Reviewerly/exHarmony huggingface dataset:
Feature (Gene) Selection in Linear Homogeneous Cuts

A layer of formal neurons ranked based on given learning data sets can linearize these sets. This means that such sets become linearly separable as a result of transforming feature vectors forming these sets through the ranked layer. After the transformation by the ranked layer, each learning set can be separated by a hyperplane from the sum of other learning sets. A ranked layer can be designed from formal neurons as a result of multiple homogenous cuts of the learning sets by separating hyperplanes. Each separating hyperplane should cut off a large number of feature vectors from only one learning set. Successive separating hyperplanes can be found through the minimization of the convex and piecewise-linear (CPL) criterion functions. The regularized CPL criterion functions can be also involved in the feature selection tasks during successive cuts.
Principal Component Analysis on the Students' Perception of a Cognitive Assistant for Content Reinforcement in Higher Education

The study in this chapter presents training by highly ontology-oriented tutoring host (THOTH), a cognitive assistant applied to students of higher education. It was developed to provide a reinforcement of contents, aiming to reach a high level of interactivity between users and interfaces. THOTH is based on the theoretical assumption that knowledge is organized in the form of ontologies constructed in the ORAV model in regard to the Ausubel's meaningful learning. THOTH processes the required objects of the ontology in order to facilitate the formulation of standard questions based on the attributes. After one session, students gave its perceptions in a Likert-scale questionnaire with 13 questions. A principal component analysis was performed with 35 questionnaires revealing eight different categories of grouped questions, ranging from the degree of functionality in the learning process to featuring how users were accepting the conversations. The evaluation of the categories is explained quantitatively, highlighting relationships between the elements of each category of study.
Four-Channel 3D Around View Monitoring Algorithm Using Hemispherical Projection Model

Most current vehicle black boxes provide a bird’s-eye view of 2D type. This viewpoint has a limited viewing angle which makes it difficult to conduct an accident investigation, parking and awareness of space. To solve these problems, we propose the 3D Around View Monitoring (AVM) algorithm using image composition which combines planar and hemispherical projection method. The experimental results indicate that the proposed method overcome the 2D AVM disadvantages and may be useful for accurate accident investigation, autonomous driving system and other system.
Impact of Shuffler Design Pattern on Software Quality

Modern software has become intricate and versatile due to the worldwide growth of new software technologies. In this regard, the evolution of software quality metrics to support software maintainability is studied. The impact of the Shuffler design pattern on software quality metrics is evaluated in this paper. The Shuffler design pattern provides an efficient design approach for shuffling. The pattern helps to choose generic shuffling alternatives that make the client program loosely coupled, and thus attaining high reusability. A few software quality metrics, which has a higher influence on software reusability and maintainability, are experimented on three gaming applications like Jigsaw, Poker, and Scramble. These gaming applications are redesigned using Shuffler design pattern and a combination of other patterns. The three software quality metrics, which show improvement on the redesigned applications, are McCabe Cyclomatic Complexity, Lack of Cohesion of Methods and Specialization Index. The authors also have tested the pattern with a reusability metrics suite, which measures the reusability of the black-box components of the aforementioned applications without any source code. The results with high cohesive and low coupling values would help software designers in the industry to be more confident in using the Shuffler pattern along with other design patterns. The interdependence of the three software metrics on the software quality attributes is finally tabulated to show their impact on software quality.
Educational Database Analysis Using Simple Bayesian Classifier

In this article, we propose the use of a new simple Bayesian classifier (SBND) that quickly learns a Markov boundary of the class variable to a network structure relating class variables and the said boundary. This model is compared to other Bayesian classifiers. Then experimental tests are carried out using a UCI database referring to the performance of high school students who take the Mathematics course in two schools in Portugal based on their personal details the tests try to anticipate who passes the course and who requires help to succeed. With these databases we compare the results obtained by such algorithms studied in the state of the art such as Naive Bayes, TAN, BAN, RPDag, CRPDag, SBND and combinations with different metrics such as K2, BIC, Akaike, BDEu. The experimental work was done in Weka software.
Interleaved Incremental/Decremental Support Vector Machine for Embedded System

Incremental and Decremental Support Vector Machine (IDSVM) is a widely used incremental learning algorithm that is highly accurate but requires high computational complexity. For IDSVM to be deployed in embedded systems, moving window architecture is needed to limit the number of support vectors in the model. This increases the complexity of the system as data need to be unlearned while learning new data. This work proposes an interleaved IDSVM (IIDSVM) architecture that performs incremental and decremental learning simultaneously. This work targets embedded system platform with limited on-chip memory. The proposed solution is able to get an improvement of 60% - 70% in terms of speed while producing similar accuracy with IDSVM.
Research on Polarization Modulated QKD Through Overhead Transmission Lines

