Reviewerly/exHarmony huggingface dataset:
Adversarial Evaluation of Robust Neural Sequential Tagging Methods for Thai Language

Sequential tagging tasks, such as Part-Of-Speech (POS) tagging and Named-Entity Recognition, are the building blocks of many natural language processing applications. Although prior works have reported promising results in standard settings, they often underperform on non-standard text, such as microblogs and social media. In this article, we introduce an adversarial evaluation scheme for the Thai language by creating adversarial examples based on known spelling errors. Furthermore, we propose novel methods including UNK masking, condition initialization with affixation embeddings, and untied-directional self-attention mechanism to enhance robustness and interpretability of the neural networks. We conducted experiments on two Thai corpora: BEST2010 and ORCHID. Our adversarial evaluation schemes reveal that bidirectional LSTM (BiLSTM) do not perform well on adversarial examples. Our best methods match the performance of the BiLSTM baseline model and outperform it on adversarial examples.
A Text-Guided Graph Structure for Image Captioning

Image captioning task requires a comprehensive understanding of visual content and has received a significant amount of attention. Recent studies have revealed that modelling relationships between visual objects imply a high-level semantic feature. However, most existing relationship modelling methods for image captioning heavily rely on the object detection results and handcrafted structured label to build the graph model. In this paper, we explore the relationships in a text-guided way via the descriptions from similar images to provide the context clues. We propose a novel framework named Text-Guided Graph (TGG) to employ image-related text to help build the relationship between objects in the image and incorporate the high-level graph information and captions associated with a certain image. Experiments conducted on the MS COCO dataset demonstrate the effectiveness of our text-guided graph model under various standard evaluation metrics.
Speaker identification based on PCC feature vector

In the research field of speaker identification, many extraction methods of speech feature have been investigated for several decades. However, several new features such as i-vector and d-vector were proposed in recent years. Mel Frequency Cepstral Coefficient (MFCC) is still wildly used in current speaker identification systems accounting for its high performance. Based on the similar generation approach of MFCC, this article proposes a novel feature extraction way based on Pearson correlation coefficient (PCC). Firstly, we also use inverse discrete cosine transform (IDCT) cepstrum coefficient as the initial speech inputs. Secondly, we employ a hierarchical clustering analysis based on PCC to merge the IDCT cepstrum coefficient until the dimension of speech inputs is reduced to 14. Finally, we output this 14-dimensional vector as speech feature named r-vector. In the experiments, Gaussian Mixture Model (GMM) was applied to compare the performance of r-vector with other speech features. According to the 630 people voice data in TIMIT database, the results of experiments claim that the r-vector could obtain higher recognition accuracy in speaker identification.
Review of recent Path Planning Optimisation Algorithms for Unmanned Combat Aerial Vehicles.

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. It is path planning is complicated optimum problem globally. The path planning is very important to been unseen from the radars. In this review paper, various path planning optimisers has been overviewed and summarised. The review paper is ended by providing a summary conclusion of different optimisers.
Two-Way Feature-Aligned And Attention-Rectified Adversarial Training

Adversarial training increases robustness by augmenting training data with adversarial examples. However, vanilla adversarial training may be overfitting to certain adversarial attacks. Small perturbations in images bring in error which is gradually amplified when forwarded through the model so that the error leads to wrong classification. Besides, small perturbations will also distract classifier's attention to significant features that are relevant to the true label. In this paper, we propose a novel two-way feature-aligned and attention-rectified adversarial training (FAAR) to improve adversarial training (AT). FAAR utilizes two-way feature alignment and attention rectification to mitigate the problems mentioned above. FAAR effectively suppresses perturbations in lowlevel, high-level and global features by moving features of perturbed images towards those of clean images with twoway feature alignment. It also leads the model into focusing more on useful features which are correlated with true label through rectifying gradient-weighted attention. Besides, feature alignment activates attention rectification by reducing perturbations in high-level feature. Our proposed method FAAR surpasses other existing AT methods in three aspects. First, it pushes the model to keep invariant when dealing with different adversarial attacks and different magnitude of perturbations. Second, it can be applied to any convolution neural networks. Third, the training process is end-to-end. For experiments, FAAR shows promising defense performance on CIFAR-10 and ImageNet.
Tandem-X satellite data application to four-dimensional of copper mineralization

