Reviewerly/exHarmony huggingface dataset:
Enabling Identity-Based Data Security with Cloud

In disseminated stockpiling organizations, customers store data indirectly onto the cloud and comprehend the data giving to others. Far away information uprightness taking a gander at is proposed to the attestation the respectability of the informational index aside in the cloud. In some fundamental appropriated amassing frameworks, for example, the EHRs structure, the cloud document may give touchy data. At the point when the cloud report is shared, the delicate information ought not to be known to anybody. Bringing the whole shared report can comprehend the inclusion of the collaborating records; however, it does not permit different gatherings to utilize this shared record instructions to recognize data offering to sensitive information stowing away in distant data uprightness exploring still has not been researched up to now. To address this issue, this paper proposes a distant data genuineness surveying plan that recognizes data offering to sensitive information concealing. To accomplish high security, this paper proposes security calculations for producing the private key and for making record label name for relating information. The entire information's are encoded twice prior to shipping off cloud. Just the information proprietor can see the entire information from cloud. Personality-based trustworthiness examining ensure that different clients could just see the required information by concealing delicate data safely.
HCov: A Target Attention-based Filter Pruning with Retaining High-Covariance Feature Map

Neural network pruning plays an important role in the deployment on resource-constrained devices by reducing the scale of the network and the computational complexity. However, existing pruning methods only consider the richness of information filters contain, without considering the distribution of information. In image classification, the information related to target area is very important. To address these limitations, we propose HCov to prune filters generating low covariance feature maps. The principle behind is that most of the feature maps generated by filters contain target area information, therefore, maps with low covariance contain either very little information or messy background information unrelated to target. Thus filters generating low covariance feature maps can be pruned with little accuracy drop. HCov calculates the covariance between feature maps in the same layer and removes filters with low covariance feature maps. Through experiments on single-branch and multi-branch networks, the results prove that HCov can prune more redundant filters while maintaining better accuracy. Notably, our method can reduce 68.6% parameters and 71.7% FLOPs of ResNet-110 with only 0.26% top-1 accuracy loss on CIFAR-10. With ResNet-50, we achieve a 44.7% FLOPs reduction by removing 40.8% of the parameters, with only a loss of 0.62% in the top-1 accuracy on ImageNet, which has advanced the state-of-the-art.
Holistic Approach to Intrinsic Capacity Assessment: An Engineering Perspective

The growing interest in healthy ageing has fostered the definition and use of proper measures to assess decline in older adults. The World Health Organisation has defined the concept of intrinsic capacity to address this issue. In this paper, we approach the intrinsic capacity concept from an engineering perspective. Our contribution is threefold: (i) we summarise the results of a literature review on the topic, which points to a lack of holistic solutions to assess intrinsic capacity automatically, by using information technology, (ii) we suggest a data-warehouse-inspired architecture to tackle the problem, and (iii) we discuss the main challenges that remain open and must be studied in the future. Overall, this paper is a first step towards the definition of an architecture allowing the practical implementation of a holistic, context-aware, automatic system to measure intrinsic capacity and monitor the healthy ageing of elders.
Research on Node Collaboration Mode Based on Mixed Meta-path

To solve the problem of Collaborative Pattern Mining in heterogeneous networks, we propose a collaborative pattern mining model based on mixed meta-path for heterogeneous networks. According to the heterogeneous network structure and information, multiple meta-paths are extracted, and the multiple meta-paths extracted are fused and analyzed. Then, random walk strategy is used to generate a large number of sequences. Frequent item-sets are extracted by the sequences in FP algorithm, and the collaborative patterns of nodes in the network are analyzed. The experimental analysis was carried out in two public data sets, tmdb5000 and DBLP, to verify the validity and scalability of the model.
Beyond Parallel Corpora

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Recognition and Application of Tunnel Water Accumulation Based on Computer Vision

Abstract Water accumulation in tunnel threatens the safety of driving and the tunnel itself. In order to detect the tunnel waterlogging in time, a method based on computer vision is proposed. This method utilizes Laplace transform for image preprocessing to remove fuzzy image, uses the network MobileNetV2 to build a tunnel waterlogging recognition model, and then smooths the prediction results. Based on this method, a tunnel waterlogging recognition and early warning platform is developed which applied to a city video surveillance system successfully. The results show that: the tunnel waterlogging recognition method based on computer vision has high accuracy, tiny computation and low cost. The existing video monitoring system can be upgraded intelligently without installing any hardware, so as to realize the active recognition and early warning of tunnel water accumulation.
Scene Image Classification Based on Improved VLAD Reprensentation

