Reviewerly/exHarmony huggingface dataset:
Clustering Ensemble of Massive Data Based on Trusted Region

Nowadays, there is a growing demand for clustering analysis of massive data, but the huge amount of data makes it a great challenge in storage and computing. Clustering ensemble forms a more robust result by ensemble multiple base clusterings, and the wrong labels in the base clusterings will greatly affect the ensemble result. This paper proposes a clustering ensemble method for massive data. Firstly, the massive data is divided into multiple small datasets by BLB sampling; K-means is used for clustering on each small dataset. In the base clusterings, find the trusted region according to a certain range from the cluster center. Combine all trusted regions to form the basic clustering structure of the data, and allocated all the remaining untrusted samples into the basic clustering structure to obtain the final clustering result. Experiments on synthetic datasets and real datasets show the effectiveness of the algorithm.
A code clone detection algorithm based on graph convolution network with AST tree edge

Detecting code cloning will prevent it from bringing risks such as vulnerabilities and intellectual property disputes in complex software systems such as large-scale defense software systems and commercial software systems. In the field of deep code clone detection, neural networks such as Tree-CNN and Tree-LSTM, which extract features from AST (abstract syntax tree), can't collect global information of upper and lower nodes, and information can't flow globally, but graph neural network can avoid this problem. This paper presents a method of edging AST, and uses GCN (Graph Convolutional Network) and GAT(Graph Attention Networks) to extract code feature vector. Finally, the experiment is carried out on BigCloneBench data set, using several common binary classification indexes, and analyzing the time consumption, it is concluded that the effect and time efficiency of using graph neural network for code clone detection are significantly improved, especially for the code fragments with completely different semantics.
Comparing Intrinsic and Extrinsic Evaluation of Sensitivity Classification

With accelerating generation of digital content, it is often impractical at the point of creation to manually segregate sensitive information from information which can be shared. As a result, a great deal of useful content becomes inaccessible simply because it is intermixed with sensitive content. This paper compares traditional and neural techniques for detection of sensitive content, finding that using the two techniques together can yield improved results. Experiments with two test collections, one in which sensitivity is modeled as a topic and a second in which sensitivity is annotated directly, yield consistent improvements with an intrinsic (classification effectiveness) measure. Extrinsic evaluation is conducted by using a recently proposed learning to rank framework for sensitivity-aware ranked retrieval and a measure that rewards finding relevant documents but penalizes revealing sensitive documents.
Mathematics and Software for Building Nonlinear Polynomial Regressions Using Estimates for Univariate Polynomial Regressions Coefficients with a Given (Small) Variance

The presented results address the building of multivariate nonlinear regressions based on experimental data and using them in various special-purpose information systems. In contrast to the well-known methods for building multivariate polynomial regressions given by a redundant representation using an active experiment, we claim that the proposed methodology and algorithms for its implementation allow finding estimates for the coefficients at nonlinear terms of multivariate regression with a variance sufficiently small for practice and a small number of tests. We show that the solution of this problem reduces to the sequential building of univariate polynomial regressions. Each regression yields corresponding strictly nondegenerate systems of linear equations with deterministic coefficients. The variables in the systems of linear equations are the coefficients at the terms of the nonlinear polynomial regression. The right parts of the systems are estimates for the coefficients at nonlinear terms of univariate regression found based on one pre-built set of normalized orthogonal polynomials of Forsythe which guarantee a given (small) value of their variance. This way, we assure efficient finding of coefficients at nonlinear terms of the multivariate polynomial regression. In conclusion, we give the software architecture that implements the proposed methodology and algorithms for building a multivariate polynomial regression given by a redundant representation.
Sharing personal ECG time-series data privately

Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics.We propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis.We conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering).Our study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors.The results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data.
Trajectory Anomaly Detection with Multi-feature and Multi-resolution Based on Direction Grouping

