Reviewerly/exHarmony huggingface dataset:
Novel Identity-Based Hash Proof System with Compact Master Public Key from Lattices in the Standard Model

As the progress of quantum computers, it is desired to propose many more efficient cryptographic constructions with post-quantum security. In the literatures, almost all cryptographic schemes and protocols can be explained and constructed modularly from certain cryptographic primitives, among which an Identity-Based Hash Proof System (IB-HPS) is one of the most basic and important primitives. Therefore, we can utilize IB-HPSs with post-quantum security to present several types of post-quantum secure schemes and protocols. Up until now, all known IB-HPSs with post-quantum security are instantiated based on latticed-based assumptions. However, all these lattice-based IB-HPSs are either in the random oracle model or not efficient enough in the standard model. Hence, it should be of great significance to construct more efficient IB-HPSs from lattices in the standard model. In this paper, we propose a new smooth IB-HPS with anonymity based on the Learning with Errors (LWE) assumption in the standard model. This new construction is mainly inspired by a classical identity-based encryption scheme based on LWE due to Agreawal et al. in Eurocrypt 2010. And our innovation is to employ the algorithm SampleGaussian introduced by Gentry et al. and the property of random lattice to simulate the identity secret key with respect to the challenge identity. Compared with other existing IB-HPSs in the standard model, our master public key is quite compact. As a result, our construction has much lower overheads on computation and storage.
Concurrent processing of cloudlets in CloudSim-SimIoT environment

People are surrounded by tons of Big Data and in fact, the rate at which big data is being generated these days is phenomenal. Every digital process and social media is contributing to the already present vast amount of Big Data. Systems, sensors and mobile devices transmit it. According to worldwide studies that are going on, it can be safely mentioned that Internet of Things is a major source of the Big Data. In this rapidly progressing world where IoT is still in its infancy, it is very important to understand and study the efficiency of these IoT devices in the cloud infrastructure. To adopt scalability in the cloud environment and to handle various kinds of file formats and complex data processing systems of big data are the inhibitions for cloud infrastructure to the development of processing time and cost effective. To fill this gap, SimIoT evolved and provide intercloud scalability. SimIoT is like an extended version of the CloudSim library which is specifically designed to test and simulate Internet of Things application under the cloud environment. In this paper, we have presented an algorithm which assimilates simultaneous finishing time for cloudlet process. This algorithm can help build the SimIoT where it has applications in sensors which can simultaneously sense multiple data and process them together.
Computer-Aided Speckle Noise Analysis in Ultrasound Images Through Fusion of Convolutional Neural Network and Wavelet Transform with Linear Discriminate Analysis

This paper presents a computer-aided system for speckle noise analysis in ultrasound images. The proposed system uses the combination of convolutional neural network (CNN) features and wavelet features to detect speckle noise in ultrasound images. The wavelet features are based on the covariance of the second-order statistical measures over the wavelet transform. Evaluations on standard databases show that the proposed system is gaining an accuracy of 98.30%, sensitivity 98.79%, and specificity of 98.52%. This approach is supported by a linear discriminate analysis (LDA) for characterization of object regions from noise regions. It produces a strong speckle reduction and edge preservation due to noise-free feature extraction scheme. The experimental result is compared with several other existing speckle reduction methods and it outperforms the state-of-the-art methods on the basis of contrast resolution and MSE.
Intuitionistic Fuzzy InterCriteria Approach to the Assessment in a Fast Food Restaurant

The InterCriteria analysis (ICrA) is a new method, which is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs), aiming at detection of the dependencies between pairs of rating criteria in both clear and fuzzy environment. It is only based on comparisons, existing between the evaluations of the objects against the system of criteria, rather than on their numerical values, which makes computations faster than the other correlation analysis methods. Here, the proposed method is applied to identify the correlations between pairs of criteria referred to the real data with estimates of the waiters at a daily fast food restaurant in Burgas, part of a chain of restaurants. A comparative analysis of the correlations between the waiters’ assessment criteria in the restaurant, resulting from the use of the correlation methods of ICrA, Pearson, Spearman and Kendall, has been performed in the paper. The four correlation analyses yielded very similar correlation coefficients, but only the ICrA can be applied to intuitionistic fuzzy evaluations. The main contribution of the paper is that it shows the effectiveness of the proposed method to reveal dependencies between evaluation criteria in the restaurant. The correlations between the criteria will lead to optimization of the system for evaluating the waiters in the chain of restaurants and will support the decision-making process related to the motivation of the human resources. The outlined approach for the ICrA, can be applied to both the crisp data and the fuzzy ones and can be expanded to retrieve information to other types of multi-dimensional data.
Preserving Privacy &amp;amp; Promoting Learning: Technology to Empower Belmont 2.0

