Reviewerly/exHarmony huggingface dataset:
Variational Bayesian Orthogonal Nonnegative Matrix Factorization Over the Stiefel Manifold

Nonnegative matrix factorization (NMF) is one of the best-known multivariate data analysis techniques. The NMF uniqueness and its rank selection are two major open problems in this field. The solutions uniqueness issue can be addressed by imposing the orthogonality condition on NMF. This constraint yields sparser part-based representations and improved performance in clustering and source separation tasks. However, existing orthogonal NMF algorithms rely mainly on non-probabilistic frameworks that ignore the noise inherent in real-life data and lack variable uncertainties. Thus, in this work, we investigate a new probabilistic formulation of orthogonal NMF (ONMF). In the proposed model, we impose the orthogonality through a directional prior distribution defined on the Stiefel manifold called von Mises-Fisher distribution. This manifold consists of a set of directions that comply with the orthogonality condition that arises in many applications. Moreover, our model involves an automatic relevance determination (ARD) prior to address the model order selection issue. We devised an efficient variational Bayesian inference algorithm to solve the proposed ONMF model, which allows fast processing of large datasets. We evaluated the proposed model, called VBONMF, on the task of blind decomposition of real-world multispectral images of ancient documents. The numerical experiments demonstrate its efficiency and competitiveness compared to the state-of-the-art approaches.
Trust Evaluation for Service Composition in Cloud Manufacturing Using GRU and Association Analysis

Service composition enables the flexible and agile collaboration of multiple services to complete personalized manufacturing tasks in cloud manufacturing. Compared with traditional manufacturing mode and cloud computing, trust problems become more serious and crucial in cloud manufacturing because of the nontransparency and short-term cooperation mode. A trust evaluation method for service composition in cloud manufacturing is proposed in this article. To quantitatively calculate the trust, a trust evaluation index system is established that comprehensively considers the influencing factors in the production, transaction, and collaboration processes of cloud manufacturing services. The trust value is synthesized based on all index values using the criteria importance through intercriteria correlation method. To extract the temporal information in historical trust data, a time-aware predictive trust evaluation method based on gated recurrent unit is proposed to learn the changing pattern of trust value over time. The trust data are trained together with their timestamps to predict the trust in the scheduled transaction time. The correlation between services in service composition is modeled to mine the correlation information by association analysis. The trust of service composition depends on the trust values of all component services and the correlations between them. The experiments demonstrate the effectiveness of the proposed method through case studies and performance comparisons with other state-of-art methods.
Early Design of a Conversational AI Development Platform for Middle Schoolers

More young people are interacting with smart conversational agents such as Alexa and Google Assistant. These platforms are extensible, providing, in principle, a compelling opportunity for young users to create and tinker with their own conversational agents. However, to date the interfaces for conversational app development are adult-focused. This paper presents the early design process for AMBY (AI Made by You), which we are building to empower young learners to create their own conversational agents. We first conducted a contextual inquiry with 14 middle school students (aged 11-13) in an AI summer camp, followed by two other usability studies. The system design has been refined after each study. Key features of AMBY include a visual dialogue management panel, testing panel with a diverse avatar, and a voice input modality. AMBY is designed to serve as a pedagogically-robust resource for K-12 AI education and as an engaging and creative way for middle schoolers to explore AI.
Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System

Recently, online social networks have enriched the users' lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in.The most advanced graph neural network based social recommendation methods start to utilize the higherorder social relations, e.g. the friends of friends, to reveal users' preferences.However, existing high-order methods ignore the implicit social relations among users and the users' interests changing dynamically over time.In this paper, we propose a Multi-Order Hypergraph Convolutional Neural Network (MOHCN) for dynamic social recommendation system to improve the recommendation task, which models the users' dynamic interest evolution at the session level.To compensate for the lack of social information of some users, we combine the implicit social relations obtained from user-item interaction graph with the explicit social relations from user-user social graph through hypergraph modeling.Extensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed MOHCN compared with the state-of-the-art methods.
Information Seeking Behavior for Bugs on GitHub: An Information Foraging Perspective

