Reviewerly/exHarmony huggingface dataset:
A KNN Accelerator Based on Approximate K-D Tree for ICP

In this paper, we propose a software/hardware co-accelerator architecture based on approximate k-d tree for accelerating k-nearest neighbor (KNN) search. Firstly, aiming at the time-consuming operation of sorting large amount of data in the tree building part, we propose an efficient-lightweight parallel pipelined merge sorting acceleration circuit. Secondly, in order to accelerate the exhaustive search of points in a subspace to the greatest extent, we propose a high-speed fully parallel searching acceleration circuit, which can calculate the Euclidean distance of all points in a subspace in parallel. The experimental results show that when the point cloud scale is 32k, our accelerator can complete a 1-NN search in only 2.3ms, and can ensure the accuracy of ICP point cloud registration.
Dependable Workflow Scheduling for Microservice QoS Based on Deep Q-Network

Workflow scheduling for microservice has become an important and challenging research topic. To design a high-performance and reliable scheduling model, we propose a dependable workflow scheduling algorithm for microservice quality of service (QoS) based on deep-Q-Network, namely the DWSM. Firstly, we utilize the redundancy strategy based on the restart strategy of the Kubernetes container to optimize the dependability of workflow. Then this paper quantifies the dependability indicator and designs the reward function based on three QoS attributes including execution time, resources consumption and dependability to generate the scheduling strategy through DQN. Finally, we utilize a real-world data set to evaluate our algorithm and compare it with several state-of-art baselines including heterogeneous earliest-finish-time (HEFT), greedy algorithm and nondominated sorting genetic algorithm (NSGA-III). Experimental results show that our DWSM algorithm achieves higher performance in dependability and saves more CPU resources. In addition to simulation, we have implemented it on a workflow engine and deployed real workflow cases to test its effectiveness in practice.
Marginal Fisher Analysis With Polynomial Matrix Function

Marginal fisher analysis (MFA) is a dimensionality reduction method based on a graph embedding framework. In contrast to traditional linear discriminant analysis (LDA), which requires the data to follow a Gaussian distribution, MFA is suitable for non-Gaussian data, and it has better pattern classification ability. However, MFA has the small-sample-size (SSS) problem. This paper aims to solve the small-sample-size problem while increasing the classification performance of MFA. Based on a matrix function dimensionality reduction framework, the criterion of the MFA method is reconstructed by using the polynomials matrix function transformation, and then a new MFA method is proposed, named PMFA (polynomial marginal fisher analysis). The major contributions of the proposed PMFA method are that it solves the small-sample-size problem of MFA, and it can enlarge the distance between marginal sample points of inter-class, so that it can get better pattern classification performance. Experiments on the public face datasets show that PMFA can get a better classification ability than MFA and its improved methods.
The Construction of Radar-Visual Surround View Dataset and Intrinsic and Extrinsic Calibration Method for the System

In this paper, we proposed the construction of the radar-visual surround view dataset with various scenes including severe environments. We first introduced the calibration of the radar and the construction of the surround view radar system. Then the segmentation and extraction of pedestrians in radar point cloud images are completed. Furthermore, a simple label matching between the radar and camera systems in the dataset is presented.
Regional Proposal Based Underwater Object Tracking

In recent years, object tracking has developed rap-idly, in which Siamese network-based trackers have occupied an important position in this field. Most algorithms are designed for images taken in the normal air environments; fewer methods are for underwater situations. Water always can bring distraction and reflection of the light, which can blur images and distort colors. A regional proposal based Siamese network combined with under-water image enhancement is proposed for the underwater tracking problem. The proposed tracking method is evaluated on some natural underwater images, and achieve better results than general tracking methods.
A Privacy-Preserving Oriented Service Recommendation Approach based on Personal Data Cloud and Federated Learning

Personal data cloud, as an emerging personal data management mode in recent years, enables to reduce the risk of privacy disclosure and protect the rights and interests of individuals given by privacy protection laws and regulations. Personal data that is generated during the interaction between individual users and various services contains a lot of useful personalized but private information and plays a crucial part in personalized service recommendation. In traditional service recommendation scenario, personal data of massive users is centralized owned/managed by service providers, which is easy to lead to privacy disclosure and personal data abuse. In the personal data cloud based service recommendation scenario, personal data of individual users is distributed stored and controlled by users themselves. To address the challenges of privacy protection and distributed storage of personalized data in this new recommendation scenario, we propose HyFL, a deep learning based recommendation algorithm with hybrid federated learning. HyFL can conduct recommendation based on the personal data from multiple services. The security of HyFL is theoretically proved, and experiments on real-world datasets demonstrate that HyFL performs better on the basis of privacy preservation than that of some traditional recommendation approaches.
Novel Contactless Sensing Technique for Real-time Human Activity Detection

