Reviewerly/exHarmony huggingface dataset:
Correlation statistical modeling between visual privacy-protection evaluation and human-pose recognition

While bringing convenience to our lives, the widespread application of image and video processing technology has recently presented particular privacy issues. Most current mainstream intelligent algorithms rely on the detailed content of images and videos, which significantly increases the risk of personal and private data being leaked. To mitigate this problem, in this paper we use a multi-layer compressed sensing (multi-CS) coding system to degrade the quality of images so their content is not visually discernible. In contrast to other privacy-protection strategies, multi-CS coding protects the content of images and videos while ensuring that intelligent recognition is still possible. A visual privacy protection (VPP) evaluation algorithm is proposed, and this is found to correspond better with subjective evaluation than general image quality-evaluation algorithms. We also propose a statistical modeling method to balance the relationship between the VPP level and the accuracy of intelligent recognition, taking human-pose recognition as an example. Experimental results show the effectiveness and feasibility of the proposed modeling approach.
Are Narrow Focus Exhaustivity Inferences Bayesian Inferences?

In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlying pragmatic inferences.
Cross ratio arrays: A descriptor invariant to severe projective deformation and robust to occlusion for planar shape recognition

Recognizing planar objects such as characters, symbols and logos is considered a hard problem in computer vision due to the possibility of these shapes suffer from different kinds of disturbances: projective or other nonlinear deformations, occlusions and spurious insertions. Different planar descriptors have been proposed by taking advantage of geometric features that are invariant to certain image transformations, usually linear ones such as scaling, rotation and affinity. In this work, a new descriptor of planar shapes robust to projective deformations is proposed: Cross Ratio Arrays (CRA). The descriptor is based on tracing rays across the images and collecting their intersections with the borders of the shapes to assemble arrays of computed cross ratios, one of the most fundamental projective invariants. Higher level arrays are built out of these sets of arrays from both query and template shapes, which ultimately allows us to identify correspondences in these shapes to estimate how projectively deformed one shape is from the other. Experiments with synthetic shapes as well as real world scene shapes suffering from severe projective deformations were conducted, with CRA outperforming state-of-the-art descriptors. In addition, tests were performed with different levels of occlusion and weak nonlinear deformations to evidence CRA’s robustness to such cases.
Disturbance Consistent Self-Ensembling for Semi-Supervised Hashing

Recently, deep semi-supervised hashing methods have attracted increasing attention, where the visual similarity of unlabeled data is usually adopted to guide the hash codes learning. However, samples with similar appearance may come from different categories, making their hash codes similar will lead to sub-optimal retrieval results. In this paper, we propose a novel Disturbance Consistent Self-Ensembling (DCSE) method to alleviate the drawback of visual similarity constraint. Specially, DCSE forms consensus hash codes for the same sample under different augmentations. These ensemble hash codes can capture the discriminative characteristics of a sample. Therefore, as more augmented data is involved, more ensemble hash codes in one category can become similar gradually. Then, we design a disturbance consistent loss to learn the discriminative hash codes by minimizing the distance between outputs of the hash layer and ensemble hash codes. Extensive experiments show that our proposed approach significantly outperforms state-of-the-art semi-supervised hashing methods.
Pearl: A Fast Deep Learning Driven Compression Framework for UHD Video Delivery

Ultra-high-definition (UHD) videos are enjoying increased popularity in people’s daily usage because of the good visual experience. However, the data size of UHD videos is 416 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. Super resolution (SR) algorithms are widely used in video delivery applications to tackle these challenges. However, applying the super resolution model on UHD videos requires much more GPU memory, as compared with HD videos, which brings a significant challenge to existing systems.In this paper, we propose a deep compression framework named Pearl, which utilizes the power of deep learning to compress UHD videos. New channel-based super resolution models are developed to overcome the GPU memory shortage problem. In pearl, instead of applying the traditional RGB-based super resolution model, three separate super resolution models are trained based on the Y, U, and V channels of UHD videos. These super resolution models are used to reconstruct a UHD video from a low-resolution video. With Pearl, super resolution algorithms can be successfully applied to UHD videos. As a result, the data size of UHD videos can be significantly reduced during network transmission. At the same time, the efficiency of video encoding and decoding can also be improved with Pearl. To the best of our knowledge, Pearl is the first deep learning driven compression framework on UHD videos. We evaluate the performance of Pearl with extensive experiments. In all considered scenarios, Pearl can compress up to 95% of video data size during the video transmission and achieve 2.4 times faster, as compared with existing systems <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament

The application of the latest Natural Language Processing breakthroughs in computational argumentation has shown promising results, which have raised the interest in this area of research. However, the available corpora with argumentative annotations are often limited to a very specific purpose or are not of adequate size to take advantage of state-of-the-art deep learning techniques (e.g., deep neural networks). In this paper, we present VivesDebate, a large, richly annotated and versatile professional debate corpus for computational argumentation research. The corpus has been created from 29 transcripts of a debate tournament in Catalan and has been machine-translated into Spanish and English. The annotation contains argumentative propositions, argumentative relations, debate interactions and professional evaluations of the arguments and argumentation. The presented corpus can be useful for research on a heterogeneous set of computational argumentation underlying tasks such as Argument Mining, Argument Analysis, Argument Evaluation or Argument Generation, among others. All this makes VivesDebate a valuable resource for computational argumentation research within the context of massive corpora aimed at Natural Language Processing tasks.
A New Approach for Educational Data Analytics with Wearable Devices

The rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population Optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.
RETRACTED ARTICLE: Application of embedded network system and distributed database in children’s psychological education

In embedded systems, embedded database systems are widely used as carriers for storing, managing, and accessing data. In internal database applications, the data associated with the system may be available in different database systems in different locations. How to determine the exchange of data and the consistency of the data and how to verify the integrity of these databases have become increasingly prominent issues in the development of systems and applications. The embedded database distribution system is an integral part of the embedded database system. Distributed database is an organic combination of computer network and database system. The rapid development of communication and network technology has promoted the development of distributed databases, but at the same time, it has also increased the complexity of the security issues of distributed databases. How to effectively ensure the security of distributed databases is a key issue that needs to be solved urgently in distributed database applications. On this basis, it also analyzes relevant data and conducts research. This article introduces the embryonic form of children’s mental health education—the development of PCT services—and focuses on the development of current mobile resources and terminal children’s mental health education. Implementing the mental health education application program, after recognizing the needs of children, we designed a mental health education platform based on the promotion of information dissemination through the use of ubiquitous learning theories and integration of current learning trends. Use the now popular micro-communication method to be embedded in the system, thereby increasing the usability of the system. The application of wireless local area network improves the comfort of platform applications and provides users with a variety of communication methods, accompanied by strong security and practical services.
AlphaZero with Real-Time Opponent Skill Adaptation

Reinforcement learning based methods achieved super-human score in many complex games. Ability to play on super-human level can be impractical when playing against casual players as the skill gap can be too big for the game to be enjoyable and challenging. In this paper, we propose modification of AlphaZero method that allows us to adapt agent to weaker opponent skill level during a single game. We added another output head to the neural network that predicts remaining game length. Based on this prediction, we added new action selection mechanism to Monte Carlo Tree Search. This mechanism allows us to make trade-off between original and new action selection strategy. The results of experiments show that the proposed modifications reduce the gap between strong and weak agents by increasing the number of draws which is our primary measurement of adaptability.
Research on Data Generation Model Based on Improved SeqGAN

With the demand of integrated energy metering business and the rise of artificial intelligence technology, the data generation model of digital equipment has become the focus of attention. As the most widely used method in the field of image generation, the implicit method based on GAN has great development potential and strong domain expansion ability. The addition of reinforcement learning method makes the GAN correlation algorithm suitable for data generation of discrete data. This paper proposes an improved SeqGAN model, reconstructs the original SeqGAN model, improves the roll-out module of the original model, uses model parameters lagging behind the generator, and increases the stability of long sequence reinforcement learning. Compared with some existing popular algorithms, the performance of the proposed model algorithm is significantly better than that of the comparison algorithm when the training times are enough (more than 150 times), which lays a foundation for its application in data generation of digital equipment.
A Survey on Tree Aggregation

The research dedicated to the aggregation of classification trees and general trees (hierarchical structure of objects) has made enormous progress in the past decade. The problem statement for aggregation of classification trees or general trees is as follows: Given k classification or general trees for a set of objects, we aim to build a consensus tree (classification or general). That is, a representative tree for the given trees. In this paper, we explore different perspectives for the motivation to construct a single tree from multiple trees given by researchers. The survey presents the approaches for the aggregation of both the classification trees as well as general trees. We bifurcate our study of the aggregation approaches into two categories: Selecting a single tree from multiple trees and merging trees. We will discuss these categories and the aggregation approaches under these categories in the paper comprehensively. We also discuss the privacy aspects of tree aggregation approaches and the possible directions for new research like using the technique of aggregating decision trees in the field of Federated Learning, which is a booming topic.
A Machine Learning Model for Product Fraud Detection Based On SVM

With the rise of IoT technology, more and more companies use this technology for daily work production. This technology will generate large amounts of data during the application process. If data can be used wisely, it will help companies make better decisions. It is very meaningful to establish a model based on supply chain data to determine whether there is fraud in the product transaction process. It can help merchants in the supply chain avoid fraud, default and credit risk, and improve market order. In this paper, we propose a fraud prediction model based on the SVM classification model. Due to the large amount of data provided by the materials, we first perform feature engineering on the data to obtain processed data that can be used for modeling, and then use the SVM classification model algorithm for data classification and regression. Experiments show that the accuracy of the SVM classification model is 98.61. Compared with logistic regression model and naive Bayes model, it has better data classification and regression capabilities.
Amplified thermal state: Properties and decoherence

