Reviewerly/exHarmony huggingface dataset:
Contour-Guided Person Re-identification

Feature representation is one of the crucial components in person re-identification(re-ID). Recently, local feature has attracted great attention from the re-ID community, and extra visual cues have been well exploited to guide local feature learning, such as pose cues, semantic parsing and etc. Besides, the latest research demonstrates that general CNN-based deep models have a bias to texture feature in pattern recognition, but ignore shape-based feature, which has been verified as significant for cross-domain invariance. As far as we know, there is little work focusing on shape-based feature on person re-ID. In this paper, we introduce a new data modality, pedestrian contour, into the re-ID community, which to our best knowledge is the first attempt to utilize contour explicitly in deep re-ID models. We hypothesize that, as an alternative of other exploited visual cues, pedestrian contour could guide deep models to learn robust shape-based feature, with build-in prior information. We propose several contour-guided architectures to explicitly use pedestrian contour, including plain ones and multi-scale one. Extensive experiments have validated the effectiveness of our models. Moreover, we transfer the methodology into a powerful part-based model, Part-based Convolutional Baseline(PCB), and boost the model performance, which verifies the promising prospect of contour-guided models to expand as an auxiliary mechanism in re-ID.
Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition

Scene text recognition has attracted rapidly increasing attention from the research community. Recent dominant approaches typically follow an attention-based encoder-decoder framework that uses a unidirectional decoder to perform decoding in a left-to-right manner, but ignoring equally important right-to-left grammar information. In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition. Firstly, the backward decoder performs decoding from right to left and generates the reverse language context. After that, the forward decoder simultaneously utilizes the visual context from image encoder and the reverse language context from backward decoder through two attention modules. In this way, the bidirectional decoders perform effective interaction to fully fuse the bidirectional grammar information and further improve the decoding quality. Besides, in order to relieve the adverse effect of noises, we devise a gated context mechanism to adaptively make use of the visual context and reverse language context. Extensive experiments on various challenging benchmarks demonstrate the effectiveness of our method.
A Survey on Data Auditing Approaches to Preserve Privacy and Data Integrity in Cloud Computing

Cloud computing is recognized as one of the prominent and rapid growing technologies in this contemporary world. Cloud allows the users to store the data without any physical infrastructure and it provides access to the users over the internet anywhere and at any time without harming its accuracy and reliability. However, on the other hand storing data in the cloud creates an inconvenience to the users by creating security issues like data integrity, privacy & availability in cloud. Security exists as one of the recent research challenges in the cloud. The data stored on the cloud can be hacked and altered by any person and sometimes even the data can be deleted by the Cloud Service Provider (CSP) for his own personal benefit. So, there exist many solutions to solve the security issues in the cloud. Data auditing is found as one of the popular approach to provide integrity and privacy using Third Party Auditor (TPA). In this paper, pros and cons of six different data auditing schemes are discussed along with the results.
Multi-layers grid environment modeling for nuclear facilities: A virtual simulation-based exploration of dose assessment and dose optimization

Dose optimization for Radioactive Occupational Personal (ROP) is an important subject in nuclear and radiation safety field. The geometric environment of a nuclear facility is complex and the work area is radioactive, so traditional navigation model and radioactive data field cannot form an effective environment model for dose assessment and dose optimization. The environment model directly affects dose assessment and indirectly affects dose optimization, this is an urgent problem needed to be solved. Therefore, this paper focuses on an environment model used for Dose Assessment and Dose Optimization (DA&DO). We designed a multi-layer radiation field coupling modeling method, and then explored the influence of the environment model to DA&DO by virtual simulation. Then, a simulation test is done, the multi-layer radiation field coupling model for nuclear facilities is demonstrated to be effective for dose assessment and dose optimization through the experiments and analysis.
DHS-Voting: A Distributed Homomorphic Signcryption E-Voting