GitHub is the largest code hosting platform for version control and collaboration. With presence of numerous files and folders within each repository, developers have to sacrifice their time to sift through them during debugging. We investigated developers&#x2019; debugging behavior with a think-aloud study involving 12 participants and reported cues and strategies utilized by them.
A Configurable Accelerator for Keyword Spotting Based on Small-Footprint Temporal Efficient Neural Network

Keyword spotting (KWS) plays a crucial role in human–machine interactions involving smart devices. In recent years, temporal convolutional networks (TCNs) have performed outstandingly with less computational complexity, in comparison with classical convolutional neural network (CNN) methods. However, it remains challenging to achieve a trade-off between a small-footprint model and high accuracy for the edge deployment of the KWS system. In this article, we propose a small-footprint model based on a modified temporal efficient neural network (TENet) and a simplified mel-frequency cepstrum coefficient (MFCC) algorithm. With the batch-norm folding and int8 quantization of the network, our model achieves the accuracy of 95.36% on Google Speech Command Dataset (GSCD) with only 18 K parameters and 461 K multiplications. Furthermore, following a hardware/model co-design approach, we propose an optimized dataflow and a configurable hardware architecture for TENet inference. The proposed accelerator implemented on Xilinx zynq 7z020 achieves an energy efficiency of 25.6 GOPS/W and reduces the runtime by 3.1× compared with state-of-the-art work.
A Variational Inference Method for Few-Shot Learning

Existing few-shot learning (FSL) methods usually treat each sample as a single feature point or utilize intra-class feature transformation to augment features. However, few-shot novel features are always vulnerable to noise, intra-class features have large variance and the direction of intra-class feature transformations is uncontrollable, which result in degradation of FSL models. Besides, existing FSL methods are one-generation based which do not utilize the prior knowledge obtained in the prior generation model to generate more robust posterior model in FSL and lack the interpretability in FSL. In this paper, we propose a novel two-generation based Latent Feature Augmentation and Distribution Regularization framework (LFADR) including prior relation net (PRN) and vae-based posterior relation net (VPORN) to generate a more robust VPORN based on PRN by transferring the prior knowledge in FSL. Firstly, we utilize a simple single-original-feature-point-based PRN to generate the more informative prior knowledge. We then propose a regularized-distribution-based VPORN driven by VAE to augment latent features by sampling from regularized class-specific distribution based on the prior knowledge transferred from PRN in order to guarantee and control the diversity of intra-class features which avoids uncontrollable feature transformations and reduces the variance of intra-class features and the impact of noise. As a result, LFADR can learn more key and robust intra-class and discriminative inter-class features and make decision boundary clearer in FSL, which is optimized as a variational inference problem. Furthermore, we analyse the feasibility and effectiveness of our framework based on Hoeffding’s inequality and Chernoff’s bounding method. Finally, experimental results validate our theoretical analysis and the effectiveness of our proposed FSL framework.
How to Improve Immersive Experience?

With the explosive growth of online multi-modal applications that typically include audio, video, and haptic signals, immersive experience (IE) improvement has been broadly regarded as one of the most important tasks. Compared with traditional quality of experience (QoE) improvement for online audio/video applications, it highlights two sequential technical challenges to be resolved: i) much more stringent demand of real-time improvement due to the incorporation of delay-sensitive haptic signals, and ii) high-dimensional instead of existing one-dimensional (<i>i.e.,</i> network-level) paradigm for better online improvement. To get over this dilemma, this work systematically addresses the following three fundamental problems: i) which factors influence IE, ii) how to online improve IE, and iii) to what extent of the corresponding IE improvement can be achieved. To this end, we first comprehensively explore and categorize the influence factors on IE from various dimensions. Then, by combing network resource scheduling with the multi-domain collaboration of user profile, device specification, and application type, an online IE improvement strategy is proposed based on the efficient linear contextual bandit with the <inline-formula><tex-math notation="LaTeX">$L_{1}$</tex-math></inline-formula>-norm estimation. Finally, we derive the theoretical bound of IE improvement, scaling at a poly-logarithmical function of data dimension. Numerical results on the practical system also demonstrate the remarkable improvement on IE.
A Progressive Quadric Graph Convolutional Network for 3D Human Mesh Recovery

