Reviewerly/exHarmony huggingface dataset:
A Survey of Streaming Protocols for Video Transmission

Communication has always been an important part of human life. In the last few years, communication over the Internet has evolved a lot and processes like live video streaming are common in the present time. To ensure video streaming in a fast and smooth way, streaming protocols are used which are continuously evolving and maturing. At present, many streaming protocols are available and many find it difficult to select a streaming protocol that best fits their requirements. This survey looks at the available streaming protocols along with their important features. The review of streaming protocols on the basis of different criteria is presented along with their performance analysis.
Two-Stage Image Dehazing with Depth Information and Cross-Scale Non-Local Attention

Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most of previous models for low-level vision tasks are always based on single-stage design. There is an issue with majority image dehazing approaches: a complex balance problem between spatial details and high-level contextualized information while recovering images. To address this issue, we propose a two-stage network, which consists of encoder-decoder subnetwork and original-resolution one. Specifically, the encoder-decoder subnetwork first learns the contextualized features, and then the features are combined with the original-resolution subnet that maintains enriched high-resolution features. For information exchange between two stages, we introduce a Cross-Scale Non-Local (CS-NL) attention module that can search more high-frequency details from low-resolution images in encoder-decoder stage and transfer directly them to the next stage. Moreover we embed the spatial feature transform (SFT) module into original-resolution subnet, which is incorporated with depth information to better achieve the purpose of image dehazing. The two-stage network, named as TSDCN-Net, which demonstrates its effectiveness according extensive experiments. The TSDCN-Net surpasses previous state-of-the-art single image dehazing methods by a large margin both quantitatively and qualitatively.
Blockchain-based Certificateless Identity Management Mechanism in Cloud-native Environments

With the widespread development of cloud-native technologies, the security of cloud-native system has gradually attracted attention. Cloud-native uses PKI (Public Key Infrastructure) to provide an important guarantee for cloud-native network security, however the heavy certificate management gradually makes the PKI mechanism be a bottleneck for cloud-native systems. In constract with PKI, the certificateless public key mechanism has many advantages such as lightness, which is very suitable for authentication in cloud-native environment. However, before introducing the certificateless public key mechanism into the cloud-native environment, the first problem that must be solved is the identity management. This paper proposes a blockchain-based certificateless identity management (BCL-IM) mechanism for cloud-native environment, which uses blockchain as a trust endorsement for service identity and service public key distribution records, and ensures the timeliness of identity management by blockchain node notification. In addition, a communication disconnect mechanism is also proposed and the usage scenarios of this mechanism are analyzed to ensure the data security. Finally, we give security analysis and performance evaluation to show that our scheme satisfies the requirements for secure service-to-service communication in cloud-native environments based on the certificateless public key mechanism.
Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.
Secure Computation of Gaussian Process Regression for Data Analysis

We propose Gaussian process regression (GPR) for encrypted data generated based on random unitary transformation. Edge cloud computing is spreading into many application fields, including data analysis services. However, new privacy concerns are being raised regarding things such as data leakage from unreliable providers and accidents. Therefore, we examine a secure GPR method that incorporates privacy protection. We prove that our GPR for encrypted data performs the same as GPR for non-encrypted data. Finally, we determine the effectiveness of our method by experimenting with diabetes data from the medical analysis field and synthetic data.
Twitter Sparrow : Reduce Event Pipeline latency from hours to seconds

Data Analytics at Twitter rely on trillions of events generated daily by micro services backing the Twitter platform. Multiple features on Twitter Mobile Application and Web interface are backed by micro services which emit events triggered by user actions. Events are well defined structured objects with fields containing important information relevant to the feature it represents. These events are aggregated and processed before making it available for internal consumption at various storage systems. Event processing pipelines in the past were designed to support scale in the order of billions of events per minute [1]. Events flowing through various software systems were batched to optimize for throughput in favor of latency. These batched event pipelines observe an end to end latency of a few hours, from the time when the event is emitted to when it is ready to be consumed. Project Sparrow is redesigning this pipeline to reduce the event latency from hours to seconds (or minutes).
Weakly Supervised Semantic Segmentation Learning on UAV Video Sequences

The domain of scene understanding from Unmanned Aerial Vehicles (UAVs) is of high interest for researchers in the computer vision domain, since it can be used for object detection and tracking in scenarios like deforestation monitoring, traffic surveillance, or for civil engineering tasks. However, the topic of dense video segmentation from drones has been insufficiently explored due to the lack of annotated ground truth data. We propose a solution based on a framework composed of a deep neural network for semantic segmentation and an optical flow generator, linked together by a spatio-temporal GRU component to efficiently solve the problem of weakly supervised semantic segmentation of video sequences recorded from UAVs. The novelty of our work comes from the employment of depthwise separable convolutions for the GRU component, which decrease the computation time and increase the segmentation accuracy. We test our methodology on the synthetic dataset Mid-Air, for low-altitude drone flight, and report results that prove the usefulness of the proposed system.
Summary of Domestic Garbage Classification and Detection Based on Deep Learning

