Reviewerly/exHarmony huggingface dataset:
Data Representation via Attribute Selection- Propagation Neural Network

Graph Convolutional Networks (GCNs) have been commonly studied for attribute graph data representation. It is known that the core of Graph Convolution (GC) operation is to define a specific graph propagation operation for graph node's attributes. Existing GCs mainly perform propagation over node's all attributes. However, this ‘full’ propagation may be unreasonable on some practical learning tasks and also be vulnerable to the noisy attributes. To address this issue, in this paper, we propose a novel Attribute Selection-Propagation (ASP) mechanism for attribute graph data representation by incorporating attribute selection into GCs. The main aspect of the proposed ASP is that it can be formulated as a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regularization model</b> based on which we can derive a simple update rule to implement ASP in a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self-supervised</b> manner. ASP aims to adaptively propagate some optimal attributes of node to better serve message passing in GCs. Using ASP, we then present a novel graph neural network, named ASPNet for attribute graph representation and learning. Experiments on several graph learning tasks including node classification, clustering and link prediction demonstrate the effectiveness of the proposed ASPNet.
A Vision-Based Method for Real-Time Traffic Flow Estimation on Edge Devices

Traffic flow estimation is an essential task in modern intelligent transportation systems. Many types of information, including vehicle type, vehicle totals, and movement direction, are vital for mitigating transportation-related tasks and effective traffic control strategies. With the development of embedded devices, systems can process captured video at the edge instead of transferring data to centralized processing servers. This paper proposes a real-time and edge-based traffic flow estimation system. The proposed system follows a detect-and-track mechanism where lightweight deep learning models perform vehicle detection. A novel scenario-based tracking and counting technique is developed to provide multi-class, multi-movement vehicle counting. The method uses predefined regions to assign the movement for each vehicle initially detected. It then performs spatial-temporal trajectory matching between the vehicle trajectory and the movement path throughout the whole video. Extensive experiments have shown that the proposed method achieves high effectiveness with multiple camera types and viewpoints.
Span Detection for Kinematics Word Problems

Solving kinematics word problems is a specialized task which is best addressed through bespoke logical reasoners. Reasoners, however, require structured input in the form of kinematics parameter values, and translating textual word problems to such structured inputs is a key step in enabling end-to-end automated word problem solving. Span detection for a kinematics parameter is the process of identifying the smallest span of text from a kinematics word problem that has the information to estimate the value of that parameter. A key aspect differentiating kinematics span detection from other span detection tasks is the presence of multiple inter-related parameters for which separate spans need to be identified. State-of-the-art span detection methods are not capable of leveraging the existence of a plurality of inter-dependent span identification tasks. We propose a novel neural architecture that is designed to exploit the inter-relatedness between the separate span detection tasks using a single joint model. This allows us to train the same network for span detection over multiple kinematics parameters, implicitly and automatically transferring knowledge across the kinematics parameters. We show that such a joint training delivers an improvement of accuracies over real-world datasets against state-of-the-art methods for span detection.
CATM: Candidate-Aware Temporal Multi-head Self-attention News Recommendation Model

User interests are diverse and change over time. Existing news recommendation models often do not consider the relationship and temporal changes between browsing news when modeling user characteristics. In addition, the wide range of user interests makes it difficult to match candidate news to users’ interests precisely. This paper proposes a news recommendation model based on the candidate-aware time series self-attention mechanism(CATM). The method incorporates candidate news into user modeling based on considering the temporal relationship of news sequences browsed by users, effectively improving news recommendation performance. In addition, to obtain more rich semantic news information, we design a granular network to obtain more fine-grained segment features of news. Finally, we also designed a candidate-aware attention network to build candidate-aware user interest representations further to better match candidate news with user interests. Extensive experiments on the MIND dataset demonstrate that our method can effectively improve news recommendation performance.
Study of Stress Field and Fracture Network Development With Rock Analogs

Analog materials are used to simplify a complex system while exhibiting comparable properties.
MEW: Evading Ownership Detection Against Deep Learning Models

