Reviewerly/exHarmony huggingface dataset:
Comprehensive Java Metadata Tracking for Attack Detection and Repair

We present ClearTrack, a system that tracks meta-data for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow/underflow and SQL/command injection vulnerabilities. Contributions include new techniques for eliminating false positives associated with benign integer overflows and underflows, new metadata-aware techniques for detecting and nullifying SQL/command command injection attacks, and results from an independent evaluation team. These results show that 1) ClearTrack operates successfully on Java programs comprising hundreds of thousands of lines of code (including instrumented jar files and Java system libraries, the majority of the applications comprise over 3 million lines of code), 2) because of computations such as cryptography and hash table calculations, these applications perform millions of benign integer overflows and underflows, and 3) ClearTrack successfully detects and nullifies all tested integer overflow and underflow and SQL/command injection vulnerabilities in the benchmark applications.
Scale Normalization Cascaded Dense-Unet for Prostate Segmentation in MR Images

Automated and accurate prostate segmentation technique from magnetic resonance images plays an important role in diagnostic and radiological planning. However, this task faces the challenge of extreme scale variation of prostate glands presented in the slices at different locations of MRI volumes. To alleviate problems arising from scale variation. We propose a cascaded prostate segmentation model that includes three stages: Coarse segmentation, segmentation result refinement, and scale normalization segmentation. Segmentation result refinement can remove the coarse segmentation results that do not contain prostates. More importantly, it normalizes the scale of the prostate region on different slice images of the same nuclear magnetic resonance volume according to the result of the coarse segmentation, thereby making the scale normalization segmentation network obtain scale-invariant magnetic resonance images as input. The experimental results demonstrate that this design can significantly reduce the degradation of segmentation performance arising from large scale variation.
EagleQR: An Application in Accessing Printed Text for the Elderly and Low Vision People

Most people ignore the difficulties for the elderly and low vision people to access the printed text, which is of critical importance in their daily lives. To resolve this issue, an application named Voiceye is designed to get the audio text by scanning the QR code. But this application has the limitation on the length of the printed text and takes no account of information security. In this paper, we design an advanced application EagleQR that store encrypted URL instead of the whole printed text. On one hand, we convert the printed text into online version and then encrypt and compress its URL into the QR code on the server. On the other hand, we decompress and decrypt the data in the QR code and then get the audio text to the client. This application has significance to broaden the information access to the printed text for the elderly and low vision people.
CASMI—An Entropic Feature Selection Method in Turing’s Perspective

Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small.
Evaluation of Bearing Capacity of Steel Rotation Pile

Steel Rotation Pile (SRP) is one kind of steel pipe pile (SPP) that has a helical blade welded to the tip. SRP has known as an environmentally friendly construction method because of no excavated soil, less noise and vibration, and no use of concrete or bentonite so that SRP is good for urban area construction. The enlarging of the blade enables SRP to have a large bearing capacity and pulling resistance capacity. This paper shows those structural characteristics statistically such as the reliability of end bearing capacity and the availability of shaft friction capacity. This paper presents the consistency between the results of the SRP vertical loading test performed in Japan and the bearing capacity formula of TCVN 11520:2016.
Optimization of Computer Resources Using OpenStack Private Cloud

The rapid development of computer technology and applications has caused the increasing need for computer use in an organization. The performance of personal computers has increased rapidly, but they cannot be utilized properly. Therefore, personal computers can be used together to perform parallel processing, in order to shorten data processing time. This paper discusses Private cloud, which could be utilized as an alternative solution to provide cloud services to organizations that are in the same network. The service provided was Infrastructure as a Service (IaaS), where users can request certain types of services and use certain operating systems.
A Visual Attention Computational Model Based on Edge Detection

In view of the inadequacy of Itti's computational model of visual attention mechanism in significance region detection, an improved computational model of visual attention based on edge detection was proposed here. It is based on the human eye's perception advantage of the edge shape information of the target object. On the basis of Itti model, this model can improve the extraction effect of significant regions in visual attention computing model by introducing edge information, and can segment significant regions more accurately. The experiment shows that the success rate of target object contour detection in this method can reach 91%, which is higher than the traditional detection method in calculation speed, and the target object contour recognition effect is better.
An improved scheme of searchable encryption algorithm based on NTRU