Recent research has looked to implement real-time contactless sensing within a healthcare application which can pro-vide monitoring of vulnerable people living at home. Currently systems make use of wearable devices to achieve this but this requires users to always be wearing devices. This paper presents a real-time contactless system which makes use of radio frequency signal propagation to determine if a person is sitting or standing. This is achieved by observing incoming channel state information to detect movements and passing detected movements to an AI model that can predict sitting or standing motions from changes the signal amplitude described in the channel state information. The system is able to make accurate real-time classifications in multiple environments.
Two-Way Long Short-Term Memory Architecture for Non-Contact Hand Gesture Identification

This study proposes a novel method for the classification of hand gestures using a frequency-modulated continuous-wave (FMCW) radar. A two-way long short-term memory (TW-LSTM) architecture is introduced to categorize the movements recorded by an FMCW radar into 12 standard types of hand gestures provided by IEEE DataPort. The TW-LSTM method initially segments the physical displacement features of 4,600 movements into smaller frames. It then applies intra- and inter-frame temporal operations on the data to identify the type of movement. Experiments on four different subjects report an F1 score and an average accuracy of 84.02% and 84.0% for the classification of hand gestures respectively. It is also demonstrated that the true positive rates for all subjects fall within the range of 71.0%-91.0%, showing excellent consistency between predicted and true labels of hand gestures.
Human Inspired Memory Module for Memory Augmented Neural Networks

Memory is an essential element in most artificial intelligence systems. Recurrent neural networks and long-short term memories are some examples of deep learning structures that have some memory capabilities. As a new approach for incorporating explicit memory into deep learning systems, memory augmented neural networks have been introduced earlier. The neural Turing machine, as a distinguished and pioneering example, is able to emulate a conventional digital computer but fails in more complex tasks. We propose an external memory module, which is composed of two separate submodules for short- and long-term memories. The long-term memory is structured as a graph, equipped with a read/write mechanism. Being fully differentiable makes this memory system easy to be trained via backpropagation. To show the superiority of the proposed system in complex tasks with longterm dependencies, some experiments are conducted. Our analysis shows that this dual-memory system outperforms the neural Turing machine in terms of convergence speed and loss.
Online Learning Using Incomplete Execution Data for Self-Adaptive Service-Oriented Systems

Service composition algorithms support the construction of complex applications by combining various web services to fulfill diverse functional and Quality of Service (QoS) requirements. Moreover, composition algorithms must fulfill diverse user requirements while adhering to constraints such as limited computational resources. Recent research has demonstrated that using online learning to select different algorithms for specific tasks of a problem domain outperforms approaches that use a single algorithm for all tasks, in terms of computational resource usage and solution quality. Problematically, existing work in service composition does not leverage these advances, leading to multiple inefficient compositions. To address these challenges, we propose online composition algorithm selection using contextual multi-armed bandits to select an algorithm for each composition task at runtime. Our evaluations demonstrate the benefits of our approach by reducing time and memory usage by up to 54.2% and 15.5% while fulfilling QoS requirements, compared to using a single composition algorithm for all tasks.
Local-to-Global Cost Aggregation for Semantic Correspondence

Establishing visual correspondences across semantically similar images is challenging due to intra-class variations, viewpoint changes, repetitive patterns, and background clutter. Recent approaches focus on cost aggregation to achieve promising performance. However, these methods fail to jointly utilize local and global cues to suppress unreliable matches. In this paper, we propose a cost aggregation network with convolutions and transformers, dubbed CACT. Different from existing methods, CACT refines the correlation map in a local-to-global manner by utilizing the strengths of convolutions and transformers in different stages. Additionally, considering the bidirectional nature of the correlation map, we propose a dual-path learning framework to work parallelly. Benefiting from the proposed framework, we can use 2D blocks to construct a cost aggregator to improve the efficiency of our model. Experimental results on the SPair-71k, PF-PASCAL, and PF-WILLOW datasets show that the proposed method outperforms the most state-of-the-art methods.
EdgeLeague: Camera Network Configuration With Dynamic Edge Grouping for Industrial Surveillance

