Reviewerly/exHarmony huggingface dataset:
"Online Transaction Security Enhancement": An Algorithm Based on Cryptography

Online transaction has become daily routine in our life. All of us are using online transaction very frequently today, so to deal with related security issues like Authorization, data protection, confidentiality is very important. Every person who is doing online transaction, every organization and every government must have the ability to control over the operations of online transactions for securing the information. It is necessary to apply better security mechanism in online transaction for maintaining data integrity, privacy of data and confidentiality. Cryptography uses encryption and decryption process for protecting the data. There are many cryptographic algorithms which uses single key which is private or two keys called public key and private key. One can select the algorithm for transferring the data on the basis of higher security and efficiency criteria of the algorithm. This paper presents role of cryptography for different issues of security and also proposes a new cryptography based algorithm for better security of online transactions. The new algorithm is based on probabilistic cipher and since it uses single key so it is a symmetric encryption algorithm.
Research on Service-oriented UAV Route Planning Component Design

Abstract In the field of aviation equipment, with the development of airborne avionics systems towards systemized multi-platform coordination, the service-oriented architecture (SOA) has become an important trend for the development of open avionics system. Based on the idea of SOA, the service-oriented design of the UAV route planning component is proposed in this paper. Firstly, the functions of avionics are abstracted out from generally involved avionics missions with focus on the conception of avionics service. Then the function-directed servitization process of the UAV avionics resources is studied, based on which, a set of callable UAV route planning components are further constructed. The UAV route planning service component set can be flexibly deployed according to different task requirements, which solves the problem of resource-application coupling for the UAV avionics, and provides support for the intelligent and autonomous operation of UAV.
Development of a PBL Workflow Model and Classification of PBL Themes

Although PBL (Project Based Learning), which can practically cultivate problem solving ability, has attracted attention, it is difficult for teachers unfamiliar with PBL to provide guidance and facilitation for PBL and for PBL beginners to make progress well. In this study, to develop an adaptive support system for PBL practice, PBL themes were classified, and the representative procedure for each theme and the correspondence between the possible issues and the facilitation were clarified. As a result, it was found that themes can be classified into five types and categorized by eight kinds of activities.
Medical Information Forensics System Based on Convolutional Neural Network with Pattern Finding Prior

Hospital Information System HIS is an indispensable technical support environment and infrastructure for modern hospitals. It reflects the comprehensive management of a modern hospital. Many hospitals have established local networks and implemented some subsystems, such as inpatient toll collection systems and drug management systems, which have yielded some success. Medical information forensics is a comprehensive process of using scientific methods to collect network data, identify intrusion, analyze data, store data, determine the reason of intrusion tp enhance security equipment and trigger alarm procedures. In this paper, we study the concepts and techniques of medical information forensics, and discuss the definition, classification, sources and characteristics of medical information forensics. The deep learning model and the data analytic framework are combined to provide the comprehensive analysis of the research. The technique novelty is validated through the experiment. The simulation compared with the other state-of-the-art methodologies proves the efficiency of the method.
Area Optimized CNN Architecture Using Folding Approach

Recently Convolution Neural Network (CNN) has become very popular in AI applications. CNN requires a lot of computational resources that has significant impact on chip size. In this paper we proposed a folding architecture to improve area efficiency of CNN chip implementation. Using the folding architecture, we can significantly reduce the chip size depending on resource availability. The proposed method exploits the resource sharing within CNN algorithm. The scalability of the design is also maintained. Therefore, similar approach can be easily applied into many different CNN image and filter size. The architecture is also easily implemented using RTL design. The result shows the comparison between speed and size using many different combinations of architecture.
Stagewise Enlargement of Batch Size for SGD-based Learning

