Reviewerly/exHarmony huggingface dataset:
Survivor

Despite the deployment of preventive security mechanisms to protect the assets and computing platforms of users, intrusions eventually occur. We propose a novel intrusion survivability approach to withstand ongoing intrusions. Our approach relies on an orchestration of fine-grained recovery and per-service responses (e.g., privileges removal). Such an approach may put the system into a degraded mode. This degraded mode prevents attackers to reinfect the system or to achieve their goals if they managed to reinfect it. It maintains the availability of core functions while waiting for patches to be deployed. We devised a cost-sensitive response selection process to ensure that while the service is in a degraded mode, its core functions are still operating. We built a Linux-based prototype and evaluated the effectiveness of our approach against different types of intrusions. The results show that our solution removes the effects of the intrusions, that it can select appropriate responses, and that it allows services to survive when reinfected. In terms of performance overhead, in most cases, we observed a small overhead, except in the rare case of services that write many small files asynchronously in a burst, where we observed a higher but acceptable overhead.
Academic Staff Professional Development via Coaching and Monitoring Technology of Adaptive Management

This paper is about concepts of development, professional development, professionalism and their relationship with the adaptive management of professional development of scientific and pedagogical staff. Author identifies the main criteria for the professional development of scientific and pedagogical staff, analyzes technology of coaching and monitoring as an effective one under conditions of society development. The qualimetric model of the professional development of scientific and pedagogical staff is considered and the results on the use of coaching and monitoring technology for their professional development are described.
The Application of Fuzzy Logic in Social Science Research (A Fuzzy Analysis of Social Development in Tehran)

The present research aimed to study the application of Fuzzy logic in social science research. In this regard, based on two variables, Social Capital and Quality of Life, the degree of Tehran’s membership in the set of social development was studied using FSQCA software.This research was an applied and descriptive research methodologically; and extensive in quantitative parts and in-depth in qualitative parts. The statistical sample contained 920 residents and 10 experts in sociology who were respectively selected by using volume-related random sampling and purposive sampling methods. Questionnaire and semi-structured interview which had face validity were the instruments for data gathering. The reliability of the questionnaire was assessed by internal consistency. As an important finding of this research, the degree of Tehran’s membership in social development set determined 0.4683. By inserting this degree of membership to the Fuzzy Logic Toolbox derived from experts’ opinions, the degree of Tehran’s membership in the set of social capital would be 0.50 in a range from 0 to 1.
ECiDA: Evolutionary Changes in Data Analysis

Modern data analysis platforms all too often rely on the fact that the application and underlying data flow are static. That is, such platforms generally do not implement the capabilities to update individual components of running pipelines without restarting the pipeline, and they rely on data sources to remain unchanged while they are being used. However, in reality these assumptions do not hold: data scientists come up with new methods to analyze data all the time, and data sources are almost by definition dynamic. Companies performing data science analyses either need to accept the fact that their pipeline goes down during an update, or they should run a duplicate setup of their often costly infrastructure that continues the pipeline operations. In this research we present the Evolutionary Changes in Data Analysis (ECiDA) platform, with which we show how evolution and data science can go hand in hand. ECiDA aims to bridge the gap that is present between engineers that build large scale computation platforms on the one hand, and data scientists that perform analyses on large quantities of data on the other, while making change a first-class citizen. ECiDA allows data scientists to build their data science pipelines on scalable infrastructures, and make changes to them while they remain up and running. Such changes can range from parameter changes in individual pipeline components to general changes in network topology. Changes may also be initiated by an ECiDA pipeline itself as part of a diagnostic response: for instance, it may dynamically replace a data source that has become unavailable with one that is available. To make sure the platform remains in a consistent state while performing these updates, ECiDA uses a set of automatic formal verification methods, such as constraint programming and AI planning, to transparently check the validity of updates and prevent undesired behavior.
Water Cycle Algorithm: Implementation and Analysis of Solutions to the Bi-Objective Travelling Salesman Problem

This research is an implementation of the Water Cycle Algorithm (WCA) to solve the biobjective Travelling Salesman Problem, based on the kroAB100 problem in the TSPLIB library, and compare its performance to an alternative metaheuristic algorithm (MO Ant Colony BiCriterionAnt). Metrics such as generational distance, inverse generational distance, spacing, dispersion and maximum dispersion were used to compare the two algorithms. Results demonstrate that the Water Cycle Algorithm generates superior solutions to this category of problem according to most of the metrics.
Survey of Attacks and Defenses on Edge-Deployed Neural Networks.

Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security challenges. Unlike classic IoT applications, edge neural networks are typically very compute and memory intensive, their execution is data-independent, and they are robust to noise and faults. Neural network models may be very expensive to develop, and can potentially reveal information about the private data they were trained on, requiring special care in distribution. The hidden states and outputs of the network can also be used in reconstructing user inputs, potentially violating users' privacy. Furthermore, neural networks are vulnerable to adversarial attacks, which may cause misclassifications and violate the integrity of the output. These properties add challenges when securing edge-deployed DNNs, requiring new considerations, threat models, priorities, and approaches in securely and privately deploying DNNs to the edge. In this work, we cover the landscape of attacks on, and defenses, of neural networks deployed in edge devices and provide a taxonomy of attacks and defenses targeting edge DNNs.
Apple and Chromebooks in Education, and zzzzzz

I read this story with some interest. It's largely a press release in interview form, but at the end of the article the interview includes
Boolean Functions with Multiple‐Valued Walsh Spectra

Compared with the method by Zhang in 2017, an extended one for constructing Boolean functions with multiple-valued Walsh spectra is given, which is derived from a bent function by complementing the values at some points. Based on which, new classes of Boolean functions with four-valued and five-valued Walsh spectra are presented, and some of their cryptographic properties are studied.
CPR Verification Analysis Based on TSA

Time stamp protocol is a message encryption protocol based on Hash function. This paper presents a scheme of electronic medical record authentication system based on timestamp protocol. This scheme encrypts the digital digest of the electronic medical record by means of digital signature, which can reduce the dependence on the communication bandwidth while encrypting. Through the analysis of the anti-attack ability, we can see that based on the context correlation of timestamp chain, this scheme can effectively prevent the modification of the content of electronic medical records and authentication time.
Properties of corporate training for social specialists

The article analyzes the properties of intra-corporate training of social workers, specialists in social work as a form of additional education.This type of training is aimed at achieving an increase of integrated efficiency from the standpoint of the system-integrative approach, representing a product, peculiar to a certain organization, which can be integrated under specific goals and objectives of the organization and a specialist in particular.The advantages of using the internal corporate training of specialists in social work, social workers by social service organizations are shown.
Pfeiffer Library: Crossref Tutorial: Welcome

Tutorial to accompany the Crossref database. Provides a description and table of contents of the guide.
Developing communicative component of psychological-pedagogical competence of potential substitute parents

This article discusses the features of the communicative component of the psychological and pedagogical competence of potential substitute parents, as well as the development of this component in the process of training sessions. It is proved that the training program, which includes exercises aimed at the development of flexibility, empathy, ability to establish contacts and competently to build relationships, promotes the development of the communicative component of psychological and pedagogical competence of potential substitute parents.
Library Guides: Maps & Geospatial: Global Partners: International Geospatial Data: South America - Lima, Peru

This guide compiles maps and geospatial resources for countries and regions of Penn State strategic partners. This guide focuses on locations where the Penn State University Libraries have partnerships.
Utilization of Optical Character Recognition Technology in Reading Identity Cards

Along with the times, the world of banking is also required to have growth in providing services to the public. The world of banking itself has a significant contribution in daily life. However, some deficiencies that arise due to the application of the procedures used are still often encountered. This situation can be seen from the number of customer self-registration data that is still done manually. Optical Character Recognition (OCR) technology on Citizenship Cards can be used to get the results of accuracy and speed and get the best reading results. The purpose of this study was to use Optical Character Recognition (OCR) technology in reading the Identity Card (KTP). The readings from OCR can be used to compare the size of the original, medium, and small images in color and grayscale images, so that the best results can be found in the processing of personal data on Identity Cards with Optical Character Recognition (OCR). This study resulted in the accuracy of reading grayscale Identity Card (KTP) data more accurately. Where the amount of 86.32% for the accuracy of the colored Identity Card (KTP) and 88.58% for the accuracy of the grayscale Identity Card (KTP).
Implementation of a Terminology Server with SNOMED CT in Graph Databases.