In electronic voting, voters encrypt their ballots and post them on a bulletin board, where the results are tallied by authorities. The right to tally is fully controlled by authorities, who generates a certificate to prove the results are correct, but their attempts to tamper with the bulletin board and falsify the certificate are inevitable. Therefore, we propose an electronic voting scheme based on distributed encryption and homomorphic signcryption, DHS-Voting, which can not only enable the verification of signatures to be completed quickly, but also allows anyone to get the result of the election with the help of the authorities, making the election results more credible. In addition, we prove that DHS-Voting satisfies the ballot privacy and some necessary properties of electronic voting, which we will prove in detail in the paper.
Mapping of ultrasonic thickness measurements using laser grid projection and image processing

A three-dimensional surface mapping method for ultrasonic thickness measurements is proposed to enable the dimensions and positions of measured values obtained by the conventional ultrasonic thickness gauges and flaw detectors on curved surfaces to be determined. The proposed system consists of a laser pattern generator and image processing methods. The laser grid produced by the pattern generator is projected onto the surface of the item to be inspected, mathematical relationships are developed to localise the grid nodes on the inspection surface and images are then captured using a complementary metal-oxide semiconductor (CMOS) camera. An image processing algorithm is developed to plot the scanned surface. The measurements performed using a thickness gauge are correlated with the plotted surface, which will enable thickness mapping. The experiments were carried out on a pipe with an artificial metal loss defect and the results showed that the developed method can accurately locate measured thickness values on a three-dimensional plotted surface.
Supervised neural machine translation based on data augmentation and improved training \& inference process

This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on English-Japanese, Japanese-English, Chinese-Japanese, and Japanese-Chinese four language pairs. We employed the Transformer model as the baseline and experimented relative position representation, data augmentation, deep layer model, ensemble. Experiments show that all these methods can yield substantial improvements.
Computational Measure of Cancer Using Data Mining and Optimization

In this paper approaches for cancer prediction through computational measures has been discussed and analyzed. This paper provides the basis of worldwide cancer impact, methodological study with discussion, attributes and parametric impact, gaps analyzed, and the suggested computational solutions. This paper also explores the impact and the association measures of the influencing factors. The methods covered in this study are from data mining and optimization. The latest trends in the methods used and applicability have been discussed with the gaps.
Robust Privacy-Preserving Gossip Averaging

Decentralized solutions are emerging as promising candidates to overcome the privacy risks associated with centralized data services. Such solutions suffer however from their own range of privacy vulnerabilities, arising from untrusted and malicious peers. In this paper, we consider the emblematic problem of privacy-preserving decentralized averaging, and propose a novel gossip protocol that exchanges noise for several rounds before starting to exchange actual data. This makes it hard for an honest but curious attacker to know whether a user is transmitting noise or actual data. Our protocol and analysis do not assume a lock-step execution, and demonstrate improved resilience to colluding attackers. We prove the correctness of this protocol as well as several privacy results. Finally, we provide simulation results about the efficiency of our averaging protocol.
Bitcoin Price Predictive Modeling Using Expert Correction

The paper describes the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends and Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.
Efficient and Accurate Property Title Retrieval Using Ethereum Blockchain

A simple Decentralized Application (Dapp) of Land Registry based on the Ethereum Blockchain can be used as an alternative to the current Land Registry System. Here the owner registers the land by providing necessary land details like the survey number of land, market value of the land/property etc. The Registration process is carried by a Superadmin who indeed acts as registrar or government authority. The Superadmin is the only responsible person who can register the land coming under particular area into the system. The owner has to transfer his full piece of land or any particular property to the buyer and no partial transaction will be allowed is specified by the Smart Contract. Even though the entire registration is done by the Superadmin acting as Government authority/Registrar still the transaction happens in a very transparent manner between the seller and the buyer without any Mediator.
Data on dissolved metals in Terengganu waters of South China Sea during pre-, post-, and Northeast Monsoon season

