Reviewerly/exHarmony huggingface dataset:
A Graph-Based Decision Support Model for Vulnerability Analysis in IoT Networks

The Internet of Things (IoT) refers to the technological phenomenon that envisages reliable connection and secure exchange of data between the real-world devices and applications. However, the vulnerabilities residing in the IoT devices are identified as the potential entry points for the attackers, thereby causing a huge security threat to the IoT network. The attackers can further advance deep into the network by exploiting the relations among these vulnerabilities. In this work, we address the security issues in the IoT network due to the existence of vulnerabilities in the network devices. We propose a multi-attacker multi-target graphical model referred to as IoT Security Graph, representing the potential attackers, targets, and the vulnerability relations in the IoT network. As the graph is derived from the network, its analysis can reveal many security-relevant parameters of the network. Security analysts are keen in evaluating threats to critical resources in the network due to the presence of inherent vulnerabilities in the devices and in analyzing cost-effective security hardening options. To aid this, we introduce the Terminator Oriented Directed Acyclic Graph (TODAG) for each terminal node representing a potential target in the network. The TODAG for a given terminal node is a sub-graph of the IoT Security Graph of the underlying network and represents all the potential attack paths in the network that orient toward it. The proposal also includes the likelihood estimation of the dominant attack paths in the TODAG. The removal of such paths can significantly reduce the threat at the targets.
An FPGA-Based HOG Accelerator with HW/SW Co-Design for Human Detection and Its Application to Crowd Density Estimation

Human detection is important in many applications and has attracted significant attention over the last decade.The Histograms of Oriented Gradients (HOG) as effective local descriptors are used with binary sliding window mechanism to achieve good detection performance.However, the computation of HOG under such framework is about billion times and the pure software implementation for HOG computation is hard to meet the real-time requirement.This study proposes a hardware architecture called One-HOG accelerator operated on FPGA of Xilinx Spartan-6 LX-150T that provides an efficient way to compute HOG such that an embedded real-time platform of HW/SW co-design for application to crowd estimation and analysis is achieved.The One-HOG accelerator mainly consists of gradient module and histogram module.The gradient module is for computing gradient magnitude and orientation; histogram module is for generating a 36-D HOG feature vector.In addition to hardware realization, a new method called Histograms-of-Oriented-Gradients AdaBoost Long-Feature-Vector (HOG-AdaBoost-LFV) human classifier is proposed to significantly decrease the number of times to compute the HOG without sacrificing detection performance.The experiment results from three static image and four video datasets demonstrate that the proposed SW/HW (software/hardware) co-design system is 13.14 times faster than the pure software computation of Dalal algorithm.
Variability Incorporated Simultaneous Decomposition of Models Under Structural and Procedural Views

This chapter presents hierarchical variability as an important development notion especially when considered together with a systems specification through decomposition. A matured domain-specific environment is the precondition for variability-centric engineering for compositional approaches as targeted in this study: Most of the requirements have been already modeled, and most of the problem domain elements have corresponding reusable solutions. Also, a mature domain enjoys a wide community of developers who are familiar with those problems and solution-space elements and an effective set of specific tools. Decomposition is a fundamental mechanism in many approaches for the specification of various dimensions of modeling. Decomposition of especially structure modeling for software is not new. Here, variability guidance is incorporated into both structure and process decomposition. This chapter combines such notions in the demonstration of variability-centric development suggesting a structural and procedural decomposition of the system. The predecessors, component-oriented approaches rely on the structural decomposition whereas service-oriented development is being supported by process decomposition. A vending machine case study is presented in this chapter for demonstrating the propagation of variability specification along with the enhancements of the component-oriented model and the process model.
Archaeology of the Digital Periphery

The computer mouse is one of the most familiar artefacts of the developed world to have been devised in the late twentieth century. The essential form remains the same as when it was first invented, but during this time the mouse has transformed our physical interaction with and perception of computers. With increased attention being paid to curating and collecting technologies of the contemporary world, and within the context of an archaeological research culture that extends to the contemporary, an archaeological examination of this ubiquitous object appeared timely. There are millions - if not billions - of mice in circulation, including models that are now outdated or obsolete. Despite their apparent uniformity they differ in significant ways, and examination of these differences can help us to understand human experiences of technology in ways that resonate with artefact types of much earlier periods. With that time depth in mind, this paper will therefore focus on the form and function of the computer mouse and its place in the contemporary imagination. This will be followed by a detailed study of five specific examples which together illustrate some of the key issues and challenges that face us, as archaeologists and curators.
Research on Network Security Quantitative Model Based on Probabilistic Attack Graph