In recent years, domestic garbage pollution has became the main source of environmental pollution, which has seriously damaged the global ecological environment and affected the normal life of residents. With the global goal of environmental protection and green development that the promulgation of corresponding garbage classification policies, various countries have started to implement them gradually. Based on the traditional garbage classification methods, there are some problems, such as reliance on manual classification, low classification efficiency and poor classification quality. A growing number of researchers are using deep learning techniques applied to garbage classification. First, the garbage classification and detection methods based on deep learning are summarized. Then, the research we have done so far on garbage classification are presented. Finally, the future challenges and directions in the field of garbage classification research are foreseen.
The Fine Calibration of the Ultra-Short Baseline System With Inaccurate Measurement Noise Covariance Matrix

The ultra-short baseline (USBL) system has been widely applied in marine positioning fields because of its small size and high positioning accuracy. However, there are particular position and angle deviations between the body frame and the USBL frame in a realistic environment. The presence of range-measuring error in the USBL system is also a non-negligible problem. Besides, the measurement noise covariance matrix may change due to the complicated underwater environment. These factors lead to the reduction in positioning accuracy. Therefore, an improved adaptive Kalman filter for fine calibration of the USBL system is proposed to deal with the problems mentioned above. First, the state model of the fine calibration is established based on installation error angles, lever arms, and range-measuring error. For the first time, the system error is included in the calibration model, thereby reducing the influence of system error on positioning accuracy. A modified nonlinear measurement model based on azimuth and slant range is presented. Second, based on the variational Bayesian method, an adaptive filter is proposed to do with the unknown and time-varying measurement noise. Finally, the numerical simulation and field trial are carried out to verify the effectiveness of the proposed algorithm. The advantage of this algorithm is that the system error and installation errors can be estimated simultaneously and the system adjusts the measurement noise covariance matrix adaptively to improve the positioning accuracy.
Intent-Aware Visualization Recommendation for Tabular Data

This paper proposes a visualization recommender system for tabular data with a visualization intent (e.g., "population trends in Italy" and "smartphone market share"). The proposed method predicts the most suitable visualization type (e.g., line, pie, and bar charts) and visualized columns (columns used for visualization) based on statistical features extracted from the tabular data, as well as semantic features derived from the visualization intent. To predict an appropriate visualization type, we propose a bi-directional attention (BiDA) model that identifies important table columns by the visualization intent, and important parts of the intent by table headers. To identify visualized columns, we employ a pre-trained neural language model to encode both visualization intents and table columns, and estimate which columns are the most likely to be used for visualization. Since there is no available dataset for this task, we developed a new dataset consisting of over 100K tables and their appropriate visualization. The experiments revealed that our proposed methods accurately estimated suitable visualization types as well as visualized columns. The code is available at https://github.com/kasys-lab/intent-viz .
Parallel Nonlinear Dimensionality Reduction Using GPU Acceleration

Dimensionality reduction is usually an essential step in data mining and classical machine learning from high-dimensional data. Uniform Manifold Approximations Projections (UMAP) is a recently developed nonlinear dimensionality reduction method that is being widely applied in biomedical informatics. However, the UMAP implementation is still not efficient enough for processing the recent big omics data from biomedicine. This paper proposes and implements a method that reduces UMAP runtime using GPU-acceleration on the GPU-RAPIDS platform. Our experiments showed that the parallel UMAP implementation performed hundred times faster than the original UMAP implementation on a cluster computer, while maintaining the effectiveness on identifying leukemic cells from clinical flow cytometry data.
An integrated fuzzy neural network with topic-aware auto-encoding for sentiment analysis

Recent advanced deep learning architectures, such as neural seq2seq and transformer, have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformer-based and seq2seq-based models have successfully enabled to capture rich contextual information of texts, they still lacked attention on incorporating global semantic information which enables to sufficiently leverage the performance of downstream SA tasks. Moreover, emotional expressions of users are normally in the form of natural human-written textual data which contains a lot of noises and ambiguities that impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy neural architecture with a topic-driven textual representation learning approach for handling the SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder–decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model compared with contemporary state-of-the-art baselines.
Database Padding for Dynamic Symmetric Searchable Encryption

Dynamic symmetric searchable encryption (DSSE) that enables the search and update of encrypted databases outsourced to cloud servers has recently received widespread attention for leakage-abuse attacks against DSSE. In this paper, we propose a dynamic database padding method to mitigate the threat of data leakage during the update operation of outsourcing data. First, we introduce an outlier detection technology where bogus files are generated for padding according to the outlier factors, hiding the document information currently matching search keywords. Furthermore, we design a new index structure suitable for the padded database using the bitmap index to simplify the update operation of the encrypted index. Finally, we present an application scenario of the padding method and realize a forward and backward privacy DSSE scheme (named PDB-DSSE). The security analysis and simulation results show that our dynamic padding algorithm is suitable for DSSE scheme and PDB-DSSE scheme maintains the security and efficiency of the retrieval and update of the DSSE scheme.
A Novel Digital watermarking Approach under the DCT and SVM