Existing research shows that the batch size can seriously affect the performance of stochastic gradient descent~(SGD) based learning, including training speed and generalization ability. A larger batch size typically results in less parameter updates. In distributed training, a larger batch size also results in less frequent communication. However, a larger batch size can make a generalization gap more easily. Hence, how to set a proper batch size for SGD has recently attracted much attention. Although some methods about setting batch size have been proposed, the batch size problem has still not been well solved. In this paper, we first provide theory to show that a proper batch size is related to the gap between initialization and optimum of the model parameter. Then based on this theory, we propose a novel method, called \underline{s}tagewise \underline{e}nlargement of \underline{b}atch \underline{s}ize~(\mbox{SEBS}), to set proper batch size for SGD. More specifically, \mbox{SEBS} adopts a multi-stage scheme, and enlarges the batch size geometrically by stage. We theoretically prove that, compared to classical stagewise SGD which decreases learning rate by stage, \mbox{SEBS} can reduce the number of parameter updates without increasing generalization error. SEBS is suitable for \mbox{SGD}, momentum \mbox{SGD} and AdaGrad. Empirical results on real data successfully verify the theories of \mbox{SEBS}. Furthermore, empirical results also show that SEBS can outperform other baselines.
Visual Analytics for Sets over Time and Space (Dagstuhl Seminar 19192).

This report documents the program and the outcomes of Dagstuhl Seminar 19192 Visual Analytics for Sets over Time and Space, which brought together 29 researchers working on visualization (i) from a theoretical point of view (graph drawing, computational geometry, and cognition), (ii) from a temporal point of view (visual analytics and information visualization over time, HCI), and (iii) from a space-time point of view (cartography, GIScience).The goal of the seminar was to identify specific theoretical and practical problems that need to be solved in order to create dynamic and interactive set visualizations that take into account time and space, and to begin working on these problems.The first 1.5 days were reserved for overview presentations from representatives of the different communities, for presenting open problems, and for forming interdisciplinary working groups that will focus on some of the identified open problems as a group. There were three survey talks, ten short talks, and one panel with three contributors.The remaining three days consisted of open mic sessions, working-group meetings, and progress reports.Five working groups were formed that investigated several of the open research questions. Abstracts of the talks and a report from each working group are included in this report.
Security System ATM Machine with One-Time Passcode on M-Banking Application

Automated Teller Machine (ATM) security system currently still uses magnetic cards and static PIN as its security system, which create many security holes. This security hole in many cases caused many bank customers to lose money mysteriously. In this paper a two-factor authentication system which uses ATM card and dynamic PIN is proposed to overcome this security hole. In this paper, a prototype of an ATM and m-banking application were built. The ATM prototype uses several components such as the Raspberry Pi 3B, smart card, smart card reader / writer, keypad number and LCD monitor. Dynamic PINs are generated using the CSPRNG-SHA1-MWC random number generator. In developing the prototypes, the framework used in this study is based on mobile applications and cloud computing. To evaluate the quality of the prototype, we performed qualitative and quantitative tests. Qualitatively we tested the prototype using a questionnaire using 165 sample respondents to provide an opinion about the safety and comfort of our prototype and quantitatively we measured the prototype to find out the level of randomness of the generated PIN and the QoS of the designed prototype.
Efficient Decentralized Tracing Protocol for Fingerprinting System with Index Table

Due to the burden at a trusted center, a decentralized fingerprinting system has been proposed by delegating authority to an authorized server so that the center does not participate in the tracing protocol. As a fingerprinting code is used to retain a collusion resistance, the calculation of correlation score for each user is required to identify illegal users from a pirated copy. Considering the secrecy of code parameters, the computation must be executed by a seller in an encrypted domain to realize the decentralized tracing protocol. It requires much computational costs as well as the communication costs between the center and a seller because encrypted database (DB) is necessary for the computation. In this paper, we propose a method to reduce such costs by using the EIGamal cryptosystem over elliptic curve instead of the Paillier cryptosystem used in the conventional scheme. Our experimental results indicate that the time consumption becomes almost 100 times shorter and the size of encrypted DB reduced by a factor of 7/32 under 112-bit security level. The encrypted DB is further compressed by introducing an index table.
Accountability and Community on the Internet: A Plea for Restorative Justice