In order to identify the threat of computer network security and evaluate its fragility comprehensively, the related factors of network security are studied, and the methods based on attack graph are improved. Based on the attribute attack graph, the probabilistic attack graph model is generated by adding various factors which affect network security. The model uses security equipment performance data, common vulnerability scoring system data and etc. to calculate priori probability, finally obtains the network security index, and carries on the exploratory analysis. The experimental results show that the model is feasible and effective. Compared with other vulnerability assessment methods, the model has the characteristics of comprehensive evaluation and concise calculation.
R2LD: Schema-based Graph Mapping of relational databases to Linked Open Data for multimedia resources data

The Web of Data used to share and interchange the diverse data of heterogeneous types on the Web has been actively established. Ontology-based Linked Open Data (LOD) that allows computers to understand and process data semantics has emerged to extend the current Web of Documents. LOD is important for the modeling of multimedia resources since it provides an efficient way for unstructured data resources. This paper proposes a noble and practical schema-based mapping method to populate Linked Open Data sets from relational databases for multimedia resources. The proposed schema-based mapping R2LD realizes the seamless mapping for RDB-to-RDF by taking advantage of the compatible conceptual schema. The schema-based mapping can resolve the complicated mapping issues in RDB-to-RDF, such as primary and foreign key relationships. The mapping description is straightforward and flexible. It can define mapping information in the form of attribute-value pairs. Especially, the proposed mapping method is suitable for the unstructured multimedia resources. The schema-based mapping R2LD provides an efficient way to implement SPARQL endpoint into RDB and preserve the performance of SQL, which is vital to the dissemination of LOD.
Error-detected single-photon quantum routing using a quantum dot and a double-sided microcavity system

Based on a hybrid system consisting of a quantum dot coupled with a double-sided micropillar cavity, we investigate the implementation of an error-detected photonic quantum routing controlled by the other photon. The computational errors from unexpected experimental imperfections are heralded by single photon detections, resulting in a unit fidelity for the present scheme, so that this scheme is intrinsically robust. We discuss the performance of the scheme with currently achievable experimental parameters. Our results show that the present scheme is efficient. Furthermore, our scheme could provide a promising building block for quantum networks and distributed quantum information processing in the future.
What Was Monet Seeing While Painting? Translating Artworks to Photo-Realistic Images

State of the art Computer Vision techniques exploit the availability of large-scale datasets, most of which consist of images captured from the world as it is. This brings to an incompatibility between such methods and digital data from the artistic domain, on which current techniques under-perform. A possible solution is to reduce the domain shift at the pixel level, thus translating artistic images to realistic copies. In this paper, we present a model capable of translating paintings to photo-realistic images, trained without paired examples. The idea is to enforce a patch level similarity between real and generated images, aiming to reproduce photo-realistic details from a memory bank of real images. This is subsequently adopted in the context of an unpaired image-to-image translation framework, mapping each image from one distribution to a new one belonging to the other distribution. Qualitative and quantitative results are presented on Monet, Cezanne and Van Gogh paintings translation tasks, showing that our approach increases the realism of generated images with respect to the CycleGAN approach.
On the Need for a General REST-Security Framework

Contemporary software is inherently distributed. The principles guiding the design of such software have been mainly manifested by the service-oriented architecture (SOA) concept. In a SOA, applications are orchestrated by software services generally operated by distinct entities. Due to the latter fact, service security has been of importance in such systems ever since. A dominant protocol for implementing SOA-based systems is SOAP, which comes with a well-elaborated security framework. As an alternative to SOAP, the architectural style representational state transfer (REST) is gaining traction as a simple, lightweight and flexible guideline for designing distributed service systems that scale at large. This paper starts by introducing the basic constraints representing REST. Based on these foundations, the focus is afterwards drawn on the security needs of REST-based service systems. The limitations of transport-oriented protection means are emphasized and the demand for specific message-oriented safeguards is assessed. The paper then reviews the current activities in respect to REST-security and finds that the available schemes are mostly HTTP-centered and very heterogeneous. More importantly, all of the analyzed schemes contain vulnerabilities. The paper contributes a methodology on how to establish REST-security as a general security framework for protecting REST-based service systems of any kind by consistent and comprehensive protection means. First adoptions of the introduced approach are presented in relation to REST message authentication with instantiations for REST-ful HTTP (web/cloud services) and REST-ful constraint application protocol (CoAP) (internet of things (IoT) services).
Malicious XSS Code Detection with Decision Tree

Dynamic applications such as e-commerce, blogs, forums, e-governance, e-banking and portals that are in these platforms have become a part of our lives. However, a tremendous increase in the use of dynamic web and mobile applications has resulted in security vulnerabilities originating from the Hypertext Markup Language (HTML) coding system. Site-to-site Script Execution (XSS) attack is the largest contributors to security exploits. There are different models according to the dynamic content that XSS attacks use. The interest of the study is composed of attacks on visual content with the "img" tag. In study, an algorithm has been developed to detect XSS attacks with the decision tree which is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. The algorithm that successfully classifies 392 of 400 malicious and clean codes in the data set with 8 different features. This result contributes to the use of secure internet without XSS attacks that use visual content..
TELAAH BUKU TEKS TEMATIK TERPADU KELAS IV SD KURIKULUM 2013

Textbooks are learning resources that are used as guidelines or handles in learning activities by students and educators (teachers). As a guide and learning material in learning activities in schools, textbooks must conform the standards of feasibility. Standards for the feasibility of textbooks as learning material, including the feasibility of content, language, and presentation. The results “Tematik Terpadu Kelas IV SD Kurikulum 2013”, namely: a) Feasibility of content: this book is appropriate and in accordance with the curriculum, KI and KD already implied in the textbook material;b) Feasibility of language: this book is feasible by using simple, communicative, and in accordance with the rules of Indonesian language that are good and correct; c) Feasibility of presentation: the presentation of this book is feasible, coherent, and interesting. Material is presented in a coherent manner, from easy to difficult, concrete to abstract. In addition, because this book is used in fourth grade,
Experiences Using Heat Maps to Help Students Find Their Bugs

Automated grading systems provide feedback to students in a variety of ways, but usually focus on identifying incorrect program behaviors. Such systems provide notices of test case failures or runtime errors, but without debugging skills, students often become frustrated when they don't know where to start. They know their code has defects, but finding the problem may be beyond their experience, especially for beginners. An additional concern is balancing the need to provide enough direction to be useful, without giving the student so much direction that you effectively give them the answer. This paper presents our experience using heat maps to visually guide student attention to parts of their code that are most likely to contain problems. These visualizations are generated using existing tools that capture execution traces from instructor-written tests to identify which portions of the code are executed during tests that pass, and which portions are executed during tests that fail. Superimposing execution footprints allows statistical identification of locations in the student's code that are most likely to contain faults. This paper describes the results of using this feedback approach to help guide student attention with heat map visualizations over two semesters of CS1 involving over 700 students. Based on this experience, we analyze the utility of the heat maps, describe student perceptions of their helpfulness, and describe the unexpected challenges arising from students attempts to understand and apply this style of feedback. We conclude with concrete solutions proposed to improve how guiding feedback is presented to students.
Evasive Malicious Website Detection by Leveraging Redirection Subgraph Similarities

Many users are exposed to threats of drive-by download attacks through the Web. Attackers compromise vulnerable websites discovered by search engines and redirect clients to malicious websites created with exploit kits. Security researchers and vendors have tried to prevent the attacks by detecting malicious data, i.e., malicious URLs, web content, and redirections. However, attackers conceal parts of malicious data with evasion techniques to circumvent detection systems. In this paper, we propose a system for detecting malicious websites without collecting all malicious data. Even if we cannot observe parts of malicious data, we can always observe compromised websites. Since vulnerable websites are discovered by search engines, compromised websites have similar traits. Therefore, we built a classifier by leveraging not only malicious but also compromised websites. More precisely, we convert all websites observed at the time of access into a redirection graph and classify it by integrating similarities between its subgraphs and redirection subgraphs shared across malicious, benign, and compromised websites. As a result of evaluating our system with crawling data of 455,860 websites, we found that the system achieved a 91.7% true positive rate for malicious websites containing exploit URLs at a low false positive rate of 0.1%. Moreover, it detected 143 more evasive malicious websites than the conventional content-based system.
Design and Application of the BiVib Audio-Tactile Piano Sample Library

