Reviewerly/exHarmony huggingface dataset:
The Tower of Babel problem: making data make sense with Basic Formal Ontology

Purpose Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications. Design/methodology/approach The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development. Findings The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data. Originality/value The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.
SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sec</u> ure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> fficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ining <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> utsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.
Supply Chain using Smart Contract: A Blockchain enabled model with Traceability and Ownership Management

The concept of openness and decentralization is one which people have desired since years. Everyone wants transactional data to be transparent. When sensitive data are on the hand of third-parties, it may be susceptible to frauds and misuse. The advent of blockchain, a decentralized technology in cryptocurrency, has revealed an appropriate solution to address such issues. Blockchain maintains the integrity of a transaction. It not only secures from tempering and fraud but also ensures transactions are verifiable without involvement of an intermediate. Therefore, blockchain is applied to different decentralized domains that require trusted computing. Supply chain is one of these domains, that can benefit from trustworthy decentralized transactions initiated by multiple stakeholders. This paper presents a general model for a blockchain enabled supply chain. We have implemented this model using three smart contracts on an Ethereum platform. We have provided evidence of our verification and validation efforts. Our results convey the feasibility of the approach, which can streamline the administrative processes, and automatize the transactions making the system more efficient and transparent.
Argumentation based reinforcement learning for meta-knowledge extraction

Abstract Knowledge integration in distributed data mining has received widespread attention that aims to integrate inconsistent information locating on distributed sites. Traditional integration methods become ineffective since they are unable to generate global knowledge, support advanced integration strategy, or make prediction without individual classifiers. In this paper, we propose an argumentation based reinforcement learning method to handle this problem. Inspired by meta-learning, we integrate distributed knowledge and extract meta-knowledge that is agreed-upon knowledge consistent to all the agents. Specifically, two learning stages are introduced: argumentation based learning stage integrates and extracts meta-knowledge, and reinforcement learning stage evaluates and refines meta-knowledge. The two learning stages run alternately to extract global meta-knowledge base, which can be used to make prediction directly. The results from extensive experiments demonstrate that our method can extract refined meta-knowledge with a much satisfied performance.
Overcoming Glossophobia Based on Virtual Reality and Heart Rate Sensors

Glossophobia or commonly called speech anxiety is the fear of public speaking. It is a psychological disorder which a person is afraid to speak in public or can be interpreted as nervous. This problem is caused by the lack of preparation or training carried out to public speaking. In Addition, the training is generally lack the atmosphere or impression like speaking in public. Therefore, this system was created for helping someone in preparation before public speaking. This system simulation for practicing public speaking based on technology such as virtual reality, video 360, and arduino heart rate sensors. The results of the functionality and non-functionality of the system have been fully implemented and are running well. In addition, based on the results of the questionnaire and test of objectivity, this system has good feedback for helping someone to prepare and practice in public speaking based on virtual reality technology.
A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

Abstract Stylometric authorship attribution aims to identify an anonymous or disputed document’s author by examining its writing style. The development of powerful machine learning based stylometric authorship attribution methods presents a serious privacy threat for individuals such as journalists and activists who wish to publish anonymously. Researchers have proposed several authorship obfuscation approaches that try to make appropriate changes (e.g. word/phrase replacements) to evade attribution while preserving semantics. Unfortunately, existing authorship obfuscation approaches are lacking because they either require some manual effort, require significant training data, or do not work for long documents. To address these limitations, we propose a genetic algorithm based random search framework called M utant -X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, M utant -X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution probability and semantic relevance. While M utant -X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate M utant -X against a variety of authorship attribution methods on two different text corpora. Our results show that M utant -X can decrease the accuracy of state-of-the-art authorship attribution methods by as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches. While M utant -X advances the state-of-the-art in automated authorship obfuscation, we find that it does not generalize to a stronger threat model where the adversary uses a different attribution classifier than what M utant -X assumes. Our findings warrant the need for future research to improve the generalizability (or transferability) of automated authorship obfuscation approaches.
Periodic template tests: A family of statistical randomness tests for a collection of binary sequences

In this work, we classify all templates according to their periods and for each template we evaluate the exact probabilities using generating functions. Afterwards, we propose a new family of statistical randomness tests, that is periodic template tests, for a collection of binary sequences. We apply these tests to the outputs of AES, SHA-3, SHA-2 family, SHA-1 and MD5 and the binary expansion of π and 2, and biased non-random data to test the power of new tests. Moreover, we give the probabilities for all templates for the overlapping template matching test in the NIST test suite. Afterwards, we analyse the power of templates and compare the periodic template tests with NIST overlapping template test.
Lightweight mediated semi-quantum key distribution protocol

Classical users can share a secret key with a quantum user by using a semi-quantum key distribution (SQKD) protocol. Allowing two classical users to share a secret key is the objective of the mediated semi-quantum key distribution (MSQKD) protocol. However, the existing MSQKD protocols need a quantum user to assist two classical users in distributing the secret keys, and these protocols require that the classical users be equipped with a Trojan horse photon detector. This reduces the practicability of the MSQKD protocols. Therefore, in this study we propose a lightweight MSQKD, in which the two participants and third party are classical users. Due to the usage of the one-way transmission strategy, the proposed lightweight MSQKD protocol is free from quantum Trojan horse attack. The proposed MSQKD is more practical than the existing MSQKD protocols.
Moving Foreground-Aware Visual Attention and Key Volume Mining for Human Action Recognition