As the intelligence level of power grid growing, the power system relies more on information and communication infrastructure. As a result, the reliability and security of the electric power system have been exposed to threats like illegal hacking interception, tamper, etc., from the informatics field. Quantum Key Distribution (QKD) is considered as a promising technology for power communication network, which can realize One Time Pad (OTP) protocol and prevent key from eavesdropping by exploring the principles of quantum mechanics. In this paper, we study the nonideal characteristics of quantum channel when single photon transmitting over overhead transmission lines. We first present a field test system for BB84 QKD protocol based on polarization modulation, and reveal the relationship between nonideal characteristics of overhead transmission, such as attenuation, internal stress, and strong electromagnetic interference, and received single photons random polarization variation, and then address the above issues by the polarization compensation scheme.
CRUST: A C/C++ to Rust Transpiler Using a “Nano-parser Methodology” to Avoid C/C++ Safety Issues in Legacy Code

CRUST, a language translator (transpiler) has been developed which converts programs in C/C++ to programs in Rust. Rust created by Mozilla has become popular as a systems programming language as it provides constructs and tool support for developing safe and secure programs without hassles. Safe Rust ensures that the programs developed using these programming constructs are safe and secure by enforcing compiler restrictions, concurrency without data races, memory safety without garbage collection, and abstractions with low overheads. CRUST is a semiautomated transpiler, to automatically convert a subset of “C/C++” code base into Rust without much effort. This is done using a unique Nano-Parser Methodology. CRUST also enhances the readability and understandability of the program by adding extensive documentation to the translated code. It yields a code which is guaranteed to be thread safe and proven to be faster.
A Novel Weighted Hybrid Multi-View Fusion Algorithm for Semi-Supervised Classification

Semi-supervised learning aims to improve the learning performance with very limited label information. To dig more available information from the collected data, we propose a weighted hybrid multi-view feature fusion approach for semi-supervised classification problem. Specifically, under the rank consistency constraint for labels predicted by view-specific learners, the proposed method estimates the optimal fusion weight for each learner to balance the incomparable square losses on different views. In this case, the learners with more powerful prediction capability are pushed to have higher weights during the fusion process. Experimental results on 6 real-world datasets demonstrate the effectiveness of the proposed technique.
Increasing Regional Original Income (PAD) Using Cloud Computing Information Communication Technology

The demographic bonus momentum in Indonesia has become a tremendous potential for economic growth, provides the widest possible prosperity of the Indonesian people. It could be realized by using technology. Technology both in the central and local government of Indonesia depends on Government and each region local budget (APBN and APBD). Technology budgeting in the APBN and APBD is very dependent on state and regional income. This study aims to examine empirical evidence on whether technology can be financed by Regional/local district Income and analyze whether cloud computing technology could increase Regional Original Income (PAD). The sample uses the district government Yogyakarta, the first phase we examine whether Regional Original Income (PAD), General Allocation Fund (DAU), and Special Allocation Fund (DAK) can finance Capital Expenditures (technology) and the second phase we analyze how to increase PAD through Cloud computing technology. Descriptive analysis of PAD, DAU and DAK data in financing technology and analysis of using cloud computing technology in order to improve PAD. The Research using four districts and one capital city in Yogyakarta. Using secondary data from 2006-2015 realized APBD, we analyzed whether the four districts and one capital city could finance the capital expenditures (technology). Using PLS We examined whether PAD, DAU, and DAK be able to finance the technology, and We analyzed whether cloud computing can improve PAD. The results conclude that technology spending in district budget cannot fully be financed by PAD, DAU, and DAK. Furthermore, DAU does not significantly affect capital expenditure (technology), but PAD and DAK positively influence the capital expenditure (technology). It is a different phenomenon findings that DAU does not significantly affect capital expenditure (technology). In order to make Indonesia is one of a developing country in the world, We conclude the demographic bonus momentum requires technology as well as computing cloud technology. Moreover Indonesia needs to increase and utilize PAD, DAK, and DAU to finance capital expenditure (technology).
Realizing an adiabatic quantum search algorithm with shortcuts to adiabaticity in an ion-trap system

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text W. Wu and J. Zhang, "Realizing an adiabatic quantum search algorithm with shortcuts to adiabaticity in an ion-trap system," in Quantum Information and Measurement (QIM) V: Quantum Technologies, OSA Technical Digest (Optica Publishing Group, 2019), paper F5A.18. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Witness indistinguishability and witness hiding against quantum attacks