Abstract R. Behnia et al proposed a NTRU-based searchable encryption scheme in 2017. Compared with the previous searchable encryption scheme, this scheme has a great improvement in security and efficiency, and can resist quantum computing. But this scheme will expose a lot of information to the server. If the server is not completely honest, an attacker can use the information to attack a user’s keywords. This paper proposes an improvement of Rouzbeh’s scheme, solves this problem and improves security.
Personalized Micro-video Recommendation Based on Multi-modal Features and User Interest Evolution

Personalized recommendation plays a critical role in maintaining active users for most micro video applications. Previous methods up to now are not always feasible, such as the problems of cold-start and mismatch meta-data. In this paper, we propose a novel method, named Multi-Modal Interest Evolution (MMIE), to deal with these problems. MMIE consists of three parts: feature extraction, user interest modeling and personalized recommendation. We extract multi-modal features rather than unreliable meta-data to represent videos’ content, which alleviates the problem of video cold-start. Furthermore, considering the diversity and evolution of user interests, we design a new evolving layer to capture the interest evolving process related to the target video. The combination of Gated Recurrent Unit and attention mechanism in this layer strengthens the effect of relevant interests and improves the accuracy of recommendation. Experiments on real-world dataset show that the MMIE performs better than other state-of-the-art methods. Moreover, the MMIE achieves 7.37% improvement on NDCG@10 by introducing multi-modal features.
Deployment of EMC-Compliant IC Chip Techniques in Design for Hardware Security

IC chips are key enablers of densely networked smart society and need to be more compliant to security and safety. The talk will start from Electromagnetic Compatibility (EMC) techniques of IC chips on the safety side, toward EMC aware design, analysis and implementation. Then, the challenges will be discussed about the deployment of such EMC techniques in the design of IC chips for the higher level of hardware security. In detail, the talk will start with Silicon experiments on electromagnetic susceptibility (noise immunity) and electromagnetic interference (noise emission) of IC chips in automotive applications, covering on-chip/in-place noise measurement (OCM) and chip-package-system board (C-P-S) simulation techniques. Then, the talk will evolve for side channel leakage analysis and resiliency by design in cryptographic IC chips.
Performance analysis in private and public Cloud infrastructures

The performance of Cloud services is a cross-cutting aspect of particular importance because it defines how the service is operating. The main purpose of this paper is to present the results of vaious performance evaluation tests run both in private and public Cloud environments. Performing such analysis helps recommending which type of Cloud is suitable with respect to the application specificity. In addition, tests performed in a private Cloud testbed environment allow recommending a certain type of hypervisor according to the user resource requirements.
Designing Technology to Encourage Healthy Eating at Work

Office vitality becomes increasingly crucial to improve individuals' quality of life [5]. Eating healthier at work can substantially promote health and vitality among office workers. Office environments and work routines offer good settings to apply healthy eating interventions [1]. In the meantime, many newly developed digital technologies, such as wearable sensors [3] and mobile apps [2] present opportunities to support healthy diet interventions. However, little is known about how to design health-promoting technologies and interventions to optimize office diet. This poster presents an experience sampling study to understand office workers' eating experiences within the workaday context and identify design opportunities to promote office diet optimization.
A Pipelined Energy-efficient Hardware Accelaration for Deep Convolutional Neural Networks

In this paper, a new architecture of an accelerator of a convolutional neural network is proposed. The suggested solution is pipelined and it reduces the band passing memory through the exploitation of sliding window images. Moreover, it is reconfigurable online at a convolution stride level. This proposal operates at a frequency equivalent to 280 mhz and offers a performance of 3.36 GMAC. The energy consumption is 475mw.
Image Segmentation of Salt Deposits Using Deep Convolutional Neural Network

