Reviewerly/exHarmony huggingface dataset:
OWGraMi: Efficient method for mining weighted subgraphs in a single graph

Recently, the problem of mining weighted subgraphs from a weighted single graph has become a vital issue because weighted graphs are generally used to restore, simulate or monitor many complex and large systems in which each object has a different role/level. This field has attracted the attention of numerous researchers, and within related studies Weighted Graph Mining (WeGraMi) can be considered as the state-of-the-art method. However, WeGraMi lacks a strategy to prune unweighted candidate subgraphs early in the process and needs a lot of time to compute the weight for all mined frequent subgraphs. In this paper, we optimize the WeGraMi algorithm with the use of two effective strategies in a new method, which we call Optimized Weighted Graph Mining (OWGraMi). Firstly, we use a strategy to prune all frequent edges which cannot reach the weighting threshold, and with this method we can decrease the number of unweighted candidates. Secondly, we reuse the weight of parent subgraphs when computing the weight for their child subgraphs, as this can reduce the running time for the mining process. On two real graph datasets, directed as well as undirected, our experiments show that both the running time and memory requirements for OWGraMi can be reduced significantly in comparison to the original algorithm.
Federated Learning Client Selection Mechanism Under System and Data Heterogeneity

Federated learning (FL) has been proposed to train a global model by distributed architecture, while keeping the training data local. Owing to the large scale of clients in FL, all clients to participate in training is not feasible. The heterogeneity of clients, including system and data heterogeneity, also poses huge challenge to the client selection problem. Traditional client selection mechanisms can’t handle these heterogeneities effectively, which lead to poor training efficiency. Hence, this paper comprehensively considers system and data heterogeneity to select clients and dynamically adjust the number of selected clients. For system heterogeneity, we build the latency model to predict the training time for selecting clients with best performance including CPU frequency, the size of dataset and transmission power. Besides, for data heterogeneity, the cluster model is established to cluster clients for alleviating the accuracy jitter owing to the non independent and identically distributed (Non-IID) dataset. We formulate the client selection problem aiming to minimize the overall training time on the premise of accuracy, and design the Federated Client Cluster and latency-Prediction Selection (FCCPS) algorithm to solve this problem. With extensive simulations, we show that the FCCPS algorithm can reduce the training time by up to 21% on Cifar-10 dataset and 13% on FashionMNIST dataset, as compared to FedAvg.
Artificial Intelligence Meets Kinesthetic Intelligence

Current mainstream biometric user authentication approaches are based on passive measurements of the subject's characteristics, and usually come with less-than-satisfactory accuracy. This paper takes a unique approach to biometric authentication. Specifically, instead of training a machine learning algorithm to recognize a legitimate user, the paper proposes a hybrid type of training, in which the legitimate user is also trained to use a customized instance of the machine. The user thus achieves a level of artificially-induced expertise to interact with the machine, which makes the user easier to recognize. We implement this concept in a mouse-based user authentication system, in which we produce customized machine instances by introducing an angle offset to the standard mouse. Human subjects then rely on their kinesthetic intelligence to achieve motor learning and visual-motor adaptation to the modified mouse. We design a 7-week IRB-approved experiment, collect data from 18 human subjects over this period, and evaluate the proposed approach with two existing state-of-the-art mouse-based authentication schemes. We find that, in both schemes, our approach significantly outperforms the baseline in which a regular unaltered mouse is used. Somewhat surprisingly, results also show that our approach improves the authentication performance even when both legitimate and non-legitimate users are trained to exactly the same instance of customized machine (i.e., the same mouse angle offset). In addition, we also observe that users can generally maintain their learned expertise even after one week of washout, which further demonstrates the practicality of the approach. Finally, we present a practical strategy to manage the enrollment of users in such a proposed system.
The ThreadRadar visualization for debugging concurrent Java programs

Abstract Due to non-deterministic behavior and thread interleaving of concurrent programs, the debugging of concurrency and performance issues is a rather difficult and often tedious task. In this paper, we present an approach that combines statistical profiling, clustering and visualization to facilitate this task. We implemented our approach in a tool which is integrated as a plugin into a widely used IDE. First, we introduce our approach with details on the profiling and clustering strategy that produce runtime metrics and clusters of threads for source-code artifacts at different levels of abstraction (class and method) and the entire program. Next, we explain the design of our visualization which represents the clusters in situ, i.e., embedded in the program text next to the related source-code artifact in the source-code editor. More detailed information is available in separate windows that also allow the user to configure thread filters interactively. In a demonstration study, we illustrate the usefulness of the tool for understanding and fixing performance and concurrency issues. Finally, we report on first formative results from a usability test and consequently implemented tool improvements. Graphical abstract
Bayesian Inference for Uncertainty-Aware Post-Disaster Damage Assessment Using Artificial Intelligence

Climate disasters cause significant loss of life and damage to properties and infrastructure. In a disaster aftermath, building inspectors conduct preliminary damage assessment (PDA), which, depending on the size and population of the affected areas, may be slow and resource-intensive. In this paper, we present an uncertainty-aware convolutional neural network (CNN) for PDA by incorporating a Bayesian framework and implementing Monte Carlo (MC) dropout sampling to efficiently approximate the Bayesian inferences. Using dropouts not only during training but also at the inference time allows the CNN model to represent softmax probabilities of the damage prediction as random variables, enabling a more rigorous and systematic characterization of uncertainty and confidence in model predictions. The model is trained and evaluated with non-deterministic ground-truth damage scores obtained through crowdsourcing from drone footage of a hurricane-affected area. Results indicate that the trained uncertainty-aware CNN model leads to more informed and reliable PDA outcomes.
Accelerating container-based deep learning hyperparameter optimization workloads

DocuSign is advancing at a great pace for artificial intelligence and embracing a continuous shift towards developing and deploying an increasing number of deep learning models. During the development stage, developers usually build a number of deep learning models and train them using a bunch of potential hyperparameter configurations to find the best-performed one, which is called hyperparameter optimization (HPO). Such HPO jobs can run for a long time due to ever-larger models and numerous hyperparameter configurations. Furthermore, the HPO jobs at DocuSign are processed in container-based environments so that the best-performed model can be deployed and maintained in production reliably and efficiently.
A Framework for Selecting Features using Various Soft Computing Algorithms

“Particle Swarm Optimization” (PSO), “Artificial Bee Colony algorithm” (ABC) are the typical example of evolutionary algorithm. In this paper pre-processing was done initially to clean the data then proposed “Grouped Artificial Bee Colony Optimization” (G-ABC) algorithm for optimizing the feature selection procedure. Proposed feature selection model includes combining employee bees to generate more efficient rule generation with less time of execution. Furthermore, experimental results were analysed and verified. The experiment compares basic PSO, basic ABC, PSO- ABC and G-ABC (proposed model) algorithms on baseball data set. The results indicate that G-ABC with Neural Network algorithm has good performance in term of selecting features(selected 91 features out of 100 records), time of execution(executedin 3 sec) and 98.2% accuracy.
Neuroendocrine Response and State Anxiety Due to Psychosocial Stress Decrease after a Training with Subject’s Own (but Not Another) Virtual Body: An RCT Study

Previous research involving healthy participants has reported that seeing a moving virtual body from the first person perspective induces the illusion of ownership and agency over that virtual body. When a person is sitting and the virtual body runs, it is possible to measure physiological, behavioral and cognitive reactions that are comparable to those that occur during actual movement. Capitalizing on this evidence, we hypothesized that virtual training could also induce neuroendocrine effects that prompt a decreased psychosocial stress response, as occurs after physical training. While sitting, 26 healthy young adults watched a virtual avatar running for 30 min from the first person perspective (experimental group), while another 26 participants watched the virtual body from the third person perspective (control group). We found a decreased salivary alpha-amylase concentration (a biomarker for the stress response) after the virtual training among the experimental group only, as well as a decreased subjective feeling of state anxiety (but no difference in heart rate). We argue that the virtual illusion of a moving body from the first person perspective can initiate a cascade of events, from the perception of the visual illusion to physiological activation that triggers other biological effects, such as the neuroendocrine stress response.
A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification

Encrypted network traffic classification is an essential task in modern communications, which is used in a wide range of applications, such as network resource allocation, QoS (Quality of Service), malicious detection, etc. With the continuous evolution of network technology, approaches used to classify network encrypted traffic have become increasingly complex, and model training relies heavily on a large amount of labeled data. However, the acquisition of correct and massive labeled data of network traffic in the real environment remains a major challenge in this field. On the opposite, unlabeled traffic is extremely easy to obtain in a network. Therefore, the effective use of unlabeled data is of great significance to the development of modern communications. In this paper, we propose the Sauce model, which can effectively use unlabeled information to obtain high-quality clustering space. It is composed of Aux (auxiliary network) and AE (auto encoder), where Aux is designed for collaborative training with AE, affecting the distribution of samples in the latent space generated by AE. Sauce uses t-SNE (t- distributed stochastic neighbor embedding) to perform secondary dimensionality reduction on the latent code, and then uses a clustering algorithm for cluster analysis. Besides, sauce applies the self-training technique, replacing the real labeled data with pseudo-labeled data to reduce the reliance on labeled data and improve the utilization of unannotated data. We conduct experiments on two real network datasets. The experiments show that Sauce can achieve clustering accuracies of 98.4% (VPN dataset) and 98.0% (TOR dataset), outperforming other unsupervised or semi-supervised learning methods.
3D Object Detection Based on Multi-view Adaptive Fusion

Aiming at the problem that multi-view features are difficult to fuse effectively, a multi-view feature adaptive fusion 3D object detection framework is proposed, and new solutions are proposed in two aspects: depth feature fusion and loss function design. It mainly cooperates the bird&#x2019;s-eye view and cylindrical view, carries out adaptive feature fusion on the premise of considering the interaction between views and the contribution of different view features to the detection task, and improves the importance of network learning structure information and local features through the information of two additional tasks: foreground classification and central regression, At the same time, the loss calculation is optimized in the detection process to improve the regression effect of the target boundary box. Experiments on KITTI dataset show that this method achieves higher performance in all single-stage fusion methods, is better than most two-stage fusion methods, and achieves a good balance between speed and accuracy on KITTI benchmark.
Achieving Secure and Efficient P2P Data Trading based on Blockchain for Internet of Things

The intelligence of the information era is accomplished through data sharing, which enables us to perform more insightful analysis and make better decisions. However, traditional data trading systems are typically centralized managed with high operating costs and associated risk of single point of failure. Furthermore, it is going to be increasingly more common to see malicious data and behavior during the transactions. This paper proposes a P2P data trading scheme based on blockchain for Internet of Things (IoT). The proposed scheme provides an efficient negotiated pricing model in the data trading process. Chameleon hashing algorithm has been adopted to advance the robustness of the trading scheme by detecting malicious data on the chain and implementing error correction and loss control. Additionally, information entropy is incorporated as data privacy measurement. The security analysis and experimental results show that the proposed data trading scheme is secure and efficient.
Beyond the Benchmarks: Toward Human-Like Lexical Representations

To process language in a way that is compatible with human expectations in a communicative interaction, we need computational representations of lexical properties that form the basis of human knowledge of words. In this article, we concentrate on word-level semantics. We discuss key concepts and issues that underlie the scientific understanding of the human lexicon: its richly structured semantic representations, their ready and continual adaptability, and their grounding in crosslinguistically valid conceptualization. We assess the state of the art in natural language processing (NLP) in achieving these identified properties, and suggest ways in which the language sciences can inspire new approaches to their computational instantiation.
A Survey of Sentiment Analysis Based on Multi-Modal Information

Multimodal sentiment analysis is a new direction in the field of emotion analysis and has become a research hotspot in recent years. This paper first briefly outlines the concept of multimodal sentiment analysis and its research background. Secondly, it sorts out the development context of existing text and image fusion, text and audio and video fusion sentiment analysis, then describes the multimodal data fusion method, and finally discusses the future development direction of multi-modal sentiment analysis. It is the intention of this study to provide corresponding reference value for workers in related fields.
Characterisation of needle edge quality using diffraction pattern

The performance of hypodermic needles is mainly dependent on quality of their edges. The quality of the needle edges directly impacts penetration forces, tissue healing time, and patient pain. At present, only subjective techniques are used to access quality of edges of the needles which are not capable to contribute during manufacturing. Thus an appropriate technique is desirable to characterize the defects of the needle edges. This study aims to develop a characterization technique for measurement of defects such as roughness, chips and tip bending on the edges of needles. The proposed technique uses the diffraction fringes, obtained from the superposition of diffracted light from needle edges and the direct propagated light from the source. Variations in the fringes and their intensity profiles are utilized to study the defects. Further, the results are compared with results obtained from a standard optical profiler and a microscope. The standard deviation of proposed technique for repeatability and reproducibility of measurements are σ = 0. 104 a.u and σ = 0. 87 a.u, respectively. This technique is simple, non-contact in nature, uses compact configuration and is capable for qualitative and quantitative evaluation of the hypodermic needles.
Multi-View Depth Estimation with Color-Aware Propagation and Texture-Aware Triangulation