The development of quantum computers has urged the cryptographic community to prepare cryptographic primitives for the eventual arrival of the post-quantum world. To this end, the authors study the witness indistinguishability (WI) and witness hiding (WH) of proof systems against quantum adversaries. They give formal definitions of quantum WI (QWI) and quantum WH (QWH), present proof systems satisfying these definitions, and specify a condition under which QWI implies QWH. Regarding the non-interactive proof systems, they prove that, even if a common reference string is used to generate polynomially many non-interactive proofs, the QWI is still preserved, while quantum zero-knowledge has no such beneficial property. To show the strength of QWI, they present two applications of them. First, they prove that the construction proposed by Feige et al. that transforms any non-interactive bounded zero-knowledge proof system to a general one is also feasible against quantum adversaries. Second, they construct a quantum-secure signature scheme in the CRS model, which is existentially unforgeable against quantum adversaries and remains secure even if a common random string is used to sign polynomially many messages.
Modelling the dynamics of forgetting and remembering by a system of belief changes

This article focusses on how belief change operations could model forgetting and remembering. Inspired by psychological findings, it is proposed that forgetting could be understood as an operation of memory decay. Remembering is then an operation which recovers already decayed beliefs. It is described how different areas of belief change could be used to build a system of these operations. An example of such a system is presented. The expected contributions and challenges of this approach are discussed, and possible applications are presented.
Emergent Scheduling of Distributed Execution Frameworks

Distributed execution Frameworks (DEFs) provide a platform for handling the increasing volume of data available to distributed computational processes, forming the creation and usage of a large number of DEFs for performing distributed computations. For example, sorting and analyzing large data sets through map and reduce operations, performing a set of operations across points in a data stream to provide near real-time analysis, and the training and testing of machine learning models for varying methods of learning, such as, supervised, unsupervised and reinforcement learning, exploiting the vast amounts of data available. Leading to varying DEFs becoming optimal for either fine or coarse grained computations, for example Apache Spark provides a framework for coarse grained data parallel processes providing data locality adding latency to scheduling decisions which would hinder performance of fine-grained computation. Whereas Ray and Apache Flink provide solutions to avoid the latency incurred by the scheduling method used by apache Spark while potentially incurring longer job completion times as data locality is no longer a priority. Therefore, this PhD will focus on overcoming the issue of trading performance for differing workloads by exploiting the capabilities presented by emergent software systems which learn how to assemble and re-assemble themselves in response to their current deployment conditions and input pattern. This allows the creation of a component based DEF capable of altering both the local behaviour of a DEF (i.e. Local Schedulers and placement polices within a centralised scheduler) to potentially improve the performance of single DEF as well as global behaviour of a DEF, for example the adaptation of a centralised to two-level scheduler.
SIGCHI Outstanding Dissertation Award

A 'data-driven life' has become an established feature of present and future technological visions. This dissertation interrogates the human experience of a data-driven life, by conceptualising, investigating, and speculating about personal informatics tools as new technologies of memory. I argue that the prevalence of quantified data and metrics is creating fundamentally new and distinct records of everyday life: a 'quantified past'. To address this, I conducted qualitative and idiographic fieldwork -- with long-term self-trackers, and subsequently with users of 'smart journals' -- to investigate how this data-driven record mediates the experience of remembering. Further, I undertook a speculative and design-led inquiry to explore the context of a 'quantified wedding'. Adopting a context where remembering is centrally valued, this Research through Design project demonstrated opportunities for the design of data-driven tools for remembering. Crucially, while speculative, this project maintained a central focus on individual experience, and introduced an innovative methodological approach 'Speculative Enactments' for engaging participants meaningfully in speculative inquiry.
AN ENHANCED RGB PROJECTION ALGORITHM FOR CONTENT BASED IMAGE RETRIEVAL

Content based image retrieval (CBIR) is one of the most popular and rising research areas of the digital image processing. Most of the available image search tools, such as Google Images and Yahoo Image search, are based on textual annotation of images. In these tools, images are manually annotated with keywords and then retrieved using text-based search methods. Therefore, the performances of these systems are not satisfactory. The goal of CBIR is to extract visual content of an image, like colour, texture, and shape automatically and to get accurate results with lower computational time. The CBIR technology can be used in several applications such as digital libraries, crime prevention, photo sharing sites, etc. Such a system has great value in apprehending suspects and identifying victims in forensics and law enforcement. This article presents an enhanced Red Green Blue (RGB) projection Algorithm to address the limitations of RGB Projection algorithm and reduce semantic gap in content-based image retrieval using bitmapping algorithm, image scale algorithm and Weighted Euclidean distance. The enhance technique was evaluated using WANG dataset, which contains 10800 colored images. The results show that the enhanced technique has higher precision than the existing system.
Research on the Five-Dimension Cooperative Education Mode of Local Universities in the Concept of Production-Education Integration —A Case Study of Applied Chemistry Major of Yancheng Teachers University

