Reviewerly/exHarmony huggingface dataset:
Detecting Model Changes and their Early Warning Signals Using MDL Change Statistics

This study is concerned with the issue of detecting changes in Gaussian mixture models (GMM) from stream data. A change in a GMM implies a change in the number of clusters as well as a change in the cluster assignments. Unlike all the existing work that addresses this issue, we aim at detecting the early warning signals of changes as well as the changes themselves. To this end, we propose a novel notion of the sequential MDL change statistics (SMCS). SMCS is a real-valued index measuring the degree of change in a GMM from an information-theoretic viewpoint. This index can also be calculated sequentially every time a dataset is generated. Therefore, by tracking the changes of SMCS in real-time, we can possibly detect the early warning signals of changes in a GMM as well as its changes. We derive error probabilities for model change detection with SMCS to show that they converge to zero exponentially as sample size increases, and we derive a suitable parameter using this theorem. Furthermore, we empirically demonstrate cases where early warning signals of the changes can be successfully tracked by SMCS.
Ciphertext Policy Attribute-Based Encryption for Circuits from LWE Assumption

Attribute-based encryption (ABE) is a standard method for achieving access control using cryptography, and is related to many other powerful primitives such as functional encryption. While classical pairing based ABE schemes support only boolean formulas as access policy, the first ABE scheme for arbitrary polynomial size circuits is given in [GVW13], and its security is based on LWE assumption. However, the GVW13 scheme is a key policy ABE (KP-ABE), and whether their method can be used to construct a ciphertext policy ABE (CP-ABE) scheme is currently unknown. In this paper, we present the first direct construction (not from universal circuits) of CP-ABE scheme for circuits. Similar to the two-to-one recoding technique used in GVW13, we introduce three-to-one recoding, and use it to construct our scheme, which can be proved for selective security assuming that LWE problem is hard, for arbitrary polynomial size circuits. Compared with universal circuit based constructions, our scheme is simpler and has lesser decryption cost.
Prevalent Crime in Nigerian Tertiary Institutions and Administrative

This study examined crime in tertiary institutions in southern Nigeria. The purpose of the study was to identify the common crime prevalent in the institution, the administrative strategies for managing crime and find out the extent to which institution managers or Administrators are employing the identified strategies to ensure a crime free environment. Three research questions were asked and answered. The sample of the study was drawn from a population of tertiary institutions in Southern Nigeria A sample of 1020 respondents were selected through stratified random sampling technique from nine tertiary institutions in South-West Nigeria. The questionnaire was the instrument for collection of data from the respondents. It is divided into three sub-scales. Cronbach Alpha for the three sub scales yielded 0.84, 0.80 and 0.76. The finding revealed the common crime prevalent in tertiary institution in South-West Nigeria as examination malpractices, assault, plagiarism, sexual harassment, and certificate forgery. The findings further showed that crime management strategies like mounting closed circuit camera in strategic locations, regular monitoring of activities, use of anti-cult group are some of the identified crime management strategies. The extent of employment of the identified strategies in crime management is low.
Disrupting the ethnographic imaginarium

This paper is a contemplation of a digital ethnography with the community surrounding Silk Road, the first widely used cryptomarket for drugs on the Dark Web. To position the study within the broader field of illegal anthropology, it provides links between the existing literature on the study of cryptomarkets with relevant anthropological scholarship. A theory of piracy is interrogated for its explanatory capacity of the digital pirates of the Dark Web. The start of the study unexpectedly coincided with the FBI seizure of Silk Road in October 2013. The field site disappearance provoked a practice-based and conceptual rewiring. The paper unpacks how the ‘hydra effect’ introduced to conceptualise resilient innovation within cryptomarkets can also apply to the multiplicity of identities linked to research practice. This effect also raises how the knowledge production within digital ethnographic practice may be reconfigured through notions of opportunism, replication, obsolescence, regeneration, iteration, adaptation and proliferation.
The Designing of Interactive Learning Media at Yogyakarta’s Sandi Museum Based on Augmented Reality

