Reviewerly/exHarmony huggingface dataset:
Graph Reciprocal Neural Networks by Abstracting Node as Attribute

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal Network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic Networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.
MPRE: Multi-perspective Patient Representation Extractor for Disease Prediction

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the Multi-perspective Patient Representation Extractor (MPRE) for disease prediction. Specifically, we propose Frequency Transformation Module (FTM) to extract the trend and variation information of dynamic features in the time-frequency domain, which can enhance the feature representation. In the 2D Multi-Extraction Network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the First-Order Difference Attention Mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation

Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previous studies to address NOCDR, several limitations still exist. Specifically, 1) while some approaches substitute overlapping users/items with overlapping behaviors, they cannot handle NOCDR scenarios where such auxiliary information is unavailable; 2) often, cross-domain preference mapping is modeled by learning deterministic explicit representation matchings between sampled users in two domains. However, this can be biased due to individual preferences and thus fails to incorporate preference continuity and universality of the general population. In light of this, we assume that despite the scattered nature of user behaviors, there exists a consistent latent preference distribution shared among common people. Modeling such distributions further allows us to capture the continuity in user behaviors within each domain and discover preference invariance across domains. To this end, we propose a Distributional domain-invariant Preference Matching method for non-overlapping Cross-Domain Recommendation (DPMCDR). For each domain, we hierarchically approximate a posterior of domain-level preference distribution with empirical evidence derived from user-item interactions. Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains. This process involves mapping them to a shared latent space and seeking a consensus on domain-invariant preference by minimizing the distance between their distributional representations therein. In this way, we can identify the alignment of two non-overlapping domains if they exhibit similar patterns of domain-invariant preference. Experiments on real-world datasets demonstrate that DPMCDR outperforms the state-of-the-art approaches with a range of evaluation metrics.
Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation

Learning-to-Rank (LTR) is a supervised machine learning approach that constructs models specifically designed to order a set of items or documents based on their relevance or importance to a given query or context. Despite significant success in real-world information retrieval systems, current LTR methods rely on one prefix ranking metric (e.g., such as Normalized Discounted Cumulative Gain (NDCG) or Mean Average Precision (MAP)) for optimizing the ranking objective function. Such metric-dependent setting limits LTR methods from two perspectives: (1) non-differentiable problem: directly optimizing ranking functions over a given ranking metric is inherently non-smooth, making the training process unstable and inefficient; (2) limited ranking utility: optimizing over one single metric makes it difficult to generalize well to other ranking metrics of interest. To address the above issues, we propose a novel listwise LTR framework for efficient and generalizable ranking purpose. Specifically, we propose a new differentiable ranking loss that combines a smooth approximation to the ranking operator with the average mean square loss per query. Then, we adapt gradient-boosting machines to minimize our proposed loss with respect to each list, a novel contribution. Finally, extensive experimental results confirm that our method outperforms the current state-of-the-art in information retrieval measures with similar efficiency.
Two-Level Graph Representation Learning with Community-as-a-Node Graphs

In this paper, we propose a novel graph representation learning (GRL) model that aims to improve both representation accuracy and learning efficiency. We design a Two-Level GRL architecture based on the graph partitioning: 1) local GRL on nodes within each partitioned subgraph and 2) global GRL on subgraphs. By partitioning the graph through community detection, we enable elaborate node learning in the same community. Based on Two-Level GRL, we introduce an abstracted graph, Community-as-a-Node Graph(CaaN), to effectively maintain the high-level structure with a significantly reduced graph. By applying the CaaN graph to local and global GRL, we propose Two-Level GRL with Community-as-a-Node (CaaN 2L) that effectively maintains the global structure of the entire graph while accurately representing the nodes in each community. A salient point of the proposed model is that it can be applied to any existing GRL model by adopting it as the base model for local and global GRL. Through extensive experiments employing seven popular GRL models, we show that our model outperforms them in both accuracy and efficiency.
Natural Language Generation Meets Data Visualization: Vis-to-Text and its Duality with Text-to-Vis

