Reviewerly/exHarmony huggingface dataset:
Hybrid BM3D and PDE filtering for non-parametric single image denoising

The BM3D method achieves excellent denoising performance, but it has artificial effects and bias effects and its performance largely depends on the noise level parameter. To address this, we propose a hybrid BM3D and PDE method for non-parametric single image denoising. First, a non-local Perona–Malik (NLPM) filtering is proposed, and we prove its discontinuity maintaining, mean invariance, convergence, and local continuity. Based on these mathematical properties, an NLPM based noise level estimator (NLPM-NLE) is explored, which involves three steps: preprocessing by NLPM filtering, sample area selection, parameter estimation. And then, we advance a stable-BM3D (SBM3D) method with NLPM filtering to avoid artificial effects and bias effects. Finally, connecting the NLPM-NLE and SBM3D by merging the same part, we develop a non-parametric single image denoising (NPSID) method. Additionally, our proposed BM3D method with NLPM-NLE and the NPSID are compared with other blind denoising methods including PCA + BM3D, WTP + BM3D, and ESM + BM3D on real image denoising. Experiments show that the proposed non-parametric method can automatically and effectively remove noise and preserve details.
Benchmarking Quantum Annealing Against “Hard” Instances of the Bipartite Matching Problem

Abstract This paper experimentally investigates the behavior of analog quantum computers as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem which is specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave “Washington” (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggest that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and hence provides additional evidences suggesting that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality. Additionally, we investigate the extent to which the qubits interconnection topologies explains these latter experimental results. In particular, we provide evidences that the sparsity of these topologies which, as such, lead to QUBO problems of artificially inflated sizes can partly explain the aforementioned disappointing observations. Therefore, this paper hints that denser interconnection topologies are necessary to unleash the potential of the quantum annealing approach.
The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.
Self-Information Loss Compensation Learning for Machine-Generated Text Detection

The technology of automatic text generation by machine has always been an important task in natural language processing, but the low-quality text generated by the machine seriously affects the user experience due to poor readability and fuzzy effective information. The machine-generated text detection method based on traditional machine learning relies on a large number of artificial features with detection rules. The general method of text classification based on deep learning tends to the orientation of text topics, but logical information between texts sequences is not well utilized. For this problem, we propose an end-to-end model which uses the text sequences self-information to compensate for the information loss in the modeling process, to learn the logical information between the text sequences for machine-generated text detection. This is a text classification task. We experiment on a Chinese question and answer the dataset collected from a biomedical social media, which includes human-written text and machine-generated text. The result shows that our method is effective and exceeds most baseline models.
Advances in MapReduce Big Data Processing: Platform, Tools, and Algorithms

This paper reviews the research progress of big data processing platforms and algorithms based on MapReduce programming model in recent years. Firstly, 12 typical ones are introduced. MapReduce-based big data processing platform analyzes and compares their implementation principles and applicable scenarios, abstracts their commonalities, then introduces based on MapReduce big data analysis algorithms, including search algorithms, data cleaning/transformation algorithms, aggregation algorithms, join algorithms, sorting algorithms, preference queries, optimization calculations method, graph algorithm, data mining algorithm, classifies these algorithms according to MapReduce implementation, analyzes the factors affecting the performance of the algorithm; finally, big data. The processing algorithm is abstracted as an external memory algorithm, and the characteristics of the external storage algorithm are sorted out. The research ideas and problems of the performance optimization method of the universal external memory algorithm are proposed. For the researcher’s reference. Specifically, it includes the disk I/O of optimizing the external memory algorithm, optimizing the locality of the external memory algorithm, and designing the incremental iterative algorithm. The existing large data processing platform and algorithm research mostly focus on platform dynamic performance optimization based on resource allocation and task scheduling, specific algorithm parallelization, specific algorithmic. This chapter provides researchers with a broad research study in MapReduce Big Data Processing: Platform, Tools, and Algorithms.
Virtual view synthesis for 3D light-field display based on scene tower blending

