Reviewerly/exHarmony huggingface dataset:
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users’ historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as “GNN Session-based New Item Recommendation (GSNIR)”. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users’ intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users’ decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module.
Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain &amp; Web3 Ecosystem

Web3, based on blockchain technology, is the evolving next generation Internet of value. Massive active applications on Web3, e.g. DeFi and NFT, usually rely on blockchain transactions to achieve value transfer as well as complex and diverse custom logic and intentions. Various risky or illegal behaviors such as financial fraud, hacking, money laundering are currently rampant in the blockchain ecosystem, and it is thus important to understand the intent behind the pseudonymous transactions. To reveal the intent of transactions, much effort has been devoted to extracting some particular transaction semantics through specific expert experiences. However, the limitations of existing methods in terms of effectiveness and generalization make it difficult to extract diverse transaction semantics in the rapidly growing and evolving Web3 ecosystem. In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. To the best of our knowledge, MoTS is the first general semantic extraction method in Web3 blockchain ecosystem. Experimental results show that MoTS can effectively distinguish different transaction semantics in real-time, and can be used for various downstream tasks, giving new insights to understand the Web3 blockchain ecosystem. Our codes are available at https://github.com/wuzhy1ng/MoTS.
Two-Stage Constrained Actor-Critic for Short Video Recommendation

The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, including WatchTime and various types of interactions with multiple videos. On the one hand, the platforms aim at optimizing the users’ cumulative WatchTime (main goal) in the long term, which can be effectively optimized by Reinforcement Learning. On the other hand, the platforms also need to satisfy the constraint of accommodating the responses of multiple user interactions (auxiliary goals) such as Like, Follow, Share, etc. In this paper, we formulate the problem of short video recommendation as a Constrained Markov Decision Process (CMDP). We find that traditional constrained reinforcement learning algorithms fail to work well in this setting. We propose a novel two-stage constrained actor-critic method: At stage one, we learn individual policies to optimize each auxiliary signal. In stage two, we learn a policy to (i) optimize the main signal and (ii) stay close to policies learned in the first stage, which effectively guarantees the performance of this main policy on the auxiliaries. Through extensive offline evaluations, we demonstrate the effectiveness of our method over alternatives in both optimizing the main goal as well as balancing the others. We further show the advantage of our method in live experiments of short video recommendations, where it significantly outperforms other baselines in terms of both WatchTime and interactions. Our approach has been fully launched in the production system to optimize user experiences on the platform.
Knowledge Graph Completion with Counterfactual Augmentation

Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of them are designed to learn from the observed graph structure, which appears to have imbalanced relation distribution during the training stage. Motivated by the causal relationship among the entities on a knowledge graph, we explore this defect through a counterfactual question: “would the relation still exist if the neighborhood of entities became different from observation?”. With a carefully designed instantiation of a causal model on the knowledge graph, we generate the counterfactual relations to answer the question by regarding the representations of entity pair given relation as context, structural information of relation-aware neighborhood as treatment, and validity of the composed triplet as the outcome. Furthermore, we incorporate the created counterfactual relations with the GNN-based framework on KGs to augment their learning of entity pair representations from both the observed and counterfactual relations. Experiments on benchmarks show that our proposed method outperforms existing methods on the task of KGC, achieving new state-of-the-art results. Moreover, we demonstrate that the proposed counterfactual relations-based augmentation also enhances the interpretability of the GNN-based framework through the path interpretations of predictions.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the de-facto standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs. In this work, we propose a novel framework KRACL1 to alleviate the widespread sparsity in KGs with graph context and contrastive learning. Firstly, we propose the Knowledge Relational Attention Network (KRAT) to leverage the graph context by simultaneously projecting neighboring triples to different latent spaces and jointly aggregating messages with the attention mechanism. KRAT is capable of capturing the subtle semantic information and importance of different context triples as well as leveraging multi-hop information in knowledge graphs. Secondly, we propose the knowledge contrastive loss by combining the contrastive loss with cross entropy loss, which introduces more negative samples and thus enriches the feedback to sparse entities. Our experiments demonstrate that KRACL achieves superior results across various standard knowledge graph benchmarks, especially on WN18RR and NELL-995 which have large numbers of low in-degree entities. Extensive experiments also bear out KRACL’s effectiveness in handling sparse knowledge graphs and robustness against noisy triples.
SkyWords: An automatic keyword extraction system based on the skyline operator and semantic similarity