The Health Insurance Portability and Accountability Act has been insufficient for guarding against ethical violations involving research on health data in the age of the “internet of medical things,” smartphone apps and artificial intelligence. Concerns regarding loss of control and privacy of health data are growing, as are calls for updated ethical guidelines for big data research. In this piece, we discuss how two innovations: blockchain technology and privacy-preserving computational models, may be foundational for the new ethical guidelines, particularly given their potential to resolve fundamental tensions between our concurrent obligations to protect individuals’ health data rights and to promote learning from health data for the good of society.
Development of a Technological Platform for Knowledge Discovery

The paper considers the architecture of the technological platform designed for construction of the knowledge base (KB) by integrating a set of logical rules with fuzzy ontologies. The KB represents the storage of knowledge and contexts of different problem areas (PrA). The PrA ontology context is a specific state of the KB content that an expert can choose from a set of available states. The state is a result of either versioning or constructing the KB content from different points of views. Also, the paper describes the application of the KB in the inference of expert recommendations on solving the problem situations that occurred in the process of local area network functioning.
Hacking women’s health: A new methodology

The paper describes an innovative methodological approach to Public Engagement already tested by the Authors in the healthcare context. We propose to apply our methodology to social innovation projects, focusing on gender perspective when promoting enhancements in health-community. Methodology mixes Psychosocial Participatory Research, Service-Design and “Human-Centered” approach, promoting an interdisciplinary, technological, and sustainable process. The final aim is to find viable solutions to real problems through the engagement of a community of experts and citizens in a three-step process. Awareness-raising actions: to increase people’s knowledge—group understanding of the subject and to stimulate reflection. Activities: Gender-Cafés, Ethnographic-Observations and Narrative-Interviews. Activation actions: group interventions in which initial stimuli are collected and processed into proposals for services and tools aimed at targeted groups. Activities: Barcamps, Generative Labs. Participation actions: multidisciplinary teams realize prototypes and elaborate concrete solutions (tools, services) addressing crucial gender issues. Activities: Hackathon, Service-Design-Labs.
“New Biology” and Its Educational Outcomes

The paper presents data of a study on the effectiveness of an experimental school course in biology based on the principles of Davydov—Elkonin developmental learning in comparison with traditional courses. The study involved 395 students of 5th, 6th, and 9th classes. The results show that children can be more successful at achieving not only subject, but also metasubject educational outcomes like reading literacy, ability to work with signs, symbols and models etc, which are all required by the federal state education standard, if the content of education is changed in such a way that the integral system of key concepts becomes the subject matter, presented not as a means of describing an object, but rather as a basis for its transformation by students. When learning is organised that way, it provides an opportunity for primary school students with underdeveloped reading skills to compensate for the lack of reading literacy, although it will still be hard for them to master subject concepts successfully enough.
Feature Extraction Acceleration to Stabilize Execution Time for Real-Time Applications in Low-Cost Embedded Systems

Extracted features are widely used for image processing. Many research endeavors have been undertaken to extract significant features of fast moving images. Appropriate algorithm processing is necessary to extract features and provide features to the other modules in real time with low-cost embedded systems. The features from accelerated segment test (FAST) algorithm is renowned for feature extraction. FAST is composed of simple arithmetic operators. In this study, FAST is employed to implement the hardware accelerator in a field-programmable gate array for small embedded systems. Meanwhile, the threshold value in FAST affects the number of extracted features and the execution time. The precarious execution time makes it difficult for the system to schedule the timing of system functions and thus degrades the performance. An appropriate method is necessary to stabilize the execution time. A dynamic threshold controller in a FAST hardware accelerator is thus proposed to enable a stable execution time. A proportional integral controller composed of an adder, subtractor, and shifter is applied for low design implementation costs. The proposed approach occupies 2,263 slice flip-flops, 3,498 look-up tables, and 17 block RAMs in a Xilinx Virtex 5 FX field-programmable gate array. It requires 3.87[Formula: see text]ms for continuous 800×480 images from the KITTI benchmark.
A study on the intelligent application of the code and the contour of the image feature

