systems, which shows the strong ability to model sequential patterns among the dataset. The classic sequential recommenders utilize the convolutional neural network, recurrent neural network, and self-attention mechanism to model the user's preferences of items. However, these existing sequential recommendation models face the "Filter Bubble" issue by putting too much attention on each user's own historical sequence, and they also ignore the feature-level item-item relationship. To address the existing challenges, we propose a novel global-aware external attention deep model (EDM) to learn both the global and local user preferences. The proposed EDM mainly contains a multi-embedding layer, an external attention layer, a feature-wise feed-forward network, and the candidate matching layer. Specifically, the external attention layer uses two external memory units shared across the entire input set to model the global interests of users. Then, by applying the feed-forward network to each feature dimension, the feature-wise feed-forward network is capable to learn the feature-level dependencies and properly model the local user preferences. In the experiments, three benchmark datasets are used with various validation metrics to show that our proposed EDM outperforms the state-of-the-art methods.
RLMixer: A Reinforcement Learning Approach for Integrated Ranking with Contrastive User Preference Modeling

There is a strong need for industrial recommender systems to output an integrated ranking of items from different categories, such as video and news, to maximize overall user satisfaction. Integrated ranking faces two critical challenges. First, there is no universal metric to evaluate the contribution of each item due to the huge discrepancies between items. Second, user's short-term preference may shift fast between diverse items during her interaction with the recommender system. To address the above challenges, we propose a reinforcement learning (RL) based framework called RLMixer to approach the sequential integrated ranking problem. Benefiting from the credit assignment mechanism, RLMixer can decompose the overall user satisfaction to items of different categories, so that they are comparable. To capture the user's short-term preference, RLMixer explicitly learns user interest vectors by a carefully designed contrastive loss. In addition, RLMixer is trained in a fully offline manner for the convenience in industrial applications. We show that RLMixer significantly outperforms various baselines on both public PRM datasets and industrial datasets collected from a widely used AppStore. We also conduct online A/B tests on millions of users through the AppStore. The results show that RLMixer brings over 4% significant revenue gain.
A Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue

Most students and academicians use anonymous item search strategies to fetch books or other library materials, including subject keywords. Also, most of the search follows keyword-based search which returns irrelevant information as a result, and in the end, result success rates will be low. To overcome such problems, especially in the library catalogue, this paper presents an ontological approach to convert syntactic search strategy to semantic search and identify major subjects using Dewey Decimal Classification (DDC), available in the Union Catalogue of Gujarat Colleges (GujCat). The semantic web is creative and relevant and provides information in a way the computer can understand. Currently, GujCat follows keyword-based searching techniques, giving only exact text-matching results. This paper has suggested a structure that implements semantic concepts with the DDC scheme that can improve the subject-wise search and the overall search result quality of GujCat. Semantic-based search requires a Resource Description Framework (RDF) or Ontology concepts. The Simple Protocol and RDF Query Language (SPARQL) is a query language recommended by World Wide Web Consortium (W3C) to access and explore the ontology and data. The DDC classification scheme refers to a hierarchy of classes to enhance and improve subject-wise search for all the subjects available in the books.
Handwriting Recognition for Predicting Gender and Handedness Using Deep Learning

This research focuses on predicting the gender and handedness and also combined prediction through Handwriting Recognition. In this paper we have analyzed various Deep learning approaches using different CNN models. Our proposed approach uses two data sets IAM English dataset and Real-Time dataset that was collected by us for recognizing the handwriting pattern to perform the defined task. Using different Deep learning approaches, experiments were conducted to analyze the performance on the given two datasets for predicting. Gender and handedness. Experiments showed Google Net using CNN is the most efficient for the prediction and recognition with the handwriting samples. For combined prediction the accuracy rate is around 94% with Real-Time dataset and 95% with the IAM dataset.
M-EBM: Towards Understanding the Manifolds of Energy-Based Models

Energy-based models (EBMs) exhibit a variety of desirable properties in predictive tasks, such as generality, simplicity and compositionality. However, training EBMs on high-dimensional datasets remains unstable and expensive. In this paper, we present a Manifold EBM (M-EBM) to boost the overall performance of unconditional EBM and Joint Energy-based Model (JEM). Despite its simplicity, M-EBM significantly improves unconditional EBMs in training stability and speed on a host of benchmark datasets, such as CIFAR10, CIFAR100, CelebA-HQ, and ImageNet 32 $$\,\times \,$$ 32. Once class labels are available, label-incorporated M-EBM (M-JEM) further surpasses M-EBM in image generation quality with an over 40% FID improvement, while enjoying improved accuracy. The code can be found in https://github.com/sndnyang/mebm .
RL-Net: Interpretable Rule Learning with Neural Networks

