Reviewerly/exHarmony huggingface dataset:
Algorithm Improvement of Pedestrians’ Red-Light Running Snapshot System Based on Image Recognition

Pedestrians' red-light snapping system is applied more and more in traffic control. The snapping technology based on face image recognition is widely used, but there are some defects such as low successful capture rate and small number of simultaneous tracking. In order to improve the capture algorithm based on face recognition technology, the MHT tracking algorithm based on upper body recognition is introduced to improve pedestrian tracking accuracy and system load performance, so that the tracking success rate is increased to 85%, and the number of simultaneous tracking reaches 25 people. At the same time, the image quality is judged by image Laplacian variance, face size and angle, and the face capture algorithm is optimized to improve the snap quality of the close-up photos of the face and improve the system availability.
Outlier Detection with Data Mining Techniques and Statistical Methods

The outlier detection in the field of data mining and Knowledge Discovering from Data (KDD) is capturing special interest due to its benefits. It can be applied in the financial area; because the obtained data patterns can help finding possible frauds and user errors. Therefore, it is essential to assess the truthfulness of the information. In this context, data auditory process uses techniques of data mining that play a significant role in the detection of unusual behavior. Here, a method for detecting values that can be considered as outliers in a nominal database is proposed. The basic idea in this method is to implement: a Global k-Nearest Neighbors algorithm, a clustering algorithm named k-means, and a statistical method of chi-square. The application of algorithms has been developed with a database of candidate people for the granting of a loan. Each test was made on a dataset of 1180 registers in which outliers have been introduced deliberately. The experimental results show that the method is able to detect all introduced values, which were previously labeled to be differentiated. Consequently, there were found a total of 48 tuples with outliers of 11 nominal columns.
Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our approach shows highly competitive performances on these datasets without deep language models (such as ELMo).
Generating Interactive WebSocket Applications in TypeScript

Advancements in mobile device computing power have made interactive web applications possible, allowing the web browser to render contents dynamically and support low-latency communication with the server.This comes at a cost to the developer, who now needs to reason more about correctness of communication patterns in their application as web applications support more complex communication patterns.Multiparty session types (MPST) provide a framework for verifying conformance of implementations to their prescribed communication protocol.Existing proposals for applying the MPST framework in application developments either neglect the event-driven nature of web applications, or lack compatibility with industry tools and practices, which discourages mainstream adoption by web developers.In this paper, we present an implementation of the MPST framework for developing interactive web applications using familiar industry tools using TypeScript and the React.jsframework.The developer can use the Scribble protocol language to specify the protocol and use the Scribble toolchain to validate and obtain the local protocol for each role.The local protocol describes the interactions of the global communication protocol observed by the role.We encode the local protocol into Type-Script types, catering for server-side and client-side targets separately.We show that our encoding guarantees that only implementations which conform to the protocol can type-check.We demonstrate the effectiveness of our approach through a web-based implementation of the classic Noughts and Crosses game from an MPST formalism of the game logic.
Progressive Face Dynamic Morphing

This work proposes a novel progressive face dynamic morphing approach, where facial structure is well preserved during the morphing process. To this end, we introduce a deep structure aligned model, by which we can achieve continuous alignment between source image and target image with high quality facial structure preservation. Also, a novel semi-supervised 3D expression animation approach is invoked for motion preservation in resulted sequence. Extensive experiments on both MMI facial expression dataset and CK AU-Coded expression dataset %wo common expression datasets demonstrate the effectiveness and efficiency of our model for face dynamic morphing.
HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