Human mesh recovery from one single image has achieved rapid progress recently, but many methods suffer from the image appearance overfitting since the training data are collected along with accurate 3D annotations in controlled settings of monotonous backgrounds or simple clothes. Some methods regress human mesh vertices from poses to tackle the above problem. However the mesh topologies have not been well exploited, and artifacts are often generated. In this paper, we aim to find an efficient low-cost solution to human mesh reconstruction. To this end, we propose a Progressive Quadric Graph Convolutional Network (PQ-GCN), and design a simple and fast method for 3D human mesh recovery from a single image in the wild. Specifically, we apply quadric-based surface simplification to human meshes and design a progressive graph convolution network, accompanied by mesh feature up-sampling, to deal with the mesh topologies. We carry out a series of studies to validate our method. The results prove that our method achieves superior performance on a challenging in-the-wild dataset, while using 66% fewer parameters than the existing method, Pose2Mesh. Artifacts have also been eliminated and better visual quality has been obtained without any further post-processing and model fitting. Besides, the recovery can be stopped at an earlier stage by adding a decoder head. Consequently, the computational complexity can be reduced greatly.
Privacy-Preserving and Real-Time Detection of Vehicular Congestion Using Multilayer Perceptron Approach for Internet of Vehicles

Detection of vehicular congestion has a significant effect on the transportation sector. Several research approaches have been proposed to improve accuracy and efficiency with the advent of the Internet of Vehicles (IoV). Nonetheless, the privacy issue has attracted little attention on congestion detection. In this paper, we put forward a privacy-preserving and efficient scheme for vehicular congestion detection with both horizontal and vertical partitioned vehicular sensing data using Multilayer Perceptron (MLP) approach. Particularly, this work considers that different vehicle manufacturers would choose their encryption systems, which poses a challenge on how to collaboratively train an MLP model on these heterogeneous encrypted sensing data. To address this challenge, we utilize proxy re-encryption and additive homomorphic encryption schemes for preserving the confidentiality of raw sensing data and learning models while maintaining secure computation efficiency. Formally security analysis is provided to demonstrate that our proposed scheme is able to guarantee the privacy of the MLP learning process. Finally, we implement our scheme and evaluate it with real datasets. Experimental results show the effectiveness and feasibility of our MLP learning under multiple encryption systems for vehicular congestion detection.
Prompt-Based Prototypical Framework for Continual Relation Extraction

Continual relation extraction (CRE) is an important task of continual learning, which aims to learn incessantly emerging new relations between entities from texts. To avoid catastrophically forgetting old relations, some existing research efforts have focused on exploring memory replayed methods by storing typical historical learned instances or embedding all observed relations as prototypes in the episodic memory and replaying them in the subsequent training process. However, they generally fail to exploit the relation knowledge contained in the pre-trained language model (PLM), which could provide enlightening information to the representations of new relations from the known ones. To this end, we investigate the CRE from a novel perspective by generating knowledge-infused relation prototypes to leverage the relational knowledge from PLM with prompt tuning. Specifically, based on the typical samples collected from the historical learned instances with K-means algorithm, we devise novel relational knowledge-infused prompts to elicit relational knowledge from PLM for generating knowledge-infused relation prototypes. Then the prototypes are used to refine the typical examples embedding and calculate the stability-plasticity balance score for adjusting the memory replayed progress. The experimental results show that our method outperforms the state-of-the-art baseline models in CRE. The further extensive analysis presents that the proposed method is robust to memory size, task order, length of the task sequence, and the number of training instances.
Game effect sprite generation with minimal data via conditional GAN

Image generation using convolutional neural networks has gained popularity in various industries in the field of computer vision over the last decade. However, thus far, generative adversarial networks (GAN) have not been utilized to their maximum potential to generate computer game sprites, especially game effects. This is because typically the amount of available open-source game sprite data is considerably less than that of other problem domains such as image classification or object detection tasks, which use hundreds of thousands of images to train classification models. This study demonstrates that GAN can be utilized to generate game effect sprites adequately with a small set of input data, thus increasing the productivity of large-scale 2D image modification work in game development. In this study, we propose a simple 2D game effect sprite generation technique called a Game Effect Sprite Generative Adversarial Network (GESGAN). The proposed model generates a new style-translated image based on the structure of an effect image and a style label. The model consists of an encoder designed to extract the feature representation of an input image and a label; a decoder to generate a synthetic image; and a discriminator that learns to determine the authenticity of images with an auxiliary classifier for the label. Experimental results show that GESGAN is capable of reliably generating style-translated images for various shapes of object images and drawing styles and can perform 2D image sprite generation and modification tasks in near real-time, thereby reducing game development costs.
A method to reduce the systematic error of line-structured light sensors based on light plane correction