Sandi Museum is one of museums that exhibits various collections of historical code in the forms of realia, replica, machine, picture and document related to codes. As the informationÂ sources of national cultural wealth, the existence of Sandi museum has not been visited by the tourists yet. It is because of the lack of museum promotion to community. Besides that, there is a stigma of community that museum is only the place to store the historical goods, therefore, the community activities when visiting the museum tend to be passive, in which they only look around the collections of historical goods without any interactions happened in visiting the museum. In order to get attraction of tourists when they are visiting the Sandi museum, the museumâ€™s officials are able to use the interactive learning media based on the technology of Augmented Reality in introducing Sandi museum. The visitorsÂ are able to visualize the object or historical good in 3 dimension (3-D) which is real time. The museum introduction based onÂ Augmented RealityÂ enables visitors to be able to interact with the objects or the historical goods through intuitive and interesting way. In this research, it is going to make AR-Sandi application as the information media to introduce Sandi museum to the community. The research approach used is research and development approach with the development model of waterfall. The Model of waterfallÂ consists of five phases, namely analysis, design, implementation, testing and maintenance. The result of the research is that the application ofÂ AR-Sandi has been successfully developed in accordance to the methodology of waterfall. Based on the acceptance testing ofÂ AR-Sandi application with the framework of TAM (Technology Acceptance Method) towards 30 visitors of Sandi museum, it is obtained that the majority of visitors agrees, the AR-Sandi application is acceptable by the users, it has functionality and work performance well.
Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru

This study aims to determine and analyze responses regarding customer satisfaction Ruangguru Application to the learning space features in the Ruangguru Application at every level of education. This is useful to know the strengths and weaknesses of the Ruangguru Application based on sentiment responses from Ruangguru users. Ruangguru is an online tutoring startup and a technology-based educational content service and provider no. 1 in Indonesia. So of course, customer satisfaction is an important thing that is the goal of the company. So that when customer satisfaction is met, that is where the company can realize their goals. To see how the level of customer satisfaction, sentiment analysis methods and topic modeling are used in processing the data so that responses can be seen as to what is provided by the customer so that it can be an evaluation for the Ruangguru Application.
A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the number of one sample is larger than that of another, but the classification effect is not ideal. Some data preprocessing methods have been proposed to reduce the imbalance ratio of data sets and combine with the basic classifiers to get better performance. In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). First, we divide the data samples in the majority class into several sub-clusters using K-means, k-value is determined by Average Silhouette Coefficient, and then adjust the number of data samples of each sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base classifier is trained on each balanced subset separately, and finally integrated into a strong ensemble classifier. In this paper, the extensive experimental results on 16 imbalanced datasets demonstrate the effectiveness and feasibility of the proposed algorithm in terms of multiple evaluation criteria, and EKR can achieve better performance when compared with several classical imbalanced classification algorithms using different data preprocessing methods.
A Study on Design and Case Analysis of Virtual Reality Contents Developer Training based on Industrial Requirements

The fourth industrial revolution has evolved at an exponential pace that has raised the level of virtual reality (VR) technology industry development. The VR-based contents industry has significantly grown in convergence with other areas; however, its production suffers from lack of skilled human resources. In this regard, this paper provides a study on educational courses dealing with VR contents production. The study identifies the current status of VR contents industry and evaluates the training contents currently being used at relevant training institutions. As a result, an industrial demand-customized educational model and its operations based on cooperative relationship between training institutions and industrial companies by means of cooperative projects and mentoring was designed. The outcome of the evaluation on the results of training courses operation served as a basis for the design of the proposed educational model and indicates the effectiveness of the training.
Petz map and Python’s lunch

A bstract We look at the interior operator reconstruction from the point of view of Petz map and study its complexity. We show that Petz maps can be written as precursors under the condition of perfect recovery. When we have the entire boundary system its complexity is related to the volume/action of the wormhole from the bulk operator to the boundary. When we only have access to part of the system, Python’s lunch appears and its restricted complexity depends exponentially on the size of the subsystem one loses access to.
Data loss prevention (DLP) by using MRSH-v2 algorithm

