Reviewerly/exHarmony huggingface dataset:
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks

We investigate to what extent alternative variants of Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. We analyse the adversarial robustness of conventional, stochastic ANNs and Spiking Neural Networks (SNNs) in the raw image space, across three different datasets. Our experiments reveal that stochastic ANN variants are almost equally as susceptible as conventional ANNs when faced with simple iterative gradient-based attacks in the white-box setting. However we observe, that in black-box settings, stochastic ANNs are more robust than conventional ANNs, when faced with boundary attacks, transferability and surrogate attacks. Consequently, we propose improved attacks and defence mechanisms for stochastic ANNs in black-box settings. When performing surrogate-based black-box attacks, one can employ stochastic models as surrogates to observe higher attack success on both stochastic and deterministic targets. This success can be further improved with our proposed Variance Mimicking (VM) surrogate training method, against stochastic targets. Finally, adopting a defender's perspective, we investigate the plausibility of employing stochastic switching of model mixtures as a viable hardening mechanism. We observe that such a scheme does provide a partial hardening.
Fərdi məlumatların mühafizəsi üçün blokçeyn texnologiyaları: imkanları və perspektivləri

The paper discusses the possibilities and prospects of using blockchain technology to protect personal data.A brief description of blockchain technology is given and an analysis of existing research is carried out.The possibilities of ensuring the security of personal data using the blockchain technology and the potential problems that arise during this are analyzed.
Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text

The growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and to use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang words such as Youth Slang are too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus and calculated sense similarities between words. We proposed to convert unknown word to semantic class id so that we might be able to process the words that were not included in the learning data. For calculation similarities between words and converting the word into word cluster id, we used the word embedding algorithms such as word2vec, or GloVe. We defined this method as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifiers, the proposed method was proved its robustness for unknown expressions.
Data Augmentation for Deep Learning-based Radio Modulation Classification

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-of-the-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning algorithm. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and shorter the classification response time.
Sentiment Analysis of Movie Review using Naïve Bayes Method with Gini Index Feature Selection

In movie reviews, there is information that determines whether the movie is good or bad. Sentiment analysis is used to process information to determine the polarity of the sentence. With unstructured reviews and a lot of data attributes so that it requires much time and computational capabilities that become a problem in the classification process. To process a lot of data selection features becomes a solution to reduce dimensions so it accelerate the classification process and reduce the occurrence of misclassification. The first Gini Index Text feature selection used to classify documents and successfully enhanced the classifier performance. Multinomial Naive Bayes (MNNB)  is a popular classifier used for document classification however, will the Gini Index Text feature selection able to improve MNNB classification performance. Therefore in this study the author aims to use the Gini Index Text (GIT) for text feature selection with MNNB classifier to classify movie review  into positive and negative classes. The data used is IMDB dataset that contains reviews in English sentences, the data will be divided into two parts, training data is 90% and data testing is 10%. The test results prove that the Gini index as a selection feature can increase accuracy where accuracy without feature selection is 56% and with feature selection of 59.54% with an increase of 3.54%.
A MODEL-DRIVEN APPROACH FOR MULTI-PLATFORM EXECUTION OF INTERACTIVE UIS DESIGNED WITH IFML

Quite recently, considerable attention has been paid to the design, implementation and evaluation of graphical user interfaces due to the apparition of the new strategic trend of computing everywhere. Accordingly, it is necessary to adopt an abstract representation of systems front-end in order to ensure this trend. IFML (Interaction Flow Modeling Language) is a user interfaces description language used to describe the content and interaction behavior of applications front-end. It has been conceived with executability aspect that is obtained via model transformations and full code generators into functional codes. however, these code generators are often accompanied by a loss of information. The main goal of this paper is to present a new virtual machine for directly executing GUIs models designed with IFML language in combination with UML domain model; that captures the content dependency. These input models will be then run on different platforms and devices. We adopted a new model driven approach that includes the hybrid approach of interpretive compiler; through a set of transformation rules, for the implementation of the desired virtual machine.
Thermoelastic Stress Analysis and Dissipated Energy Evaluation Using Infrared-Optical Synchronous Measurement