Abstract In this article, I analyze norm enforcement on social media, specifically cases where an agent has committed a moral transgression online and is brought to account by an Internet mob with incongruously injurious results in their offline life. I argue that users problematically imagine that they are members of a particular kind of moral community where shaming behaviors are not only acceptable, but morally required to ‘take down’ those who appear to violate community norms. I then demonstrate the costs that are associated with this strategy; the most worrisome being those that distort the nature of moral dialog and the purpose and effectiveness of accountability practices online, creating a vitriolic and polarising online environment. Because of these negative consequences, I suggest that we ought to hold others accountable for restorative ends. I argue that restorative accountability practices can help us cultivate new norms online that rely less for their enforcement upon negative acts such as shame, and more upon positive acts that focus upon the most appropriate way to make amends to the victim(s) and the community. In this sense, restorative accountability incorporates important elements from the ethics of care, a relational ethics that values creating, promoting, and restoring good social and personal relationships. I conclude by arguing that accountability practices premised on the ethics of care produce better outcomes for the victim(s) of a moral violation, the transgressor, and the community.
DialGraph: Sparse Graph Learning Networks for Visual Dialog

Visual dialog is a task of answering a sequence of questions grounded in an image utilizing a dialog history. Previous studies have implicitly explored the problem of reasoning semantic structures among the history using softmax attention. However, we argue that the softmax attention yields dense structures that could distract to answer the questions requiring partial or even no contextual information. In this paper, we formulate the visual dialog tasks as graph structure learning tasks. To tackle the problem, we propose Sparse Graph Learning Networks (SGLNs) consisting of a multimodal node embedding module and a sparse graph learning module. The proposed model explicitly learn sparse dialog structures by incorporating binary and score edges, leveraging a new structural loss function. Then, it finally outputs the answer, updating each node via a message passing framework. As a result, the proposed model outperforms the state-of-the-art approaches on the VisDial v1.0 dataset, only using 10.95% of the dialog history, as well as improves interpretability compared to baseline methods.
Citation content/context data as a source for research cooperation analysis

Using citation relationships, one can build three groups of papers: (1) the papers of a selected author; (2) those papers cited by the author; (3) papers citing the author. Authors of papers from these three groups can be presented as a fragment of a research cooperation network, because they use/cite research outputs of each other. Their papers' full texts and especially the contexts of their in-text citations contain some information about the character of this research cooperation. We present a concept of research cooperation, based on publications and the current results of the Cirtec project for building the research cooperation characteristics. This work is based on the processing of citation content/context data. The results include an on-line service for authors to monitor the citation content data extractions and three types of built indicators/parameters: co-citation statistics, spatial distribution of citations over papers' body and topic models for citation contexts.
Code and Named Entity Recognition in StackOverflow

There is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet. For example, StackOverflow currently has over 15 million programming related questions written by 8.5 million users. Meanwhile, there is still a lack of fundamental NLP techniques for identifying code tokens or software-related named entities that appear within natural language sentences. In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types. We trained in-domain BERT representations (BERTOverflow) on 152 million sentences from StackOverflow, which lead to an absolute increase of +10 F-1 score over off-the-shelf BERT. We also present the SoftNER model which achieves an overall 79.10 F$_1$ score for code and named entity recognition on StackOverflow data. Our SoftNER model incorporates a context-independent code token classifier with corpus-level features to improve the BERT-based tagging model. Our code and data are available at: this https URL
Juice dispenser for hard-working students

We want to increase the quality of our educational system, so we developed a juice dispenser for the hard-working students. In order to stimulate the creativity and critical thinking among students we suggest online quizzes with credits. So, if you had enough credit you could get free juice from the juice dispenser. The paper presents how this juice dispenser works and an unusual way for getting the tastiest fruits juice using neural networks.
A security metric for assessing the security level of critical infrastructures

The deep integration between the cyber and physical domains in complex systems make very challenging the security evaluation process, as security itself is more of a concept (i.e., a subjective property) than a quantifiable characteristic. Traditional security assessing mostly relies on the personal skills of security experts, often based on best practices and personal experience. The present work is aimed at defining a security metric allowing evaluators to assess the security level of complex cyber-physical systems (CPSs), as critical infrastructures, in a holistic, consistent and repeatable way. To achieve this result, the mathematical framework provided by the open source security testing methodology manual (OSSTMM) is used as the backbone of the new security metric, since it allows to provide security indicators capturing, in a non-biased way, the security level of a system. Several concepts, as component lifecycle, vulnerability criticality and damage potential - effort ratio are embedded in the new security metric framework, developed in the scope of the H2020 project ATENA.
Intelligent forecasting of time series based on evolving distributed Neuro‐Fuzzy network