Training deep neural network (DNNs) requires massive computing resources and data, hence the trained models belong to the model owners’ Intellectual Property (IP), and it is very important to defend against the model stealing attack. Recently, a well-known approach named Dataset Inference (DI) claimed that by measuring the distance from the sample to the decision boundary, it can be determined whether the theft has occurred. In this paper, we show that DI is not enough for IP protection. To demonstrate this, we propose a new system called MEW, which combines the Model Inversion (MI) attack and Elastic Weight Consolidation (EWC) to evade the detection of DI. We first use the pre-trained adversary model to generate a data pool and adaptively select samples to approximate the Fisher Information Matrix of the adversary model. Then we use an adaptation of EWC to slightly fine-tune the adversary model which moves it decision boundary slightly. Our empirical results demonstrate that the adversary model evaded the DI detection with 40 samples. We also lay out the limitations of MEW and discuss them at last.
Image Anomaly Detection and Localization Using Masked Autoencoder

Generally speaking, abnormal images are distinguished from normal images in terms of content or semantics. Image anomaly detection is the task of identifying anomalous images that deviate from normal images. Reconstruction based methods detect anomaly using the difference between the original image and the reconstructed image. These methods assume that the model will be unable to properly reconstruct anomalous images. But in practice, anomalous regions are often reconstructed well due to the network’s generalization ability. Recent methods propose to decrease this effect by turning the generative task to an inpainting problem. By conditioning on the neighborhood of the masked part, small anomalies will not contribute to the reconstrued image. However, it is hard to reconstruct the masked regions when neighborhood exists much anomalous information. We suggest that it should include more useful information of the image when doing inpainting. Inspired by masked autoencoder (MAE), we propose a new anomaly detection method, which called MAE-AD. The architecture of the method can learn global information of the image, and it can avoid being affected by the large anomalous region. We evaluate our method on the MVTec AD dataset, and the results outperform the previous inpainting based approach. In comparison with the methods which use pre-trained models, MAE-AD also has a competitive performance.
Hybrid-Supervised Network for 3D Renal Tumor Segmentation in Abdominal CT

Renal cell carcinoma (RCC) is one of the top ten tumors threatening human health. Before renal tumor surgery, doctors need to understand the spatial structure and relative position of the kidney and tumor, and be familiar with the intraoperative environment in advance to improve the success rate. However, current tools for automatic contouring and localization of renal tumors are flawed. For example, the segmentation accuracy is not high and requires a lot of manual operations. This work proposes a 3D segmentation method for CT renal and tumor based on hybrid supervision. Hybrid supervision improves segmentation performance while using few labels. In the test on the public dataset KITS19 (Kidney Tumor Segmentation Challenge in 2019), the hybrid supervised method outperforms other segmentation methods, with an average Dice coefficient improvement of three percentage points under the condition of using the same amount of annotation, and the kidney and kidney tumor are well separated. Hybrid supervision provides a new intention for clinical renal tumor segmentation.
Automatic Language Identification for Celtic Texts

Language identification is an important Natural Language Processing task. Though, it has been thoroughly researched in the literature, some issues are still open. This work addresses the identification of the related low-resource languages on the example of the Celtic language family. In this work, we collected a new dataset for the combination of the selected languages: Irish, Scottish, Welsh and also English. Language identification is a classification task, so we applied supervised models such as SVM and neural networks. Traditional statistical features were tested alongside the output of clustering, autoencoder, and topic modelling methods. The analysis showed that the unsupervised features could serve as a valuable extension to the n-gram feature vectors. It led to an improvement in performance for more entangled classes. The best model achieved 98% F1 score and 97% MCC. The dense neural network consistently outperformed the SVM model.
Real-Time Inertial Foot-Ground Contact Detection Based on SVM

At present, inertial motion capture systems are widely used by consumer users due to their advantages of easy deployment and low price. The inertial sensor itself does not accurately locate itself in 3D space. The most accurate localization method is currently represented by the algorithm of forward kinematic estimation of crotch position, and the accurate estimation of this algorithm needs to be based on the accurate judgment of foot-ground contact. In this work, we implemented plain Bayesian, decision tree, random forest, SVM, and GBDT models to find the model with the highest recognition rate of classified foot-ground contact states. This work evaluates the quality of each algorithm in terms of computational speed and accuracy, achieving SOTA under the condition of wearing only crotch, left and right thigh, and left and right calf IMUs compared to most people in the field using 17 IMUs for the whole body. Finally, this paper uses SVM models to classify foot-ground contact states for captured poses, yielding an average foot-ground contact accuracy of 97% for various motions. The method in this paper is applicable to any inertial motion capture system and satisfies the accuracy of an inertial motion capture system for foot-ground contact detection, providing kinematic constraints for pose estimation and global position estimation.
A Novel Optimized Context-Based Deep Architecture for Scene Parsing