Vector of Locally Aggregated Descriptors (VLAD) method, which aggregates descriptors and produces a compact image representation, has achieved great success in the field of image classification and retrieval. However, the original VLAD method is a hard assignment strategy that only assigns each descriptor to the nearest neighbor visual word in dictionary, which leads to large quantization error. In this paper, improved VLAD based on adaptive bases and saliency weights is proposed to solve the above problem. The new method considers the local density distribution when assigning local descriptors, adaptively selects several nearest neighbor visual words, and takes the coding coefficients obtained by utilizing saliency as the weights of the selected visual words. Experimental results on Corel 10, 15 Scenes and UIUC Sports Event datasets show that the new coding method proposed in this paper achieves better classification performance compared with the existing five VLAD based methods and two commonly used representation methods.
Influence of Speaker Pre-training on Character Voice Representation

Finding professional voice-actors for cultural productions is performed by a human operator and suffers from several difficulties. Researchers have therefore been interested for several years in mimicking the process of vocal casting to help human operators find new voices. However, voice casting appears to be an underdefined task with many difficulties. The main issue is that no label is available to accurately assess the performance of voice casting systems. To tackle these problems, recent works have focused on building a speech representation of acted voices able to highlight the character dimension. The proposed approach relies on an initial sequence extractor issued from a speaker recognition system which is able to represent a time variable speech sequence by a unique fixed-size vector, followed by a dedicated neural network where the character-based embedding, called p-vector, is extracted. It is legitimate to wonder if the sequence extractor is not guiding p-vectors too much towards speaker information. We then propose to study the impact of the speaker pre-training on the character representation learning. In comparison to a directly trained character representation, the results show that the use of a speaker pre-training provides more character information while retaining the speaker-independent part.
SIntactical Distance Attention Guided Graph Convolutional Network for aspect-based sentiment analIsis

Aspect-based sentiment analIsis (ABSA) aims to detect the sentiment polaritI of a specific aspect in an opinionated sentence. Current work focuses on exploiting the sIntactic tree to shorten the distance between the aspect term and context words. However, the “hard-pruning” strategI on the sIntactic tree maI lead to the reduction of importa nt sIntactic information. In this paper, we propose a novel sInt actical distance attention guided graph convolutional network (SDGCN) for ABSA. Our model is capable of fullI exploiting the sIntactic knowledge with a “soft pruning” strategI and learning crucial fine-grain sIntactic distance info rmation. AdditionallI, an effective denselI connected graph convolutional laIer is applied to avoid the over-sm oothing problem of standard GCN. Experiments conducted on three benchmark datasets show that our model achieves promising results comparing to the baseline models.
Provability logic: models within models in Peano Arithmetic

Abstract In 1994 Jech gave a model-theoretic proof of Gödel’s second incompleteness theorem for Zermelo–Fraenkel set theory in the following form: $${{\,\mathrm{\mathrm {ZF}}\,}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace /> <mml:mi>ZF</mml:mi> <mml:mspace /> </mml:mrow> </mml:math> does not prove that $${{\,\mathrm{\mathrm {ZF}}\,}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace /> <mml:mi>ZF</mml:mi> <mml:mspace /> </mml:mrow> </mml:math> has a model. Kotlarski showed that Jech’s proof can be adapted to Peano Arithmetic with the role of models being taken by complete consistent extensions. In this note we take another step in the direction of replacing proof-theoretic by model-theoretic arguments. We show, without the need of formalizing the proof of the completeness theorem within $${{\,\mathrm{\mathrm {PA}}\,}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace /> <mml:mi>PA</mml:mi> <mml:mspace /> </mml:mrow> </mml:math> , that the existence of a model of $${{\,\mathrm{\mathrm {PA}}\,}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace /> <mml:mi>PA</mml:mi> <mml:mspace /> </mml:mrow> </mml:math> of complexity $$\Sigma ^0_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>Σ</mml:mi> <mml:mn>2</mml:mn> <mml:mn>0</mml:mn> </mml:msubsup> </mml:math> is independent of $${{\,\mathrm{\mathrm {PA}}\,}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace /> <mml:mi>PA</mml:mi> <mml:mspace /> </mml:mrow> </mml:math> , where a model is identified with the set of formulas with parameters which hold in the model. Our approach is based on a new interpretation of the provability logic of Peano Arithmetic where $$\Box \phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>□</mml:mo> <mml:mi>ϕ</mml:mi> </mml:mrow> </mml:math> is defined as the formalization of “ $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> is true in every $$\Sigma ^0_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>Σ</mml:mi> <mml:mn>2</mml:mn> <mml:mn>0</mml:mn> </mml:msubsup> </mml:math> -model”.
Automated Subsurface Knowledge ASK Thamama Retrieval Engine Driven by Conversational Text Analytics and NLP - Lessons Learned in Managing Large Volume of Documents in Abu Dhabi Assets