PatchMatch-based multi-view stereo relies on photometric consistency to estimate the depth map for each view. However, due to the matching ambiguity introduced by photo-consistency metrics, it often lacks completeness on textureless areas. In this paper, we propose a novel Multi-View Stereo algorithm for the multi-view depth map estimation problem. First, assuming local areas with similar colors are on the same piecewise plane, depth hypotheses are propagated with color-aware weights during PatchMatch propagation. Second, we propose a texture-aware triangulation to generate reliable planes to approximate the textureless regions, which helps to decrease the matching ambiguities in textureless areas. Moreover, a probabilistic filter is proposed aiming to optimize the estimated depth map with small speckles. The effectiveness of our method has been validated on the ETH3D benchmark. Experiments of our method show competing performance against the state-of-the-art.
Point-of-Interest Recommendation System based on DeepWalk and Tensor Decomposition

With the popularization of mobile devices and the development of satellite positioning technology, it is more convenient to collect the travel data of users. A large amount of user sign-in data provides strong data support for points of interest recommendation. The rapid development of transportation and the improvement of people’s material living standards have made point-of-interest recommendations an urgent need. Because the user’s travel is a continuous behavior with a trajectory. We can dig out important information from the adjacent sign-in records. For example, a large number of users will go to place B after they have been to place A, which shows that the connection between place A and place B is very close. In this paper, the graph embedding method is used to mine users’ favorite degree of places, and the tensor decomposition method is used to mine users’ travel interests, so as to analyze the relationship between users’ travel characteristics and places. Based on the idea of collaborative filtering, we need to find similar user groups in the dataset. In this paper, a method for calculating weighted virtual users is proposed, and similar users are clustered by using Gaussian mixture clustering method to get similar user groups. Experimental results show that the method implemented in this paper has better effect than other methods.
ShaderNet

This paper demonstrates ShaderNet --- our graph analytics framework with shader codes, which are machine-level codes and are important for GPU designers to tune the hardware, e.g., adjusting clock speeds and voltages. Due to a wide spectrum of use-cases of modern GPUs, engineers generally find it difficult to manually inspect a large number of shader codes emerging from these applications. To this end, we present a system, ShaderNet, which converts shader codes into graphs, and applies advanced graph mining and machine learning techniques to simplify shader graphs analysis in an effective and explainable manner. By studying shader codes' evolution with temporal graphs analysis and structure mining with frequent subgraphs, we demonstrate several key functionalities of our framework, such as a frame's scene detection, clustering scenes, and a new application's inefficient shaders prediction, which can accelerate GPU's performance tuning. Our code base and demonstration video are at: https://lzlz15.github.io/D_E_M_O/.
A Holistic Client Selection Scheme in Federated Mobile CrowdSensing Based on Reverse Auction

Federated Mobile CrowdSensing is applied to collect massive sensory data and exploits the computing power of mobile devices brought by their embedded specialized computing engines (e.g., Neural Engine in iPhone) to train machine learning (ML) models. However, the heterogeneity of mobile devices includes significant differences in the size and quality of datasets, different computing power, and some unreliable clients using unreliable data for training. The heterogeneity of mobile devices reduces FL’s performance. Therefore, selecting high-quality clients for Federated learning (FL) is vital. This study proposes a client selection scheme based on the reverse auction. First, each client’s training time is predicted, the total FL time threshold is optimized, and the reputation value is calculated based on the historical performance of each client. Then, each client’s current computing power and dataset size are converted into an efficiency value. Finally, the selection value of each client is calculated based on the efficiency value and reputation value. The results of the experiments show that our scheme can select high-quality clients. Compared with FedRep, our scheme can reduce training time by 91.5%. Compared with FedEff, our scheme can reduce communication rounds by 87.5%. In the same communication rounds (5000), our scheme has higher accuracy than RandomFL, and the average accuracy is improved by about 4.4%.
Design of image recognition system based on FPGA

With the development of digital image processing technology, image recognition has a wide application prospect. This technology is widely used in military, industrial, people&#x2019;s livelihood and other fields. For example, face recognition system often used in our life is a typical application of image recognition technology. But the hardware realization of image recognition technology is not very mature. In this paper, an image recognition system based on FPGA is proposed, which uses the advantages of high concurrency and real time of FPGA to process the image quickly and realize the real time recognition of some simple objects.