This study presents a hybrid keyword extraction method called SkyWords. It implements a novel supervised step based on the skyline operator and the majority voting principle for high-quality candidate keyword selection and an unsupervised step based on contextual semantics for the candidate keyword ranking. To achieve this, firstly, we build a feature vector database using the features of known keywords and then apply the skyline operator to retrieve the dominating feature vectors. To select the candidate keywords of a document, we compare each word of the document against the dominating feature vectors and assume the words that are as good as the majority of the dominating feature vectors as candidate keywords. To obtain the final set of keywords, we rank the candidate keywords based on their semantic similarity to the document based on their vector representation using the MPNet sentence transformer. We conducted experiments on six benchmark scholarly datasets to evaluate the performance of SkyWords and compared the results against eleven baseline keyword extraction systems. The experimental results show that the proposed novel keyword selection algorithm reduced the number of candidate keywords by several folds. Moreover, SkyWords achieved statistically significant improvements over the baseline methods in precision, recall, and F1 score. Compared to the baseline regarding ranking-based metrics, SkyWords achieved the highest mean average precision score for all datasets and the highest mean reciprocal rank score for all datasets but one. Furthermore, SkyWords extracted more relevant keywords than the baseline methods.
Hypergraph Association Weakly Supervised Crowd Counting

Weakly supervised crowd counting involves the regression of the number of individuals present in an image, using only the total number as the label. However, this task is plagued by two primary challenges: the large variation of head size and uneven distribution of crowd density. To address these issues, we propose a novel Hypergraph Association Crowd Counting (HACC) framework. Our approach consists of a new multi-scale dilated pyramid module that can efficiently handle the large variation of head size. Further, we propose a novel hypergraph association module to solve the problem of uneven distribution of crowd density by encoding higher-order associations among features, which opens a new direction to solve this problem. Experimental results on multiple datasets demonstrate that our HACC model achieves new state-of-the-art results.
Novel medical question and answer system: Graph convolutional neural network based with knowledge graph optimization

In order to effectively integrate medical data and alleviate the problem of uneven distribution of medical resources. In this paper, we combine the techniques of expert systems, graph neural networks, and knowledge graphs to propose a disease guidance model combining semi-supervised graph neural networks and knowledge graphs. We use the MASR speech recognition module combined with gated convolutional units for effective text processing of different types of speech; then we use the LTP module in natural language processing for semantic analysis and segmentation matching of interrogative sentences; we combine keywords with the number of diseases and divide and construct the set of nodes with knowledge graphs. And we use semi-supervised graph neural network type analysis to give treatment results and rehabilitation suggestions effectively. We optimize the Chinese and English corpora respectively, adding consideration for local dialect audiences. We performed a comprehensive comparison of the accuracy and training time of several mainstream GCN algorithms and our GCN semi-supervised (SGS) under various graphical text datasets to validate the efficiency and accuracy of our own algorithm choices. We preprocess the number of different symptoms for classification and simplify the redundant nodes to optimize the running time while taking into account the overall convergence. The operational mechanism of the model as well as the convergence and hits under different symptom parameters are explained through hit rate and convergence rate metrics to demonstrate the effectiveness and stability of the model under proprietary medical conditions.
APF-RRT*: An Efficient Sampling-Based Path Planning Method with the Guidance of Artificial Potential Field