Data visualizations (DVs) refer to the methodologies and tools where visual elements like charts, bars and scatters are used to convey summaries behind the raw data. However, it usually takes much effort to master DV, even for data scientists and experts, not to mention beginners. Hence, a popular task named Text-to-Vis, focusing on automatically generating data visualizations (DVs) from natural language questions (NLQs), has been introduced and has recently been gaining great attention from both the database and the data mining communities. In this paper, we propose the reversed task Vis-to-Text, which aims to generate human-readable descriptions to explain complicated DVs for educational purposes. Intuitively, text-to-vis and vis-to-text are strongly correlated to one another, i.e., the input of text-to-vis is the output of vis-to-text, and vice versa. This relationship is generally known as duality and has been validated to be important for improving the performances of both tasks in machine translation, question answering, and dialogue systems. However, its effectiveness in text-to-vis and vis-to-text is under-explored. In this paper, we make use of the duality to optimize both tasks. We first design a Transformer-based network to tackle the vis-to-text task. Then, we further explore a dual training framework to simultaneously optimize the two tasks. More specifically, we analyze the duality and finally convert it into a corresponding regularization term to constrain the loss function to guide the model training process. Finally, we evaluate our approach on a public dataset, and the experimental results validate the rationale of this new proposed vis-to-text task and also show that this dual framework can boost the performance of the vis-to-text task over existing baselines.
Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification

Representation learning in unlabeled heterogeneous graphs has gained significant interest. The heterogeneity in graphs not only provides rich information but also poses challenges to model complex relations in self-supervised learning (SSL) manner. Existing SSL-based approaches are usually designed for node-level tasks and are unable to capture global graph-level features. Also, they often employ computationally expensive meta-path-based techniques, to learn the intrinsic graph structure, that are intractable. Importantly, they overlook non-pairwise relationships among nodes in heterogeneous graphs, for instance in protein-protein interaction networks or collaboration networks, limiting the effectiveness of graph-level learning. To address these issues, we propose a novel self-supervised heterogeneous hypergraph learning framework that captures the richness of heterogeneity, and high-order connectivity in graph-level classification. Unlike traditional methods that rely on meta-path-based approaches to incorporate high-order information, we introduce a k-hop neighborhood strategy to construct intra-graph hyperedges, and a shared attribute-based approach for inter-graph hyperedges to construct the heterogeneous hypergraph. Furthermore, we introduce a context-aware graph-level pooling mechanism that facilitates adaptive aggregation of relevant information across the hypergraph, considering both local and global contexts. Lastly, we design a self-supervised contrastive learning framework by introducing a high-order-aware adaptive augmentation mechanism. This enables the model to learn meaningful graph-level representations from less-labeled data. We evaluate our proposed model against graph kernels, graph neural networks, and graph pooling-based baselines on real-world datasets, demonstrating an overall performance improvement of 5.81% that validates the effectiveness and superiority of the proposed method.
A Symbolic Representation of Two-Dimensional Time Series for Arbitrary Length DTW Motif

Over the last two decades, time series motif discovery has emerged as a useful primitive; in parallel, it has been known that Dynamic Time Warping (DTW) outperforms other similarity measures, such as Euclidean Distance, under most settings. In this background, an algorithm for scalable DTW motif discovery was proposed; however, it is limited to one-dimensional (1D) and fixed-length subsequences. In contrast, two-dimensional (2D) DTW presents distinct challenges as it involves warping against the target time series and managing lags between dimensions. We propose a general symbolic representation for 2D time series, enabling the discovery of arbitrary-length motifs with warping and lags. In the motion capture (MoCap) community, a ’text-like representation’ for similarity search has been previously proposed. However, it requires multiple parameters and an exhaustive dataset. In contrast, our method relies on just one parameter: the “magnitude threshold,” which captures the value fluctuations without requiring an exhaustive dataset. Furthermore, we empirically demonstrate that our method is scalable enough for various real-world datasets and can effectively identify 2D DTW motifs of arbitrary lengths.
ATTA: Adversarial Task-transferable Attacks on Autonomous Driving Systems