Identifying if a subsurface target is salt or not automatically and accurately is of vital importance to oil drilling. But unfortunately, obtaining the precise position of large salt deposits is very difficult. Professional seismic imaging still requires the interpretation of salt bodies by experts. This leads to very subjective, highly variable renderings. More alarmingly, it leads to potentially dangerous situations for drillers in oil and gas companies. In this paper, a Squeeze-Extraction Feature Pyramid Networks (referred to as Se-FPN) was proposed to tackle the task of image segmentation of salt deposits. Specifically, we utilized SeNet as backbone so as to implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for the task. Considering the importance of multi-scales information, we proposed an improved FPN to integrate information of different scales. In order to further fuse the information from multiple scales, the Hypercolumns module was inserted at the end of the network. The proposed Se-FPN has been applied to the TGS Salt Identification Challenge and achieved high quality segmentation effect. The Mean Intersection over Union value can reach 0.86.
How University Acts in the Development of “Smart Cities”

“Smart city” is a new model of territorial development, taking into account the growing importance of information, innovation and human capital. One of the main elements of the “smart city” is a developed system of higher education. The aim of this work was to study the impact of higher education on "smart" and innovative cities. The objectives of this work were to obtain quantitative characteristics of the impact of the University on the smart city. Approach to solving this problem was to build indicators based on indicators that characterize the quality of life, and ratings to compare cities on their basis. The hypothesis of the study is that the higher education system has a positive impact on the development of smart and innovative cities. A theoretically justified method of constructing an indicator of a certain direction of socio-economic development is a component analysis of indicators characterizing this direction. As a result, the rankings of Russian and foreign cities based on the characteristics of quality of life, which prove that education is a key indicator of the development of "smart" and innovative cities.
Performance Inspection of Denoising Method Implemented by Decision Based Adaptive Median Filter for Using under RVIN Condition

In 2010, the DBAMF (decision based adaptive median filter) denoising method based on ROAD that was proposed for eradicating an impulsive noise because ROAD (Rank Order Absolute Difference) has the obviously high performance in noisy classification and is mathematically developed from similar measurement method in computer vision for identifying whether the pixel is contaminated by impulsive noise or the noise-free. For that reason, the scholar thesis article intents to inspect the performance of the denosing method implemented by DBAMF for RVIN (Random Value Implosive Noise) condition. For inspecting the performance and its impediment of the DBAMF denoising method, the two worldwide assessment images integrated of Girl and Airplane has been manipulated in these performance inspections under RVIN condition by originally denigrated by accumulating the RIVN at generous power. Later, every denigrated image is enhanced by the denosing method implemented by DBAMF. For indicating the DBAMF performance, the classical SMF (Standard Median Filter) and AMF (Adaptive Median Filter) are comparatively integrated in this performance inspection.
Information-Analysis Services based on Scientific News in the Academy Research Libraries

In the context of the development of modern information and communication environment, the top challenge of library institutions is to renew their activities and fill it with new functionalities. As science communications advance in accordance with the general evolution of the web sphere, the organization systems of information support for scientific research therefore undergo changes too. Scientific and academic libraries with their unique position in science communications are faced with the need to rethink their role and functions, with the problem of finding new ways of information and library support to research activities, and new services that meet the needs of modern scientists. Since one of the indicators for scientific institutions reporting is the number of institution references in the media, scientific libraries have begun to provide science news services. The authors consider the news information resources and services for scientific research institutions. The paper presents a brief description of news aggregator “Siberian Science News”, an information platform for distribution, storage and browsing of factual, bibliographic and full-text data. Based on this platform, staff members of the Branch of the State Public Scientific-Technological Library of the Siberian Branch of the Russian Academy of Sciences (SB RAS) have set up a service to provide specialists with information to maintain and update the online news feeds of the research institutions of the Novosibirsk Scientific Center of SB RAS. The authors note that currently, according to the preferences of subscribers, there are used two types of information-analysis reports based on media publications. The results of the conducted research allow the authors to conclude that there is a need for more active promotion of the resource “Siberian Science News” and the service for the maintenance of news information. In the case of the research institutions, the service maintains the development of their news pages and news feeds on the websites of organizations, which in turn makes the organizations more visible in science communication environment and promotes research areas and scientific results.
Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models