Metals are natural elements existed in the environment. However, due to the rapid development of urbanisation and economic, high content of anthropogenic metals are being perceived in polluting the environment. The oceans are known to be a part of the sinking basin for anthropogenic metals ends. Dataset provided is purposely to give an overview of dissolved metals spatial distribution in the South China Sea off the east Peninsular of Malaysia during the pre-, post- and Northeast (NE) Monsoon period. Seawater samples were collected in a grid of 18 stations at 3 different water depth. Dissolved metals were pre-concentrated on-board ship using Chelex-100 resin and analysed using Inductively Coupled Plasma Mass Spectrophotometry (ICPMS). The dataset shows the effect of NE Monsoon on dissolved metals spatial distribution mainly at the area closer to the land. Therefore, this dataset could reveal the past information on anthropogenic metals intrusion in the South China Sea, since Terengganu state was recently pointed to be one of the Malaysian waterfront city. Additionally, this dataset also could help in studying the cycle of metals in the southern South China Sea waters.
Dealing with software estimates distortions from the perspective of negotiation theories

Software estimation is a critical task in software projects, and the accuracy of software estimates has been a concern for researchers and practitioners. Researchers have already identified some factors that impact estimates accuracy, like cognitive biases, that ultimately lead to unintentional distortions of software estimates. However, there is evidence that intentional distortions of software estimates are also part of the reality of estimation in software projects. The goal of this research project is to investigate whether negotiation theories can be effective to aid software engineers in defending their estimates and, therefore, reduce distortions. To achieve this, we apply the Design Science Research (DSR) approach by defining and evaluating negotiation guidelines adapted to the context of software project estimation. The problem addressed in this project is investigated through a systematic literature mapping (SLM) and a case study. Additionally, the guidelines are going to be applied in real scenarios in software companies. The expected contributions in this project are (i) the negotiation guidelines aiming at reducing software estimate distortions, (ii) empirical evidence about whether negotiation theories are effective to aid software engineers to deal with estimate distortions, and (iii) a set of technological rules about the use of these guidelines.
Collusion Resistant Traitor Tracing from Learning with Errors

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 July 2018Accepted: 05 June 2019Published online: 05 November 2019Keywordstraitor tracing, private linear broadcast encryption, functional encryption, Mixed FE, attribute-based encryption, lattice trapdoors, encryption, decryption, learning with errorsAMS Subject Headings68P25, 94A60Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Investigating Feature Reduction Strategies for Replay Antispoofing in Voice Biometrics

One of the biggest challenges for voice based biometric solutions is in handling replay spoofing attacks. These attacks pose enormous threat on speaker verification system wherein the recorded voice of a genuine user is played in front of the authentication system to attempt unauthorized access. The problem with such system is to distinguish between origin of the input signal whether it comes from a human (live signal) or a device (spoofed signal). In this work, we compare filterbank based features and attempt to choose prominent features by employing some dimensionality reduction strategies. Low level, short-term spectral features have been used to represent audio files. Three methods for feature selection and feature construction are implemented and tested on these features. Results obtained on ASVspoof 2017 version 2 corpus indicate that entropy based feature selection approach gains 9.98% relative improvement over other approaches for feature reduction studied in this work, and an overall performance gain of 13.2% in terms of equal error rate reduction.
STUDY OF THE EFFECT OF EXAMINATION STRESS ON THE INDICES OF THE MENTAL WORKING CAPACITY OF UNIVERSITY STUDENTS

Introduction. In this study, there were examined features of the impact of exam stress on mental performance and anxiety level in 2-3 year students of medical and pedagogical universities. Materials and methods. The levels of reactive and personal anxiety were studied according to the C. Spielberger anxiety scale adapted by Yu. L. Khanin, attention was assessed using the Pieron-Rüser test, the Bourdon-Anfimov correction test was used to study the level of mental performance, the type of temperament was determined using a two-factor model of extraversion and neuroticism using questionnaire H.J. Eysenck. The study was implemented in two stages: the first stage - during the semester period, and the second stage - during the examination session. For processing the data obtained during the study, a standard statistical software package was used, and correlation analysis was used to identify the relationships between the variables studied. Results. The assessment of situational anxiety in the second stage showed a statistically significant increase in the number of students with high and extremely high levels of anxiety in 24 and 3 respondents from the 1 group respectively (p ≤ 0.05), and 17 and 3 respondents in group 2, respectively (p ≤ 0.05). Correlation analysis of the relationship between reactive anxiety and mental performance showed a weak and medium correlation among students in both groups. The attention study in both groups showed that in the second stage of the study, the indices change: in 1 group, the proportion of students with a low level of attention increased to 40%, and in 2 the number of students with low and very low level of attention decreased by 2.77% and 11.08%, respectively (p ≤ 0.05). Conclusion. Increased study loads and requirements for students were proved to contribute to an increase in the level of reactive anxiety, in the period between sessions indices of mental performance in students decrease, respectively, and the level of attention declines.
Translation technology evaluation research