Deep learning (DL) based perception models have enabled the possibility of current autonomous driving systems (ADS). However, various studies have pointed out that the DL models inside the ADS perception modules are vulnerable to adversarial attacks which can easily manipulate these DL models’ predictions. In this paper, we propose a more practical adversarial attack against the ADS perception module. Particularly, instead of targeting one of the DL models inside the ADS perception module, we propose to use one universal patch to mislead multiple DL models inside the ADS perception module simultaneously which leads to a higher chance of system-wide malfunction. We achieve such a goal by attacking the attention of DL models as a higher level of feature representation rather than traditional gradient-based attacks. We successfully generate a universal patch containing malicious perturbations that can attract multiple victim DL models’ attention to further induce their prediction errors. We verify our attack with extensive experiments on a typical ADS perception module structure with five famous datasets and also physical world scenes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We release our code at https://github.com/qingjiesjtu/ATTA
Interpretable Subgraph Feature Extraction for Hyperlink Prediction

Hyperlink prediction aims to predict interactions among multiple entries, constituting a practical yet challenging problem in the literature. While a handful of solutions have been proposed, they generally operate on the entire hypergraph. A practical subgraph-based solution not only enables better identification of localized characteristics of the central hyperedge but also alleviates scalability concerns. In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features. The rationale behind SSF is that hyperedges and non-hyperedges exhibit distinct local patterns, which can be unveiled through the assimilation of subgraph structural features. To this end, we utilize well-established structural heuristics such as walks and loops as the fundamental building blocks. We commence by extracting a subgraph encompassing each focal hyperedge, subsequently integrating an edge weakening scheme to facilitate feature extraction from the initial subgraph and its variations. The extracted feature vector is interpretable, and the designed edge weakening scheme empowers SSF with an adaptive capability to handle hypergraphs with varying densities. Lastly, a multilayer perceptron classifier is trained for prediction. Experiment results on ten real-world hypergraph networks demonstrate the effectiveness of the proposed approach. The source code of SSF is available at this URL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/KXDY233/SSF
Efektivitas Skor Wells dengan Pemeriksaan D-Dimer, Prothrombin Time, Activated Partial Thromboplastin Time dan Fibrinogen Terhadap Deteksi Dini Deep Vein Trhombosis di Ruang ICU RSPAD Gatot Soebroto

Deep vein thrombosis (DVT) is a clinical disorder that occurs in the vein. The purpose of this study is to identify the effectiveness of the Wells score with D-Dimer, PT, APTT, and fibrinogen examinations for DVT early detection. cross-sectional research design to assess the validity of Wels scores and D-Dimer, PT, APTT, and Fibrinogen's examinations on DVT early detection. 70 respondents were chosen by the purposive sampling technique. In the results of sensitivity analysis using the True Positives/(True Positives + True Negatives) formula, the percentage of D-Dimer's sensitivity was obtained on the Wells score of 85.4%, PT on the Wells score percentage of 81.3%, Aptt against the Wells percentage score of 83.3%, and fibrinogen against the Wells scores with a percentage of 64.6%. It can be concluded that all variables have sufficient sensitivity values in determining DVT events by using Wells scores, but the most sensitive D-Dimer variable is D-Dimer in detecting the risk of DVT events. Researchers recommend that the results of this research be utilized as a reference in the development of instruments for the application of DVT early detection.
Infographics as a Tool to Facilitate English Learning Activities: Student’s Perceptions

The research focuses on the impact of infographics as a tool for English learning trough student’s perceptions. The purpose of this research is to identify the positive impact of using infographics for students' learning and teaching activities in the classroom and also the appropriate learning media by educators. The researchers used qualitative research methods in analyzing the data that was collected. The instrument of the research was a questionnaire that consists of questions and statements that must be filled in by the elected respondents. The questionnaire was targeted to students of Universitas Tidar, especially the student of the English Education Department study program in the second and fourth semesters. There were many as 41 participants as a sample in this study. The results of this study show that based on the students' experience of knowing and using infographics in the learning activity, most of them agree that the use of infographics as learning media has a very positive impact. Infographics are also the suitable medium to improve students' quality in understanding the material and their interest in learning.
FACTORS AFFECTING BUSINESS PERFORMANCE THROUGH KNOWLEDGE COMPETENCE AS A MEDIATION VARIABLE