The important direction of information hiding is digital watermarking technology. The traditional digital watermarking is advantage to against general attacks, but it is difficult to the geometrial distortions. This paper proposes a novel digital watermarking based on discrete cosine transform and support vector machine. Firstly, the block discrete cosine transformation is used for watermarking embedding, then the trained support vector machine is used to roughly correct the geometric parameters and the template matching method is used to correct the existing errors. Experiments show that the purposed algorithm is not only against traditional attacks such as noise, filtering and clipping, but also better robustness against geometrial distortions.
An evaluation of fast segmented sorting implementations on GPUs

Many real-world problems require a sorting operation as part of their efficient solution. Some examples of this are real-time plasma diagnostic, image re-ranking, and suffix array construction. These problems usually involve a large amount of data, so their solutions need a particular application of the sorting procedure, consisting of sorting several arrays in matrix rows or array segments, an operation called segmented sorting. Previous studies showed that a merge sort-based strategy and a strategy called fix sort executed this operation on GPUs with good performance for different array sizes. In this work, we compare the fastest segmented sorting GPU implementations on seven different GPU models with various input data scenarios, including scenarios with varying numbers of segments, segment sizes, and considering segments of the same and different sizes. We first performed algorithm analysis to explain how the number of segments affects each implementation’s performance. Then, we perform an S-curve analysis and observe that, even though each strategy might be the fastest option for a subset of the sorting scenarios, some approaches may cause very high slowdowns on specific scenarios. We also compare the strategies using heat maps, show that their performance depends on the array size and number of segments, and propose a recommendation map to support selecting the best overall implementation based on the size and number of segments. Our experimental results show that choosing a strategy based on our recommendation map leads to the best strategy on 47.57% of the cases and a maximum slowdown of less than 1.5 times in 93.58% of the cases. Moreover, on average, the recommended strategy is only 1.11× worse than the optimum one. Finally, we evaluated how each strategy behaves when sorting arrays with different and equal segment sizes and showed that the fix sort-based approaches take roughly the same time to sort arrays with equal or different segment sizes, while the approach proposed by Hou et al. usually takes longer to sort arrays with different segment sizes than arrays with equal segment sizes.
Survey on Technique and User Profiling in Unsupervised Machine Learning Method

In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques. Furthermore, many marketing teams are unfamiliar with data-driven classification methods. The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes. A thorough literature study was undertaken on the most popular UML techniques and their dataset attributes needs. For UP, a structure is developed that outlines several UML techniques. In terms of data size and dimensions, it offers two-stage clustering algorithms for category, quantitative, and mixed types of datasets. The clusters are determined in the first step using a multilevel or model-based classification method. Cluster refining is done in the second step using a non-hierarchical clustering technique. Academics and professionals may use the framework to figure out which UML techniques are best for creating strong profiles or data-driven user segmentation.
A New Approach for Side Channel Analysis on Stream Ciphers and Related Constructions

Side Channel Analysis (SCA) is among the newly emerged threats to small scale devices performing a cryptographic operation. While such analysis is well studied against the block ciphers, we observe that the stream cipher counterpart is not that much explored. We propose novel modelling that can work with a number of stream ciphers and related constructions. We show practical state/key recovery attacks on the lightweight ciphers, LIZARD, PLANTLET and GRAIN-128-AEAD. We consider the software platform (where the Hamming weight leakage is available) as well as the hardware platform (where the Hamming distance leakage is available). Through the modelling of Satisfiability Modulo Theory (SMT), we show that the solution can be obtained in a matter of seconds in most cases. In a handful of cases, however, the entire state/key recovery is not feasible in a practical amount of time. For those cases, we show full recovery is possible when a small number of bits are guessed. We also study the effect of increasing/decreasing the number of keystream bits on the solution time. Following a number of literature, we initially assume the traces that are obtained are noiseless. Later, we show how an extension of our model can deal with the noisy traces (which is a more general assumption).
Knowledge Graph Integrated Graph Neural Networks for Chinese Medical Text Classification

Text classification is a well-developed task in natural language processing. In the medical area, this task is still difficult since the discriminative ability requires domain-specific knowledge, such as high specialization, terminology, and structured relationships. In this work, we leverage a large Chinese medical knowledge graph (KG) to derive the above knowledge and propose graph neural networks (GNN) to make the best use of such knowledge. Particularly, for a medical text, we build two graphs: text-graph, which is based on the occurrences of contextualized words; text-specific knowledge graph, retrieved from KG in light of the common terms between the text and KG. The above two graphs are bridged by the common terms and then merged into a joint one. We propose GNN to learn this graph. As such, our model builds interactions between adjacent nodes, and meanwhile, the medical knowledge can be propagated from KG to text. To enhance the node representations and improve knowledge interaction, we introduce general prior knowledge to the text-graph and domain-specific prior knowledge to the text-specific knowledge graph. We conduct extensive experiments on three medical datasets. The experimental results show that our model outperforms strong baseline methods significantly.