Path planning is a decisive module of mobile robots and its time efficiency significantly affects the safety of the robots. Sampling-based methods have achieved great success in the robotic path planning domain. However, poor time efficiency is still a serious limitation when they are applied to a crowded environment. In this paper, we combine the RRT* algorithm and artificial potential field(APF) technic and propose an efficient sampling-based path planning method named APF-RRT*. Utilizing the prior knowledge of the mission and the environment, we construct APFs for the start point, the goal point, the reference path, and the obstacles. Then we modify the random sampling step of the RRT* algorithm. With the guidance of APF, the random sample points are closer to the optimal path, and useless sample points greatly decrease. Results show that the proposed APF-RRT* outperforms state-of-the-art sampling-based methods in convergence rate, sampling effectiveness, and time efficiency.
<b>COLTR</b>: Semi-Supervised Learning to Rank With Co-Training and Over-Parameterization for Web Search

While <i>learning to rank</i> (LTR) has been widely used in web search to prioritize most relevant webpages among the retrieved contents subject to the input queries, the traditional LTR models fail to deliver decent performance due to two main reasons: 1) the lack of well-annotated query-webpage pairs with ranking scores to cover search queries of various popularity, and 2) ill-trained models based on a limited number of training samples with poor generalization performance. To improve the performance of LTR models, tremendous efforts have been done from above two aspects, such as enlarging training sets with pseudo-labels of ranking scores by self-training, or refining the features used for LTR through feature extraction and dimension reduction. Though LTR performance has been marginally increased, we still believe these methods could be further improved in the newly-fashioned “interpolating regime”. Specifically, instead of lowering the number of features used for LTR models, our work proposes to transform original data with random Fourier feature, so as to over-parameterize the downstream LTR models (e.g., GBRank or LightGBM) with features in ultra-high dimensionality and achieve superb generalization performance. Furthermore, rather than self-training with pseudo-labels produced by the same LTR model in a “self-tuned” fashion, the proposed method incorporates the diversity of prediction results between the listwise and pointwise LTR models while co-training both models with a cyclic labeling-prediction pipeline in a “ping-pong” manner. We deploy the proposed <i><u>C</u>o-trained and <u>O</u>ver-parameterized <u>LTR</u></i> system <b>COLTR</b> at Baidu search and evaluate <b>COLTR</b> with a large number of baseline methods. The results show that <b>COLTR</b> could achieve <inline-formula><tex-math notation="LaTeX">$\Delta NDCG_{4}$</tex-math></inline-formula> = 3.64% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4.92%, compared to baselines, under various ratios of labeled samples. We also conduct a 7-day A/B Test using the realistic web traffics of Baidu Search, where we can still observe significant performance improvement around <inline-formula><tex-math notation="LaTeX">$\Delta NDCG_{4}$</tex-math></inline-formula> = 0.17% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 0.92% in real-world applications. <b>COLTR</b> performs consistently both in online and offline experiments.
DG-ALETSK: A High-Dimensional Fuzzy Approach With Simultaneous Feature Selection and Rule Extraction

Fuzzy or neuro-fuzzy systems have been successfully employed in many areas, but their limitation in solving high-dimensional problems remains a challenging task. On the other hand, fuzzy model-based feature selection (FS) approaches have been well studied but, because of the above limitation, are rarely applied to high-dimensional data, which exactly needs feature reduction. In this article, we design a novel adaptive Ln-Exp softmin operator as the approximator of the minimum T-norm, which is equipped in Takagi–Sugeno–Kang (TSK) fuzzy systems to make them capable of dealing with high-dimensional problems. The adaptive Ln-Exp softmin-based TSK (ALETSK) model is developed. Then, we improve the gate function by introducing an enhanced scheme and propose a double groups of gates-based ALETSK (DG-ALETSK) fuzzy approach to simultaneously conduct FS and rule extraction (RE), where the gate function is a function measuring the importance of the features or rules and it acts like a gate. More specifically, a group of feature gates are embedded in the antecedents of ALETSK for FS. Meanwhile, another group of rule gates are embedded in the consequents of ALETSK for RE. In one training phase, the gate parameters are trained along with the system parameters, so that the FS and RE are done together. Our proposed DG-ALETSK is effective and time-saving for FS and RE in the high-dimensional tasks, which is verified by the numerical experiments.
Digital Work – Transforming the Higher Education Landscape in South Africa

Abstract There are twenty-six public universities in South Africa. Yet, there is no digital transformation in most higher education workplaces. In some universities, digital technologies are advanced, and in others, they are not. The education landscape is partially transformed, and in others, it is a work in progress. Therefore, the study explored the digital environment in the twenty-six public universities in South Africa by using qualitative methods and found that the digital environment in higher education poses digital inequalities that make it difficult for academic staff to work smarter across the board. The playfield is not seamless and given the digital society of a new normal, digital work deserves close attention. The study concludes that digital work in South African higher education requires digital transformation to enable the academic staff to optimally work from everywhere in any educational environment to maximize productivity in the advancement of the academic project and to produce globally competitive and locally relevant graduates.
Modeling Trust Dimensions and Dynamics in Human-Agent Conversation: A Trajectory Epistemic Network Analysis Approach

Human-AI conversation provides a natural, unobtrusive, yet under-explored way to investigate trust dynamics in human-AI teams (HATs). In this paper, we modeled dynamic trust evolution in conversations using a novel method, trajectory epistemic network analysis (T-ENA). T-ENA captures the multidimensional aspect of trust (i.e., analytic and affective), and trajectory analysis segments conversations to capture temporal changes of trust over time. Twenty-four participants performed a habitat maintenance task assisted by a conversational agent and verbalized their experiences and feelings after each task. T-ENA showed that agent reliability significantly affected people's conversations in the analytic process of trust, t(38.88)=15.18,p<0.001,Cohen′s d=4.72, such as discussing agents' errors. The trajectory analysis showed that trust dynamics manifested through conversation topic diversity and flow. These results showed trust dimensions and dynamics in conversation should be considered interdependently and suggested that an adaptive conversational strategy for managing trust in HATs.
Global-local least-squares support vector machine (GLocal-LS-SVM)

This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning. GLocal-LS-SVM addresses the challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm is a double-layer learning approach that employs multiple local LS-SVM models in the first layer and one global LS-SVM model in the second layer. The key idea behind GLocal-LS-SVM is to extract the most informative data points, known as support vectors, from each local region in the input space. Local LS-SVM models are developed for each region to identify the most contributing data points with the highest support values. The local support vectors are then merged at the final layer to form a reduced training set used to train the global model. We evaluated the performance of GLocal-LS-SVM using both synthetic and real-world datasets. Our results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models. In addition, our experiments show that GLocal-LS-SVM outperforms standard LS-SVM in terms of computational efficiency. For instance, on a training dataset of 9, 000 instances, the average training time for GLocal-LS-SVM was only 2% of the time required to train the LS-SVM model while maintaining classification performance. In summary, the GLocal-LS-SVM algorithm offers a promising solution to address the challenges associated with decentralised data sources and large datasets while maintaining high classification performance. Furthermore, its computational efficiency makes it a valuable tool for practical applications in various domains.
Effect of fast noise on the fidelity of trapped-ion quantum gates

High fidelity single and multi-qubit operations compose the backbone of quantum information processing. This fidelity is based on the ability to couple single- or two-qubit levels in an extremely coherent and precise manner. A necessary condition for coherent quantum evolution is a highly stable local oscillator driving these transitions. Here we study the effect of fast noise, that is noise at frequencies much higher than the local oscillator linewidth, on the fidelity of one- and two-qubit gates in a trapped-ion system. We analyze and measure the effect of fast noise on single qubit operations including resonant $\pi$ rotations and off-resonant sideband transitions . We further analyze the effect of fast phase noise on the Molmer-Sorensen two-qubit gate. We find a unified and simple way to estimate the performance of all of these operations through a single parameter given by the noise power spectral density at the qubit response frequency. While our analysis focuses on phase noise and on trapped-ion systems, it is relevant for other sources of fast noise as well as for other qubit systems in which spin-like qubits are coupled by a common bosonic field. Our analysis can help in guiding the deign of quantum hardware platforms and gates, improving their fidelity towards fault-tolerant quantum computing.