Object detection is crucial for surveillance in edge-enabled Industrial Internet-of-Things. Massive high-dimensional video streams without considering priority differences connect to edges via narrow and time-varying uplink channels, which should be analyzed efficiently for accurate and fast surveillance responses. However, time-varying network environments and constrained edge resources degrade surveillance's accuracy and real-time performance. This article proposes EdgeLeague for multiple video streams with different quality of service, which maintains high surveillance performance under edge resource limitations and uplink bandwidth dynamics by edge collaboration and camera network configuration. The EdgeLeague scheme is formulated by an NP-hard integer nonlinear problem to dynamically configure camera network resolutions and detection models on cooperative edges. To accelerate configuration responses, the formulated problem is decomposed into edge league grouping, video-league matching, and video configuration, solved by low-complexity algorithms. Theoretical analysis is provided for optimal video-league matching. Simulations show EdgeLeague achieves 0.312 s latency and 86.3% surveillance accuracy.
A Multi-graph Fusion Based Manifold Embedding for Face Beauty Prediction

Automatic facial beauty prediction is an interesting research topic in computer vision and aesthetic medicine. Most of the existing FBP methods rely on supervised solutions based on geometric features or deep features. Recently, multi-graph fusion techniques have been used to construct more accurate graphs which better represent the data. In this work, we propose a semi-supervised manifold embedding method in which multiple graphs with geometric features, deep features and label information are constructed. The proposed method fuses the geometric features with deep features to generate a high-level representation of a face image. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. The experimental results on the SCUTFBP-5500 face beauty dataset demonstrated the superiority of the proposed algorithm compared with other state-of-the-art methods.
MisuseHint: A Service for API Misuse Detection Based on Building Knowledge Graph from Documentation and Codebase

Developers often call APIs to improve development efficiency, but they misuse APIs due to lack of understanding of source code logic and other unavoidable reasons, resulting in serious consequences such as program crashes. Many studies that extract API usage constraints from API documentation or codebases expect to get out of this dilemma through API misuse detection. However, low recall remains a hurdle for researchers to overcome. In this work, we make full use of API documentation and codebases to construct constraint knowledge graph, and propose a new API misuse detector, MisuseHint. We precisely define API constraints into seven categories, utilize API caveat knowledge in documentation and API usage patterns in codebases, and fuse knowledge from both to build knowledge graph with rich constraints. To detect API misuses, we obtain API usage constraints in the knowledge graph and analyze static code to propose different strategies to determine whether API misuses exist. Through defect pattern analysis, object variable tracking, and Z3 SAT solver, our detector can identify various complex situations of code at a fine-grained level, especially solving various complex problems of Call Order and State Checking constraints. Experimental results on MUBench show that our recall reaches 39.78%, demonstrating the validity and theoretical feasibility of fusing documentation and codebases using knowledge graphs. MisuseHint achieves a recall of 76.34% when it is always given sufficient API constraints. This detector can practically help developers program effectively.
An Intelligent Data-Centric Web Crawler Service for API Corpus Construction at Scale

The number of web APIs is growing rapidly. API adoption is increasing across all industries with executives prioritizing investments in the API economy. Each API provider offers API documentation which includes complex descriptions. In order to collect and understand the applications and operations of diverse APIs, software engineers read lengthy and complicated API documentations. Understanding the variety of API documentations is a labor intensive and error-prone process. In this paper, we introduce a data-centric web crawler service to collect, analyze, and construct a large corpus of API documentations. The generated API Corpus can be used in machine programming (i.e., code generation, code search). The proposed API web-crawler intelligently harvests more than 2.8M API documentation pages where it uses a machine-learning-based approach with an accuracy of 91.32% to select only web API pages (REST). We also conducted an extensive and end-to-end real-world evaluation, where the proposed API web-crawler not only collects a sheer number of API pages, but also successfully validates 1,222 APIs out of 1,521 target APIs with a success rate of 80.34%.
Applicable and Partial Learning of Graph Topology Without Sparsity Priors

This paper considers the problem of learning the underlying graph topology of Gaussian Graphical Models (GGMs) from observations. Under high-dimensional settings, to achieve low sample complexity, many existing graph topology learning algorithms assume structural constraints such as sparsity to hold. Without prior knowledge of graph sparsity, the correctness of their results is difficult to check. In this paper, we aim to do away with these assumptions by developing algorithms for learning degree-bounded GGMs and separable GGMs without any sparsity priors. The proposed algorithms, which are based only on the knowledge of conditional independence relations in the data distribution, require minimal structural assumptions while still achieving low sample complexity, and hence are ‘applicable’. Specifically, for any user defined sparsity parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> , we prove that the proposed algorithms can consistently identify whether a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -dimensional GGM is degree-bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> (or strongly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -separable) with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Omega (k \log p)$</tex-math></inline-formula> sample complexity. Besides, our algorithms also demonstrate ‘partial’ learning properties whenever the overall graph is not entirely sparse, that is, not all nodes are degree-bounded (or are strongly separable). In this case, we can still learn the sparse portions of the graph, with theoretical guarantees included. Numerical results show that existing algorithms fail even in some simple settings where sparsity assumptions do not hold, whereas our algorithms do not.
HSDN: A High-Order Structural Semantic Disentangled Neural Network