This paper reprocesses the information of the boundary intelligent contour so as to effectively extract the codes of image contour features. The algorithm based on the coordinates of contour extracted through the level set evolution algorithm is used to obtain several 2D contour matrixes with the s ame size after repeated conversions. Moreover, the matrixes are diagonally, column-wise and horizontally coded to obtain new coded features. The anti-interference analysis of algorithm indicates that the algorithm of extracting feature code has variable and flexible extraction schemes and high stability. Distinguishable information can be obtained in similar images more easily. In order to prove the validity of the proposed algorithm, feature code exaction algorithm is used to facial expression recognition, and a facial expression recognition model on the basis of facial part contour code exaction is established. According to the experimental results, this facial expression recognition system can eliminate the interference with recognition resulted from the similarity of samples. The comprehensive recognition rate of facial expression is up to 97.20%.
Artful Integrations of Infrastructures by Community-based Telecoms

This one-day workshop considers the experiences of communities that own and operate their own telecoms systems. We will reflect on what communities do, when deploying, running and adopting telecoms, to tackle infrastructures, or taken for granted arrangements, that were not set-up to support them, for instance telecommunications laws.
Pengembangan Buku Ajar Visual Menulis Kreatif untuk Peserta Didik Madrasah Ibtidaiyah

Creative writing skills are still a challenge that must be faced by students of 6th grade in the Ar- Roihan Lawang Integrated Ibtidaiyah Madrasah. One of the contributing factor is the lack of interesting textbooks available at the school. This study aims to develop creative writing visual textbooks that improve students' skills in writing. This type of research is Research and Development (R &amp; D) with the stage model Borg and Gall (2003). The stages include: needs analysis, product design, product development, product validity testing, testing on predetermined subjects, and product revisions. The results of this research and development of creative writing visual textbook products were based on the assessment of the media validator team with linguistic averages, presentation, graphics, scores of 4.72. Whereas from expert learning content with indicators of accuracy, attractiveness, and usability of 4, 29 are categorized very well. The product trial series found 33% addition in the experimental class post-test results and tested significantly with the number 0,000 SPSS t-test. Interviews in students indicated that creative writing visual textbook was very interesting so it can be concluded that this textbook product can improve the writing skills of 6th grade students of the Ibtidaiyah Ar-Roihan Lawang Madrasah.Keywords: Developing visual textbook, creative writing, writing skill.
Different Schemes for Improving Fuzzy Clustering Through Supervised Learning

Clustering is an important unsupervised learning tool to find natural grouping of instances from a given unlabeled data based on some similarity/dissimilarity measures. However, clustering the data points at the boundary of multiple overlapping clusters is really a challenge. The challenge also lies in many real life applications where the boundaries among clusters are not rigid and clear. There are several hard and soft clustering algorithms which attempt to address these challenges. Hard clustering algorithms though perform well in the presence of well separated clusters, it shows poor performance in presence of such difficulties mentioned above. Researchers have recommended several soft clustering algorithms for handling such dilemma. In this article, we have proposed different schemes toward improvement of Fuzzy clustering employing supervised classification. The superiority of our prescribed schemes are demonstrated by comparing its performance with that of Fuzzy c-means clustering. The comparison has also been made among different schemes we have proposed in this article.
Predicting the Listing Status of Chinese Listed Companies Using Twin Multi-class Classification Support Vector Machine

Multi-class classification problem is research challenge in many applications. Listing companies’ statuses are signals on different risk levels in China’s stock markets. The prediction of the listing statuses is complex problem due to imbalance in the data, due to different values and features. In the literature when the list status is divided into two categories for simple measurements using binary classification model, accurate risk management cannot achieved correctly. In this work, we have used SMOTE and wrapper feature selection to reprocess data. Accordingly, we have proposed an algorithm named as Twin-KSVC (twin multi-class support vector machine) which is used for multi-class classification problem by “1-versus-1-versus-rest” structure. Our experiments tested on large sample of data set; show that we could achieve better performance, in comparison with other approach. We have tested our algorithm on different strategies of feature selection for comparison purposes.
Fault Region Extraction of Electric Equipment Based on PCNN

To localize the fault in infrared images of electric equipment, an efficient fault region extraction method is presented. On the basis of pulse coupled neural network (PCNN), the inner parameters of each neuron is reduced, and local features of fault and non-fault regions are combined to achieve adaptive iteration. Through experimental analyses, this method is proven to be able to extract the fault region more effectively compared with conventional extraction methods based on OTSU, EM, conventional PCNN and normalized cuts.
Orienting to the Wild

Understandings of what 'in the wild' might mean differ enormously according to the kind of research being undertaken. This chapter inspects the orientations to the wild visible in various kinds of research deployments and assesses their implications. Very broadly, one can identify four potentially overlapping concerns: wanting to deploy a technology so that it will be seamlessly integrated into real-world activities; deploying a rather crude technology with an interest in finding out by doing so what it will take to make it fit; deploying a technology to encourage specific kinds of engagement that may or may not have any permanence; and deploying a technology with the express goal of being disruptive. Having examined how these orientations are visible in the literature the chapter goes into greater depth regarding two specific and contrasting deployments to examine the outcomes of different research orientations to the wild in greater depth. In particular, the chapter highlights the potential disjuncture between researcher orientations to the wild and the orientations of the inhabitants of the settings where research deployments take place and considers what the consequences of that might be.
Mobile Robots Applied on Virtual and Real Environments