The purpose of this study was to determine the effect of entrepreneurial orientation and market orientation on business performance through knowledge competence as a mediating variable in Sunter culinary entrepreneurs. This study uses a non-probability sampling technique with a purposive sampling technique. The sample criteria include respondents who are culinary entrepreneurs in Sunter, North Jakarta. Then, business actors who have been running their business for more than 1 year. So, in this study researchers used 100 respondents. This study was analyzed using SmartPLS version 3.3.9. The results of the study show that entrepreneurial orientation and market orientation have a positive and significant effect on business performance. Knowledge competency can mediate the effect of entrepreneurial orientation and market orientation positively and significantly on business performance. entrepreneurial orientation and market orientation have a positive and significant effect on knowledge competence.
Extraction of Subjective Information from Generative AI Models

Pre-trained language models based on deep learning have achieved significant improvements in natural language processing. Many studies have been published to investigate the knowledge in language models. In this paper, we investigate the extraction of subjective information from generative artificial intelligence models, show that the extraction of subjective information from language models is possible, and discuss superior extraction methods.
Efficient Cryptographic Processing Using On-Chip GPUs in System-on-Chip Architectures

In recent years, with the spread of telework, there has been an increase in demand for encryption when working on laptops at home or in the office. However, since the performance is lower than that of desktop PCs, the CPU load increases, thereby reducing overall performance. Therefore, we propose a system that offloads encryption processing to the GPU in the SoC, so that it can be done even on notebook PCs. As a result of evaluation experiments, we found that the GPU kernel implemented in this research is faster than the CPU implementation if the size of the data to be encrypted is above a certain level, and has a maximum throughput of about 10.3 times higher, so that sufficient effects can be obtained. Therefore, we can expect to improve processing efficiency by devising scheduling, such as using different CPUs and GPUs depending on the data size. However, when compared to cryptographic processing using AES-NI, it was confirmed that the throughput was approximately 13.3 times lower even at the point where the GPU implementation showed the highest Gbps. We also confirmed that this system can perform encryption without occupying the CPU.
Improved Vulnerability Handling Framework to Automatically Fix Vulnerable Web Applications

With the spread of smartphones and personal computers, many people are using Web applications. Web applications are subject to various attacks from all over the world through the Internet. However, it is difficult for web application developers to always create secure web applications. This paper aims to support the creation of secure Web applications by automatically fixing vulnerable code with a Web application framework. Therefore, we use a web application framework called VHF (Vulnerability Handling Framework) [1]. We propose to add a new variable to the source code update system, which is part of the VHF functions. By doing so, we hope to reduce the cost of adding methods to fix vulnerabilities in VHF and increase the flexibility of the fix methods. Using an Electronic Commerce site as an example situation, a simple Web application will be created to confirm the effectiveness of the proposed method.
Polynomial-Time Cloudlet Placement Scheme with an Improved Approximation Ratio

In this paper, we consider the optimal cloudlet placement problem (OCP) and improve the performance ratio of an approximate scheme proposed by Bhatta and Mashayekhy [IEEE TPDS 2022]. The OCP entails the task of strategically placing cloudlets for a given collection of user devices, cloudlet options, and potential placement locations. The objective is to optimize both device latency and the total cost of cloudlet placement, while ensuring complete coverage of user devices by the deployed cloudlets. ACP proposed by Bhatta and Mashayekhy achieves a (2,4)-approximation to this problem. That is, the latency of the solution obtained by ACP is at most twice the optimal latency when the placement cost is not considered, and the placement cost of the solution is at most four times the optimal placement cost when the latency is not considered. We improve this ratio to (2,3.4) and (1.74,4). As a specific technique for the improvement, an optimal packing of items into bins of different sizes is used.
EMERGING SECURITY CONCERNS BECAUSE OF AI USAGE

Recently, there has been a lot of hype surrounding AI, bringing urgent concerns along with lots of myth, as the future of AI is still uncertain. While social media headlines warn that AI is about to outperform humans in near future, there's a good chance that attacks made possible by the increased application of AI will be very potent, precisely targeted, hard to identify, and likely to take advantage of holes in AI systems. This article analyzes the emerging security issues that are forming in the way AI is used in practice.