Reviewerly/exHarmony huggingface dataset:
Framework for concealing medical data in images using modified Hill cipher, multi-bit EF and ECDSA

Digitisation spread to the core area of the medical field and one of the results is telemedicine. Telemedicine provides the transfer of medical information of patients such as medical results, reports and images which closely deals with privacy. The real essence of telemedicine application relies on the secure storage and transfer of medical data over unsecured platform or network where the patient is remotely located from the service provider. The proposed work focused on combining security aspects into a single framework providing the CIA triad of security, i.e., confidentiality, integrity and authentication to achieve the above criteria, we use modified Hill cipher and multi-bit encoding function (EF) for confidentiality, ROI-based hashing for integrity and elliptic curve digital signature algorithm (ECDSA) for authentication. The experimental results prove the framework have a better embedding capacity with a comparatively higher PSNR value.
Decidability of compatibility for data-centric web services

The problem of checking compatibility of data-centric services is discussed herein. It focuses, more specifically, on compatibility of data-centric services' protocols such that a service protocol is the description of the service's external behaviour. Our study comprises two parts: the first part consists of checking the services' protocols compatibility, which is represented by the same database instance with different queries. Here, we prove that the verification problem is decidable; the second part consists of studying the same problem but including a database. Hence, the problem of data infinity, which remains undecidable although the use of classic verification algorithms. This study is concluded by the implementation of a verification tool based specifically on guarded services with a finite database.
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification

Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, and then fine-tuning the last few layers in order to capture specific information related to the target situation. This approach is particularly useful when only limited or weakly labeled data are available for the new task. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models, especially if only limited data are available for the new domain task. Further, we observe that adversarial training biases the learnt representations to retaining shapes, as opposed to textures, which impacts the transferability of the source models. Finally, through the lens of influence functions, we discover that transferred adversarially-trained models contain more human-identifiable semantic information, which explains -- at least partly -- why adversarially-trained models transfer better.
ИСПОЛЬЗОВАНИЕ ДАННЫХ И ПРОДУКТОВ ОРГАНИЗАЦИИ ДОГОВОРА О ВСЕОБЪЕМЛЮЩЕМ ЗАПРЕЩЕНИИ ЯДЕРНЫХ ИСПЫТАНИЙ В СЕЙСМИЧЕСКОМ МОНИТОРИНГЕ РОССИИ В ОПЕРАТИВНОМ РЕЖИМЕ

The paper describes experience of Geophysical Service (GS) RAS gained from use of CTBTO data and its data products as well as of KNDC data and data products in seismic monitoring in Russia and in bordering areas in near-real time mode data processing in 2016–2017. It is demonstrated the usage of waveforms, arrivals and Bulletin SEL1 data when in location by EEAS events on Russian territory.
Efficient T2 brain region extraction algorithm using morphological operation and overlapping test from 2D and 3D MRI images

The main purpose of segmentation in MRI images is to diagnose the problems in the normal brain anatomy and to find the location of tumour. Many of the algorithms have been found in recent years which aid to segment the medical images and identify the diseases. This paper proposes a novel 3D brain extraction algorithm (3D-BEA) for segmentation of MRI images to extract the exact brain region. Transverse relaxation time (T2) weighted images are used as an input for the development of algorithm as these images provide bright compartments and dark fat tissues in the MRI brain region. The images are first denoised and smoothed for further processing. The final brain volume has been generated using this 3D-BEA process. The result of this developed proposed algorithm is validated by comparing proposed algorithm with the results of the existing segmentation algorithm.
Unlabeled facial expression capture method in virtual reality system based on big data

In view of the problems of high error rate and low efficiency in the traditional method of facial expression capture without markers, a method of facial expression capture without markers based on large amount of data is proposed. Haar feature is used to determine the initial position of human face, and active shape model is used to extract unmarked facial feature points. The extracted feature points and the generated triangle mesh are tracked by the optical flow tracking method. The displacement of the face feature points is used to promote the overall change of the mesh and complete the unmarked facial expression capture. The experimental results show that the error rate of this method is in the range of 1.2%-1.7%, the error rate is small, and it needs 20 s-34 s to capture facial expression, which is more practical and efficient.
modum.io (B): Bidding Farewell to Crypto

