Reviewerly/exHarmony huggingface dataset:
A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network

Sequential recommendation systems seek to learn users' preferences to predict their next actions based on the items engaged recently. Static behavior of users requires a long time to form, but short-term interactions with items usually meet some actual needs in reality and are more variable. RNN-based models are always constrained by the strong order assumption and are hard to model the complex and changeable data flexibly. Most of the CNN-based models are limited to the fixed convolutional kernel. All these methods are suboptimal when modeling the dynamics of item-to-item transitions. It is difficult to describe the items with complex relations and extract the fine-grained user preferences from the interaction sequence. To address these issues, we propose a knowledge-aware sequential recommender with the attention-enhanced dynamic convolutional network (KAeDCN). Our model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence. Meanwhile, we enhance the representations of items with Knowledge Graph (KG) information through an information fusion module to capture the fine-grained user preferences. The experiments on four public datasets demonstrate that KAeDCN outperforms most of the state-of-the-art sequential recommenders. Furthermore, experimental results also prove that KAeDCN can enhance the representations of items effectively and improve the extractability of sequential dependencies.
Markovian Quantum Neuroevolution for Machine Learning

Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks. It bears a number of intriguing capabilities that are typically inaccessible to gradient-based approaches, including optimizing neural-network architectures, hyperparameters, and even learning the training rules. In this paper, we introduce a quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine-learning tasks. In particular, we establish a one-to-one mapping between quantum circuits and directed graphs, and reduce the problem of finding the appropriate gate sequences to a task of searching suitable paths in the corresponding graph as a Markovian process. We benchmark the effectiveness of the introduced algorithm through concrete examples including classifications of real-life images and symmetry-protected topological states. Our results showcase the vast potential of neuroevolution algorithms in quantum architecture search, which would boost the exploration towards quantum-learning advantage with noisy intermediate-scale quantum devices.
STARec: Adaptive Learning with Spatiotemporal and Activity Influence for POI Recommendation

POI recommendation has become an essential means to help people discover attractive places. Intuitively, activities have an important impact on users’ decision-making, because users select POIs to attend corresponding activities. However, many existing studies ignore the social motivation of user behaviors and regard all check-ins as influenced only by individual user interests. As a result, they cannot model user preferences accurately, which degrades recommendation effectiveness. In this article, from the perspective of activities, this study proposes a probabilistic generative model called STARec. Specifically, based on the social effect of activities, STARec defines users’ social preferences as distinct from their individual interests and combines these with individual user activity interests to effectively depict user preferences. Moreover, the inconsistency between users’ social preferences and their decisions is modeled. An activity frequency feature is introduced to acquire accurate user social preferences because of close correlation between these and the key impact factor of corresponding check-ins. An alias sampling-based training method was used to accelerate training. Extensive experiments were conducted on two real-world datasets. Experimental results demonstrated that the proposed STARec model achieves superior performance in terms of high recommendation accuracy, robustness to data sparsity, effectiveness in handling cold-start problems, efficiency, and interpretability.
Dimensionality Reduction Using Hybrid Brainstorm Optimization Algorithm

Bacanin, Nebojsa Zivkovic, Miodrag Bezdan, Timea Cvetnic, Dusan Gajic, LukaIn this work, a swarm intelligence-based algorithm, brainstorm optimization, is proposed for reducing dimensionality (feature selection) in datasets that are used for classification. Dimensionality reduction is a well-known and widely used technique in analyzing big data. Its role is to reduce the number of features in high-dimensional datasets and to keep only those that contain useful and rich information. This results in better understanding and interpretation of data, higher accuracy, and boosting the training process of machine learning method used for classification. After extracting features from the dataset, it should be decided which subset of features will be used in the training process. Since, in high-dimensional datasets many features exist, this problem is categorized as NP hard and it is necessary to employ metaheuristics for its solving. For tackling this issue, a binary hybrid brainstorm optimization metaheuristics that overcome the drawbacks of original algorithm, is proposed. For performance evaluation, 21 datasets are used. The comparative analysis is made between the proposed approach and the original brainstorm optimization algorithm, as well as with nine other metaheuristics adopted for feature selection. Experimental results prove the robustness of proposed method, since it is capable to reduce the number of features by simultaneously achieving better classification accuracy than other methods taken for comparative analysis.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

Abstract Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. Methods To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Results Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. Conclusions In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.
Experimental demonstration of entanglement delivery using a quantum network stack