Two algorithms are integrated to construct 4-D of the copper
Blind Integer Wavelet Transform Medical Image Watermarking Based Logistic Map Encryption

A new blind Medical image watermarking (MIW) scheme with a high capacity for e-healthcare applications is presented in this paper. Integer Wavelet Transform (IWT) is used as an efficient alternative to treat the cover image in the frequency domain. In this study, three watermarks are embedded in the cover image for content authentication at the receiver side. The watermarks embedding is performed using a variable gain factor in the floating part of the IWT coefficients values. For security improvement of the embedded data and the cover image, a non-linear chaos dynamics is exploited for encrypting the watermarked image. The proposed scheme is evaluated in terms of imperceptibility and capacity payload by subjecting it to various image quality metrics (MSE, PSNR, and SSIM) and multiple metrics for encryption strength measurement (NPCR and UACI). Experimental results prove that the proposed scheme, besides blind is capable of providing high quality watermarked images for relatively high payload. Further, it is observed that the proposed technique provides a good encryption quality to maintain data privacy and security.
Minimizing Supervision in Multi-label Categorization

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.
CONDUCTING THE LIBRARY PROJECT ACTIVITIES: CREATIVE MANAGEMENT

Project development is viewed upon as a process of creative approaches to solving problems and making effective management decisions, where creative management is a key component of project management and thereby radically increases the efficiency of the library activity. Reflected is the experience of the State Universal Scientific Library of the Krasnoyarsk Territory on conducting socio-cultural projects, the implementation of which is carried out using creative management. The curator's role in the modern library project activity is stated.
StochasticRank: Global Optimization of Scale-Free Discrete Functions

In this paper, we introduce a powerful and efficient framework for direct optimization of ranking metrics. The problem is ill-posed due to the discrete structure of the loss, and to deal with that, we introduce two important techniques: stochastic smoothing and novel gradient estimate based on partial integration. We show that classic smoothing approaches may introduce bias and present a universal solution for a proper debiasing. Importantly, we can guarantee global convergence of our method by adopting a recently proposed Stochastic Gradient Langevin Boosting algorithm. Our algorithm is implemented as a part of the CatBoost gradient boosting library and outperforms the existing approaches on several learning-to-rank datasets. In addition to ranking metrics, our framework applies to any scale-free discrete loss function.
Interpolative fuzzy reasoning method based on the incircle of a generalized triangular fuzzy number

Fuzzy Rule Interpolation (FRI) is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. This paper introduces a new method called "Incircle F RI" for fuzzy interpolation which is based on the incircle of a triangular fuzzy number. The suggested method is defined for triangular CNF fuzzy sets, for a single antecedent universe and two surrounding rules from the rule-base. The paper also extends the suggested "Incircle FRI" to trapezoidal, and hexagonal shaped fuzzy sets by decomposing their shapes to multiple triangulars. The generated conclusion is also a CNF fuzzy set. The performance of the suggested method is evaluated based on numerical examples and a comprehensive comparison to other current FRI methods.
Rain detection in image using convolutional neural network

Abstract Weather is a phenomenon occurs in the earth’s atmosphere. Weather affects human daily activities, especially outdoor activities. Weather observations including rainfall observation in Indonesia conducted by Meteorology, Climatology, and Geophysical Agency (BMKG). BMKG facing a major problem in terms of rainfall data spatial density. The insufficient amount and unevenly distributed rainfall measurement instrument, are two main factors contributing to rainfall data special density problems. One of the very prominent methods to gain a larger amount of rainfall measurement location is using the image obtained from existing Closed Circuit Television (CCTV) spread over vast areas, especially in the Jakarta region. The approach to recognize and classify the rainfall in a certain area from the CCTV image used in this research is the Convolutional Neural Network (CNN) method. The image data was taken from CCTV located in Kamal, Kalideres, West Jakarta. The images taken is split into two categories, the one that shows a rainy day and the one that shows a clear day. These two categories of images will be used as sample data to train CNN, an effort to obtain a suitable model. By using the CNN method, it’s possible to recognize and classify the rainfall condition within an image based on the model. Python is an open-source programming language that widely used nowadays to run CNN. The image classification using this CNN, scored approximately 98.30% of accuracy, which means that the model is optimal to recognize and classify rainfall conditions in a certain area based on the CCTV images.
Best neural simultaneous approximation