• The systematic error model caused by inaccurate light plane equation is established. • The distribution function of systematic error is derived. • The measurement method of the systematic error is proposed. • The inaccurate light plane equation is corrected by the measured systematic error. Line-structured light sensors are widely used in 3-dimentional (3D) reconstruction, however, there are few studies on the systematic error and the correction method. To further improve the measurement accuracy, this paper establishes the systematic error model of the line-structured light sensor, and the distribution function of the systematic error caused by the inaccurate light plane equation is derived. Then, the measurement method of the systematic error is proposed, the distance error and the orientation error of the light plane equation are corrected in two steps according to the distribution characteristics of the systematic error. The experimental results show that the systematic error of the sensor is reduced by at least 80% after correction. This method effectively reduces the systematic error of the line-structured light sensor and has good robustness.
Detecting and Processing Anomalies in a Factory of the Future

Production systems are changing in many aspects on the way to a Factory of the Future, including the level of automation and communication between components. Besides all benefits, this evolution raises the amount, effect and type of anomalies and unforeseen behavior to a new level of complexity. Thus, new detection and mitigation concepts are required. Based on a use-case dealing with a distributed transportation system for production environments, this paper describes the different sources of possible anomalies with the same effect, anomaly detection methods and related mitigation techniques. Depending on the identified anomaly, the FoF should react accordingly, such as fleet or AGV reconfiguration, strong authentication and access control or a deletion of adversarial noises. In this paper, different types of mitigation actions are described that support the fleet in overcoming the effect of the anomaly or preventing them in the future. A concept to select the most appreciate mitigation method is presented, where the detection of the correct source of the anomaly is key. This paper shows how various techniques can work together to gain a holistic view on anomalies in the Factory of the Future for selecting the most appropriate mitigation technique.
Extract Class Refactoring Based on Cohesion and Coupling: A Greedy Approach

A large class with many responsibilities is a design flaw that commonly occurs in real-world object-oriented systems during their lifespan. Such a class tends to be more difficult to comprehend, test, and change. Extract class refactoring (ECR) is the technique that is used to address this design flaw by trying to extract a set of smaller classes with better quality from the large class. Unfortunately, ECR is a costly process that takes great time and effort when it is conducted completely by hand. Thus, many approaches have been introduced in the literature that tried to automatically suggest the best set of classes that can be extracted from a large class. However, most of these approaches focus on improving the cohesion of the extracted classes yet neglect the coupling between them which can lead to the extraction of highly coupled classes. Therefore, this paper proposes a novel approach that considers the combination of the cohesion and coupling to identify the set of classes that can be extracted from a large class. The proposed approach was empirically evaluated based on real-world Blobs taken from two open-source object-oriented systems. The results of the empirical evaluation revealed that the proposed approach is potentially useful and leads to improvement in the overall quality.
Resource efficient AI: Exploring neural network pruning for task specialization

This paper explores the use of neural network pruning for transfer learning applications for more resource-efficient inference. The goal is to focus and optimize a neural network on a smaller specialized target task. With the advent of IoT, we have seen an immense increase in AI-based applications on mobile and embedded devices, such as wearables and other smart appliances. However, with the ever-increasing complexity and capabilities of machine learning algorithms, this push to the edge has led to new challenges due to the constraints imposed by the limited availability of resources on these devices. Some form of compression is needed to allow for state-of-the-art convolutional neural networks to run on edge devices. In this work, we adapt existing neural network pruning methods to allow them to specialize networks to only focus on a subset of what they were originally trained for. This is a transfer learning use-case where we optimize large pre-trained networks. This differs from standard optimization techniques by allowing the network to forget certain concepts and allow the network's footprint to be even smaller. We compare different pruning criteria, including one from the field of Explainable AI (XAI), to determine which technique allows for the smallest possible network while maintaining high performance on the target task. Our results show the benefits of using network specialization when executing neural networks on embedded devices both with and without GPU acceleration.
Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Abstract Integrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety.