Reviewerly/exHarmony huggingface dataset:
MVC Frameworks Modernization Approach

The use of web development frameworks has grown significantly, specially the Model-View-Controller (MVC) based frameworks.The ability to immigrate web applications between different frameworks available becomes more and more relevant.The automation of the migration through transformations avoid the necessity to rewrite the code entirely.Architecture Driven Modernization (ADM) is the most successful approach that standardizes and automates the reengineering process.In this paper, we define an ADM approach to generate MVC web applications models in the highest level of abstraction from Struts 2 and Codeignitter Models.To do this, we add the MVC concepts to the KDM metamodel and then we specify a set of transformations to generate MVC KDM models.This proposal is validated through the use of our approach to transform CRUD (Create, Read, Update and Delete) applications models from MVC frameworks to MVC KDM.
Ontology Learning from Relational Databases: Transforming Recursive Relationships to OWL2 Components

Relational databases (RDB) are widely used as a backend for information systems, and contain interesting structured data (schema and data).In the case of ontology learning, RDB can be used as knowledge source.Multiple approaches exist for building ontologies from RDB.They mainly use schema mapping to transform RDB components to ontologies.Most existing approaches do not deal with recursive relationships that can encapsulate good semantics.In this paper, two technics are proposed for transforming recursive relationships to OWL2 components: (1) Transitivity mechanism and (2) Concept Hierarchy.The main objective of this work is to build richer ontologies with deep taxonomies from RDB.
A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets

In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches.
Micro-Doppler Based Detection of Hovering UAVs

This paper presents a method for detecting hovering UAVs in ground clutter background. The proposed method is based on both Range-Doppler (RD) map and micro-Doppler signatures, aiming to distinguish the hovering UAV from ground clutter. Frequency modulation characteristic induced by rotating blades is extracted as a unique micro-Doppler signature to discriminate between the hovering UAV and ground clutter. Simulation data is used to verify the performance of the proposed method and results demonstrate the effectiveness of the proposed method.
Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

&lt;span lang="EN-US"&gt;Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies &lt;/span&gt;&lt;span lang="EN-US"&gt;using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection. &lt;/span&gt;
BFO: Batch-File Operations on Massive Files for Consistent Performance Improvement

Existing local file systems, designed to support a typical single-file access pattern only, can lead to poor performance when accessing a batch of files, especially small files. This single-file pattern essentially serializes accesses to batched files one by one, resulting in a large number of non-sequential, random, and often dependent I/Os between file data and metadata at the storage ends. We first experimentally analyze the root cause of such inefficiency in batch-file accesses. Then, we propose a novel batch-file access approach, referred to as BFO for its set of optimized Batch-File Operations, by developing novel BFOr and BFOw operations for fundamental read and write processes respectively, using a two-phase access for metadata and data jointly. The BFO offers dedicated interfaces for batch-file accesses and additional processes integrated into existing file systems without modifying their structures and procedures. We implement a BFO prototype on ext4, one of the most popular file systems. Our evaluation results show that the batch-file read and write performances of BFO are consistently higher than those of the traditional approaches regardless of access patterns, data layouts, and storage media, with synthetic and real-world file sets. BFO improves the read performance by up to 22.4× and 1.8× with HDD and SSD respectively; and boosts the write performance by up to 111.4× and 2.9× with HDD and SSD respectively. BFO also demonstrates consistent performance advantages when applied to four representative applications, Linux cp, Tar, GridFTP, and Hadoop.
Traceable Decentralized Anonymous E-Cash System Against Active Attackers

Decentralized e-cash schemes such as Bitcoin have seen widespread adoption due to the elimination of dependence on trusted authorities. However, the decentralization property also brings difficulties on privacy preserving and auditing. Although several approaches have been suggested to support anonymity, none of them has a reliable anonymity revocation mechanism. We fill this gap by presenting a novel e-cash scheme called traceable decentralized anonymous e-cash (TDAE). In TDAE, the transactions can be publicly verified, and the identities of the payers and payees are hidden from everyone except a designated authority. Moreover, we consider active attackers who can corrupt selected authorities along with many users within the e-cash system. We present rigorous security definitions for a TDAE scheme, ensuring that even an active attacker cannot spend more money than he owns, neither can he frame any user that is not corrupted. We also provide a secure generic construction of a TDAE scheme. This construction is built upon several basic cryptographic tools, and thus it enables one to obtain a TDAE with better security and efficiency when advantageous tools are available.
Features of the Development of the Digital Educational Environment in Russia

Goal of the investigation: This article aims to distinguish the characteristics of the development of the digital educational environment in Russia in the context of the global digitalization process, and to identify threats to information security. Methodology: The leading method for studying this problem is a comparative analysis of the level of evolution of the digital administration in various nations of the world, which passes to comprehensively estimate the obstacle of receiving the learning and informative space as a portion in the sustainable expansion of the Russian economy. Results: The authors revealed the peculiarities of the Russian “digitalization” management system, which is still distinguished by spontaneity and the lack of a coordinated action program on the part of the state. It was revealed that Russia has the necessary potential for the further development of the digital economy: the scientific and intellectual base, a good level of secondary and higher technical education. The article substantiates the results of the development of the Russian digital educational environment: a data source (portal) has been created that is convenient to all levels of residents and stores each user with entrance to online classes for all levels of training and online support for mastering general education subjects; an infrastructure has been created to train teachers and administrative staff, to spread the experience of introducing successful methods and practices of online learning, to track the dynamics of creating a digital educational environment; and a system of advanced training for teachers in the development, use and examination of online courses has been created. Applications of this study: The materials of the article can be useful to relevant state and economic institutions for the formation of a mechanism for informatization of the country's development management system and education in particular, which is part of certain development strategies and programs; researchers of socio-economic processes associated with the development of the information society and the economy, and the increasing distribution of digital data processing technologies. The analysis of the digitalization process in the country and the world allowed us to identify problems that require close attention and finding solutions for the implementation of project activities for the formation of a modern digital educational environment in the Russian Federation. Novelty/Originality of this study: The novelty and originality of the study lies in the fact that for the first time it offers a comprehensive approach to assessing the specifics and factors of the digitalization process in the educational space of the country, developing mechanisms to reduce threats to information security by developing a system for identifying users, technical controls, and improving the legislative framework.
Frequent itemset mining: technique to improve eclat based algorithm

&lt;span lang="EN-US"&gt;In frequent&lt;/span&gt;&lt;span lang="EN-US"&gt; itemset mining, the main challenge is to discover relationships between data in a transactional database or relational database. Various algorithms have been introduced to process frequent itemset. Eclat based algorithms are one of the prominent algorithm used for frequent itemset mining. Various researches have been conducted based on Eclat based algorithm such as Tidset, dEclat, Sortdiffset and Postdiffset. The algorithm has been improvised along the time. However, the utilization of physical memory and processing time become the main problem in this process. This paper reviews and presents a comparison of various Eclat based algorithms for frequent itemset mining and propose an enhancement technique of Eclat based algorithm to reduce processing time and memory usage. The experimental result shows some improvement in processing time and memory utilization in frequent itemset mining.&lt;/span&gt;
A Large-Scale Empirical Study of Internet Users' Privacy Leakage in China

In recent years, information leakage incidents occur and have involved hundreds of millions of accounts on Tencent QQ. Despite recent efforts to study privacy leakages in online social networks (OSNs) such as Facebook and Twitter, little attention has been given to evaluating the risk of the leaked datasets. In this paper, we developed several methods that can successfully correlate the leaked datasets and accurately learn millions of users' confidential information such as real-name, OSN ID, age, birth date, social connection, and education background, etc. This privacy leakage is possible even when a large number of users' attributes such as real-name and age are intentionally set wrongly or not directly observable in the leaked datasets.
Heterogeneous Computing System for Deep Learning

Various forms of Deep Neural Network (DNN) architectures are used as Deep Learning tools for neural inspired computational systems. The computational power, the bandwidth and the energy requested by the current developments of the domain are very high. The solutions offered by the current architectural environment are far from being efficient. We propose a hybrid computational system for running efficiently the training and inference DNN algorithms. The system is more energy efficient compared with the current solutions, and achieves a higher actual performance per peak performance ratio. The accelerator part of our heterogeneous system is a programmable many-core system with a Map-Scan/Reductive only the cells where architecture. The chapter describes and evaluates the proposed accelerator for the main computational intensive components of a DNN: the fully connected layer, the convolution layer, the pooling layer, and the softmax layer.
Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks

&lt;span lang="EN-US"&gt;The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system.&lt;/span&gt;
Research on Reliable Path Planning of Manipulator Based on Artificial Beacons

In order to solve the problem that manipulators can only move in a fixed trajectory in common industrial production at present, we propose a monocular visual positioning method based on artificial beacons, combining visual position technology with the movement of manipulators to complete the reliable path planning of manipulators. When it comes to practicability, the traditional point pair-matching method is equipped with robust factors, to increase the reliability. We adopt a linear predict algorithm and ROI (region of interest) in the solving of PnP (Perspective-n-Point) to ensure the real-time performance and improve the robustness. Finally, the reliability is verified by experiments, which provides theoretical basis and practical reference for the path planning of manipulator based on artificial beacons.
Feature Extraction of Facial Electromyograph (EMG) Signal for Aceh Languages Speech using Discrete Wavelet Transform (DWT)

The facial electromyograph (FEMG) signal is a signal that occurs in the muscles of the contracted human face. This FEMG signal is one of the techniques used to study human speech recognition. It can be acquired by placing an electrode surface on the skin around the facial articulation muscle. Three types of muscles in this study are the masseter, risorius and depressor muscle. This study aims to extract and analyze the features in the FEMG signal. The extraction method is the discrete wavelet transform (DWT). The type of wavelet transform is Daubechies2 with level 5. After extraction and analysis of FEMG signals, the FEMG signal pattern for each spoken word indicated by differences in the approximation and detail coefficient of the FEMG signal. In addition, the level of difference in the FEMG signal pattern is also indicated by the histogram of the approximation coefficient of the FEMG signal. Thus, the discrete wavelet transform method can be used as one of the methods for extracting the FEMG signal feature in a human facial electromyograph (FEMG) signal.
DEVELOPING SCREAMBLE MEDIA TO ENHANCE SPEAKING SKILL FOR PRESCHOOLERS

This study aims to develop SCREAMBLE media to enhance speaking skill among preschoolers in Cahaya Bangsa School Metro. A research question is formulated: is SCREAMBLE media effective to enhance speaking skill? The participants are 18 preschoolers of Kindergarten B chosen by purposive cluster sampling. This is a kind of educational research and development designed by certain procedures; setting the instructional goals, procedures, and materials, conducting performance objectives, field-testing, assessing instruments, doing formulative and summative evaluation. The result gathered by Wilcoxon test represents Asymp.Sig (2-tailed) values 0,000 which is smaller than 0,05 indicates that SCREAMBLE media is effective to enhance speaking skill. Besides, observation reveals that preschoolers are excited to speak since their fine motor and cognitive aspects are included simultaneously.
Dual Layer Data Security in Cloud Computing

One of the major advantage of cloud computing is that data can be shared among various organizations. However, this advantage itself have a risk to data. In order to negate potential risk to the data, it is necessary to protect data. Encryption is a hugely important tool when it comes to protecting data. This paper propose a new era of cryptographic dual layer encryption to make the data store in cloud more secure and trustworthy. There are readily available many multiple encryption techniques that are available in present time but unable to provide sufficient security. This paper aim to suggest the new encryption technique named as dual encryption. It is based on popular encryption algorithm AES which is symmetric-key algorithm. We are going to propose an additional layer of rabbit algorithm around encrypted data which will help to provide more security against Bruteforce and other type of attacks. Therefore, if an intruder detects a single key of cryptosystem even then it is not possible to decrypt the original message. This paper aims to suggest a perspective which is a double layer encryption method to ensure security in cloud. In this proposed double layers encryption schemes, the data will be extremely secured while protecting and sharing in cloud environment. This scheme not only makes full use of the great processing skill of cloud computing but also can efficiently ensure cloud data privacy and security.I.
Is Your Game Generator Working? Evaluating Gemini, an Intentional Generator

Determining whether a game generator is working properly is challenging, since it entails conducting potentially many evaluations of generated games and synthesizing these into a net evaluation of the system. The problem is compounded when the generator has a human-centered goal: for example, that the generated games should be interpreted as having certain mechanics or as being about particular ideas. In this paper, we examine the Gemini game generator and develop an evaluation instrument that tests the interpretability of its generated games’ mechanics and higher-order proceduralist arguments. In the process we build empirical evidence for the claim that some amount of non-systems-based framing is required in order for arguments made by procedural rhetorics to be sensible to players. The tools we have assembled for this evaluation can be applied to game generators more broadly; game generators should be allowed to invent games which go beyond merely formally “good” or subjectively “fun.”
A modal logic for subject-oriented spatial reasoning

We present a modal logic for representing and reasoning about space seen from the subject's perspective. The language of our logic comprises modal operators for the relations front, behind, the left, and the of the subject, which introduce the intrinsic frame of reference; and operators for behind an object, between the subject and an object, the left of an object, and the right of an object, employing the relative frame of reference. The language allows us to express nominals, hybrid operators, and a restricted form of distance operators which, as we demonstrate by example, makes the logic interesting for potential applications. We prove that the satisfiability problem in the logic is decidable and in particular PSpace-complete.
Science in the Archives

A review of Science in the Archives, ed. Lorraine Daston.
Detecting Public Sentiment of Medicine by Mining Twitter Data

The paper presents a computational method that mines, processes and analyzes Twitter data for detecting public sentiment of medicine.Self-reported patient data are collected over a period of three months by mining the Twitter feed, resulting in more than 10,000 tweets used in the study.Machine learning algorithms are used for an automatic classification of the public sentiment on selected drugs.Various learning models are compared in the study.This work demonstrates a practical case of utilizing social media in identifying customer opinions and building a drug effectiveness detection system.Our model has been validated on a tweet dataset with a precision of 70.7%.In addition, the study examines the correlation between patient symptoms and their choices for medication.
Towards More Believable Characters Using Personality and Emotion

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. So far, there have been considerable advances in 3D and AI design to enhance virtual environments. Virtual characters are one of the crucial aspects of interactive narratives. The interaction of rich virtual characters can produce interesting narrative and as a result, enhance the experience of virtual environments. As one of those characters, the user would feel immersed and engaged when interacting with compelling characters. There are many characteristics that improve believable behavior generation, including beliefs, goals, desires, affect, and personality. My goal is to propose an affective model of personality for multi-agent narrative planning systems that is domain independent and thus minimizes authorial burden. I aim to combine existing models into a unified framework. Although the framework may oversimply those models, it would not overlook their key ingredients.