As there is a need for interpretable classification models in many application domains, symbolic, interpretable classification models have been studied for many years in the literature. Rule-based models are an important class of such models. However, most of the common algorithms for learning rule-based models rely on heuristic search strategies developed for specific rule-learning settings. These search strategies are very different from those used in neural forms of machine learning, where gradient-based approaches are used. Attempting to combine neural and symbolic machine learning, recent studies have therefore explored gradient-based rule learning using neural network architectures. These new proposals make it possible to apply approaches for learning neural networks to rule learning. However, these past studies focus on unordered rule sets for classification tasks, while many common rule-learning algorithms learn rule sets with an order. In this work, we propose RL-Net, an approach for learning ordered rule lists based on neural networks. We demonstrate that the performance we obtain on classification tasks is similar to the state-of-the-art algorithms for rule learning in binary and multi-class classification settings. Moreover, we show that our model can easily be adapted to multi-label learning tasks.
Binary Black Widow with Hill Climbing Algorithm for Feature Selection

Feature Selection (FS) is a pre-processing step in most big data processing applications. Its purpose is to remove inconsequential and redundant features from data to determine a final set of data properties that best describe the data as a whole. The FS process is an NP-hard problem. It tries to determine the optimal subset, i.e., produces all conceivable solutions to acquire only the best. In the last few years, metaheuristic algorithms (MAs) have been coined as an ideal solution for FS problems, particularly in high-dimensional data cases. This work is an extension of our previous effort in finding an effective solution to FS problems by applying a recently developed metaheuristic algorithm called the Black Widow Optimization (BWO) algorithm. We combine our previous algorithm, the Binary Black Widow Algorithm (BBWO), with a Hill-Climbing Algorithm to solve the slow convergence problem of the BBWO. The newly developed algorithm, BBWO-HCA, is tested using 28 UCI datasets and compared with six well-regarded algorithms in the domain. The test results show that the BBWO-HCA outperforms our previous BBWO solution and almost all comparable solutions tested.
Adaptive Bi-nonlinear Neural Networks Based on Complex Numbers with Weights Constrained Along the Unit Circle

Traditional real-valued neural networks can suppress neural inputs by setting the weights to zero or overshadow other inputs by using extreme weight values. Large network weights are undesirable because they may cause network instability and lead to exploding gradients. To penalize such large weights, adequate regularization is typically required. This work presents a feed-forward and convolutional layer architecture that constrains weights along the unit circle such that neural connections can never be eliminated or suppressed by weights, ensuring that no incoming information is lost by dying neurons. The neural network’s decision boundaries are redefined by expressing model weights as angles of phase rotations and layer inputs as amplitude modulations, with trainable weights always remaining within a fixed range. The approach can be quickly and readily integrated into existing layers while preserving the model architecture of the original network. The classification performance was tested and assessed on basic computer vision data sets using ShuffleNetv2, ResNet18, and GoogLeNet at high learning rates.
Mixing Data Augmentation Methods for Semantic Segmentation

Deep learning models are the state-of-the-art approach to deal with semantic segmentation tasks. However, training deep models require a considerable amount of images that might be difficult to obtain. This issue can be faced by means of data augmentation techniques that generate new images by applying geometric or colour transformations, or more recently by mixing several images using techniques such as CutMix or CarveMix. Unfortunately, mixing strategies are usually implemented as ad-hoc methods and are difficult to incorporate into the pipeline to train segmentation models. In this work, we present a library that implements several mixing strategies for data augmentation in semantic segmentation tasks. In particular, we provide a set of callbacks that can be integrated into the training pipeline of FastAI segmentation models. We have tested the library with a vineyard dataset and show the benefits of combining mixing strategies with traditional data augmentation techniques; namely an improvement of almost 5% was achieved using these methods regarding models trained only with traditional data augmentation methods.
A Hierarchical Cooperative Coevolutionary Approach to Solve Very Large-Scale Traveling Salesman Problem

In this paper, we propose a hierarchical Cooperative Coevolution framework (hCC) to deal with the Very Large-scale Traveling Salesman Problem (VLSTSP). Due to the existence of the curse of dimensionality, it is difficult to find an acceptable solution for VLSTSP with conventional Evolutionary Algorithms (EAs). Cooperative Coevolution (CC) framework, which divides the problems into multiple subcomponents and optimizes them independently, offers a potential opportunity to find suitable solutions. However, conventional CC with large-scale sub-size decomposition will still be affected by the curse of dimensionality, and small-scale sub-size decomposition ignores many interactions between subcomponents. Although the initial sub-size of the proposed hCC is small-scale, the ignored interactions will be reconsidered in the higher layer of optimization. Another issue is how to decompose the VLSTSP. In the numerical experiments, we design two strategies: (1). Random decomposition. (2). Decomposition based on the greedy solution. 10 symmetric instances of VLSTSP ranging from 38,478 to 238,025 are employed to evaluate our proposal, and the basic optimizer is greedy Local Search (gLS). Experimental results show that our proposal has great potential and scalability to deal with VLSTSP, which can be easily extended to deal with various types of TSP.
A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules

Link forecasting in a temporal Knowledge Graph (tKG) involves predicting a future event from a given set of past events. Most previous studies suffered from reduced performance as they disregarded acyclic rules and enforced a tight constraint that all past events must exist in a strict temporal order. This paper proposes a novel explainable rule-based link forecasting framework by introducing two new concepts, namely ‘relaxed temporal cyclic and acyclic random walks’ and ‘link-star rules’. The former concept involves generating rules by performing cyclic and acyclic random walks on a tKG by taking into account the real-world phenomenon that the order of any two events may be ignored if their occurrence time gap is within a threshold value. Link-star rules are a special class of acyclic rules generated based on the natural phenomenon that history repeats itself after a particular time. Link-star rules eliminate the problem of combinatorial rule explosion, thereby making our framework practicable. Experimental results demonstrate that our framework outperforms the state-of-the-art by a substantial margin. The evaluation measures hits@1 and mean reciprocal rank were improved by 45% and 23%, respectively.
Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling

In recent years, groundbreaking transformer-based language models (LMs) have made tremendous advances in natural language processing (NLP) tasks. However, the measurement of their fairness with respect to different social groups still remains unsolved. In this paper, we propose and thoroughly validate an evaluation technique to assess the quality and bias of language model predictions on transcripts of both spoken African American English (AAE) and Spoken American English (SAE). Our analysis reveals the presence of a bias towards SAE encoded by state-of-the-art LMs such as BERT and DistilBERT and a lower bias in distilled LMs. We also observe a bias towards AAE in RoBERTa and BART. Additionally, we show evidence that this disparity is present across all the LMs when we only consider the grammar and the syntax specific to AAE.
An Improved Visual Assessment with Data-Dependent Kernel for Stream Clustering

The advances of 5G and the Internet of Things enable more devices and sensors to be interconnected. Unlike traditional data, the large amount of data generated from various sensors and devices requires real-time analysis. The data objects in a stream will change over time and only have a single access. Thus, traditional methods no longer meet the needs of fast exploratory data analysis for continuously generated data. Cluster tendency assessment is an effective method to determine the number of potential clusters. Recently, there are methods based on Visual Assessment of cluster Tendency (VAT) proposed for visualising cluster structures in streaming data using cluster heat maps. However, those heat maps rely on Euclidean distance that does not consider the data distribution characteristics. Consequently, it would be difficult to separate adjacent clusters of varied densities. In this paper, we discuss this issue for the latest inc-siVAT method, and propose to use a data-dependent kernel method to overcome it for clustering streaming data. Extensive evaluation on 7 large synthetic and real-world datasets shows the superiority of kernel-based inc-siVAT over 4 recently published state-of-the-art online and offline clustering algorithms.
Enhanced Topic Modeling with Multi-modal Representation Learning

Existing topic modelling methods primarily use text features to discover topics without considering other data modalities such as images. The recent advances in multi-modal representation learning show that the multi-modality features are useful to enhance the semantic information within the text data for downstream tasks. This paper proposes a novel Neural Topic Model framework in a multi-modal setting where visual and textual information are utilized to derive text-based topic models. The framework includes a Gated Data Fusion module to learn the textual-specific visual representations for generating contextualized multi-modality features. These features are then mapped into a joint latent space by using a Neural Topic Model to learn topic distributions. Experiments on diverse datasets show that the proposed framework improves topic quality significantly.
Federated Learning Under Statistical Heterogeneity on Riemannian Manifolds

Federated learning (FL) is a collaborative machine learning paradigm in which clients with limited data collaborate to train a single “best” global model based on consensus. One major challenge facing FL is the statistical heterogeneity among the data for each of the local clients. Clients trained with non-IID or imbalanced data whose models are aggregated using averaging schemes such as FedAvg may result in a biased global model with a slow training convergence. To address this challenge, we propose a novel and robust aggregation scheme, FedMan, which assigns each client a weighting factor based on its statistical consistency with other clients. Such statistical consistency is measured on a Riemannian manifold spanned by the covariance of the local client output logits. We demonstrate the superior performance of FedMAN over several FL baselines (FedAvg, FedProx, and Fedcurv) as applied to various benchmark datasets (MNIST, Fashion-MNIST, and CIFAR-10) under a wide variety of degrees of statistical heterogeneity.