Automatic detection of Leukemic B-lymphoblast cells from normal cells is both crucial and difficult with subject variability being a major obstacle. While deep learning models are widely adopted in image recognition and demonstrate state-of-the-art performance, deep learning models in their vanilla form do not provide an estimate of prediction uncertainty. This is especially problematic when there is a high discrepancy between training and testing data such as in blood smear images where subject level variability and sample noise from staining and illumination are non-negligible. To address these issues, we propose a novel ensemble method by weighting each base model according to its respective predictive confidence obtained from Monte Carlo dropout. Our ensemble method can effectively prevent overfitting and increases classification performance compared to any single model result. Furthermore, we demonstrate the model’s ability to identify samples that do not fall in the training data distribution. We achieved 0.8929 prediction score according to the leaderboard on the SBILab challenge website.
Exponential Distribution Parameter Estimation with Bayesian SELF Method in Survival Analysis

Abstract This Paper discussed the Exponensial distribution parameter estimation using Bayesian SELF method in survival analysis with <?CDATA $\hat \theta $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mi>θ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> </mml:math> as SELF Bayesian estimator for the θparameter. Survival analysis corresponds to a method that relates to time starting from the start point to the occurrence of a particular event or an endpoint. The event referred in this research is the event of death. The distribution used in this research is an exponential distribution. Bayesian SELF estimation of <?CDATA $\hat \theta $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mi>θ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> </mml:math> on exponential distribution for the censored data was obtained by minimizing expectations of loss function. Application of Bayesian SELF method on acute coronary syndrome patient’s data, it was obtained <?CDATA $\hat \theta $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mi>θ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> </mml:math> = 0.18583 that indicates the patients’ probability to survive is high and the probability for the patients to fail is low.
The Russian State Library at the 32nd Moscow International Book Fair: New Event-Image — New Format of Participation

The 32nd Moscow International Book Fair (MIBF) was held on 4—8 September 2019. The purpose of this article is to rethink the role of the Russian State Library (RSL) in the MIBF event and communication space: on the one hand, as one of the subjects of bookwork and preserver of cultural values, and on the other hand — as the largest library publisher. The concept of the RSL stand as the exhibitor at the exhibition-fair was developed and built on the basis of this binary mode of the Library, with emphasis on those elements that reflect the chain of transition from the early printed book to the modern printed book, and from it — to the new digital formats. The RSL stand contained the dedicated areas of the “Pashkov Dom” Publishing house and the Scientific research Department of rare books (Book Museum), the Module of preservation and restoration of collections, the Advisory-expository legal module providing communications with publishers and authors on the issues of the National Electronic Library (NEL) and legal deposit copy of the printed publications in electronic form. The content program of stand work was correspondingly diverse and rich. It presented new products of the “Pashkov Dom” Publishing house, new projects prepared for publication independently and in cooperation with partners. The article considers the main theses of the authors, compilers and publishers, who made presentations at the RSL stand, as well as the topics of public lectures in open areas. The author reveals the new perspectives of NEL as one of the leading projects of the RSL, which combines different models of relationship between publishers, authors and libraries. The article highlights the results of the library competition of the Russian Library Association (RLA) “Best Professional Book of the Year”. The new format of participation of the RSL in exhibitions, fairs and similar events has been set. This format, in turn, forms a new event-image of the RSL, which positions the Library not only as an Institute of memory, which preserves the greatest cultural heritage — books, as a centre of intellectual development and professional interaction, but also as an active participant in the book publishing process.
Calibrated Multi-probabilistic Prediction as a Defense Against Adversarial Attacks

Machine learning (ML) classifiers—in particular deep neural networks—are surprisingly vulnerable to so-called adversarial examples. These are small modifications of natural inputs which drastically alter the output of the model even though no relevant features appear to have been modified. One explanation that has been offered for this phenomenon is the calibration hypothesis, which states that the probabilistic predictions of typical ML models are miscalibrated. As a result, classifiers can often be very confident in completely erroneous predictions. Based on this idea, we propose the MultIVAP algorithm for defending arbitrary ML models against adversarial examples. Our method is inspired by the inductive Venn-ABERS predictor (IVAP) technique from the field of conformal prediction. The IVAP enjoys the theoretical guarantee that its predictions will be perfectly calibrated, thus addressing the problem of miscalibration. Experimental results on five image classification tasks demonstrate empirically that the MultIVAP has a reasonably small computational overhead and provides significantly higher adversarial robustness without sacrificing accuracy on clean data. This increase in robustness is observed both against defense-oblivious attacks as well as a defense-aware white-box attack specifically designed for the MultIVAP. We make our code available at https://github.com/saeyslab/multivap .