Under the background of production-education integration, cooperation between school and government, school and industry, school and enterprises, and school and research institutes, the construction of five-dimensional collaborative education mode needs to be oriented to serve the regional chemical industry. The five parties, such as schools, local governments, enterprises, industries and research institutes, participated in the construction of five-dimensional collaborative education mode, focusing on the construction of collaborative education platform, talent training objectives, curriculum system, teaching staff, practical teaching system and institutional mechanism in order to promote the integration of the curriculum chain, the talent chain and the industrial chain. The construction of this mode has an important reference value for realizing the scientific development of applied chemistry talent training in China.
SIGCHI Outstanding Dissertation Award

Text input methods are an integral part of our daily interaction with digital devices. However, their design poses a complex problem: for any method, we must decide which input action (a button press, a hand gesture, etc.) produces which symbol (e.g., a character or word). With only 26 symbols and input actions, there are already more than 1026 distinct solutions, making it impossible to find the best one through manual design. Prior work has shown that we can use optimization methods to search such large design spaces efficiently and automatically find a good user interface with respect to the given objectives [6]. However, work in the text entry domain has been limited mostly to the performance optimization of (soft-)keyboards (see [2] for an overview). The Ph.D. thesis [2] advances the field of text-entry optimization by enlarging the space of optimizable text-input methods and proposing new criteria for assessing their optimality. Firstly, the design problem is formulated as an assignment problem for integer programming. This enables the use of standard mathematical solvers and algorithms for efficiently finding good solutions. Then, objective functions are developed, for assessing their optimality with respect to motor performance, ergonomics, and learnability. The corresponding models extend beyond interaction with soft keyboards, to consider multi-finger input, novel sensors, and alternative form factors. In addition, the thesis illustrates how to formulate models from prior work in terms of an assignment problem, providing a coherent theoretical basis for text entry optimization. The proposed objectives are applied in the optimization of three assignment problems: text input with multi-finger gestures in mid-air [8], text input on a long piano keyboard [4], and - for a contribution to the official French keyboard standard - input of special characters via a physical keyboard [3]. Combining the proposed models offers a multi-objective optimization approach able to capture the complex cognitive and motor processes during typing. . .
Extracting Suspicious IP Addresses from WhatsApp Network Traffic in Cybercrime Investigations

Sniffers are among the commonest approaches for capturing network traffic activities and collecting digital evidences in cybercrime investigations. The ubiquity of instant messaging (IM) apps on smartphones has provided criminals with communication channels that are difficult to decode. Moreover, investigators and analysts of cybercrimes are encountering increasingly large datasets. To combat criminal activity, law enforcement agencies (LEAs) often rely on call-record analysis. In this paper, cybercriminals are investigated by network forensics and sniffing techniques. Retrieving valuable information from specific IM apps is difficult because the criminal's IP address records are not easily recognisable on the Internet. Here, a criminal's identity is located more effectively by a packet filter framework that isolates the WhatsApp communication features from huge collections of network packets. A rule extraction method for sniffing packets is proposed that retrieves the relevant attributes from high-dimensional analysis based on geolocation and a pivot table. The utility of this methodology is illustrated on real-time network forensics and a lawful interception system in Taiwan. The methodology also meets the ISO/IEC 27043:2015 standards of fear, uncertainty, and doubt avoidance. Besides supporting LEAs in discovering criminal communication payloads, prosecuting cybercriminals and bringing them to justice, it improves the effectiveness of modern call-record analysis.
Metro-Viz

Millions of time-based data streams (a.k.a., time series) are being recorded every day in a wide-range of industrial and scientific domains, from healthcare and finance to autonomous driving. Detecting anomalous behavior in such streams has become a common analysis task for which data scientists employ complex machine learning models. Analyzing the behavior and performance of these models is a challenge on its own. While traditional accuracy metrics (e.g., precision/recall) are often used in practice to measure and compare the performance of different anomaly detectors, such statistics alone are insufficient to characterize and compare the algorithms in a systematic, human-interpretable way. In this extended abstract, we present Metro-Viz, a visual analysis tool to help data scientists and domain experts reason about commonalities and differences among anomaly detectors, and to identify their strengths and weaknesses.