Sensitive data may be stored in different forms. Not only legal owners but also malicious people are interesting of getting sensitive data. Exposing valuable data to others leads to severe Consequences. Customers, organizations, and /or companies lose their money and reputation due to data breaches. There are many reasons for data leakages. Internal threats such as human mistakes and external threats such as DDoS attacks are two main reasons for data loss. In general, data may be categorized based into three kinds: data in use, data at rest, and data in motion. Data Loss Prevention (DLP) are good tools to identify important data. DLP can do analysis for data content and send feedback to administrators to make decision such as filtering, deleting, or encryption. Data Loss Prevention (DLP) tools are not a final solution for data breaches, but they consider good security tools to eliminate malicious activities and protect sensitive information. There are many kinds of DLP techniques, and approximation matching is one of them. Mrsh-v2 is one type of approximation matching. It is implemented and evaluated by using TS dataset and confusion matrix. Finally, Mrsh-v2 has high score of true positive and sensitivity, and it has low score of false negative.
Design and Analysis of Secure Emerging Crypto-Hardware Using HyperFET Devices

The emergence of new devices to be used in low-power applications are expected to reach impressive performance compared to those obtained by equivalent CMOS counterparts. However, when used in lightweight security applications, these emerging paradigms are required to be reliable and safe enough during the task of protecting important and valuable data. In this work, the usage of HyperFET devices for security applications has been analyzed and new paradigms for enhancing security against Power Analysis attacks have been developed for the first time. To perform this analysis, classical dual-precharge logic primitives implemented with 14nm FinFET have been upgraded to incorporate HyperFET devices. The proposed primitives incorporating HyperFETs, as well as a 4-bit Substitution box of PRIDE algorithm as demonstrative example, have been designed and simulated using predictive models. Simulation-based Differential Power Analysis attacks demonstrate high improvements in security levels in a x25 factor at least, with negligible degradation in performance. This first approach could be easily extensible to other ciphers or crypto-circuits, where the incorporation of HyperFET devices will enhance security for most future applications.
Green entrepreneurship prospects and challenges: the context of Afghanistan

The main purpose of this chapter is to provide a picture on green entrepreneurship challenges, drivers and prospects in Afghanistan. Furthermore, it provides suggestions on how to benefit from green entrepreneurship in order to avoid environmental degradation. The study uses factor analysis to analyse the data. The variables include motivational factors and those of barriers to green entrepreneurship in Afghanistan. The primary data is gathered from 100 business management students through a well-designed questionnaire. This chapter is expedient to understand the motivational factors that will create a platform for entrepreneurs to develop their strategies accordingly. The findings show that growing awareness and demand for organic products amongst consumers, providing a sustainable solution to ecological dangers and prevention of further degradation, and social responsibility are the motivational factor for green entrepreneurship in Afghanistan.
Connected Bike-smart IoT-based Cycling Training Solution

The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, cadence and power generated by the cyclist. Also, the trainer can see at every moment where the rider is with the aid of a GPS module. The system is built out of both hardware and software components. The hardware is in charge of collecting, scaling, converting and sending data from sensors. On the software side, there is the server, which consists of the Back-End and the MQTT (Message Queues Telemetry Transport) Broker, as well as the Front-End of the Web Application that displays and manages data as well as collaboration between cyclists and trainers. Finally, there is the Android Application that acts like a remote command for the hardware module on the bike, giving the rider control over how and when the ride is monitored.
Rapid Re-Identification Risk Assessment for Anonymous Data Set in Mobile Multimedia Scene

Ubiquitous mobile multimedia applications bring great convenience to users.However, when enjoying mobile multimedia services, users provide personal data to service platforms.Although the service platforms always claim that the collected personal data are de-identified, the risk of re-identifying users through linkage attacks still exists and is incalculable.This paper proposes a rapid prediction model for the overall re-identification risk based on the statistics of data sets (i.e., the number of individuals, number of attributes, distribution of attribute values, and attribute dependency).Our proposed model reveals the impact of statistics on the overall re-identification risk and adopts random sampling and semi-random sampling methods to predict the overall re-identification risk of data sets with and without strong dependency ordered attribute pairs.Experimental results show that for the data sets without strong dependency ordered attribute pairs, the random sampling method has a high prediction accuracy (the prediction error is less than 0.05).For the data sets with strong dependency ordered attribute pairs, the semi-random sampling method has a high prediction accuracy (the prediction error is less than 0.09).Exploiting our model, governments and individuals can quickly assess the privacy leakage risk of their data sets, given only the statistic of the data sets.Besides, this model can also evaluate the privacy risk of data collection schemes in advance according to historical statistics, and identify suspected services.
MeteoVis: Visualizing Meteorological Events in Virtual Reality