Three-dimensional (3D) light-field display has achieved a great improvement. However, the collection of dense viewpoints in the real 3D scene is still a bottleneck. Virtual views can be generated by unsupervised networks, but the quality of different views is inconsistent because networks are separately trained on each posed view. Here, a virtual view synthesis method for the 3D light-field display based on scene tower blending is presented, which can synthesize high quality virtual views with correct occlusions by blending all tower results, and dense viewpoints on 3D light-field display can be provided with smooth motion parallax. Posed views are combinatorially input into diverse unsupervised CNNs to predict respective input-view towers, and towers of the same viewpoint are fused together. All posed-view towers are blended as a scene color tower and a scene selection tower, so that 3D scene distributions at different depth planes can be accurately estimated. Blended scene towers are soft-projected to synthesize virtual views with correct occlusions. A denoising network is used to improve the image quality of final synthetic views. Experimental results demonstrate the validity of the proposed method, which shows outstanding performances under various disparities. PSNR of the virtual views are about 30 dB and SSIM is above 0.91. We believe that our view synthesis method will be helpful for future applications of the 3D light-field display.
High-Performance Linguistic Steganalysis, Capacity Estimation and Steganographic Positioning

With the rapid development of natural language processing technology, various linguistic steganographic methods have been proposed increasingly, which may bring great challenges in the governance of cyberspace security. The previous linguistic steganalysis methods based on neural networks with word embedding layer could only extract the context-independent word-level features, which are insufficient for capturing the complex semantic dependencies in sentences, thus may limit the performance of text steganalysis. In this paper, we propose a novel linguistic steganalysis model. We first employ the BERT or Glove component to extract the contextualized association relationships of words in the sentences. Then we put these extracted features into BiLSTM to further get context information. We use the attention mechanism to find out local parts that may be discordant in text. Finally, based on these extracted features, we use the softmax classifier to decide if the input sentence is cover or stego. Experimental results show that the proposed model can achieve currently the best performance of text steganalysis and hidden capacity estimation. Further experiments found that proposed model can even locate where the secret information may be embedded in the text to a certain extent. To the best of our knowledge, we made the first attempt to achieve text steganography positioning in the field of text steganalysis (Code and datasets are available at https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis ).
Dietary life and mukbang- and cookbang-watching status of university students majoring in food and nutrition before and after COVID-19 outbreak

Purpose: With increased time spent at home due to prolonged online classes, this study sought to determine how the coronavirus disease 2019 (COVID-19) pandemic has impacted the mukbang- and cookbang-watching patterns and dietary life of college students
From Quantitative Analysis to Synthesis of Efficient Binary Neural Networks

Binary Neural Networks (BNNs) offer an effective way to slash the cost of computation and memory accesses in inference. Recently, a plurality of ideas has been proposed, some of which are complementary while others are incompatible. This work presents a thorough review of state-of-the-art methods and an analysis of their computational cost based on the energy consumption of fixed-point, ternary and binary MAC vector operations. We derive an approach on how to systematically design a cost-efficient BNN. Our quantized LeNet and VGGNet architectures highlight the benefit of prudent capacity augmentation, with layer-wise ternarization providing best improvement of accuracy over μJ/dassificatíon in BNNs.
Task Planning with Belief Behavior Trees

In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized following a planning strategy for BTs proposed recently: from a set of goal conditions we iteratively select a goal and find the action, or in general the subtree, that satisfies it. Such action may have preconditions that do not hold. For those preconditions, we find an action or subtree in the same fashion. We extend this approach by including, in the planner, actions that have the purpose to reduce the uncertainty that affects the value of a condition node in the BT (for example, turning on the lights to have better lighting conditions). We demonstrate that BBTs allows task planning with non-deterministic outcomes for actions. We provide experimental validation of our approach in a real robotic scenario and - for sake of reproducibility - in a simulated one.
An ontology-based deep learning approach for triple classification with out-of-knowledge-base entities

Knowledge graphs (KGs) are one of the most common frameworks for knowledge representation. However, they suffer from a severe scalability problem that hinders their usage. KG embedding aims to provide a solution to this issue. Nonetheless, general approaches are incapable of representing and reasoning about information not previously contained in the graph. This paper proposes to leverage semantic and ontological information for a significant benefit of knowledge graph completion, focusing on triple classification. The goal of this task is to determine whether a given fact holds. Furthermore, this paper also considers the classification of facts that include entities that have not been seen during training, denoted out-of-knowledge-base or OOKB entities. An incremental method is presented, composed of six stages. Although the proposal can be applied to any KG embedding model, this work focuses on its application for semantic matching models, such as ComplEx and DistMult. Compared to other approaches, our proposal is model-agnostic, computationally inexpensive, and does not require retraining. The results show that triple classification accuracy scales up to 15% with the proposed approach, as well as accelerating the convergence of the model to its optimal solution. Furthermore, facts containing OOKB entities can be classified with a reasonable accuracy.
A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF

As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost.Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper.The RPP mainly includes three rules, i.e.Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function.We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function.The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule.Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF.Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack.It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger.Besides, the proposed approach is much faster than other model-based methods.
A Hybrid Camera System for High-Resolutionization of Target Objects in Omnidirectional Images

Recent improvements in cameras and image processing techniques have advanced research on object detection and classification. The cameras capable of capturing omnidirectional images are advantageous for detecting surrounding objects, we focus on their usage method. Omnidirectional camera images generally have lower resolution than available cameras, leading to difficulty in identifying objects farther from the camera. To address this problem, we propose a hybrid camera system that first detects indistinct target regions during omnidirectional viewing, then uses a pan-tilt camera to capture a high-resolution image of the target. We also propose an algorithm for reducing the number of required complementary shots to realize efficient shooting.
Cloud Pricing: The Spot Market Strikes Back

Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance or for users with long-term contracts). A natural idea, which should intuitively increase the provider’s profit, is to sell these idle instances on a secondary market, for example, via a preemptible spot market. However, this ignores possible “market cannibalization” effects that may occur in equilibrium as well as the additional costs the provider experiences due to preemptions. To study the viability of offering a spot market, we model the provider’s profit optimization problem by combining queuing theory and game theory to analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which a provider can simultaneously achieve a profit increase and create a Pareto improvement for the users by offering a spot market (using idle resources) alongside a fixed-price market. Finally, we illustrate our results numerically to demonstrate the effects that the provider’s costs and her strategy have on her profit. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
An intelligent decision support system for software plagiarism detection in academia

The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre-processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics.
Fostering Engagement With Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort

Background Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale. Objective This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community. Methods The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes. Results The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact. Conclusions Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public.
Anomaly Detection of Periodic Multivariate Time Series under High Acquisition Frequency Scene in IoT

Anomaly Detection of Multivariate Time Series is an intensive research topic in data mining, especially with the rise of Industry 4.0. However, few existing approaches are taken under high acquisition scene, and only a minority of them took periodicity of time series into consideration. In this paper, we propose a novel network Dual-window RNN-CNN to detect periodic time series anomalies of high acquisition frequency scene in IoT. We first apply Dual-window to segment time series according to the periodicity of data and solve the time alignment problem. Then we utilize Multi-head GRU to compress the data volume and extract temporal features sensor by sensor, which not only solves the problems caused by high acquisition but also adds more flexible transfer ability to our network. In order to improve the robustness of our network in different periodic scenes of IoT, three different kinds of GRU mode are put forward. Finally we use CNN-based Autoencoder to locate anomalies according to both temporal and spatial dependencies. It should also be note that Multi-head GRU broadens the receptive field of CNN-based Autoencoder. Two parts of experiment were carried to verify the validity of Dual-Window RNN-CNN. The first part is conducted on UCR/UEA benchmark to discuss the performance of Dual-Window RNN-CNN under different structures and hyper parameters, for datasets in UCR/UAE benchmark contain enough timestamps to monitor the high acquisition and periodicity in IoT. The second part is conducted on Yahoo Webscope benchmark and NAB to compare our network with other classic time series anomaly detection approaches. Experiment results confirm that our Dual-Window RNN-CNN outperforms other approaches in anomaly detection of periodic multivariate time series, demonstrating the advantages of our network in high acquisition scene.
Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters

Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the proposed tracker is equipped with robust discriminative power against challenging factors while maintaining high operational efficiency in UAV scenarios. Quantitative and qualitative experiments on two well-known benchmarks with 173 challenging UAV video sequences demonstrate the effectiveness of the proposed framework. The proposed tracking algorithm favorably outperforms 12 state-of-the-art methods, yielding 4.8% relative gain in UAVDT and 8.2% relative gain in UAV123@10fps against the baseline tracker while operating at the speed of ~28 frames per second.