Unreasonable trajectory segmentation will lead to inaccurate results about pedestrian anomaly detection. To solve this problem, this paper proposed a multi-feature and multiresolution feature representation method combining global and local features of the trajectory. First, the trajectories are grouped using the DBSCAN clustering algorithm according to the direction of movement. Generate a Fitting trajectory based on all trajectories in the group. Then, the trajectory is segmented to use different resolutions, The offset distance of the sub-trajectory and the Fitting trajectory at each resolution is calculated using the position, velocity, and direction features Abnormal trajectories are detected using DBSCAN clustering. The motion pattern of the trajectory is accurately represented using a multi-resolution method combined with contextual features. The abnormal trajectory can be detected more comprehensively through the multi-feature method. The experimental results show that the anomaly detection effect of our method is better than the existing trajectory anomaly detection methods using clustering.
Speech Recording for Dietary Assessment: A Systematic Literature Review

Traditional methods of capturing people’s dietary intake are complex and labour-intensive, requiring a high level of literacy and time. Speech recording has potential to reduce these barriers, and recent technological advances have greatly increased the viability of this approach. The aim of this paper is to establish the current state of research on the usage of speech records in dietary assessment. To this end, we performed a systematic literature review and summarised the current state of research along a conceptual framework that captures the components involved in using speech records for dietary assessment. Six databases from the nutrition and computing domains were interrogated, resulting in 21 relevant papers. Speech recording in an unstructured format was preferred when compared against other methods by all three studies reporting comparisons. High technological satisfaction and ease of use were noted by all eight studies reporting user acceptance. When recording data, 78% of studies focused on collecting prospective food records. The choice of device reflected this, with 15 of 18 studies reporting a form of handheld, portable collection device intended to be always available. To process data, nine studies performed automated speech transcription achieving an average accuracy of 83%, seven of which utilized a readily available commercial service. Of the five studies that used natural language processing to further automate analysis, an average accuracy of 82% was reported. Further research is required to adapt these prototypes to address practical challenges in dietary assessment and monitoring (e.g. self-monitoring for low-literacy users).
Soft Actor-Critic Algorithm for 360-Degree Video Streaming with Long-Term Viewport Prediction

In the tile-based 360-degree video streaming, it is essential to predict future viewport and to allocate higher bitrates to tiles inside the predicted viewport to optimize the Quality of Experience (QoE) of the users. However, the majority of existing work focuses on short-term viewport prediction, which is prone to rebuffering in dynamic network conditions. On the other hand, the recently developed on-policy Deep Reinforcement Learning (DRL)-based bitrate allocation approaches suffer from poor sample efficiency. To address these issues, in this paper we present a tile-based adaptive 360-degree video streaming system, named LS360, which consists of long-term viewport prediction and adaptive bitrate allocation. First, we propose a Long Short-Term Memory (LSTM)-based viewport prediction model to make use of the heatmap feature from all users’ previous movement information and the target user’s fixation movement feature to improve prediction accuracy. Next, we employ the off-policy Soft Actor-Critic (SAC) algorithm to make optimal tile bitrate allocation decisions by taking the predicted long-term viewport, playback buffer, and bandwidth-related information into account. Experiments on real-world datasets demonstrate that LS360 outperforms state-of-the-art streaming algorithms in terms of long-term viewport prediction accuracy and QoE under different bandwidth conditions.
DWANet: Focus on Foreground Features for More Accurate Location

Object detection can locate objects in an image using bounding boxes, which can facilitate classification and image understanding, resulting in a wide range of applications. Knowing how to mine useful features from images and detect objects of different scales have become the focus for object-detection research. In this paper, considering the importance of foreground features in the process of object detection, a foreground feature extraction module, based on deformable convolution, is proposed, and the attention mechanism is integrated to suppress the interference from the background. To learn effective features, considering that different layers in a convolutional neural network have different contributions, we propose methods to learn the weights for feature fusion. Experiments on the VOC datasets and COCO datasets show that the proposed algorithm can effectively improve the object detection accuracy, which is 12.1% higher than Faster R-CNN, 1.5% higher than RefineDet, and 2.3% higher than the Hierarchical Shot Detector (HSD).
Research On Data Model Migration In Image Semantic Segmentation Based On Deep Learning