Determining the optimal parameter values for a scene parsing network architecture is an important task as a network with optimal parameters produces the best performance. A manual selection of parameter values requires expert domain knowledge of the intrinsic structure and extensive trial and error, which does not guarantee optimal results. An automatic search of the optimal parameters is desirable to harness the full potential of a scene parsing framework. The network architecture needs to be evaluated with various combinations for several parameters to achieve optimum performance. We propose a stacked three-level deep context-adaptive end-to-end network. The end-to-end network architecture extracts visual features, and contextual features, and optimally integrates both features in three logical levels for scene parsing. Particle Swarm Optimization (PSO) algorithm has been used to efficiently search for optimal solutions. Using PSO, we set an optimal set of parameters in all three levels to obtain optimum performance. The PSO aims to optimize the network globally by considering all hyperparameters in the network to achieve the best performance.
DALedger: Towards High-Performance Transaction Processing for Collaborative Decentralized Applications

Despite recent intensive research, existing blockchain systems still have limitations in supporting decentralized applications. In particular, although existing blockchain systems execute internal transactions of different applications concurrently, it is difficult to process the concurrent control considering both the cross-application transactions and internal transactions. The reason is that each application can only access the transactions related to it, and the conflicts between all transactions cannot be detected by the applications. To improve the concurrency of blockchain systems for decentralized applications, we propose a novel blockchain named as DLedger, which is designed based on the directed acyclic graph ledge structure. DALedger supports the concurrent execution of not only internal transactions in the same application but also internal transactions and cross-DApp transactions. We prove that cycles in the dependency graph must have a special dangerous structure, which can be detected in a single application's partial dependency graph. Based on that, we propose a novel concurrency control mechanism to resolve concurrency conflicts, while ensuring serializability. We conduct more extensive experiments compared with state-of-the-art blockchain ledgers for decentralized applications. Experimental results show that our method outperforms existing works significantly.
A Static Bi-dimensional Sample Selection for Federated Learning with Label Noise

In real-world Federated learning(FL), client training data may contain label noise, which can harm the generalization performance of the global model. Most existing noisy label learning methods rely on sample selection strategies that treat small-loss samples as correctly labeled ones, and large-loss samples as mislabeled ones. However, large-loss samples may also be valuable hard samples with correct labels. Also, these sample selection strategies have an issue in FL setting: Moreover sample selection strategies require training two models simultaneously and are executed in every mini-batch, which increases the communication cost and client computation. In this paper, we propose an efficient multi-stage federated learning framework to tackle label noise. Firstly, we use self-supervision pre-training to extract category-independent features without using any labels. Secondly, we propose a federated static two-dimensional sample selection(FedSTSS) method, which uses category probability entropy and loss as separation metrics to identify samples. Notably, to improve the accuracy of recognition samples, we add a weight entropy minimization term to the cross-entropy loss function. Finally, we use a semi-supervised method to finetune the global model on identified clean samples and mislabeled samples. Extensive experiments on multiple synthetic and real-world noisy datasets demonstrate that our method outperforms the state-of-the-art methods.
Generating Textual Description Using Modified Beam Search

Generating textual descriptions of images by describing them in words is a fundamental problem that connects computer vision and natural language processing. A single image may include several entities, their orientations, appearance, and position in a scene as well as their complex spatial interactions, thus leading to a lot of possible captions for an image. Search algorithm of Beam Search has been employed for the task of sentence for the last couple of decades, although it returns around the similar captions with minor changes of wordings. We came across another search strategy, Diverse M-Best which uses M (M denotes the number of independent, diverse beam searches) beam searches from diverse starting statements and keeps the best output from each beam search, and removes the rest of (B-1) captions. This method would mostly lead us to many possible diverse generated sequences, but running Beam Search M several times would be computationally expensive. With the above stated works in vision, we have devised and implemented a novel algorithm, Modified Beam Search (MBS), for generation of Diverse and better captions, with an increase in the computational complexity as compared to the Beam Search. We obtained improvements on BLEU-3 and BLEU-4 scores by 1–3% over the top-2 predicted captions from the original beam search captions.
Intra- and Inter-behavior Contrastive Learning for Multi-behavior Recommendation