For many years, approximation concepts has been investigated in view of neural networks for the several applications of the two topics. Researchers studied simultaneous approximation in the 2-normed space and proved essential theorems concern with existence, uniqueness and degree of best approximation. Here, we define a new 2-norm in -space, with so we call it quasi 2- normed space ( ). The set of approximations is a space of feedforward neural networks that is constructed in this paper. Existence and uniqueness of best neural approximation for a function from is proved, describing the rate of best approximation in terms of modulus of smoothness.
Argumentative Approach for the Discovery Truth: The Role of Source Dependence

In many real-world applications, the information available especially in the Web is often obtained from non original and dependent sources i.e information comes from a variety of dependant sources. So that, obtaining original and dependant sources of information is a challenging task since these sources often provide a large amount of conflicting information. In this paper, we propose a new way to improve the discovery of the truth, taking into account the dependency between the data sources. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. For that purpose, we develop an argumentative approach based on propositional logic to discover the truth using a dependence measure and a set of principles between sources of information.
DESERV IT: A Method for Devolving Service Tasks in IT Services

Abstract Nowadays, IT operations devolve many tasks in IT services to internal customers (i.e., IT self-service). The rationale for this service task devolvement is often to reduce the IT personnel’s workload. However, prior research has shown that IT operations often fail to achieve this goal. Existing methods for modeling and analyzing services fall short of supporting service providers in identifying and specifying service tasks suitable to be devolved to (internal) customers. This paper presents, therefore, the first method for devolving service tasks in IT services (DESERV IT). DESERV IT is a compound of four method components encompassing a joint meta-model, a visual notation for modeling IT services, and procedural recommendations. The DESERV IT meta-model extends the meta-model of service blueprinting by means of concepts required to analyze service task devolvement. DESERV IT is evaluated in four evaluation episodes. The results of the evaluation episodes show that DESERV IT is perceived as effective, useful, complete, and generalizable by experts in the IT service management and enterprise architecture discipline. This paper contributes to enterprise modeling by demonstrating the feasibility of DESERV IT in an example case and describing DESERV IT’s evolution during the evaluation episodes. DESERV IT supports practitioners (e.g., request fulfillment managers) in modeling and analyzing IT services.
Automatic Generation of Dialogues based on Grammatical Inference and the use of a Knowledge Base

In this work, we present a model for the automatic generation of written dialogues, through the use of grammatical inference. This model allows the automatic recognition of grammars from a set dialogues employed as a training set. The inferred grammars are then used to generate templates of responses within the dialogues. The final objective is to apply this model in a specific domain dialogue system that answers questions in Spanish with the use of a knowledge base. The experiments carried out have been performend using the DIHANA project corpus which contains dialogues written in Spanish about schedules and prices of a rail system.
An Online Path Planning Algorithm for Automated Vehicles for Slow Speed Maneuvering

Today the level of automation of road vehicles increases due to the new requirements of the society. In the last century the main motivation was to make driving easier or more comfortable, nowadays lower fuel consumption, higher traffic safety and reduced environmental impact are in the focus of the developments. The improvement of automation will result a safer and environmental friendlier road transport. In automated vehicles a software does the driving tasks. It is a complex, multiple level controlling task which has several steps. An important step is, when the vehicle plans its own trajectory. The trajectories can also be meant aspects: it can mean the exact path and speed profile of the vehicle or it can mean the nodes on a traffic network where the vehicle should go. This paper presents the development of path planning algorithms. Based on the earlier offline path planning research this paper will present a basic online path planning algorithm. To reach the aim a map will be given as a 2204 × 1294 size matrix where the roads, the obstacles and the parking areas will be defined by different numbers. The map presents a smaller area of the campus of the Budapest University of Technology and Economics. The development, the testing and validation will be done in Prescan simulation environment. The aim is to make an algorithm which can modify the pre-planned path of the vehicle to avoid the collision with the LIDAR-sensed obstacles. The vehicle speed will be assumed slow enough to ignore the dynamical properties of the vehicle.
Opinion-Unaware No-Reference Image Quality Assessment of Smartphone Camera Images Based on Aesthetics and Human Perception