Scaling current quantum communication demonstrations to a large-scale quantum network will require not only advancements in quantum hardware capabilities, but also robust control of such devices to bridge the gap to user demand. Moreover, the abstraction of tasks and services offered by the quantum network should enable platform-independent applications to be executed without knowledge of the underlying physical implementation. Here we experimentally demonstrate, using remote solid-state quantum network nodes, a link layer and a physical layer protocol for entanglement-based quantum networks. The link layer abstracts the physical-layer entanglement attempts into a robust, platform-independent entanglement delivery service. The system is used to run full state tomography of the delivered entangled states, as well as preparation of a remote qubit state on a server by its client. Our results mark a clear transition from physics experiments to quantum communication systems, which will enable the development and testing of components of future quantum networks.
Identity-Based Matchmaking Encryption Without Random Oracles

Identity-based matchmaking encryption (IB-ME) is a generalization of identity-based encryption where the sender and the receiver can both specify a target identity: If both the chosen target identities match the one of the other party, the plaintext is revealed, and otherwise the sender’s identity, the target identity, and the plaintext remain hidden. Previous work showed how to construct IB-ME in the random oracle model. We give the first construction in the plain model, based on standard assumptions over bilinear groups.
Critical Success Factors of Component-Based Software Outsourcing Development From Vendors’ Perspective: A Systematic Literature Review

Component-Based Software Development (CBSD) is a very demanding approach for developing complex products or systems. In CBSD, software or product is creating from reusable pieces of components. With the help of CBSD, a complex system or software is developed with high quality, security, and within limited time and cost. However, this research will convince the outsourcing vendor organization to adopt the component software development using the CBSD approaches. A Systematic Literature Review (SLR) is used to extract all the possible success factors regarding CBSD that have positively impact software outsourcing vendor organization. The identified success factors comprise ‘Well Trained and Skilled Team’; ’ Proper Selection of Components’; ’ Using Standard for Designing’; ’ Well defined Architecture’; ’ Proper Testing and Analysis; ’ Efficient Integration’; ’ Quality Assurance’; ’ Proper Documentation’; ’well-organized security’; ’ Trusted and Certified.’ This research paper is also identifying the differences and similarities of success factors in various continents. This research paper is also convincing the software outsourcing vendor organization to adopt the concept of CBSD. The proposed method has analyzed the identified challenges continent-wise using the Chi-Square test to determine the significant difference.
Learning Rates for Stochastic Gradient Descent With Nonconvex Objectives

Stochastic gradient descent (SGD) has become the method of choice for training highly complex and nonconvex models since it can not only recover good solutions to minimize training errors but also generalize well.Computational and statistical properties are separately studied to understand the behavior of SGD in the literature.However, there is a lacking study to jointly consider the computational and statistical properties in a nonconvex learning setting.In this paper, we develop novel learning rates of SGD for nonconvex learning by presenting high-probability bounds for both computational and statistical errors.We show that the complexity of SGD iterates grows in a controllable manner with respect to the iteration number, which sheds insights on how an implicit regularization can be achieved by tuning the number of passes to balance the computational and statistical errors.As a byproduct, we also slightly refine the existing studies on the uniform convergence of gradients by showing its connection to Rademacher chaos complexities.
The INTERSPEECH 2020 Computational Paralinguistics Challenge: Elderly Emotion, Breathing &amp; Masks

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart.We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.
IoT based Multiple Vital Health Parameter Detection and Analyzer System

The health care checking system is one of the most crucial systems that became high-tech over history. In the contemporary world, it is very challenging to carry patients to hospitals for a regular checkup. People are facing an issue of sudden demise because of different types of diseases or viruses and also due to the absence of required treatment to the suffered victims on time. It is very difficult for the patients to stand in a queue for taking treatment. S o, this research intends to develop a novel framework to detect the patient’s health parameters like temperature, oxygen levels in the blood, and pulse rate. We are developing this framework based on the IOT and things peak database. This proposed system provides a piece of information about the mental and emotional conditions of a victim. We are using Arduino and programmed with software (Arduino IDE). In this proposed work we are detecting the patient health parameters with their respective sensors. If any abnormal parameters are detected automatically LED will turn on indicating caution and also a SMS is sent to the victim’s relatives or doctor by the GSM module. And also, the observed parameters are graphically seen in the things peak database. All the collected information regarding health is stored in the database. Thus, the proposed work based on the Internet of Things effectively monitors the patient health parameter status every second to second and saves the life of a patient by taking suitable steps when their health parameters are in an abnormal condition.
Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field

Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of the liver and tumours is a time-consuming and labour-intensive task; therefore, a fully automated method for performing this segmentation is particularly desired. An automatic two-step liver and tumour segmentation method is presented in this paper. A cascade framework is used during the segmentation process, and a fully connected conditional random field (CRF) method is used to refine the tumour segmentation result. First, the proposed fractal residual U-Net (FRA-UNet) is used to locate and initially segment the liver. Then, FRA-UNet is further used to predict liver tumours from the liver region of interest (ROI). Finally, a three-dimensional (3D) CRF is used to refine the tumour segmentation results. The improved fractal residual (FR) structure effectively retains more effective features for improving the segmentation performance of deeper networks, the improved deep residual block can utilise the feature information more effectively, and the 3D CRF method smooths the contours and avoids the tumour oversegmentation problem. FRA-UNet is tested on the Liver Tumour Segmentation challenge dataset (LiTS) and the 3D Image Reconstruction for Comparison of Algorithm Database dataset (3DIRCADb), achieving 97.13% and 97.18% Dice similarity coefficients (DSCs) for liver segmentation and 71.78% and 68.97% DSCs for tumour segmentation, respectively, outperforming most state-of-the-art networks.
OASIS: Only Adversarial Supervision for Semantic Image Synthesis