This paper described the implementation of a terminology server performed at a health institution in Uruguay, whose architecture is based on SNOMED CT using graph databases (NoSQL). The aim of this development was to create an intuitive terminological service, making the most of SNOMED CT's ontology, and which can be used from a clinical, statistical, management, decision support and research point of view, among others, with good performance.
Stilometriai Elemzés Lehetőségei Magyar Történeti Szövegkorpuszon

This paper discusses the potentials of the computer assisted analysis of Hungarian historical texts, which relies on the methods of linguistic, literary studies, information technology and statistics. It reviews the characteristics and applicability of different stylometric methods and demonstrates their use by case studies based on Kelemen Mikes’ (1690-1761) works. It examines the relationships between Mikes’ own works and his translations as well as the thematic separation of his works. The case studies highlight that the effectiveness of the stylometric analyses of Mikes’ writings can be improved by applying the digital Mikes’ dictionary.
Acceleration classification of earthquake signals and linear signals of human activity using smartphone accelerometer based on support vector machine algorithm

The threat of earthquake calamity spread throughout most of the Indonesian archipelago. Smartphone’s accelerometer usage as a seismic parameter detector in Indonesia, of which the noise has obstacles, mainly due to human activities. This study aims to classify linear acceleration signals caused by human activity and earthquake acceleration signals as an initial effort to reduce noise caused by human activity in the smartphone’s accelerometer signals. Both signals are classified by using the Support Vector Machine (SVM) algorithm of which consists of several steps, respectively, data collection, data preprocessing, data segmentation, feature extraction, and classification. These algorithms are tested to 2545 human activity signals in trouser pocket, 2430 human activity signals in shirt pocket and earthquake acceleration signals. Based on the test results by using the confusion matrix, linear acceleration signal data caused by human activity and earthquake acceleration signals can be classified properly using an SVM algorithm with Polynomial or Gaussian kernel with a small kernel scale value. The algorithms can achieve an accuracy of 87.74% to 97.94%.
Kerjasama Australia dan Indonesia dalam Upaya Pengembangan Sektor Pendidikan Di Australia Melalui Organisasi ACICIS (Australian Consortium For In-Country Indonesian Studies)

ABSTRACT&#x0D; This study analyzes the collaboration between Australia and Indonesia in the development of the education sector in Australia through the ACICIS (Australian Consortium for In-Country Indonesian Studies) organization which was established in 1994. ACICIS is a consortium of non-profit universities that develops and coordinates various study options quality in Indonesia for Australian, non-Australian and individual students. The reason Australia and Indonesia collaborate in the education sector is to improve the quality and quality of education in both countries, at the university level. In this journal, we discuss how cooperation between Australia and Indonesia in the education sector through the ACICIS organization. To find out the system of cooperation between the two parties, in carrying out this collaboration, researchers used an approach to the theory of international cooperation.&#x0D; Keywords: ACICIS (Australian Consortium for In-Country Indonesian Studies), international cooperation, education.
Additive Bayesian Network Modelling with the R Package abn

The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production.
Forme di cortesia nella storia dell’italiano: cambiamenti nella lingua e nei rapporti sociali

In the history of the Italian language, the politeness system is a fascinating theme from all points of view because it involves developments in social history, contacts with other cultures, variations of sociolinguistic nature and changes within the language. It is therefore a key to understanding society and the relationships between people in synchrony and in diachrony, even beyond the linguistic system. This paper focuses on the main factors involved in the politeness system and its evolution in Italian, with particular regard to nominal and pronominal address forms.
Jumping on the Bandwagon: Overcoming Social Barriers to Public Display Use

The fear of social embarrassment has been identified as a significant barrier to people's interactions with public large interactive displays (PLIDs). Prior research has also shown that the presence of others at a display can help to mitigate this issue by drawing on people's innate need to belong and social curiosity. This research investigates the potential to replicate this social effect, within the display itself, by drawing on prior interface design approaches that attempt to emulate the bandwagon-effect. This effect refers to the tendency for people to mimic the thoughts and behaviors of others. In a four-day field experiment, we deployed three different PLID interfaces featuring bandwagon and call-to-action design concepts. The study found that both the bandwagon and call-to-action designs were effective for engaging passersby, but each influenced different stages of the overall interaction process. We discuss the design implications of our findings, especially the need for socially-safe PLID interactions.
Knowledge-Based Short Text Categorization Using Entity and Category Embedding.