Modern meteorologists in the National Oceanic and Atmospheric Administration (NOAA) use the Advanced Weather Interactive Processing System (AWIPS) to visualize weather data. However, AWIPS presents critical challenges when comparing data from multiple satellites for weather analysis. To address its limitations, we iteratively design with Earth Science experts and developed MeteoVis, an interactive system to visualize spatio-temporal atmospheric weather data from multiple sources simultaneously in an immersive 3D environment. In a preliminary case study, MeteoVis enables forecasters to easily identify the Atmospheric River event that caused intense flooding and snow storms along the western coast of North America during February 2019. We envision that MeteoVis will inspire future development of atmospheric visualization and analysis of the causative factors behind atmospheric processes improving weather forecast accuracy. A demo video of MeteoVis is available at https://youtu.be/pdkXhkTtimY.
Understanding Account Recovery in the Wild and its Security Implications

Account recovery (usually through a password reset) on many websites has mainly relied on accessibility to a registered email, due to its favorable deployability and usability. However, it makes a user&#x0027;s online accounts vulnerable to a single point of failure when the registered email account is compromised. While previous research focuses on strengthening user passwords, the security risk imposed by email-based password recovery has not yet been well studied. In this article, we first conduct a measurement study to characterize the password recovery activities in the wild. Specifically, we examine the authentication and password recovery protocols from 239 traffic-heavy websites, confirming that most of them use emails for password recovery. We further scrutinize the security policy of leading email service providers and show that a significant portion of them takes no or marginal effort to protect user email accounts, leaving compromised email accounts readily available for mounting password recovery attacks. Then, we conduct case studies to assess potential losses caused by such attacks. Finally, we propose and implement a lightweight email security enhancement called Secure Email Account Recovery (SEAR) to defend against password recovery attacks by adding an extra layer of protection to password recovery emails.
Tree Inheritance Distance

Tree comparison is an important method in various areas. In order to compare class hierarchy in object-oriented languages, there is a need to compare trees in the context of the hierarchy changes. This paper addresses tree dissimilarity based on two measures. First, changes in edges between nodes and parents are measured by introducing Edge Edit Distance (EED). Second, changes in inheritance relationships between nodes are measured by introducing Tree Inheritance Distance (TID). It is shown that EED and TID increase with the dissimilarity in the class hierarchies of the compared program versions.
Financial development, income inequality and governance institutions

The paper investigates empirically how governance institutions mediate the link between financial development and inequality. To this aim, we assemble a dataset of 48 middle- and high-income countries for the period 1996-2014. Results, obtained by means of instrumental variables dynamic panel data models, reveal that financial development is pro-inequality; however, the strength of the relationship is attenuated in contexts with stricter control of corruption, better regulatory quality, political stability and rule of law. Institutional domains less directly related to the market economy ? political voice and accountability and government effectiveness ? do not play any mediating role.
Multicultural orientation of inclusive education in the modern school of the USA

И. С. Бессарабова, Е. С
A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discriminate the polarity of sentiments. This paper proposes a hybrid method of linguistic and statistical features along with classification methods for Arabic sentiment analysis. Linguistic features contains stemming and POS tagging, while statistical contains the TF-IDF. A benchmark dataset of Arabic tweets have been used in the experiments. In addition, three classifiers have been utilized including SVM, KNN and ME. Results showed that SVM has outperformed the other classifiers by obtaining an f-score of 72.15%. This indicates the usefulness of using SVM with the proposed hybrid features.
Image Encryption System Based on Joint Transformation Correlation and Ptychography

In this paper, a new image encryption system based on joint transformation correlation principle and ptycholographic iterative engine is proposed.When the encryption is performed, the original image can be divided into several parts by using the scanning movement of the probe.Each part is encrypted by the joint transformation correlation technology, and the ciphertexts are finally transmitted in the form of many encrypted images.The receiver uses the working principle of the joint transform correlator to decrypt and reconstructs the image by using the ptycholographic iterative engine, which can integrate multiple images with less information into one high-precision decrypted image.Computer simulations prove its possibility.