With the development of artificial intelligence and deep learning, semantic segmentation algorithm based on deep learning has become the mainstream of computer vision technology, which is applied in the fields of medical image, automatic search, unmanned driving, scene analysis and so on. However, in the research process, due to the difference of "domain drift" between the data distribution of computer synthetic data and the real world, an unsupervised domain adaptation based on regularization model migration method is proposed. Without any manual annotation, domain adaptation processing is performed on the image semantic segmentation model through GTA5, A series of experiments of cityscapes have proved the credibility of the regularized migration model method on large-scale data. This method can effectively cooperate with a variety of backbone networks and domain adaptation frameworks to improve the cross domain semantic segmentation ability of the model.
One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems

Cyber-Physical Systems (CPS) are prevalent in critical infrastructures and a prime target for cyber-attacks. Multivariate time series data generated by sensors and actuators of a CPS can be monitored for detecting cyber-attacks that introduce anomalies in those data. We use Signal Temporal Logic (STL) formulas to tightly describe the normal behavior of a CPS, identifying data instances that do not satisfy the formulas as anomalies. We learn an ensemble of STL formulas based on observed data, without any specific knowledge of the CPS being monitored. We propose an algorithm based on Grammar-Guided Genetic Programming (G3P) that learns the ensemble automatically in a single evolutionary run. We test the effectiveness of our data-driven proposal on two real-world datasets, finding that the proposed one-shot algorithm provides good detection performance.
Comprehensive Exploration of Machine Learning based models in Digital Forensics – A plunge into Hate Speech Detection

From the prime state of distress to the gigantic flood of hatred thriving online, the humanity seems to be poignant. Social media is endemic with hate speech. Moreover, online along with offline hate events are rising at a high pace. This, eventually, makes a call to develop automatic hate speech detection systems. The present study aims to endow with a thorough relative analysis of machine learning algorithms in this regard. The methodologies are investigated with respect to their potencies and limitations for better understanding. The paper also imparts the milieu information about hate speech followed by the challenges faced by researchers while developing the appropriate models. This paper will act as a guide for future researchers of this domain by showing them the path for selecting most suitable machine learning based models.
Reflect on the Application of Human-Machine Cooperation Technology in Film Art Creation in Virtual Production Era

Film art creation is not only artistic creation, but also inseparable from the support of technology. It plays an important role in film production. With the advent of the era of film virtual production, film digital assets will become the main role of film art creation. In the face of efficient virtual production rhythm, the use of artificial intelligence and human-machine cooperation technology will greatly improve the efficiency of film art creation. It can not only give greater play to the artists' creative inspiration and ideas, but also make full use of the massive storage capacity and amazing computing speed of the computer. The human-machine cooperation film art creation method will also become one of the main methods of film art creation in the future. This paper proposes a framework of film art creation using human-machine cooperation technology, which will be the future efficient method in film art creation field.
A Robust and Ultra-High Extinction Ratio Optical Switch Enabled by Optical Diffractive Network

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> N optical switches are essential building blocks for modern information communication. Moreover, the recent development of a new on-chip solid-state light detection and ranging (Lidar) technology scheme has made this kind of switch attractive. In this work, we propose a new robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> N optical switch structure that can get a 100 dB extinction ratio in theory by using two cascaded N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> N optical diffractive units. As a proof-of-concept, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times $ </tex-math></inline-formula> 5 optical switch is demonstrated, and a more than 40 dB extinction ratio for all channels is achieved at the center wavelength of 1550 nm. The ultra-high extinction ratio and large fabrication tolerance of our structure will pave a way for future information communication and Lidar.
ChevOpt: Continuous-Time State Estimation by Chebyshev Polynomial Optimization

In this paper, a new framework for continuous-time maximum a posteriori estimation based on the Chebyshev polynomial optimization (ChevOpt) is proposed, which transforms the nonlinear continuous-time state estimation into a problem of constant parameter optimization. Specifically, the time-varying system state is represented by a Chebyshev polynomial and the unknown Chebyshev coefficients are optimized by minimizing the weighted sum of the prior, dynamics and measurements. The proposed ChevOpt is an optimal continuous-time estimation in the least squares sense and needs a batch processing. A recursive sliding-window version is proposed as well to meet the requirement of real-time applications. Comparing with the well-known Gaussian filters, the ChevOpt better resolves the nonlinearities in both dynamics and measurements. Numerical results of demonstrative examples show that the proposed ChevOpt achieves remarkably improved accuracy over the extended/unscented Kalman filters and extended batch/fixed-lag smoother, closes to the Cramer-Rao lower bound.
Speaker Conditioning of Acoustic Models Using Affine Transformation for Multi-Speaker Speech Recognition