Recently, many deep learning approaches have shown remarkable progress on human action recognition. However, it remains unclear how to extract the useful information in videos since only video-level labels are available in the training phase. To address this limitation, many efforts have been made to improve the performance of action recognition by applying the visual attention mechanism in the deep learning model. In this article, we propose a novel deep model called Moving Foreground Attention (MFA) that enhances the performance of action recognition by guiding the model to focus on the discriminative foreground targets. In our work, MFA detects the moving foreground through a proposed variance-based algorithm. Meanwhile, an unsupervised proposal is utilized to mine the action-related key volumes and generate corresponding correlation scores. Based on these scores, a newly proposed stochastic-out scheme is exploited to train the MFA. Experiment results show that action recognition performance can be significantly improved by using our proposed techniques, and our model achieves state-of-the-art performance on UCF101 and HMDB51.
Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs

Although most robots use probabilistic algorithms to solve state estimation problems, path planning is often performed without considering the uncertainty about the robot's position. Uncertainty, however, matters in planning, but considering it often leads to computationally expensive algorithms. In this paper, we investigate the problem of path planning considering the uncertainty in the robot's belief about the world, in its perceptions and in its action execution. We propose the use of an uncertainty-augmented Markov Decision Process to approximate the underlying Partially Observable Markov Decision Process, and we employ a localization prior to estimate how the belief about the robot's position propagates through the environment. This yields to a planning approach that generates navigation policies able to make decisions according to the degree of uncertainty while being computationally tractable. We implemented our approach and thoroughly evaluated it on different navigation problems. Our experiments suggest that we are able to compute policies that are more effective than approaches that ignore the uncertainty, and that also outperform policies that always take the safest actions.
Outer-Points shaver: Robust graph-based clustering via node cutting

Graph-based clustering is an efficient method for identifying clusters in local and nonlinear data patterns. Among the existing methods, spectral clustering is one of the most prominent algorithms. However, this method is vulnerable to noise and outliers. This study proposes a robust graph-based clustering method that removes the data nodes of relatively low density. The proposed method calculates the pseudo-density from a similarity matrix, and reconstructs it using a sparse regularization model. In this process, noise and the outer points are determined and removed. Unlike previous edge cutting-based methods, the proposed method is robust to noise while detecting clusters because it cuts out irrelevant nodes. We use a simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy and robustness to noisy data. The comparison results confirm that the proposed method outperforms the alternatives.
Image steganography based on LSB substitution and encryption method: adaptive LSB+3

The goals of image steganography are to simultaneously reach maximum embedding capacity, enhanced embedding efficiency, better image quality, and more security. We present a two-step data hiding method that improves image quality, capacity, and embedding efficiency, and also provides stego security. In the first step of the proposed method, the hidden data are encrypted with the key words and shifting method to provide security. In the second stage, we propose adaptive least-significant-bit (LSB)+3 type I and adaptive LSB+3 type II methods to hide encrypted data in the cover image. The image quality of the stego image obtained from the proposed adaptive LSB+3 method is better than the traditional three-bit LSB methods. When maximum data are embedded in the cover image, a peak signal-to-noise ratio (PSNR) greater than 41 dB is reached. Experimental studies show that adaptive LSB+3 type I and adaptive LSB+3 type II methods are higher PSNR values (3.48% and 5.73%) than the standard three-bit LSB substitution methods.
On the Shortness of Vectors to Be Found by the Ideal-SVP Quantum Algorithm

The hardness of finding short vectors in ideals of cyclotomic number fields (hereafter, Ideal-SVP) can serve as a worst-case assumption for numerous efficient cryptosystems, via the average-case problems Ring-SIS and Ring-LWE. For a while, it could be assumed the Ideal-SVP problem was as hard as the analog problem for general lattices (SVP), even when considering quantum algorithms. But in the last few years, a series of works has lead to a quantum algorithm for Ideal-SVP that outperforms what can be done for general SVP in certain regimes. More precisely, it was demonstrated (under certain hypotheses) that one can find in quantum polynomial time a vector longer by a factor at most $$\alpha = \exp ({\widetilde{O}(n^{1/2})})$$ than the shortest non-zero vector in a cyclotomic ideal lattice, where n is the dimension. In this work, we explore the constants hidden behind this asymptotic claim. While these algorithms have quantum steps, the steps that impact the approximation factor $$\alpha $$ are entirely classical, which allows us to estimate it experimentally using only classical computing. Moreover, we design heuristic improvements for those steps that significantly decrease the hidden factors in practice. Finally, we derive new provable effective lower bounds based on volumetric arguments. This study allows to predict the crossover point with classical lattice reduction algorithms, and thereby determine the relevance of this quantum algorithm in any cryptanalytic context. For example we predict that this quantum algorithm provides shorter vectors than BKZ-300 (roughly the weakest security level of NIST lattice-based candidates) for cyclotomic rings of rank larger than about 24000.
LSTM‐based dynamic probability continuous hand gesture trajectory recognition