On the infrared thermography image, a false temperature change caused by the relative displacement between moving object and infrared camera is obtained. This paper shows the motion compensation system with the infrared-optical synchronous measurement. Displacement information of the specimen is calculated from the series of visible light image using the digital image correlation method (DIC). The displacement information on the visible image is reflected to the infrared image by the homography transformation. The random white pattern which can’t be detected by the infrared camera was drawn on the measured surface for DIC process. The developed motion compensation system was applied to the thermoelastic stress analysis and dissipated energy measurement. It was confirmed that a false temperature change and edge effect can be removed by using developed motion compensation system.
A Pilot Study on the Evaluation of Clinical Chemistry Laboratory Test Performance using Six Sigma Metrics

Introduction Six sigma has been used over the years, initially in manufacturing industries to improve quality by reducing the number of wastes and defects. In the laboratory, it can also provide measurement of quality using the sigma scale. Objectives The main objective of the study is to evaluate the performance of tests in two chemistry analyzers using the six sigma scale. Methodology A total of twenty (28) tests were evaluated on two Abbott Architect c8000 chemistry analyzers from September 2014 to July 2019 using results of quality control mean, coefficient of variation, bias and total allowable error to compute for the six sigma value. Both level one and level two third party quality controls were included in the evaluation. Results OResults of the study showed the tests that were >6 sigma for both levels 1 and 2 throughout the 5 years. Di-Bil, CK, HLD, TG and UA were consistently >6 sigma for one machine while CK, Di-Bil, HDL, Mg, TG and UA were consistently >6 sigma for the other. Level 1 and Level 2 sigma scores were noted to be incongruent in some analytes as follows: ALB, ALT, K, TP for one instrument and ALB, ALP and AST for the other instrument. Electrolytes Ca, Cl, and Na were generally low ( Conclusion Using six sigma metrics allowed the laboratory to evaluate the performance of the chemistry tests objectively. Tests that are >6.0 sigma signifies world class performance and entail application of fewer Westgard rules with fewer number of runs while those that are
A Practical Conflicting Role-Based Cloud Security Risk Evaluation Method

Background: Currently, Cloud Service Provider (CSP) or third party usually proposes principles and methods for cloud security risk evaluation, while cloud users have no choice but to accept them. However, since cloud users and cloud service providers have conflicts of interests, cloud users may not trust the results of security evaluation performed by the CSP. Different cloud users may have different security risk preferences, which makes it difficult for the third party to consider all users' needs during evaluation. In addition, current security evaluation indexes for the cloud are too impractical to test (e.g., indexes like interoperability, transparency, portability are not easy to be evaluated). Methods: To solve the above problems, this paper proposes a practical cloud security risk evaluation method of decision-making based on conflicting roles by using the Analytic Hierarchy Process (AHP) with Aggregation of Individual priorities (AIP). Results: Not only can our method bring forward a new index system based on risk source for cloud security and corresponding practical testing methods, but also can obtain the evaluation result with the risk preferences of conflicting roles, namely CSP and cloud users, which can lay a foundation for improving mutual trusts between the CSP and cloud users. The experiments show that the method can effectively assess the security risk of cloud platforms and in the case where the number of clouds increased by 100% and 200%, the evaluation time using our methodology increased by only by 12% and 30%. Conclusion: Our method can achieve consistent decision based on conflicting roles, high scalability and practicability for cloud security risk evaluation.
MAP ADVERTISEMENTS AS HISTORICAL SOURCES