After a successful initial coin offering (ICO) in the fall of 2017, the Switzerland-based supply chain technology company modum.io began trading its MOD token on various exchanges. The financial resources generated from the token sale set up the modum.io team to focus on operations, launch the company’s product, increase revenue, onboard new customers, and hire new staff.In the course of the cryptocurrency bear market of 2018–2019, however, the value of the MOD token deteriorated to the point where it was delisted from major exchanges. Mainly caused by the lack of regulatory compliance of security tokens on the exchanges, these delistings partially killed the liquidity of the MOD token. In light of market and regulatory developments, and with external pressure increasing, modum.io scrambled to identify alternatives to solve its predicament. In 2019, cofounder Marc Degen and his partners decided to elaborate different options in order to secure the future of modum.io while simultaneously offering a satisfactory solution for all stakeholders. In their discussions, they saw three potential paths forward from which they must choose: (1) continue as is, (2) convert outstanding tokens into some form of company equity, or (3) liquidate the company and distribute the proceeds to the share- and tokenholders. Because this decision was strategically crucial for the future of modum.io, the board of directors did not want to rush to a conclusion; they preferred to thoroughly review the benefits and pitfalls of each option.
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction

Reinforcement Learning (RL) agents have great successes in solving tasks with large observation and action spaces from limited feedback. Still, training the agents is data-intensive and there are no guarantees that the learned behavior is safe and does not violate rules of the environment, which has limitations for the practical deployment in real-world scenarios. This paper discusses the engineering of reliable agents via the integration of deep RL with constraint-based augmentation models to guide the RL agent towards safe behavior. Within the constraints set, the RL agent is free to adapt and explore, such that its effectiveness to solve the given problem is not hindered. However, once the RL agent leaves the space defined by the constraints, the outside models can provide guidance to still work reliably. We discuss integration points for constraint guidance within the RL process and perform experiments on two case studies: a strictly constrained card game and a grid world environment with additional combinatorial subgoals. Our results show that constraint-guidance does both provide reliability improvements and safer behavior, as well as accelerated training.
A Low Power Architecture for 1D Median Filter using Carry Look Ahead adder

In image and signal processing applications, it is very essential to suppress the noisy signal while protecting the required information. In this paper, one dimensional median filter is used to red...
The Effect of Application of Cooperative Script Learning Model on Social Studies Learning Outcomes of Grade V Students of SD Negeri 35 Parak Karakah, Padang City

Education is a process of human interaction between education and students' subjects to achieve educational goals. The process takes place in certain environments using a variety of actions called educational tools.Wahidmurni (2017: 17) states that Social Sciences are subjects that are sourced from the social life of the people who are selected by using social science concepts that are used for learning purposes. The use of learning models in the learning process has significant significance. The learning model is a plan or concept to help students in the learning process and understand learning easily. By using models in learning, will help eliminate the boredom of students in receiving lessons. One way is to use Cooperative Script learning models in social studies learning.This type of research is an experimental research design Post-test-only control design. The sample in this study were 24 VA class students and 21 VB class students. The technique in sampling is by total sampling. The data taken in this study are cognitive learning outcomes of cognitive domain students obtained through the final test in an objective form.The results showed the average value of the experimental class was higher than the control class (the experimental class the average value of 82.3 and the control class an average value of 76.4). In statistical test the price of tcount> t (0.05; 43) (2.11> 2.02) with a level (α = 0.05) then the H1 hypothesis is accepted.From these data it can be concluded that the Cooperative Script learning model influences the learning outcomes of fifth grade students in social studies learning at SD Negeri 35 Parak Karakah, Padang. Thus the Cooperative Script learning model is expected to be used in the learning process to have a better influence on student learning outcomes.Keywords: Cooperative Script, learning outcomes, social studies.
Performance evaluation of a computational model for brain shift calculation

This article shows a solution for the computation of deformable tissues displacements in the brain shift problem in neurosurgery. In this type of surgery, the brain moves and deforms, changing the pre-surgical reference the surgeon had before the intervention. Among the causes of brain shift are the effect of gravity, loss of cerebrospinal fluid as a consequence of the resection practiced, the effect of the drugs supplied, among others. This document refers to the physical model of this displacement to later simulate them in multiphysics software. A phantom test was constructed by means of hydrogels, imitating the porcine brain tissue, which is subjected to compression along the z-axis. The results show that the simulation proposed reproduces the behaviour of the real phantom with a high level of accuracy. The application developed may serve in the future to reproduce the total behaviours of the brain and thus obtain a better calculation of the brain shift.
Towards Differential Query Service in Cost-Efficient Clouds

Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server without leaking any information.
The impacts of Knowledge Management on Organizational Entrepreneurship with the Moderating Role of Social Capital in the Melli Bank (Mashhad Branches)

The main goal of this research is the relationship between knowledge management and entrepreneurship with the role of social capital adjustment in Melli banks' employees. The sample was analysed using Cochran table and two-stage cluster sampling of the 117 employees of the Mashhad's Melli's bank. In this research, four standard questionnaires are used to measure the variables under study. The results of this study showed that there is a relationship between knowledge management and organisational entrepreneurship, and the social capital has a moderating effect on knowledge management and entrepreneurship.
字音定義한 代表 字義의 교육적 대안 모색 -중학교 한문교육용 기초한자 900자를 중심으로-

Of the 900 basic Chinese characters for middle school Chinese characters education used in the middle school Chinese characters textbook written according to the 2015 revised Chinese characters subject curriculum, this study attempts to find an educational alternative for meanings of Chinese characters of which the representative meaning was defined with the character pronunciation, and intends to contribute to the spread of research on this and to reflect it in the Chinese characters textbook of middle schools that will be written according to the next curriculum. In 17 kinds of middle school Chinese characters textbooks written according to the 2015 revised Chinese characters subject textbook curriculum, the Chinese characters presented as the representative meaning of a Chinese character by character pronunciation are 34 characters such as Gak(各) character. Among these, through a questionnaire survey, this study examined the degree of understanding of the meaning of a Chinese character for 17 characters, such as Gong(功) character, which are frequently presented. As a result, the students understood 9 characters such as Mun(門), Bang(房), Seong(城), Shi(時), Yak(藥), Yang(羊), Eun(銀), Chaek(冊), Hyeong(兄) as the original meaning of the meaning of a Chinese character, but they had a low degree of understanding for the remaining 8 Chinese characters such as Gong(功), Deok(德), Ban (半), Byeong(病), Bok(福), Seong(姓), Chang(窓), Han(恨). As for the search of educational alternatives for Chinese characters defining the meaning with the pronunciation, 8 meanings of Chinese character meanings of Chinese characters, such as Gong(功) character, were given to the same students in the order presented in the dictionary of classical Chinese characters, and among them, the Chinese characters that are easy to understand were numbered in the order of 1, 2, and 3, and weight was added to determine the degree of understanding. As a result, Gong(功) character should present the representative meanings of the Chinese character as ‘merit’ or ‘official’, Deok(德) character should be presented as ‘moral', Ban(半) character should be presented in 'half', and Byeong(病) character should be presented as ‘disease'. Bok(福) character should be presented as ‘happiness', Seong(姓) character should be presented as ‘surname', Chang(窓) character should be presented as 'window', and Han(恨) character should be presented as ‘to lament.’ This study is only an attempt to find various problems and educational alternatives of the meaning of a Chinese character that defines the meaning of a Chinese character with the character pronunciation. In future, it is hoped that many researchers will be interested in this and that it will be a reference for the spread of research, and when writing the next Chinese character textbook.
Deep Multi-View Stereo gone wild.

Deep multi-view stereo (deep MVS) methods have been developed and extensively compared on simple datasets, where they now outperform classical approaches. In this paper, we ask whether the conclusions reached in controlled scenarios are still valid when working with Internet photo collections. We propose a methodology for evaluation and explore the influence of three aspects of deep MVS methods: network architecture, training data, and supervision. We make several key observations, which we extensively validate quantitatively and qualitatively, both for depth prediction and complete 3D reconstructions. First, we outline the promises of unsupervised techniques by introducing a simple approach which provides more complete reconstructions than supervised options when using a simple network architecture. Second, we emphasize that not all multiscale architectures generalize to the unconstrained scenario, especially without supervision. Finally, we show the efficiency of noisy supervision from large-scale 3D reconstructions which can even lead to networks that outperform classical methods in scenarios where very few images are available.
Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images