Central to the successful research, development and application of translation technology is its evaluation. Accordingly, there has been a growing demand to evaluate translation technologies based on the systematic and rigorous application of scientific methods to assess their design, development and outcomes. Diverse needs of different stakeholders have led to a proliferation of evaluation methods and measures involving human- and machine-based approaches that are used across academia and industry research contexts. The chapter provides a critical description of contemporary direct and indirect translation technology evaluation research in order to identify trends, emerging issues and potential solutions. It traces advances made in the field to date and indicates remaining gaps calling for further work. The author anticipates future research in the field to be able to deliver more standardized and inclusive approaches towards more meaningful and nuanced evaluation indicators for the different stakeholders involved in the process and products of translation.
Public WiFi Security Network Protocol Practices in Tourist Destination

Paper addresses security issues with public access WiFi networks, with emphasis on networks deployed in touristic places, because of their popularity. Such networks are often poorly administered and guarded whereas tourist-services they support, are massively used by thousands of occasional users daily. With intention to put emphasis on the security awareness of the users, filed research was conducted to investigate current security preferences of wireless computer networks in tourist destination, in the City of Zadar, Croatia. The research was conducted during preparation and early in the tourist season, spring/summer 2018. Hardware research support include AP beacon used a TL-WN722N card with a data rate of 150 Mbps, a 5 db antenna, a chip Atheros AR9271, all powered by Linux operating. Small suite was a passive scan tool for the beacon area. The data set used include the default AP settings that transmits its current SSID every 100 ms. WLAN card was used in the vehicle that was set up in the monitor mode used to collect all the available beacon frames. In addition to field research, we conduct additional survey with aim to investigate the general habits of users of wireless computer networks, from personal perspective. Overall goal was to put attention on WiFi security awareness and to expose security behaviour at router level.
Cross-channel Communication Networks

Convolutional neural networks process input data by sending channel-wise feature response maps to subsequent layers. While a lot of progress has been made by making networks deeper, information from each channel can only be propagated from lower levels to higher levels in a hierarchical feed-forward manner. When viewing each filter in the convolutional layer as a neuron, those neurons are not communicating explicitly within each layer in CNNs. We introduce a novel network unit called Cross-channel Communication (C3) block, a simple yet effective module to encourage the neuron communication within the same layer. The C3 block enables neurons to exchange information through a micro neural network, which consists of a feature encoder, a message communicator, and a feature decoder, before sending the information to the next layer. With C3 block, each neuron accounts for the channel-wise responses from other neurons at the same layer and learns more discriminative and complementary representations. Extensive experiments for multiple computer vision tasks show that our proposed mechanism allows shallower networks to aggregate useful information within each layer, and performances outperform baseline deep networks and other competitive methods.
An Ontology-Based Approach for Developing Offshore and Onshore Process Equipment Inspection Knowledge Base

Abstract A collection of inspection results is an essential input in assessing and managing the technical integrity of offshore and onshore process equipment. The inspection results enable the current condition of the equipment, the type of damage mechanism, and the severity and location of the degradation to be investigated. Typically, the inspection results are documented in a text-format report and stored in a conventional data repository. Conventional inspection data storage has drawbacks in terms of the sharing, exchange, and retrieval of information within an inspection knowledge domain, due to the lack of knowledge representation. This study proposes an ontology-based approach for developing an inspection knowledge base, in order to improve the degree of retrieval, distribution, and administration of inspection results. Ontology provides a semantic structure and relations for concepts in the inspection knowledge domain, which facilitate semantic search capability and enable increased utilization, enhanced communication and improved exchange of inspection information. A case study of a static equipment inspection is shown, to demonstrate the application of an ontology-based approach in facilitating data and information retrieval from an inspection knowledge base.