The paper presents a VIPRO versatile, intelligent and mobile platform for real time robot control, by applying an original virtual projection method which involves the representation of modern mobile robots in a 3D virtual environment using a strong robotic simulator, an open architecture system and adaptive networks over the classical control system of the robot, developing intelligent control interfaces that use advanced control technologies adapted to the robot environment such as extended control (Extenics), neutrosophic control, human adaptive mechatronics. The obtained results lead to application development on mobile robots in virtual and real environments, allowing a correct evaluation of force-position robot behaviors and high level real-time simulation in order to correctly model interactions among the robot and the motion environment.
Features of the representation of the Kazakh sign language with the use of gestural notation

In the article we give the description of the features of the functioning of gestural speech as a specific communicative system. As well we present a brief description of the gestural notations used to record the structure of gestures in writing. The rationale for the choice of gestural notation by L.S. Dimskis in relation to the Kazakh sign language is offered. The dictionary of frequently used gestures of the Kazakh sign language has been compiled using this sign system.
GPU-Based Bat Algorithm for Discovering Cultural Coalitions

Nowadays, artificial intelligence makes a great success in our modern social life. The human should be prepared to be able to live with social robots that can provide him comfort and help in solving complex processes. Extending the use of robot’s technology is certainly desirable, but preventing certain catastrophes from the misdeeds of artificial intelligence is crucial. One of these troubles could be for instance the creation of robots’ coalitions to impose pernicious decisions. As a contribution to cope with such issue, we propose a parallel approach for the detection of cultural coalitions based on Bat Algorithm. This GPU-based bat algorithm approach can treat very large datasets due to the possibility of launching several artificial bats simultaneously, which contribute to reducing the runtime without affecting the performance. To proof the effectiveness of the parallel detection coalition method, we conducted several experiments on datasets of different sizes. These datasets represent the result of cultural artificial agents playing the colored trails (CT) game. For the creation of agents’ profiles, we use real cultural datasets generated based on the WV survey. The experimental analysis demonstrates that the use of the proposed method will considerably reduce the runtime.
An Approach Towards Analyzing Various VM Allocation Policies in the Domain of Cloud Computing

Cloud Computing has become a new age technology that has got the high degree of potency nowadays. Cloud Computing uses the concept of virtualization. It also provides resources to applications by allocating virtual machines to specific application. Optimizing resources in the cloud environment is more beneficial, minimizing allocation cost and satisfying client requests are the main purpose of working with VM allocation strategy. So, the resource allocation policies play crucial role for allocating and controlling the resources among several applications in cloud computing environment. This paper illustrates a comparative study on various VM allocation policies. The study and performance analysis of these algorithms are done on the basis of total allocation cost in between VM to Host considering different attributes and different service level agreements (SLA) in the domain of cloud computing.
Risk Analysis of Agile Framework in Project Management Exploration Drilling in Oil and Gas Sector

Energy sector in particular of oil and gas industry is the industry that became main income in the State of Indonesia before 2015, but in the same year there was a significant change in oil and gas prices. A more rapid and systematic project management pattern is required using Agile, Agile is a management project pattern that will be implemented in the current decade, using agile activities to make it easier to think, understand and implement prioritization at each stage. In this research will be mapping priority and risk based on expert on determining risk matrix, job priority and risk mapping. The results of this study indicate that subsurface activity is the main sub-project that must be prioritized.
Polarity Classification of Tweets Considering the Poster’s Emotional Change by a Combination of Naive Bayes and LSTM

Twitter, as a popular social networking service, is used all over the world, with which users post tweets for various purposes. When users post tweets, an emotion may be behind the messages. As the emotion changes over time, we should better consider their emotional changes and states when analyzing the tweets. In this study, we improve polarity classification by considering the poster’s emotional state. Firstly, we analyze the sentence structure of a tweet and calculate emotion scores for each category by Naive Bayes. Then, the poster’s emotion state is estimated by the emotion scores, and a prediction model of emotional state is created by Long Short Term Memory (LSTM). Based on the predicted emotional state, weights are added to the scores. Finally, polarity classification is performed based on the weighted emotion scores for each category. In our experiments, our approach showed better accuracy than other related studies.