In this paper, we introduce the amplified thermal state (ATS) by operating [Formula: see text] on the thermal state (TS). Here, [Formula: see text] is the amplification factor and [Formula: see text] is the photon number operator. We study its properties, such as light intensity, signal-to-noise ratio (SNR), Fock matrix elements and Wigner function. In addition, we study its decoherence in photon-loss channel by analyzing evolution of all above properties. All considered properties are derived analytically and simulated numerically. Compared with the original TS, the amplification can enhance light intensity and SNR, remain the mixed character, and exhibit non-Gaussianity. While the decoherence will weaken light intensity and SNR, remain the mixed character, and return to Gaussian state.
MulStepNET: stronger multi-step graph convolutional networks via multi-power adjacency matrix combination

Abstract Graph convolutional networks (GCNs) have become the de facto approaches and achieved state-of-the-art results for circumventing many real-world problems on graph-structured data. However, these networks are usually shallow due to the over-smoothing of GCNs with many layers, which limits the expressive power of learning graph representations. The current methods of solving the limitations have the bottlenecks of high complexity and many parameters. Although Simple Graph Convolution (SGC) reduces the complexity and parameters, it fails to distinguish the feature information of neighboring nodes at different distances. To tackle the limits, we propose MulStepNET, a stronger multi-step graph convolutional network architecture, that can capture more global information, by simultaneously combining multi-step neighborhoods information. When compared to existing methods such as GCN and MixHop, MulStepNET aggregates neighborhoods information at more distant distances via multi-power adjacency matrix while fitting fewest parameters and being computationally more efficient. Experiments on citation networks including Pubmed, Cora, and Citeseer demonstrate that the proposed MulStepNET model improves over SGC by 2.8, 3.3, and 2.1% respectively while keeping similar stability, and achieves better performance in terms of accuracy and stability compared to other baselines.
A insider threat detection system based on user and entity behavior analysis

Abstract Under the background of “digital new era”, the trend of network environment diversification and personnel technical requirements complexity is becoming more and more intense. After the “Prism Gate” incident was exposed, the public began to think deeply about insider security. At present, most organizations adopt security information and event management (SIEM) security policies and the rules to carry out insider security detection. However, with the surge of insider information data, the number of false alarms and false alarms due to the lack of context increases, which consumes a lot of time and human and material resources. Based on these problems, it is particularly important to develop a new insider safety inspection system and tools. This work proposes to develop an insider threat detection system based on the security strategy of user and entity behavior analysis to realize the detection and analysis of insider threat with high precision. The main work is as follows:This work abandons the traditional SIEM combined rules to determine the anomaly, but adopts the detection strategy of User and Entity Behavior Analysis (UEBA).This work proposes an improved LSTM-GaN insider threat detection algorithm.
Comparative Performances of Neural Networks of Variant Architectures Trained with Backpropagation and Differential Evolution

Work on Neuro-evolution has of late tended to focus on the design of alternative architectures and hyper-parameters for different classes of Neural Networks to improve performance and computational efficiency, using Evolutionary Algorithms (EA). Predominantly, the underlying mechanism for learning weight parameters remains traditional backpropagation (BP), now considered a paradigm of Deep Learning (DL). Many important facets of DL, like hierarchical construction of features across layers in image recognition, vanishing gradients, etc., are taken for granted without recognizing that these may implicitly be induced by the properties of BP itself. EAs that perform global optimization in contrast to local gradient descent of BP, if used extensively for ANN training, can potentially disrupt these assumed facets of DL - and construct alternative and interesting perspectives. There is a surprising lack of research activity towards one-to-one performance comparison between EA and BP, keeping precisely the same architectures, activation functions and datasets, for complex regression and classification problems. This work partially fills this gap. It is demonstrated that Differential Evolution enhanced with local search, can generate mildly better accuracies on regression problems on noisy industrial datasets compared to standard BP solutions, and comparable accuracies on popular image classification datasets. Consequently, this establishes the baseline for revisiting the above-mentioned assumed facets of DL, and potentially engender new and interesting perspectives.
Towards Language-Based Mitigation of Traffic Analysis Attacks

Traffic analysis attacks pose a major risk for online security. Distinctive patterns in communication act as fingerprints, enabling adversaries to de-anonymise communicating parties or to infer sensitive information. Despite the attacks being known for decades, practical solution are scarce. Network layer countermeasures have relied on black box padding schemes that require significant overheads in latency and bandwidth to mitigate the attacks, without fundamentally preventing them, and the problem has received little attention in the language-based information flow literature. Language-based methods provide a strong foundation for fundamentally addressing security issues, but previous work has overwhelmingly assumed that interactive programs communicate over secure channels, where messages are undetectable by unprivileged adversaries. This assumption is too strong for online communication where packets can be trivially observed by eavesdropping. In this paper we introduce SELENE, a small language for principled, provably secure communication over channels where packets are publicly observable, and we demonstrate how our program level defence can reduce the latency and bandwidth overheads induced compared with program-agnostic defence mechanisms. We believe that our results constitute a step towards practical, secure online communication.