In the field of continuous hand-gesture trajectory recognition, aiming at the problems of existing a lot of noise for handwriting trajectories, and difficult to segment multiple continuous hand gestures accurately, a long short-term memory-based dynamic probability (DP-LSTM) method is proposed. Firstly, obtain the classification result for each sub-period in the whole time period by using LSTM; secondly, cluster the classification results by non-maximum suppression for trajectory algorithm to eliminate interference of invalid subsets; Finally, the end point of the valid trajectory is obtained according to the characteristics of the probability change, thus realising dynamic trajectory segmentation and recognition. In order to evaluate the performance of the DP-LSTM, this method is evaluated by using an Arabic numerals gesture database. The experiments show that the DP-LSTM has a high recognition rate for continuous hand gestures and can recognise its in real time.
Nonces Are Noticed: AEAD Revisited

We draw attention to a gap between theory and usage of nonce-based symmetric encryption, under which the way the former treats nonces can result in violation of privacy in the latter. We bridge the gap with a new treatment of nonce-based symmetric encryption that modifies the syntax (decryption no longer takes a nonce), upgrades the security goal (asking that not just messages, but also nonces, be hidden) and gives simple, efficient schemes conforming to the new definitions. We investigate both basic security (holding when nonces are not reused) and advanced security (misuse resistance, providing best-possible guarantees when nonces are reused).
Automatic velocity picking based on deep learning

At present, the stacking velocity mainly are acquired by manual picking on velocity spectra, which is a tedious and time-consuming process due to the growing number of the seismic data, especially 3D seismic data. To improve the efficiency in velocity picking, we have developed a new automatic velocity picking method based on deep learning. We combine two different structures of artificial neural networks, the You Only Look Once (YOLO) and Long Short-Term Memory (LSTM) for constructing an algorithm of automatic velocity picking, which are designed according to two aspects: (1) The process of velocity picking may be considered as the detection of maximum coherency values associated with primary reflections in the velocity spectra. (2) The time-velocity pairs picked from velocity spectra is a time sequence. The test with real velocity spectra from a marine seismic data set demonstrates that the deep neural network of YOLOLSTM model for velocity auto-picking is much more efficient than manual picking. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: Poster Station 2 Presentation Type: Poster
Advanced deep learning techniques for image style transfer: A survey

Image style transfer is an emerging technique based on deep learning, which takes advantage of the impressive feature extraction of convolutional neural networks (CNN). The extraction of high-level features of images makes the separation of style information and image content possible. Image style conversion technique aims to learn the style characteristics of various paintings, and then apply the learned style to another image. The combination of artificial intelligence and art makes this technique highly concerned in the relevant technical fields and art fields, and has been applied in many different fields of society. In this paper, we conduct a comprehensive study on image style transfer techniques. Firstly, we analyze and classify the existing algorithms of the current style transfer algorithms, and then elaborate on their applications in different fields. In addition, we also summarize the future development and prospect of the image transfer technique.
Contextual Bandit Learning-Based Viewport Prediction for 360 Video

Accurately predicting where the user of a Virtual Reality (VR) application will be looking at in the near future improves the perceive quality of services, such as adaptive tile-based streaming or personalized online training.However, because of the unpredictability and dissimilarity of user behavior it is still a big challenge.In this work, we propose to use reinforcement learning, in particular contextual bandits, to solve this problem.The proposed solution tackles the prediction in two stages: (1) detection of movement; (2) prediction of direction.In order to prove its potential for VR services, the method was deployed on an adaptive tile-based VR streaming testbed, for benchmarking against a 3D trajectory extrapolation approach.Our results showed a significant improvement in terms of prediction error compared to the benchmark.This reduced prediction error also resulted in an enhancement on the perceived video quality.
Optimizing Visual Element Placement via Visual Attention Analysis

Eye-tracking enables researchers to conduct complex analysis on human behavior. With the recent introduction of eye-tracking into consumer-grade virtual reality headsets, the barrier of entry to visual attention analysis in virtual environments has been lowered significantly. Whether for arranging artwork in a virtual museum, posting banners for virtual events or placing advertisements in virtual worlds, analyzing visual attention patterns provides a powerful means for guiding visual element placement. In this work, we propose a novel data-driven optimization approach for automatically analyzing visual attention and placing visual elements in 3D virtual environments. Using an eye-tracking virtual reality headset, we collect eye-tracking data which we use to train a regression model for predicting gaze duration. We then use the predicted gaze duration output of our regressors to optimize the placement of visual elements with respect to certain visual attention and design goals. Through experiments in several virtual environments, we demonstrate the effectiveness of our optimization approach for predicting gaze duration and for placing visual elements in different practical scenarios. Our approach is implemented as a useful plug-in that level designers can use to automatically populate visual elements in 3D virtual environments.