A library of piano samples composed of binaural recordings and keyboard vibrations has been built, with the aim of sharing accurate data that in recent years have successfully advanced the knowledge on several aspects about the musical keyboard and its multimodal feedback to the performer. All samples were recorded using calibrated measurement equipment on two Yamaha Disklavier pianos, one grand and one upright model. This paper documents the sample acquisition procedure, with related calibration data. Then, for sound and vibration analysis, it is shown how physical quantities such as sound intensity and vibration acceleration can be inferred from the recorded samples. Finally, the paper describes how the samples can be used to correctly reproduce binaural sound and keyboard vibrations. The library has potential to support experimental research about the psycho-physical, cognitive and experiential effects caused by the keyboard’s multimodal feedback in musicians and other users, or, outside the laboratory, to enable an immersive personal piano performance.
Public-Key Encryption with Integrated Keyword Search

Since the last decade, the public-key encryption with keyword search (PEKS) has been studied as a popular technique for searching data over encrypted files. The notion finds useful application for fine-grained data search on outsourced encrypted data like iCloud, mobile cloud data, etc. In this paper, we present a concrete public-key encryption (PKE)+PEKS scheme and prove its security in the standard model. We prove that our scheme is both IND-PKE-CCA secure, that is, provides message confidentiality against an adaptive chosen-ciphertext adversary, and IND-PEKS-CCA secure, that is, provides keyword privacy against an adaptive chosen-ciphertext adversary, under the Symmetric eXternal Diffie-Hellman (SXDH) assumption. Our construction uses asymmetric pairings which enable a fast implementation useful for practical applications. Our scheme has much shorter ciphertexts than other known PKE+PEKS schemes. Particularly, we compare our scheme with other proposed PEKS and integrated PKE+PEKS schemes and provide a relative analysis of various parameters including assumption, security, and efficiency.
Analysis of convergence of adaptive single­step algorithms for the identification of non­stationary objects

The study deals with the problem of identification of non-stationary parameters of a linear object which can be described by first-order Markovian model, with the help of the simplest in computational terms single-step adaptive identification algorithms – modified algorithms by Kaczmarz and Nagumo-Noda. These algorithms do not require knowledge of information on the degree of non-stationarity of the studied object. When building the model, they use the information only about one step of measurements. Modification involves the use of the regularizing addition in the algorithms to improve their computing properties and avoid division by zero. Using a Markovian model is quite effective because it makes it possible to obtain analytic estimates of the properties of algorithms. It was shown that the use of regularizing additions in identification algorithms, while improving stability of algorithms, leads to some slowdown of the process of model construction. The conditions for convergence of regularizing algorithms by Kaczmarz and Nagumo-Noda at the evaluation of stationary parameters in mean and root-mean-square and existing measurement interference were determined. The obtained estimates differ from the existing ones by higher accuracy. Despite this, they are quite general and depend both on the degree of non-stationarity of an object, and on statistical characteristics of interference. In addition, the expressions for the optimal values of the parameters of algorithms, ensuring their maximum rate of convergence under conditions of non-stationarity and the presence of Gaussian interferences, were determined. The obtained analytical expressions contain a series of unknown parameters (estimation error, degree of non-stationarity of an object, statistical characteristics of interferences). For their practical application, it is necessary to use any recurrent procedure for estimation of these unknown parameters and apply the obtained estimates to refine the parameters that are included in the algorithms
Research on the Adaptive Control in Sugar Evaporative Crystallization Using LSSVM and SaDE-ELM

Abstract The process of sugar evaporative crystallization is a nonlinear process with large time lag and strong coupling. It is difficult to establish a reasonable mechanism model. In this paper, we use the data driving modeling method to establish an Adaptive Control model for batch boiling sugar crystallization process. First, by analyzing the main influencing factors of the evaporative crystallization process of intermittent boiling sugar, the most important two parameters, brix and liquid level, are selected as the control object. The self-adaptive differential evolution Extreme Learning Machine (SaDE-ELM) is used to construct the control model. A least squares support vector machine (LSSVM) is established and connected in the control loop to control the opening of the feed valve so that to control the feed flowrate according to the objective values of syrup Brix and liquid level. Experiments are conducted and the obtained data are used to train and verify the learning machines. Experiments indicate that the learning machines are able to realize adaptive control to key parameters of the crystallization process. Comparison of different neural networks indicates that the LSSVM performs better than BP, RBF and ELM and SaDE-ELM with prediction error of below 0.01, and training time of below 0.05 s.
Visualization of Clouds and Atmospheric Air Flows

The IEEE Scientific Visualization Contest 2017 addressed the arising challenges in the visualization and analysis of atmospheric cloud-resolving simulations. In this paper, we utilize direct and indirect methods to represent atmospheric attributes such as cloud water content and air pressure, and employ Eulerian and Lagrangian techniques for air flow visualization.