Reviewerly/exHarmony huggingface dataset:
Conversation Frames: Yet Another Contextual Dimension for IPAs

At first, we aimed at comparing the only two Intelligent Personal Assistants (IPAs) that support Arabic language, namely Siri of Apple and Salma of Mawdoo3. We compared them according to two features concerning the given answers: 1) Correctness and 2) Naturality. During our evaluation, we found out that both IPAs have a shortcoming that is present for other languages as well. This shortcoming is their inability to take into consideration the contextual information derived from consequent questions. To address this shortcoming, we propose a model that improves the naturality of an IPA without downgrading the correctness of its answers.
Emitter Threat Evaluation Based on IFS-TWD

In view of the shortcomings of the existing radar emitter threat evaluation, the model is applied to emitter threat evaluation by utilizing the uncertainty problem processing ability and delayed decision making ability of intuitionistic fuzzy set(IFS)-three-way decision(TWD) model. Firstly, five PDW parameters of radar emitter are extracted, fuzzy matrix of target set and attribute set and weight matrix of PDW parameters are constructed, and then fuzzy transformation is used to construct synthetic intuitionistic fuzzy set matrix. Then, an intuitionistic fuzzy set-three-way decision model is constructed by combining threshold value and intuitionistic fuzzy set matrix. Finally, the threat degree of emitter is evaluated according to the model. The experimental results show that the model overcomes the uncertainty in the process of processing and avoids the judgment error easily caused by two-way decision. The emitter is divided into different domains according to the degree of emitter threat, and the emitter threat evaluation is realized with high speed and reliability.
Analysis Instrument of Smart Technology Capability for an Industry

In fourth industrial revolution, all kinds of industry fields have utilized smart technology to increase their competitiveness in a global industrial environment. It is a core industrial strategy that most industry has built its smart technology environment appropriate for its industrial tasks and activities. In this environment, the smart technology capability of an industry is critical to efficiently perform industrial activities and effectively advance industrial performance. A reasonable analysis tool is necessary for objectively examining a smart technology ability of an industry in order to synthetically control and improve its smart technology capability. The developed 16-item framework is confirmed by reliability analysis and factor analysis based on previous studies. This study presents a 16-item analysis instrument that can properly examine a smart technology capability of an industry in an entire smart technology perspective. Our findings will contribute to the management and advancement of the smart technology capability of industry fields.
A Novel Text Encryption Algorithm based on Core Adaptive Fourier Decomposition

The progress of modern science and technology has accelerated the development of communication, providing more focus on the security of data transmission. Although there are many recognized encryption techniques, more high-performance algorithms are required to withstand the sophisticated analysis and cracking techniques. In this paper, we propose an innovative algorithm that combines with Core Adaptive Fourier Decomposition (Core-AFD) for text encryption. In addition, two versions of algorithm, initial version and enhanced version, are discussed for different forms and strength of the secret key and ciphertext. Due to the one-to-one correspondence between the decomposition and the error terms, the encryption algorithm is theoretically feasible and safe. The algorithm is further demonstrated by conducting experiments on text encryption with different lengths. The results are promising. Finally, the time complexity of cracking our algorithm in different cases is given.
Dual-Supervised Instance Segmentation Network Combined with Priori Corner Information

Instance segmentation is a classic problem in the field of computer vision, where the goal is to classify every pixel and differentiate object instances. Mask R-CNN is one of the most advanced and efficient algorithms in the field of instance segmentation. In practice, however, Mask R-CNN algorithm does not perform well when segmenting slender parts and edge parts of the object, not only in the standard COCO dataset but also in self-made dataset. Aiming at this problem, we propose a dual-supervised network combined with priori corner information called Corner Mask R-CNN. By using corner points as a priori information to allow the network to fit the edge parts better, the details of the instance corner points can be maintained, thereby achieving more accurate results of predictions. In addition, in order to enhance the feature expression ability of the network, a Squeeze & Excitation module is added which is similar to the attention mechanism. The method proposed is more accurate than Mask R-CNN in the MS-COCO dataset. Specifically, the average precision is 1.2 percent higher than Mask R-CNN. Besides, Corner Mask R-CNN is more effective in segmenting large objects, which is 2.5 percent higher than the benchmark network.
Optimization of a Novel Nakagami-m Fading Affected Multiuser Cognitive Radio System Using a New Hybrid DE/BBO/SA