Graph disentangling is a new promising direction that can help us to discover the latent patterns in the data and understand the behaviors of a graph learning model. Despite the many efforts in disentangling representation learning, few works focus on disentangling the latent factors behind a graph. Most current foci are mainly on studying node-level semantics in the graphs. Compared with node-level, the structure-level view can provide a new interpretable and in-depth insight into graph data. The study of structure-level relations enables us to reveal the high-order structural semantics in the data. To explore the complex high-order structural semantics in the data, we propose the High-order Structural Semantic Disentangled Neural Network (HSDN) to model the graph structure units and disentangle structural semantics. It's the first attempt to hypergraph disentangled networks. Unlike prior methods that disentangle factor graphs based on pair-wise relations only, we introduce hyperedges on pair-wise graphs to model structure units and disentangle the complex high-order structural semantics between different structures. Extensive experiments demonstrate that HSDN achieves state-of-the-art performances in terms of both disentangling and downstream tasks.
Model-Based Navigation Control for Communication-Aware Motion Using Harmonic Potential Fields

This paper suggests an optimal and provably-correct method for inducing communication-aware, context-sensitive mobility in an autonomous agent. The method can fuse crisp representations of arbitrarily shaped hard-obstacles with soft representations of the wireless signal strength field that forms within the confines of the hard-obstacles. The procedure can produce a navigation control signal able to induce in a wide class of realistic agents optimal, communication-aware, dynamically-friendly trajectories to a specified target point in the environment. The approach is developed with the aid of the potential-field method for motion planning. Its immediate goal is to tackle the model-based case where the navigator assumes full knowledge of the obstacles and Wireless Communication Link (WCL). The treatment in this part provides the theoretical foundations and the tools necessary to extend the method to the sensor-based case where the agent does not a-priori know the obstacles or the WCL which have to be sensed online. Theoretical proofs of the communication-aware navigator’s performance are provided and its behavior is verified using intensive realistic simulation. Note to Practitioners—In this paper we target applications like search and rescue, where mobile agents may be utilized for information gathering and mapping purposes in regions that are hazardous to humans. Our proposed approach can simultaneously combine obstacle information and signal strength information to generate guidance signals. Trajectories manifested using these guidance signals can avoid regions with very low signal strength. Typically, guidance signals from a planner need to be smoothed before generating actuation signals. Our proposed planner provides guidance signal that can be directly used to generate actuation signals. Also the proposed approach results in trajectories that are very smooth (infinitely differentiable), therefore, energy consumption due to motion is reduced. Simulations show the effectiveness of our approach. In future work, we intend to use real-time signal strength and obstacle information data in out approach.
An Integrated Node Selection Model Using FAHP and FTOPSIS for Data Retrieval in Ubiquitous Computing

Ubiquitous computing (UC) is an advanced computing concept that makes services and computing available everywhere and anytime. In UC, data lies at the heart of all UC applications, and the key technologies that are required to make UC a reality are data and task management. In this context, retrieving data is influenced by the dynamic nature of these systems in addition to human and sensor failures. So the main problem is how to select the most appropriate service provider for retrieving data. Retrieving data is a complex issue that requires an extensive evaluation process and is one of the biggest challenges in UC. In addition, not every eventuality in these systems can be predicted due to their dynamic nature. As a result, there is a strong need to address the uncertainty in context data. In this paper, to assist users to efficiently select their most preferred service provider for retrieving data, a new fuzzy integrated multicriteria decision-making model, which meets quality of context (QoC) and quality of service (QoS) and satisfies user quality requirements and needs, is proposed. The proposed model is based on four stages. In the initial stage, the identification of evaluation criteria is performed due to the varying importance of the selected criteria. In the second stage, a fuzzy Analytical Hierarchy Process (FAHP) procedure is utilized to assign importance weights to each criterion. In the third stage, the fuzzy Technique for Order Preference by Similarity of an Ideal Solution (FTOPSIS) is used to evaluate and measure the performance of each alternative. Finally, sensitivity analysis is performed to check the robustness and the applicability of the proposed model.