Multi-behavior recommendation (MBR) aims to improve the prediction of target behavior by exploiting multi-typed auxiliary behaviors. However, most MBR suffers from data sparsity in real-world scenarios and thus performs mediocrely. In this paper, we propose a novel Intra- and Inter-behavior Contrastive Learning (IICL) framework to exploit contrastive learning to enhance MBR with two key challenges to be addressed: i). Difficult to learn reliable representations under different behaviors; ii). Sparse supervised signals under target behavior. For the first challenge, we devise an intra-behavior contrastive learning objective, which enhances the representation learning by incorporating potential neighbors into the contrastive learning from the graph topological space and the semantic space, respectively. As for the second challenge, we design an inter-behavior contrastive learning objective, which has the benefit of capturing commonalities between different behaviors and integrating them into the target behavior to alleviate the sparse supervised signal problem. In addition, we also propose an adaptive weight network to customize the integration of all losses efficiently. Extensive experiments on three real-world benchmark datasets show that our proposed method IICL is significantly superior to various state-of-the-art recommendation methods.
A Fast and Robust Photometric Redshift Forecasting Method Using Lipschitz Adaptive Learning Rate

With the recent large astronomical survey experiments using high-resolution cameras and telescopes, there has been a tsunami of astronomical data that has been collected and is being utilized for important analysis. Based on pure photometric information, Redshift estimation is a crucial task of cosmology. The application of neural networks (NN) in this area is gaining popularity of late as NN performs well for large training samples. In this paper, we use Mean Absolute Error (MAE), as a metric, with a neural network to estimate the redshift of galaxies and quasars and show that MAE can be used as an alternate metric for this regression task. This paper uses Lipschitz constant based adaptive learning rate that involves hessian-free computation for faster training of the neural network. Results show that an adaptive learning rate based neural network with MAE converges much faster compared to a constant learning rate and reduces training time while providing MAE of 0.28 and Normalized Median Absolute Deviation (NMAD) is 0.03 for a data sample of 5 lakhs.
HOPFIELD NEURAL NETWORK FOR A TRANSNATIONAL ENTERPRISE'S MANUFACTURING NETWORK

Multinational enterprises (MNEs) have to deal with two main issues. They need to define a suitable configuration for the activities of the chain value and to coordinate the so obtained network of facilities. The complexity of such issues and the impact of the managerial decisions on MNEs' performance are emphasized due to the international-wide economic scenario with which MNEs have to face. In this paper we focus on the coordination problems. In particular, we refer to transnational enterprises, i.e. MNEs which pursue a high coordination among their activities dispersed throughout the world.<br> We utilize an approach based on neural networks and, in particular, on a Hopfield neural network (HNN). We consider a simple model of the logistic network of a MNE, in order to describe the approach. The objective approach is the minimization of the total cost, that is the sum of production and transportation costs. The approach allows to handle non-linear cost functions and proves effective in determining the optimal solution.
PERFORMANCE TESTING OF HAND HELD SCANNERS FOR TWO DIMENSIONAL BAR CODE SYMBOLOGIES

In this paper, a suite of standard tests are presented to measure the decode time of hand held scanners. These tests were applied to a PDF417 two-dimensional bar code symbol, and used to compare the performance of five newly developed hand-held scanners. The tests focused on the following factors that typically affect decode time: lighting conditions, reading distance, orientation angles of the scanner, and use of the scanners in a typical freehand application. Results show the tests to be a valid means for comparing performance of differing hand-held scanner technologies typically used for two-dimensional symbologies.
Query2Trip: Dual-Debiased Learning for Neural Trip Recommendation

Trip recommendation (TripRec) seeks to recommend a trip that consists of an ordered sequence of points-of-interest (POIs) for a tourist through a user-specific query. Recent neural TripRec methods with sequence-to-sequence models have achieved remarkable performance. However, alongside the exposure bias in general recommender systems, the selection bias caused by the lack of explicit feedback (e.g., ratings) from the trip data exacerbates the tendency toward users' unsatisfactory experience in TripRec. To this end, a novel debiased representation learning method for neural TripRec is proposed to fulfill sequence generation from Query to Trip named Query2Trip. It develops dual-debiased learning to mitigate selection bias and exposure bias in TripRec. The former happens as the visit by a user does not necessarily mean the user exhibits a positive preference for the visit. Benefiting from the query provided by a user, Query2Trip designs a debiased adversarial learning module by conditional guidance to alleviate this selection bias from positives (visited). The latter happens as unvisited is not equivalent to negative. Query2Trip devises a debiased contrastive learning module by negative weighting to mitigate this exposure bias from negatives (unvisited). Experiments conducted on eight real-world datasets empirically demonstrate the superior performance of Query2Trip compared to the state-of-the-art baselines.