Abstract The purpose of this paper is to communicate the experiences in the development of an innovative concept named "ASK Thamama" as an automated data and information retrieval engine driven by artificial intelligence techniques including text analytics and natural language processing. ASK is an AI enabled conversational search engine used to retrieve information from various internal data repositories using natural language queries. The text processing and conversational engine concept is built upon available open-source software requiring minimum coding of new libraries. A data set with 1000 documents was used to validate key functionalities with an accuracy of 90% of the search queries and able to provide specific answers for 80% of queries framed as questions. The results of this work show encouraging results and demonstrate value that AI-enabled methodologies can provide natural language search by enabling automated workflows for data information retrieval. The developed AI methodology has tremendous potential of integration in an end-to-end workflow of knowledge management by utilizing available document repositories to valuable insights, with little to no human intervention.
VR Technology and Its Application in Financial Mathematics Teaching

This paper systematically analyses virtual reality (VR) technology and its characteristics of immersion, interaction, imagination and intelligence, puts forward the fusion method of virtual reality technology and financial mathematics specialty teaching, conducts a pioneering research on the application of VR technology in financial mathematics specialty teaching, and carries out a practical exploration. The results show that the financial mathematics specialty teaching based on VR technology can be not affected by the limitation of time and space, make the teaching more vivid, intuitive and vivid, make it easier for students to set up correct innovative ideas, create a good learning atmosphere, greatly improve students’ learning enthusiasm, initiative and creativity. It is of great significance to improve the quality and efficiency of financial mathematics teaching.
Born-Again Decision Boundary: Unsupervised Concept Drift Detection by Inspector Neural Network

In the actual operation of machine learning models, differences between training and operational data distributions are a problem referred to as concept drifts. Most existing detection approaches assume that labels can be made readily available, but it is unrealistic to acquire all labels instantaneously. As concept drift is often unpredictable, the deployed machine learning model is not always amenable to concept drift detection. In this paper, we propose the Born-Again Decision Boundary algorithm, which is a novel unsupervised concept drift detection method for inspecting a deployed black box model without labels. The proposed method builds an inspector model that recreates the decision boundaries from the deployed model using black-box rule extraction techniques. This model calculates the distance from the decision boundary to the data point and monitors a region of uncertainty in the input space. Our method removes the dependency on machine learning algorithms, which has hindered the existing methods in practical applications and can be widely applied regardless of the classification algorithm. Experimental results on synthetic and real-world data show that the proposed method is capable of detecting concept drift on unlabeled data.
REFLECTION OF THE PROBLEM OF THE DEVELOPMENT OF GENETIC KNOWLEDGE OF STUDENTS IN THE ORGANIZATION OF INDEPENDENT WORK

Currently, the problem of teaching genetics through students’ independent work is one of the most important areas of higher education. In this article, we offer several methods and techniques for organizing students’ independent work on the subject of genetics in a multilingual education. The methodological improvement course of the biology subject is comprehensively studied from year to year and the results are being published. From this point of view, analyses were conducted on the biology teaching at the university and the independent work of foreign and domestic scientific works in recent years. The article presents the experience of teaching genetics based on the independent work organization of future biology teachers in the conditions of multilingual education at Abai University. The results of experimental studies aimed at testing the effectiveness of independent work have been presented. The analysis of the studies made it possible to conclude that the methodological training of biology students as an object of pedagogical research should include various professional training system aspects. The teaching genetics process in English through independent work encourages students to further in-depth subject and language study.
Black-Box Buster: A Robust Zero-Shot Transfer-Based Adversarial Attack Method

Recent black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models are either pre-trained models or trained with the target model’s training examples, which is hard to obtain because of the security and privacy of training data. In this paper, we proposed a zero-shot adversarial black-box attack method that can generate high-quality training examples for the substitute models, which are balanced among the classification labels and close to the distribution of the real training examples of the target models. The experiments demonstrate the effectiveness of our method that significantly improves the non-target black-box attack success rate around 20%–30% of the adversarial examples generated by the substitute models.
Elementary Methods for Generating Three-Dimensional Coordinate Estimation and Image Reconstruction from Series of Two-Dimensional Images