Abstract Despite their recent successes, generative adversarial networks (GANs) for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Previously, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the same time limited the progress of GAN models for semantic image synthesis. In this work, we propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity and with a better alignment to their input label maps, making the use of the perceptual loss superfluous. Furthermore, we enable high-quality multi-modal image synthesis through global and local sampling of a 3D noise tensor injected into the generator, which allows complete or partial image editing. We show that images synthesized by our model are more diverse and follow the color and texture distributions of real images more closely. We achieve a strong improvement in image synthesis quality over prior state-of-the-art models across the commonly used ADE20K, Cityscapes, and COCO-Stuff datasets using only adversarial supervision. In addition, we investigate semantic image synthesis under severe class imbalance and sparse annotations, which are common aspects in practical applications but were overlooked in prior works. To this end, we evaluate our model on LVIS, a dataset originally introduced for long-tailed object recognition. We thereby demonstrate high performance of our model in the sparse and unbalanced data regimes, achieved by means of the proposed 3D noise and the ability of our discriminator to balance class contributions directly in the loss function. Our code and pretrained models are available at https://github.com/boschresearch/OASIS .
Edge Detection Guide Network for Semantic Segmentation of Remote-Sensing Images

The acquisition of high-resolution satellite and airborne remote sensing images has been significantly simplified due to the rapid development of sensor technology. Several practical applications of high-resolution remote sensing images (HRRSIs) are based on semantic segmentation. However, single-modal HRRSIs are difficult to classify accurately in the complex situation of some scene objects; therefore, the semantic segmentation of multi-source information fusion is gaining popularity. The inherent difference between multimodal features and the semantic gap between multi-level features typically affect the performance of existing multi-mode fusion methods. We propose a multimodal fusion network based on edge detection to address these issues. This method aids multimodal information fusion by utilizing spatial information contained in the boundary. An edge detection guide module is included in the feature extraction stage to realize the boundary information through the fusion of details and semantics between high-level and low-level features. The boundary information is extended into the well-designed multimodal adaptive fusion block (MAFB) to obtain the multimodal fusion features. Furthermore, a residual adaptive fusion block (RAFB) and a spatial position module (SPM) in the feature decoding stage have been designed to fuse multi-level features from the standpoint of local and global dependence. We compared our method to several state-of-the-art (SOTA) methods using the International Society for Photogrammetry and Remote Sensing’s (ISPRS) Vaihingen and Potsdam datasets. The final results demonstrate that our method achieves excellent performance.
A distributed semi-supervised learning algorithm based on manifold regularization using wavelet neural network

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to solve D-SSL problems, where training samples are often extremely large-scale and located on distributed nodes over communication networks. Training data of each node consists of labeled and unlabeled samples whose output values or labels are unknown. These nodes communicate in a distributed way, where each node has only access to its own data and can only exchange local information with its neighboring nodes. In some scenarios, these distributed data cannot be processed centrally. As a result, D-SSL problems cannot be centrally solved by using traditional semi-supervised learning (SSL) algorithms. The state-of-the-art D-SSL algorithm, denoted as Distributed Laplacian Regularization Least Square (D-LapRLS), is a kernel based algorithm. It is essential for the D-LapRLS algorithm to estimate the global Euclidian Distance Matrix (EDM) with respect to total samples, which is time-consuming especially when the scale of training data is large. In order to solve D-SSL problems and overcome the common drawback of kernel based D-SSL algorithms, we propose a novel Manifold Regularization (MR) based D-SSL algorithm using Wavelet Neural Network (WNN) and Zero-Gradient-Sum (ZGS) distributed optimization strategy. Accordingly, each node is assigned an individual WNN with the same basis functions. In order to initialize the proposed D-SSL algorithm, we propose a centralized MR based SSL algorithm using WNN. We denote the proposed SSL and D-SSL algorithms as Laplacian WNN (LapWNN) and distributed LapWNN (D-LapWNN), respectively. The D-LapWNN algorithm works in a fully distributed fashion by using ZGS strategy, whose convergence is guaranteed by the Lyapunov method. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that the D-LapWNN algorithm is a privacy preserving method. At last, several illustrative simulations are presented to show the efficiency and advantage of the proposed algorithm.