This study addresses the problem of single-channel Automatic Speech Recognition of a target speaker within an overlap speech scenario. In the proposed method, the hidden representations in the acoustic model are modulated by speaker auxiliary information to recognize only the desired speaker. Affine transformation layers are inserted into the acoustic model network to integrate speaker information with the acoustic features. The speaker conditioning process allows the acoustic model to perform computation in the context of target-speaker auxiliary information. The proposed speaker conditioning method is a general approach and can be applied to any acoustic model architecture. Here, we employ speaker conditioning on a ResNet acoustic model. Experiments on the WSJ corpus show that the proposed speaker conditioning method is an effective solution to fuse speaker auxiliary information with acoustic features for multi-speaker speech recognition, achieving +9% and +20% relative WER reduction for clean and overlap speech scenarios, respectively, compared to the original ResNet acoustic model baseline.
Postdigital-Biodigital: An Emerging Configuration

This dialogue (trilogue) is an attempt to critically discuss the technoscientific convergence that is taking place with biodigital technologies in the postdigital condition. In this discussion, Sarah Hayes, Petar Jandrić and Michael A. Peters examine the nature of the convergences, their applications for bioeconomic sustainability and associated ecopedagogies. The dialogue chapter raises issues of definition and places the technological convergence (‘nano-bio-info-cogno’) – of new systems biology and digital technologies at the nano level – in an evolutionary context to speculate, on the basis of the latest research, future possibilities. The chapter also reviews these developments within familiar landscapes of posthumanism and postmodernism, raises the question of political bioeconomy and the role of postdigital education within it.
Characterization of disruptive education in a public university based on the use of digital tools on the cisco WebEx digital platform

research-article Share on Characterization of disruptive education in a public university based on the use of digital tools on the cisco WebEx digital platform Authors: Ibis Lopez Education Faculty, National University of San Martin, Peru Education Faculty, National University of San Martin, PeruView Profile , Manuel Padilla Education Faculty, National University of San Martin, Peru Education Faculty, National University of San Martin, PeruView Profile , Monica Juarez Education Faculty, National University of San Martin, Peru Education Faculty, National University of San Martin, PeruView Profile , Mario Chauca Engineering Faculty, National Technological University of South Lima - UNTELS, Peru Engineering Faculty, National Technological University of South Lima - UNTELS, PeruView Profile , Wildoro Ramirez Education Faculty, National University of San Martin, Peru Education Faculty, National University of San Martin, PeruView Profile Authors Info & Claims ICETM'21: 2021 4th International Conference on Education Technology ManagementDecember 2021 Pages 59–66https://doi.org/10.1145/3510309.3510319Online:14 March 2022Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Depth Motion Map Based Human Action Recognition Using Adaptive Threshold Technique

Nowadays, human action recognition (HAR) has become an emerging research topic for movie understanding, video clip retrieval, human-computer interactions, autonomous driving systems etc. This paper introduces an efficacious feature representation strategy and novel framework for identifying human action by employing depth motion maps (DMMs) based on adaptive thresholding technique. Firstly, each 3D depth frame is projected onto three orthogonal Cartesian planes to form 2D projected maps and concatenating the front view, side view and top view to generate the DMM features.The DMMs capture the motion characteristics from the video sequence. After that, we have adopted adaptive thresholding for separating image objects from video sequence based on pixel intensities so that the radial basis kernel based support vector machine (SVM) are used to classify and recognize the action sequences effitively. We carried out the experiment on MSR-Action3D action dataset and evaluated the classification performance by measuring confusion matrices to provide details and visualization about recognized actions. From the experimental results, we reveal that the proposed framework attains always better performance than other competitive methods on this database.