The increase in computational power in recent years has opened a new door for image processing techniques. Three-dimensional object recognition, identification, pose estimation, and mapping are becoming popular. The need for real-world objects to be mapped into three-dimensional spatial representation is greatly increasing, especially considering the heap jump we obtained in the past decade in virtual reality and augmented reality. This paper discusses an algorithm to convert an array of captured images into estimated 3D coordinates of their external mappings. Elementary methods for generating three-dimensional models are also discussed. This framework will help the community in estimating three-dimensional coordinates of a convex-shaped object from a series of two-dimension images. The built model could be further processed for increasing the resemblance of the input object in terms of its shapes, contour, and texture.
Person Re-identification Algorithm Based on Siamese LSTM

In this paper, we propose a new siamese long short-term memory (SLSTM) network model to solve the problem that the recognition accuracy is affected by the occlusion of pedestrian images. The backbone network is composed of two modules, CNN and LSTM, in order to use the local detail information of pedestrian images, firstly, the image is divided into blocks and the features of each image sub-block are extracted, then use the LSTM module to learn the dependency relationship between the features of the pedestrian image block, obtain the feature representation of pedestrian images through memory coding; at the same time, A new loss function is designed by combining the new triplet verification loss and Softmax recognition loss to reduce the intra-class difference and increase the inter-class difference. Comparative experiments were carried out on three pedestrian benchmark datasets of PRID-2011, Market-1501 and CUHK03. The experimental results show that the model proposed in this paper can reduce the impact of occlusion phenomena on re-identification, and the experimental results are better than other state-of-the-art methods.
Improving Text Summarization Using Feature Extraction Approach Based on Pointer-generator with Coverage

The sequence-to-sequence models have been widely used in the abstractive summarization tasks. However, the existing models have inaccuracy issues due to the insufficient understanding of overall semantics, the emergence of Out-Of-Vocabulary (OOV) words and the duplication of generated summarizations. In order to solve the above issues, in this paper, we propose an automatic text summarization model through combining the feature extraction, pointer-generator and coverage mechanism. First, in order to obtain input text with the multiple semantics, a feature extraction method is proposed to extract hidden features in the source text. Then, the pointer-generator (PGN) is used to select words from the source text or vocabulary to form a summarization, which solves the problem of OOV words in the generated summarizations. Finally, the coverage mechanism is applied to reduce the duplication of generated summarizations. The experimental results show that the model achieves better performance on the real-world CNN/Daily Mail summarization dataset.
Feature Interaction Based Graph Convolutional Networks for Image-Text Retrieval

To solve the challenge of heterogeneous gap between visual and linguistic data in image-text retrieval task, many methods have been proposed and significant progress has been made. Recently, some works use more refined information of the relation between regions in images or the semantic connection between words in text to further improve the representation of text and image data, while the cross-modal relation between image region and text word is not well explored in the representation. The current methods lack feature interaction in the data representation. For this purpose, we propose a novel image-text retrieval method which introduces inter-modal feature interaction in the graph convolutional networks (GCN) of image and text fragments. By the feature interaction between fragments of different modalities and the information propagation of GCN, the proposed method can capture more inter-modal interaction information for image-text retrieval. The experimental results on MS COCO and Flickr30K datasets show that the proposed method outperforms the state-of-the-art methods.
Examining the Perception of Drilling Depth Using Auditory Cues

This paper presents the results of a preliminary experiment that examined the accuracy of drill depth perception using auditory cues only. The results of this experiment will form the basis of a larger study that will investigate the effects of simulated drilling within medical-based training/educational tasks for online teaching employing non-specialized, consumer-level devices/hardware available to trainees in their homes.
Poster: Angry Birding: Evaluating Application Exceptions as Attack Canaries

Application exceptions are anomalous events occurring within the application. These can be caused by common issues such as simple programming errors; however, they can also originate from the side-effects of a trial-and-error process used in active attacks. Utilising attacker-induced exceptions as a canary for intrusion detection has been demonstrated as a feasible technique for SQL injection detection, but this has not been applied to other types of attacks. This paper proposes an approach to consider attacker-induced application exceptions as attack canaries. The work is part of an ongoing investigation on integrating detective defences into applications through established development practices.