Nowadays, location-based services are being widely popularized due to their massive usage in current and emerging technologies.These services are based on searching out areas of interest which are likely to be accessed by users.Despite helping users worldwide, Location Based Services (LBSs) Systems endanger users' privacy because a user must provide personal information in order to use the services.Users thus become easy prey for assailants to access their social and personal lives.This problem is a giant issue for contemporary technologies because they are increasingly being used with the passage of time.Many existing solutions have attempted to resolve the challenges, but they face some serious dilemmas regarding the preservation of privacy.In order to address the privacy challenges in LBS systems, in this paper we have introduced a new Hierarchy Based Location Privacy (HBLP) model that protects the user's privacy, including the user's query time and identity and location information.The proposed model protects the user's privacy by using pseudo identity exchange, an aggregation protocol, and the concepts of Forest User (FU), Tree User (TU), and Child Users (CU) with k-anonymity and t-closeness, which is a reasonable combination for privacy provision for a user's query time, identity, and location.In order to evaluate the privacy protection level, we implemented the HBLP model in a Riverbed (Opnet) simulation and compared the results with existing state-of-the-art privacy-provisioning methods.The results showed that HBLP protected all the privacy attributes when a user interacts with an LBS system.
Credit Card Fraud Identification Based on Principal Component Analysis and Improved Adaboost Algorithm

As the issuance of credit cards grows rapidly, its risk cannot be underestimated. Due to the high dimension and sparsity nature of credit cards fraud data, it is easy to occur overfitting, low robustness and poor generalization in data mining. Therefore, this paper proposes a risk control algorithm for integrated learning, which is based on combining smote and principal component analysis for data processing, by using a single-layer decision tree and the improved Adaboost algorithm, as well as applying the trained risk control system to individual data for fraud identification. After testing the data sample of a commercial bank, the simulation results show that the accuracy rate of the improved integrated machine learning method reaches 96.50% and F-Measure value is 97.3%. In addition, comparing with other detection methods, the result proved that the proposed method is superior to traditional risk control technology, which also effectively completed the fraud detection work.
UAF Model Verification Method based on Description Logic

Abstract The grammatical structure of model based on Unified Architecture Framework (UAF) adopts formal specification, but the lack of formal and accurate description of semantics may result in information inconsistency between different views. In this paper, an UAF model verification method based on Descriptive Logic SHIQ is proposed. The knowledge base of SHIQ is constructed in formal description, with the help of Racerpro reasoning engine based on Tableau algorithm, conflicting or contradictory information in the models is reasoned and judged. The feasibility and convenience of this method are proved by verifying the "Requirement" model of the Major Disaster Relief System.
A discussion on the research process and illusory line motion

Scientific research involves a statement involving quantities and their values with respect to three different conceptual spaces. There is theoretical space, containing explanatory statements involving non-observable quantities and values. On the whole, theoretical space is concerned with why things happen. There is methodological space, containing statements about what must be done. While methodological space involves what is required to produce a given effect, it also involves aspects related to data analysis and, in the case of psychological research, quantities that are associated with the participants, many of which are both unknown and unmeasured. Data space involves statements concerning what was found, and the particular values obtained from a given study. Using Illusory Line Motion as an example, this article will explore how these ideas interact. We will explore why we see motion when none exists under different circumstances and offer multiple explanations for illusory line motion. We view these not as alternative explanations for a common illusion but as independent explanations for different illusions.
Adversarial Training Improved Multi-Path Multi-Scale Relation Detector for Knowledge Base Question Answering

Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on relation detection, a subtask of KBQA and proposes an adversarial training improved multi-path multi-scale relation detector (AdvT-MMRD) to improve the performance of a common KBQA system. To solve the problem of matching the casual form of a question with the logical form of a predicate, we use question pattern-relation matching, in which an attention-based bidirectional recurrent neural network with gated recurrent units (Bi-GRUs) is used to match semantic similarity and a convolutional neural network (CNN) is used to learn literal similarity between question and relation. We also explore two ways to measure the relevance of entity type-relation pairs through several level representations. Additionally, an adversarial training strategy is conducted to enhance our model. The experimental results demonstrate that our approach not only achieves a state-of-the-art accuracy of 93.8% on relation detection task, but contributes our KBQA system to reaching an outstanding accuracy of 79.0% on the SimpleQuestions benchmark.
From Video Game to Real Robot: The Transfer Between Action Spaces

Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training. To address this, transfer learning can be used to train the policy first in a simulated environment and then transfer it to physical agent. As the simulation never matches reality perfectly, the physics, visuals and action spaces by necessity differ between these environments to some degree. In this work, we study how general video games can be directly used instead of fine-tuned simulations for the sim-to-real transfer. Especially, we study how the agent can learn the new action space autonomously, when the game actions do not match the robot actions. Our results show that the different action space can be learned by re-training only part of neural network and we obtain above 90% mean success rate in simulation and robot experiments.
Camera Pose Estimation Using First-Order Curve Differential Geometry

This paper considers and solves the problem of estimating camera pose given a pair of point-tangent correspondences between a 3D scene and a projected image. The problem arises when considering curve geometry as the basis of forming correspondences, computation of structure and calibration, which in its simplest form is a point augmented with the curve tangent. We show that while the resectioning problem is solved with a minimum of three points given the intrinsic parameters, when points are augmented with tangent information only two points are required, leading to substantial robustness and computational savings, e.g., as a minimal engine within ransac. In addition, algorithms are developed to find a practical solution shown to effectively recover camera pose using synthetic and real datasets. This technology is intended as a building block of curve-based structure from motion systems, allowing new views to be incrementally registered to a core set of views for which relative pose has been computed.
Sentiment Analysis Based on Attention Mechanisms and Bi-Directional LSTM Fusion Model

This Sentiment analysis is mainly found in the user's social platform for a hot event or product point of view and attitude. Most existing sentiment analysis approaches heavily rely on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations. In order to avoid the dependence on the manual annotation dictionary and reduce the human intervention in the machine learning process, In this paper, Based on the researches on sentiment analysis and deep learning, we propose a hybrid framework AM-Bi-LSTM that combines Attention Mechanism and Bi-directional Long-Short-Term Memory (Bi-LSTM) neural networks for sentence classification. We demonstrate the effectiveness and efficiency of our approach on a representative Stanford Sentiment Treebank (SST) dataset. For SST-1 and SST-2, Compared with the currently published state-of-the-art methods Conv-RNN, the accuracy of AM-Bi-LSTM is improved by 2.787% and 1.946% respectively.
Investigating Generalization in Neural Networks Under Optimally Evolved Training Perturbations

In this paper, we study the generalization properties of neural networks under input perturbations and show that minimal training data corruption by a few pixel modifications can cause drastic overfitting. We propose an evolutionary algorithm to search for optimal pixel perturbations using novel cost function inspired from literature in domain adaptation that explicitly maximizes the generalization gap and domain divergence between clean and corrupted images. Our method outperforms previous pixel-based data distribution shift methods on state-of-the-art Convolutional Neural Networks (CNNs) architectures. Interestingly, we find that the choice of optimization plays an important role in generalization robustness due to the empirical observation that SGD is resilient to such training data corruption unlike adaptive optimization techniques (ADAM).
Joint Enhancement And Denoising of Low Light Images Via JND Transform

Low light images suffer from low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint enhancement and denoising of low light images via just-noticeable-difference (JND) transform. We achieve contrast enhancement and noise reduction simultaneously based on human visual perception. First, we perform contrast enhancement based on perceptual histogram to effectively allocate a dynamic range while preventing over-enhancement. Second, we generate JND map based on an HVS response model from foreground and background luminance, called JND transform. Then, we refine JND map using Weber's law and visual masking. Weber's law enhances the JND map based on the luminance variation after enhancement, while visual masking provides noise suppression for smooth regions and detail enhancement for texture regions. Finally, we conduct chroma denoising that transfers texture information of the denoised luma channel to the chroma channels by guided image filtering. Experimental results show that the proposed method achieves good performance in contrast enhancement and noise reduction while successfully preserving details.
BDMF: A Biased Deep Matrix Factorization Model for Recommendation