Background and Objective: This communication introduces a multiple secondary user (SU) cognitive radio (CR) system in a dynamic fading environment, specifically Nakagami-m fading. The transmission parameters of the CR system are optimized to turn it into an optimal design. Methods: For this purpose, a new hybrid of differential evolution, biogeography-based optimization with simulated annealing, namely DE/BBO/SA is proposed. The suggested method searches the best CR parameter values while accomplishing general communication objectives. Fitness functions of these objectives are modified to include fading as well as to accommodate multiple carriers. Results and Conclusion: DE/BBO/SA generated results in terms of optimized parameters, fitness core and values of the objectives are compared with the ones acquired by other available techniques in the literature to show the efficacy of DE/BBO/SA in cognitive radio optimization. The performances are further validated by conducting a non-parametric statistical test to prove the use of proposed technique for solving CR problem.
Extreme Learning Regression for nu Regularization

Extreme learning machine for regression (ELR), though efficient, is not preferred in time-limited applications, due to the model selection time being large. To overcome this problem, we reformulate ELR to take a new regularization parameter nu (nu-ELR) which is inspired by Schölkopf et al. The regularization in terms of nu is bounded between 0 and 1, and is easier to interpret compared to C. In this paper, we propose using the active set algorithm to solve the quadratic programming optimization problem of nu-ELR. Experimental results on real regression problems show that nu-ELR performs better than ELM, ELR, and nu-SVR, and is computationally efficient compared to other iterative learning models. Additionally, the model selection time of nu-ELR can be significantly shortened.
Quantum Cryptography Key Distribution

Quantum cryptography is actions to protect transactions through executing the circumstance of quantum physics. Up-to-the-minute cryptography builds security over the primitive ability of fragmenting enormous numbers into relevant primes; however, it features inconvenience with ever-increasing machine computing power along with current mathematical evolution. Among all the disputes, key distribution is the most important trouble in classical cryptography. Quantum cryptography endows with clandestine communication by means of offering a definitive protection statement with the rule of the atmosphere. Exploit quantum mechanics to cryptography can be enlarging unrestricted, unfailing information transmission. This chapter describes the contemporary state of classical cryptography along with the fundamentals of quantum cryptography, quantum protocol key distribution, implementation criteria, quantum protocol suite, quantum resistant cryptography, and large-scale quantum key challenges.
FeatureCoPP

Annotation-based and composition-based variability mechanisms have complementary strengths regarding software maintenance and evolution. Consequently, several proposals have been made to combine, integrate, and substitute both mechanisms. An open challenge is to provide a unified, automatic, and practical technique to adopt such proposals. In this paper, we present a technique to convert variable feature code that is enclosed in the C preprocessor's conditional compilation into compositional feature modules and vice versa. We facilitate the usability of our technique by keeping the annotation-based representation of the C preprocessor. Besides contributing a practicable implementation, we describe the core principles of our technique and demonstrate its functionality based on previous empirical studies and by analyzing the Linux kernel. While our technique is fast in transforming projects, we also illustrate the challenges of maintaining fine-grained feature modules.
Teacher Candidate Experiences Related to Augmented Reality Applications

This study aimed to compare and contrast educational presentations prepared with the help of PowerPoint (PPT) program and augmented reality (AR) and to explore teacher candidates' presentation experiences in terms of impact on learning, motivation, usability/employability, and positive and negative aspects. In this context, a qualitative study with a phenomenological design was conducted. The study group was composed of teacher candidates attending the Department of Computer Education. Study data were collected via the interview form developed by the researchers. Research results show that AR applications are superior to PPT presentations in many respects, and while both AR and PPT presentations have positive and negative aspects, traditional learning environments with PPT presentations are no longer sufficient to attract the attention and interest of today's students and meet current demands. Moreover, there is a need to increase educational content and infrastructure to improve the use of AR in addition to providing training to teacher candidates and teachers.
Humans Enter the Age of Avatarism

The internet, social networks, emerging virtual/augmented/mixed reality technology platforms and portals are beginning to utilize and display interactive, spatially relative, three-dimensional versions of objects, persons, and environments. The human need to document and archive one's form, behavior, beliefs, experiences, and wishes is an inherent need and desire of our species to preserve and tell their unique life stories. An ability to track and/or capture human movement, expression, environment, and experience with technology designed to acquire hand gestures, body and facial tracking inputs, as well as speech will play an important role in the life documentation process. The eventual goal will be for humankind to interact with, and be remembered as, autonomous virtual agents beyond the scope of physical life, providing “virtual immortality” to any and all that adopt the capability as it evolves in our culture, and with the machines and applications that we utilize. This chapter explores the age of avatarism.
Chinese Character Recognition by Pseudo-Zernike Moments