High research potential of maps and advertisements was recognized by scientists for a long time. The majority of research investigations contain analysis of separate documents. If a map contains an advertisement, it provides more opportunities for investigation. The purpose of the article is to reveal the research potential of maps containing advertisements. The author studied a map of Asian part of Russia and Turkestan Krai kept in the collection of Omsk State Library named after Alexander Pushkin. The research enabled the author to identify the time when the map was published (1901-1904), reveal the specifics of advertised goods and their manufacturers, and outline the supply geography and target audience of the advertised products. The author concludes that advertisements enhance the potential of maps as historical sources.
PENGARUH KOMPETENSI GURU DAN SELF DIRECTED LEARNING TERHADAP HASIL BELAJAR MATA PELAJARAN FIKIH PADA SISWA KELAS VIII MTsN 17 JAKARTA TAHUN PELAJARAN 2018/2019

The purposes of this research are: (1) To know the influence of teacher competence on learning achievement of FIQH subject at the eight grade student of MTsN 17 Jakarta, (2) To know the influence of self directed learning on learning achievement of FIQH subject at the eight grade student of MTsN 17 Jakarta, (3) To know the influence of teacher competence and self directed learning simultanously on learning achievement of FIQH subject at the eight grade student of MTsN 17 Jakarta&#x0D; The writer took sample of 127 the eight grade students of MTsN 17 Jakarta. The kinds of data were questionnaire and documentation. To analize data the writer used product moment correlation analysis.&#x0D; The conclusions of this research are as follows: teacher competence influences on learning achievement of FIQH subject 17,4%, self directed learning influences on learning achievement of FIQH subject 15,5% , teacher competence and self directed learning simultanously on learning achievement of FIQH subject 36,4%.
Need Assessment of Colaboration between the Higher Education and the School based on Management Information System

This study aims to analyze the needs of schools and higher education to collaborate in education by a management information system approach. This study used a qualitative approach. The method used is a mix method, through the stages of initial data collection and needs assessment and interview to the Principal of Elementary School and to Lecturers from Universitas Serambi Mekkah. The results of the study show that collaboration information management systems in education still use conventional information and technology, and are needed to expand information access needs between the two parties. Nevertheless, the readiness of some human resources to use technology still needs to be improved, the lack of supporting facilities is still an obstacle.
Computing Quality of Navigation Designed into a WEB-Site

People around the world are at web sites for want of information, doing shopping, conduct literature surveys, gain knowledge, post activities and actions, do chatting, develop communities, and organize events and many such purposes.A vast number of websites are in use for information dissemination.It has become difficult to figure the web site where the information is available for which the user is looking.The search engine invented and implemented to help the user to locate the website where the information is available desired by the users.One of the most critical aspects of extensive usage of the web site is the navigation implemented within the development of web sites.People must be able to navigate across the web sites with ease and land up at the location where the information posted.Users get satisfied when the information that they are looking for is available with ease and within the negligible amount of time.Many factors influence the quality of a web site out, which the factor "Navigation" is the key as it forms the backbone of any web site.If the web sites poorly designed, it leads to the implementation of poor navigation, and the user usually tends to ignore surfing such a website.Some contributions exist in the literature relating the definition and meaning of the factor "Navigation," but no formal methods, techniques, frameworks exist used for computing the quality of the website.An approach / Model presented in this paper for computing quality of a web site considering "Navigation" of the website as the primary focus.
Usability Evaluation of sMouse: Software-Interface that Provides Sensory Reality of Use

Because of smartfication in societies, various kinds of physical interfaces have become softwarized. Regarding mobile environments, devices are designed on the basis that users do not need to use physical keyboards or mice. Most of the softwareized interfaces have higher performance than physical ones. However, there are many people who feel that the softwarized interfaces are hard to use despite of their high performance. They carry physical keyboards or external mice instead of softwareized interfaces. We suppose that this phenomenon is caused because humans want “tangible feeling of operations (the use)” when they use computer or machine interfaces. Almost all software-interfaces are hard to provide humans with “tangible feeling of operations (the use)”. Implementing “sensory reality of the use” to the applications integrated to the mobile devices is indispensable to the software-interfaces. We proposed “sMosue”, which is the application (app) that turns a smartphone into a mouse. With sMouse, the users can obtain the mice interfaces utilizing smartphones, which most people always bring .with them at all times. The user can use a smartphone as a mouse without carrying a physical mouse, streamlining of works is expected. In this paper, we describe outline of sMouse and its development purpose. In addition, we also explain the newly added function to provide “tangible feeling of operations (the use)”, and performed its usability evaluation.
An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