In this work, we evaluate the quality of images taken by smartphone cameras. By extracting localized features from different contents, images can be analyzed in terms of exposure, noise, color and texture. Coarse-grained method, which simulates the Ising model system, is utilized to evaluate the exposure performance. Gabor transform and Tamura texture features, which imitate the human visual perception, are used to grade noise and texture. For color, hue histograms are adopted. Comparing our computational result with the ranking given by experts, it is found that the averaged SROCC values are greater than 0.35.
Generation of Realistic Navigation Paths for Web Site Testing Using Recurrent Neural Networks and Generative Adversarial Neural Networks

A robust technique for generating web navigation logs could be fundamental for applications not yet released, since developers could evaluate their applications as if they were used by real clients. This could allow to test and improve the applications faster and with lower costs, especially with respect to the usability and interaction aspects. In this paper we propose the application of deep learning techniques, like recurrent neural networks (RNN) and generative adversarial neural networks (GAN), aimed at generating high-quality weblogs, which can be used for automated testing and improvement of Web sites even before their release.
Joint Facial Action Unit Intensity Prediction And Region Localisation

Facial Action Unit (AU) intensity prediction is essential to facial expression analysis and emotion recognition, and thus has attracted much attention from the community. In comparison, AU localization, albeit being important to emotion visualization and tracking, was relatively unexplored. In addition, as most existing AU intensity prediction methods take a cropped face image as input, their actual speed at run-time is often penalized by the pre-processing steps such as face detection and alignment. At the same time, their performance (in terms of inference speed), also does not scale well to multi-face images. To alleviate these problems, we propose a joint AU intensity prediction and localization method that works directly on the whole input image, thus eliminating the need of any pre-processing step and achieving the same inference speed regardless of the number of faces in the image. Based on the observation that different relevancy exists between AU intensities categories, a flexible cost function is proposed. At inference time, we introduce a non-maximum intensity suppression model to refine the prediction result. In order to leverage existing datasets without AU region groundtruth, we also propose an automatic AU region labeling method. Experiments on two benchmark databases, DISFA and FERA2015, show that the proposed approach outperforms the state-of-the-art methods on three metrics, ICC, MAE and F1 for the AU intensity prediction task.
SYSTEM BASED COMPONENT IDENTIFICATION USING COORDINATE DETERMINATION IN MIXED REALITY

Abstract The maintenance of an industrial plant is mainly based on analogue and manual methods and processes. In the future, the maintenance data of the real object should be determined digital and transferred to a PDM system in real time. This is made possible with a digital method of component identification. This publication presents a system-based component identification using Mixed Reality. Coordinates are determined by the terminal device and compared with a reference structure. After successful identification, maintenance data can be determined and automatically transferred to a PDM System.
Attention Meets Normalization and Beyond

To make Convolutional Neural Networks (CNNs) more efficient and accurate, various lightweight self-attention modules have been proposed. In this paper, we systematically study state-of-the-art attention modules in CNNs and discover that self-attention mechanism can be closely related to normalization. Based on this observation, we propose a novel attention module, named Normalization-Attention module (NA module in short), which is almost parameter-free. The NA module calculates the mean and standard deviation of intermediate feature maps and processes the feature context with normalization, which makes a CNN model easier to be trained and more responsive to informative features. Our proposed Normalization-Attention module can be integrated into various base CNN architectures, and used for many computer vision tasks, including image recognition, object detection, and more. Experimental results on ImageNet and MS COCO benchmarks show that our method outperforms state-of-the-art works using fewer parameters. Codes are made publicly available.
Community Cleanup: Incentivizing Network Hygiene via Distributed Attack Reporting

Residential networks are difficult to secure due to resource constraints and lack of local security expertise. These networks primarily use consumer-grade routers that lack meaningful security mechanisms, providing a safe-haven for adversaries to launch attacks, including damaging distributed denial-of-service (DDoS) attacks. Prior efforts have suggested outsourcing residential network security to experts, but motivating user adoption has been a challenge. This work explores combining residential SDN techniques with prior work on collaborative DDoS reporting to identify residential network compromises. This combination provides incentives for end-users to deploy the technique, including rapid notification of compromises on their own devices and reduced upstream bandwidth consumption, while incurring minimal performance overheads.