Chinese character recognition systems based on local structure features face difficulties to recognize characters with similar structures. Since moment-based features could capture the global statistic properties of an image rather than local structure features, they are introduced into Chinese character recognition systems. This paper proposed a set of feature vectors based on pseudo-Zernike moments for Chinese character recognition. Three different feature vectors are composed of different parts of four selected lower pseudo-Zernike moments. Experiments on a set of 6,762 Chinese characters show that this method performs well to recognize similar-shaped Chinese characters. The rotational invariant property of pseudo-Zernike moments is also verified.
RELEVANT TECHNOLOGIES FOR DEVELOPING READINESS OF TEACHING STAFF FOR THE STANDARDIZATION OF JUNIOR SPECIALISTS’ TRAINING

Relevance: Professional training quality of prospective junior specialists mainly relies on teaching staff competence in professional training standards in colleges and technical schools.&#x0D; Aim: The aim of this article is to determine the ways of implementing educational technologies to develop teaching staff competence in junior specialists’ training standards.&#x0D; Methods: A number of theoretical methods were used in this research including the analysis and the synthesis (to identify the current issue research level), summary (to make conclusions and recommendations).&#x0D; Results: The implementation of this type of training system is based on educational methods including structural, logical, integrative, play-based, dialogic and other teaching techniques. The authors briefly mention the educational technologies which are relevantly used to develop teaching staff competence in junior specialists’ educational standards including training seminars, discussion seminars (group discussions), brainstorm, polylogues, positional discussions, training games (exercise games), staging (business simulation), complex action games (case-study method), contest games, projects (alternatively called “project management”), training, case problem solution, criteria kaleidoscope, talk-show.&#x0D; Conclusions: Workshops, trainings, case methods, round tables and other educational technologies help achieve practical results in developing teaching staff competence in junior specialist training standards.
An Improved Biomedical Event Trigger Identification Framework via Modeling Document with Hierarchical Attention

Biomedical event extraction has wide applications in biomedicine field. As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Although many approaches have been proposed for biomedical event trigger identification, two main challenges still remain for researchers: 1) most of the existing approaches treat each sentence separately in biomedical documents, failing to make full use of the semantics in the global document context; 2) the sparseness of event triggers leads to a serious issue of imbalanced class for trigger identification. In this paper, we propose an end-to-end framework for biomedical event trigger identification which addresses effectively the two mentioned challenges accordingly. Specifically, a hierarchical attention mechanism is used to model the global document context, including the semantic relationships both among words in the same sentence and among sentences in the same document. In addition, an adaptive class weight learning method is proposed to treat the class imbalance issue in biomedical event trigger identification. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed framework.
Variability meets security

We present a framework for quantitative security modeling and analysis of highly customizable attack scenarios, which resulted as a spin-off from our research in software product line engineering. The graphical security models are based on attributed attack-defense diagrams to capture the structure and properties of vulnerabilities, defenses and countermeasures---with notable similarities to feature diagrams---and on probabilistic models of attack behavior, capable of capturing resource constraints and attack effectiveness. In this paper, we provide an overview of the framework that is described in full technical detail in twin papers, which present the formal syntax and semantics of the domain-specific language and showcase the associated tool with advanced IDE support for performing analyses based on statistical model checking. The properties of interest range from average cost and success probability of attacks to the effectiveness of defenses and countermeasures. Here we illustrate the capabilities of the DSL and the tool by applying them to an example scenario from the security domain. This shows how techniques from variability modeling can be applied to security. We conclude with a vision and roadmap for future research.
Solar forecasting as an enablement tool for the distribution system operator (DSO)

Low voltage network operators, as well as other electricity sector actors and academia more broadly, are beginning to explore additional business models for more proactive management of Distributed Energy Resources (DER), including Behind-the-Meter (BtM) solar and energy storage. One such business model designed for managing higher penetrations of DER technologies is the "Distribution System Operator" (DSO). In this paper, we explore the potential future role of solar forecasting technologies in DER management through interviews with a variety of key stakeholders whom are developing the DSO model for Australia. The work frames interview results with a selection of three cases studies from Australian low voltage network operators (DNSPs - distribution network service providers) who have been given access to high resolution solar forecasting data. DNSPs are relevant in the DSO context, as it is possible they will create or partner with DSO entities in Australia.
Low-Cost Architecture of the Universal Security Threat Detection System for Industrial IoT