As a representative collaborative filtering method, matrix factorization has been widely used in personalized recommendation. Recently, deep matrix factorization model, which utilizes deep neural networks to project users and items into a latent structured space, has received increased attention. In this paper, inspired by the idea of BiasedSVD that introduces bias to both users and items, we propose a novel matrix factorization model with neural network architecture, named BDMF, short for Biased Deep Matrix Factorization. Specifically, we first construct a user-item interaction matrix with explicit ratings and implicit feedback, and randomly sample users and items as the input. Next, we feed this input to the proposed BDMF model to learn latent factors of both users and items, and then use them to predict the ratings for personalized ranking. We also formally show that BDMF works on the same principle as BiasedSVD, which means that BDMF can be viewed as a deep neural network implementation of BiasedSVD. Finally, extensive experiments on real-world datasets are conducted and the results verify the superiority of our model over other state-of-the-art.
Comparative Analysis of Combined Finite State Machine Implementation on Chips of Different Manufacturers

Certain ways of combined finite state mashine implementing on chips of different architectures and manufacturers are considered. Each discussed method allows achieving a certain reduction of hardware amount in the control device logic circuit. The aim of studies is to define the range of efficiency for each method correspondently to chips of the top manufactorers - Xilinx, Altera/Intel, Microsemi. The studies were conducted on chips of different series. Analysis of the experimental results is proposed and it may be used as a tip in practice of control unit design and optimization.
Learning from Dances: Pose-Invariant Re-Identification for Multi-Person Tracking

Most existing multi-person tracking approaches rely on appearance based re-identification (re-ID) to resolve fragmented tracklets. However, simply using appearance information could be insufficient for videos containing severe pose changes, such as sports or dance videos. With the goal of learning pose-invariant representations, we propose an end-to-end deep learning framework Sparse-Temporal ReID Network. Our proposed network not only realizes human pose disentanglement in an image recovery manner, but also makes efficient linkages between the identical subjects via a unique Sparse temporal identity sampling technique across time steps. Experimental results demonstrate the effectiveness of our proposed method on both multi-view re-ID benchmarks and our newly collected dance video dataset DanceReID <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Image super resolution via nonlocal and second-order feature fusion network

Recently, deep neural networks have made remarkable performance in the image super-resolution (SR) field. However, they mainly focus on wider or deeper architectural design, neglecting to capture the inherent property of natural images, hence hindering the representational ability of convolutional neural networks. To address this issue, we propose a deep network based on nonlocal (NL) and second-order (SO) feature fusion for image SR. In particular, we draw the observation that a SO attention mechanism could achieve more powerful feature expression and feature correlation learning. On the other hand, NL module is proved to be an effective prior to explore spatial contextual information. Thus, we introduce an SR network architecture by embedding NL operations and SO feature to capture intrinsic statistical characteristics of images. Furthermore, long skip connection is applied in the network to pass more abundant low-frequency information from low-resolution images and ease the network training. Experimental results on a variety of images demonstrate that our proposed method can achieve more desirable performance over several state-of-the-art methods in terms of quantitative metrics and visual quality.
Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging

High dynamic range (HDR) imaging is used to represent scenes with a greater dynamic range of luminance on a standard dynamic range display. Usually, HDR images are synthesized through base–detail separations. The base layer is used for tone compression and the detail layer is used for detail preservation. The representative detail-preserved algorithm iCAM06 has a tendency to reduce the sharpness of dim surround images, because of the fixed edge-stopping function of the fast-bilateral filter (FBF). This paper proposes a novel base–detail separation and detail compensation technique using the contrast sensitivity function (CSF) in the segmented frequency domain. Experimental results show that the proposed rendering method has better sharpness features and image quality than previous methods correlated by the human visual system.
Cost Aware Adversarial Learning

The problem of making the classifier design resilient to test data falsification is considered. In the literature, a few countermeasures have been proposed to defend machine learning algorithms against test data falsification, but a common assumption employed therein is that feature entries of test data are equally vulnerable to falsification. When test data entries consist of data collected from various sources such as different types of sensor devices, vulnerability levels of data entries to falsification attacks can differ significantly depending on how data creation and transmission procedures are secured. In this paper, we present an attack-cost-aware adversarial learning framework that takes into account the (potentially inhomogeneous) vulnerability characteristics of test data entries in designing an attack-resilient classifier. We demonstrate the efficacy of the proposed approach using experiments with the MNIST handwritten digit database.