Short text categorization is an important task due to the rapid growth of online available short texts in various domains such as web search snippets, etc. Most of the traditional methods suffer from sparsity and shortness of the text. Moreover, supervised learning methods require a significant amount of training data and manually labeling such data can be very time-consuming and costly. In this study, we propose a novel probabilistic model for Knowledge-Based Short Text Categorization (KBSTC), which does not require any labeled training data to classify a short text. This is achieved by leveraging entities and categories from large knowledge bases, which are further embedded into a common vector space, for which we propose a new entity and category embedding model. Given a short text, its category (e.g. Business, Sports, etc.) can then be derived based on the entities mentioned in the text by exploiting semantic similarity between entities and categories. To validate the effectiveness of the proposed method, we conducted experiments on two real-world datasets, i.e., AG News and Google Snippets. The experimental results show that our approach significantly outperforms the classification approaches which do not require any labeled data, while it comes close to the results of the supervised approaches.
EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network

Although deep encoder-decoder networks have achieved astonishing performance for mitochondria segmentation from electron microscopy (EM) images, they still produce coarse segmentations with lots of discontinuities and false positives. Besides, the need for labor intensive annotations of large 3D dataset and huge memory overhead by 3D models are also major limitations. To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline. Therefore, the centerline detection sub-network is able to enhance the accuracy and robustness of segmentation task, especially when only a small set of annotated data are available. To achieve a light-weight 3D network, we introduce a novel hierarchical view-ensemble convolution module to reduce number of parameters, and facilitate multi-view information aggregation.Validations on public benchmark showed state-of-the-art performance by EM-Net. Even with significantly reduced training data, our method still showed quite promising results.
EXTRACURRICULAR COURSE «FUNCTIONAL EQUATIONS» FOR THE SENIOR PUPILS OF THE PROFESSION-ORIENTED SCHOOL

Вісник Черкаського національного університету імені Богдана Хмельницького 103 image theory, drawing curves and surfaces in differential geometry promotes the development of spatial ideas and spatial thinking of students, facilitates the understanding of learning, it is better to form spatial representations of the studied figures.The article gives an example of using the GeoGebra program when constructing a pyramid cross section.When using information and communication technologies students are formed a qualitatively new thinking, which is necessary in the conditions of the information society, thereby improving the quality of professional training of the future mathematics teacher.Conclusion.Therefore, the use of modern software in the process of training future mathematics teachers is a promising way to develop and improve the educational process at a pedagogical university.
On Symbiosis of Attribute Prediction and Semantic Segmentation

In this paper, we propose to employ semantic segmentation to improve person-related attribute prediction. The core idea lies in the fact that the probability of an attribute to appear in an image is far from being uniform in the spatial domain. We build our attribute prediction model jointly with a deep semantic segmentation network. This harnesses the localization cues learned by the semantic segmentation to guide the attention of the attribute prediction to the regions where different attributes naturally show up. Therefore, in addition to prediction, we are able to localize the attributes despite merely having access to image-level labels (weak supervision) during training. We first propose semantic segmentation-based pooling and gating, respectively denoted as SSP and SSG. In the former, the estimated segmentation masks are used to pool the final activations of the attribute prediction network, from multiple semantically homogeneous regions. In SSG, the same idea is applied to the intermediate layers of the network. SSP and SSG, while effective, impose heavy memory utilization since each channel of the activations is pooled/gated with all the semantic segmentation masks. To circumvent this, we propose Symbiotic Augmentation (SA), where we learn only one mask per activation channel. SA allows the model to either pick one, or combine (weighted superposition) multiple semantic maps, in order to generate the proper mask for each channel. SA simultaneously applies the same mechanism to the reverse problem by leveraging output logits of attribute prediction to guide the semantic segmentation task. We evaluate our proposed methods for facial attributes on CelebA and LFWA datasets, while benchmarking WIDER Attribute and Berkeley Attributes of People for whole body attributes. Our proposed methods achieve superior results compared to the previous works.