The chapter presents the new architecture of the distributed threat detection system for broadly understood information system security. Unlike existing systems, the system is heterogeneous in terms of information, communication, operation and hardware, architecturally using industrial Internet of Things solutions. Additionally, it is separated from the entity’s basic IT structure and based on the extensive use of parallel and distributed processing. In most of the available literature, improvements are sought in the way of wireless communication. In this work, the wired communication environment is examined, that is the backbone of any wireless sensor network. From the algorithmic point of view, it uses intelligent data analysis and biologically inspired methods. Communication environment is described using mainly graph theory, graph algebra and probability. The data collected by a heterogeneous group of autonomous traffic analyzers located at all levels of the communication hierarchy of the industrial information system is subjected to permanent analysis. Depending on the operating mode, the analyzers communicate with the central security node in one of three possible, complementary modes of transmission. Operating mode also determine how data is processed. In the simplest case it is carried out on the central node resources. On the other hand, in the most advanced one the system is in fact a parallel computer based on analyzers resources. The proposed architecture in the standard operating mode is integrated with the classic information system, in each of the emergency modes is separated from it. The work ends with the presentation of the empirical research results on the effectiveness of the proposed architecture.
A CRS-based convolution algorithm for NTRUEncrypt

The convolution operation consumes the main amount of computation and memory in the NTRUEncrypt public-key cryptosystem, which is the key to the implementation of NTRUEncrypt. A Circular Right Shift(CRS)-based algorithm is presented to calculate the convolution in the NTRUEncrypt. According to the closure of the ring, this algorithm is designed to seek the order of the term whose coefficient is `1' in the polynomial with binary coefficients, and then the coefficients of another polynomial participating in the convolution are rotated to the right by the corresponding order to replace the multiplication operation, thus the computation of the convolution is reduced. And the extra memory to store the convolution product is removed, hence the temporary memory for the convolution is decreased. The analysis result shows that the convolution algorithm proposed in this study has nearly the same performance as the Bailey's algorithm, but the temporary memory is reduced by 50% compared with the Bailey's algorithm.
Automatic Evaluation of UML Class Diagrams Using the XML Schema Matching and the Machine Learning Algorithm

During the learning process, the evaluation of learners’ products becomes difficult with immense number of students in universities, and it can be a heavy task for a teacher in such complicated subjects, for this purpose we propose to develop an automatic system for evaluating the learners’ product. In this article we present a new method for evaluating the UML class diagram built by the student and compare them with that of the teacher, and also, we compare the commonly used machine learning methods.
Modeling How Humans Judge Dot-Label Relations in Point Cloud Visualizations

When point clouds are labeled in information visualization applications, sophisticated guidelines as in cartography do not yet exist. Existing naive strategies may mislead as to which points belong to which label. To inform improved strategies, we studied factors influencing this phenomenon. We derived a class of labeled point cloud representations from existing applications and we defined different models predicting how humans interpret such complex representations, focusing on their geometric properties. We conducted an empirical study, in which participants had to relate dots to labels in order to evaluate how well our models predict. Our results indicate that presence of point clusters, label size, and angle to the label have an effect on participants' judgment as well as that the distance measure types considered perform differently discouraging the use of label centers as reference points.
Improved strong tracking particle filter

Aiming at the estimation of the higher nonlinear dynamic system which contains both saltatory states and the condition of likelihood function locating at the tail end of state space, this paper proposes improved strong tracking particle filter(ISTPF) method. By running a strong tracking Kalman filter, a correction item can be obtained and then is used to correct the particle sample of particle filter, which incorporates the current measurement into the sampling process, so the particles in the ISTPF method can be pushed to the high likelihood region quickly, and the sampling efficiency can be improved greatly as well as ISTPF has better ability to tracking saltatory states. Comparing with adaptive iterated particle filter (AIPF), ISTPF has the same estimation accuracy with great reduced computational cost. The ISTPF proposed in this paper not only has the same optimal estimation accuracy, but also can save lots of running time. Simulation results demonstrate that ISTPF algorithm is valid and advantageous.
Effectiveness and Efficiency for Document Clustering in Biomedicine

It is crucial for biomedical information retrieval, or clinical decision support in particular, to discover relevant biomedical/clinical information buried in scientific publications. At present, typical search interface is based on keywords as queries and returns a ranked list of documents, which is suited for finding simple factoids but not ideal for more complex information needs required for clinical decision support. A search interface deemed more suitable for this kind of tasks is cluster-based browsing, where retrieved documents are topically grouped for more intuitive exploration. To adopt this model, however, one needs to consider not only the effectiveness but also the efficiency of clustering framework as clustering is a computationally costly operation. As a first step toward a cluster-based browsing information exploration, this paper empirically studies representative feature selection/extraction methods and clustering algorithms for their effectiveness and efficiency.