The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing CAD schemes, Machine Learning (ML) plays an essential role because it is widely used to identify effective image features from complex datasets and optimally integrate them with the classifiers, which aims to assist the clinicians to more accurately detect early disease, classify disease types and predict disease treatment outcome. In my dissertation, in different studies, I assess the feasibility of developing several novel CAD systems in the area of medical imaging for different purposes. The first study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict the likelihood of cases being malignant. CADx scheme is applied to pre-process mammograms, generate two image maps in the frequency domain using discrete cosine transform and fast Fourier transform, compute bilateral image feature differences from left and right breasts, and apply a support vector machine (SVM) method to predict the likelihood of the case being malignant. This study demonstrates the feasibility of developing a new global image feature analysis based CADx scheme of mammograms with high performance. This new CADx approach is more efficient in development and potentially more robust in future applications by avoiding difficulty and possible errors in breast lesion segmentation. In the second study, to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, I investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. To this purpose, a computer-aided image processing scheme is applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, an embedded LLP algorithm optimizes the feature space and regenerates a new operational vector with 4 features using a maximal variance approach. This study demonstrates that applying the LPP algorithm effectively reduces feature dimensionality, and yields higher and potentially more robust performance in predicting short-term breast cancer risk. In the third study, to more precisely classify malignant lesions, I investigate the feasibility of applying a random projection algorithm to build an optimal feature vector from the initially CAD-generated large feature pool and improve the performance of the machine learning model. In this process, a CAD scheme is first applied to segment mass regions and initially compute 181 features. An SVM model embedded with the feature dimensionality reduction method is then built to predict the likelihood of…
Road signs recognition: state-of-the-art and perspectives

Making cars safer is a crucial element of saving lives on roads. In case of inattention or distraction, drivers need a performant system that is capable of assisting and alerting them when a road sign appears in their field of vision. To create such type of systems, we need to know first the major difficulties that still face traffic signs recognition, as presented in the first and second sections of this paper. We should also study the different methods proposed by researchers to overcome each of these challenges, as proposed in the third section. Evaluation metrics and criteria for proving the effectiveness of these approaches represents also an important element which section three of this article presents. Ameliorating the existing methods is crucial to ensure the effectiveness of the recognition process, especially by using deep learning algorithms and optimisation techniques, as discussed in the last section of this paper.
A novel multi-purpose watermarking scheme using multiple watermarks

Most of the existing digital watermarking schemes mainly target a single or a few required objectives. The novelty of the proposed technique is to meet the four main objectives of the digital water...
FairAccess 2.0: a smart contract-based authorization framework for enabling granular access control in IoT

In this paper, we explore access control area as one of the most crucial aspect of security and privacy in IoT. Actually, conventional security and privacy solutions tend to be less tailored for IoT. Then, designing a distributed access control with user-driven approach and privacy-preserving awareness in an IoT environment is of paramount importance. In this direction, we have investigated in our previous work a new way to build a distributed access control framework based on the blockchain technology through our proposed framework, FairAccess. The first version of FairAccess was based on the Bitcoin's UTXO model. However, this version presented limitations in expressing more granular access control policies. To tackle this issue, this paper upgrades the proposed framework to FairAccess2.0 that uses SmartContract concept instead of the locking/unlocking scripts. Thus, we show a possible working implementation based on ABAC policies, deployed on the ethereum blockchain. The obtained results show the efficiency of FairAccess2.0 and its compatibility with a wide range of existing access control models mainly the ABAC model. Finally, a performance and cost evaluation, discussion and future work are elaborated.
The UCLA Library and Wikidata: A Pilot

Author(s): Zhang, Erica; Dagher, Iman; Biswas, Paromita | Abstract: Since the fall of 2020, the UCLA Library has been participating in the Program for Cooperative Cataloging’s Wikidata Pilot, an initiative to explore contributing linked data to a free and public platform as well as incorporating linked data work and principles into current cataloging practices. This talk will introduce the linked data platform Wikidata and the benefits it provides, including opportunities to share siloed metadata with the public, serving as a hub of identifiers, and supporting multilingualism, among others. We will walk through current projects that the UCLA Pilot team is working on, and the lessons learned thus far. We hope that this talk will inspire our colleagues to think of other Wikidata projects and to spark new ideas and discussion about the potential of Wikidata in not just cataloging, but also broader research and scholarship.