In image processing and computer vision, image segmentation plays a fundamental role since it can make images easier to analyze. However, noise is easily introduced into images and brings great challenges to image segmentation. This paper focuses on the segmentation problem of noisy images and proposes an efficient variational level set model based on adaptive local fitted image to handle it. By utilizing normalized local entropy and local means, an adaptive local fitted image is proposed and introduced into the data term to enhance the robustness of the model against noise. Then a penalty term is proposed to reduce the deviation of the adaptive local fitted image from the original image by punishing the difference between them, so as to guarantee the accuracy of segmentation results. Later, the total variational regularization term is introduced into the model, so the level set function can be smoothed and the effect of noise on the active contour can be further reduced. The energy functional of the whole model is convex rigorously, which can reach the minimum and should have good properties in noisy image segmentation. Numerous experiments on synthetic, natural, synthetic aperture radar and oil spill images demonstrate that the proposed model is strongly robust to different types and levels of noise, which indicates its good performance in noisy image segmentation.
Selfish Behavior in the Tezos Proof-of-Stake Protocol

Proof-of-Stake consensus protocols give rise to complex modeling challenges. We analyze the recently-updated Tezos Proof-of-Stake protocol and demonstrate that, under certain conditions, rational participants are incentivized to behave dishonestly. In doing so, we provide a theoretical analysis of the feasibility and profitability of a block stealing attack that we call selfish endorsing, a concrete instance of an attack previously only theoretically considered. We propose and analyze a simple change to the Tezos protocol which significantly reduces the (already small) profitability of this dishonest behavior, and introduce a new delay and reward scheme that is provably secure against length-1 and length-2 selfish endorsing attacks. Our framework provides a template for analyzing other Proof-of-Stake implementations for selfish behavior.
Developing Complex Problem-Solving Skills: An Engineering Perspective

While technical skills remain the core foundation for engineering graduates, professional competencies, including communication, teamwork, and complex problem solving, are increasingly important to succeed in work environments. To enhance the employability of our students, we have integrated several professional skills courses into our “Software Design and Engineering” Master study curriculum, and combined each of them with a technical course. In this paper, we present a blended-learning course design including didactic methods for teaching complex problem solving. The course is intertwined with a lecture on software integration topics, which enables the students to apply their complex problem solving skills on real projects in a domain-specific context.
PIDForest: Anomaly Detection via Partial Identification

We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anomaly measure for a point that we call PIDScore, which measures the minimum density of data points over all subcubes containing the point. We present PIDForest: a random forest based algorithm that finds anomalies based on this definition. We show that it performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.
EFFECTIVE WAYS OF MANAGING WEBSITES FOR ACADEMIC INSTITUTIONS

The paper describes the process of building and developing a proper website for academic institutions. Development needs to follow certain guidelines like planning a good structure and writing quality content in order to be able to convey the information that needs to be delivered to the target audience. The study surveyed existing higher education institutions websites from the current best top 100 list by QS World University Rankings. Although the scale is different the audience is the same and the structure of the websites does not differ. We found that educational websites keep a similar structure, but some manage to present the information in a much subtle manner to their audience. One of the most important features of an academic website is to make information accessible to all. The authors used a documentation review as a means of surveying to gather information about university websites. The purpose being to make informed recommendations about the best practices used in higher education website development. In conclusion we found that information needs to be displayed clearly, we recommend contact information to be placed on the front page as well as relevant information about the institution.