Abstract An evolving methodology based on Neuro‐Fuzzy Takagi‐Sugeno network (NF‐TS) for distributed forecasting of univariate time series, is proposed. First, the unobservable components, or hidden patterns, are extracted from experimental data of the time series. Then, a distributed forecasting is performed separately for each component, considering an evolving NF‐TS associated with each extracted pattern. The evolving NF‐TS uses components data to adapt and adjust its structure, as the number of fuzzy rules increases or decreases according the behavior of the unobservable components. A recursive version of singular spectral analysis (SSA) technique is formulated, as one of the main contributions of this article, and it is applied to extract the components. The efficiency of proposed methodology is illustrated from results of comparison to others state‐of‐the‐art techniques for forecasting of various univariate time series.
Color and Edge-Aware Adversarial Image Perturbations

Adversarial perturbation of images, in which a source image is deliberately modified with the intent of causing a classifier to misclassify the image, provides important insight into the robustness of image classifiers. In this work we develop two new methods for constructing adversarial perturbations, both of which are motivated by minimizing human ability to detect changes between the perturbed and source image. The first of these, the Edge-Aware method, reduces the magnitude of perturbations permitted in smooth regions of an image where changes are more easily detected. Our second method, the Color-Aware method, performs the perturbation in a color space which accurately captures human ability to distinguish differences in colors, thus reducing the perceived change. The Color-Aware and Edge-Aware methods can also be implemented simultaneously, resulting in image perturbations which account for both human color perception and sensitivity to changes in homogeneous regions. Because Edge-Aware and Color-Aware modifications exist for many image perturbations techniques, we also focus on computation to demonstrate their potential for use within more complex perturbation schemes. We empirically demonstrate that the Color-Aware and Edge-Aware perturbations we consider effectively cause misclassification, are less distinguishable to human perception, and are as easy to compute as the most efficient image perturbation techniques. Code and demo available at this https URL
Interpreting Neural Networks’ Reasoning

New methods that help researchers understand the decision-making processes of neural networks could make the machine learning tool more applicable for the geosciences.
Improving Children's Language Development By Storytelling Method Through Series Images In Group B3 Kindergarten: Literature Study: Meningkatkan Perkembangan Bahasa Anak Dengan Metode Bercerita Melalui Gambar Seri Di Kelompok B3 Tk: Studi Literatur

This research is attracted by the ineffective language development of children. This is because less of children stimulation to reveal the language skill. Therefore, it is important to have the media that can increase the development of children language like media of picture series. The aim of this study was to know the increasing of children language development.The kind of this research was literatur research. This study did by seeing many of studied that relevant with this research. In this research, the researcher took the result of comprising topic from 7 journals, 3 of books, and 4 of thesis that connected with story of picture series method.The result of this research showes the children language development was increase with story of picture series method. Based on the evidance, the researcher found out three of main results. They are increase the listening skill of children, improve the speaking skill of children, and enhance the reading skill of children.
A hybrid approach of firefly and genetic algorithm for solving optimisation problems

Firefly algorithm (FA) is a newly developed nature-inspired, metaheuristic, stochastic algorithm that has seen many applications in solving problems of optimisation nature since its introduction just a couple of years ago. FA is a simple, flexible, easily implementable and robust approach inspired from natural phenomenon of light emission by fireflies but a major drawback associated with FA is the random initial solution set generation that degrade the solution quality. In this work, the targeted issue has been resolved by introducing genetic algorithm (GA) operators namely selection, mutation and cross over operators during initial solution set generation for standard FA. The proposed technique has been applied to few standard benchmark minimisation and maximisation functions and the results have been compared with standard FA and GA. A significant amount of improvement in the convergence rate can be observed that results in high quality solution for solving optimisation problems.