Reviewerly/exHarmony huggingface dataset:
Asynchronous sessions with implicit functions and messages

Session types are a well-established approach to ensuring protocol conformance and the absence of communication errors such as deadlocks in message passing systems. Haskell introduced implicit parameters, Scala popularised this feature and recently gave implicit types first-class status, yielding an expressive tool for handling context dependencies in a type-safe yet terse way. We ask: can type-safe implicit functions be generalised from Scala's sequential setting to message passing computation? We answer this question in the affirmative by generalising the concept of an implicit function to an implicit message, its concurrent analogue. We present two calculi, each with implicit message passing. The first, Im, is a concurrent functional language that extends Gay and Vasconcelos's calculus of linear types for asynchronous sessions (Last) with implicit functions and messages. The second, MpIm, is a π-calculus with implicit messages that extends Coppo, Dezani-Ciancaglini, Padovani and Yoshida's calculus of multiparty asynchronous sessions (Mpst ). We argue, via examples, that these new language features provide utility to the programmer, and prove each system sound by translation into its respective base calculus.
Processing and Communication on Quantum Ensemble Transductions and Physical Observables

We investigate quantum-enabled processing on detectable physical variables in quantum photo-electronic devices and assemblies. The measurable photon-electron transductions can be used to accomplish dynamically-reconfigurable seamless optical communication, robust processing and interfacing. We investigate quantum processing on quantum ensemble. Integrated micro-optoelectronic solutions and control schemes should support physical implementation of data processing solutions and schemes. Performing fundamental studies and examining scalable technologies, processing estimates and device-level solutions are reported. We consider controlled photon emission and absorption by electrostatically-regulated semiconductor quantum dots, photon detection, photon propagation in optical waveguides, and, photon-electron transductions. Some of these schemes are tested conducting low-fidelity studies. The electrostatically controlled silicon nitride waveguides with quantum dots are fabricated and characterized. We report engineering solutions in silicon micro-optoelectronics, as well as in quantum-enabled processing and communication schemes. Quantum mechanics, microelectronics, nonlinear optics and microfabrication are applied to conduct fundamental, analytic, numeric and experimental studies. The energy switching, transduction frequency, channel capacity and measurement estimates are derived.
How Competitive Is Genetic Programming in Business Data Science Applications?

The paper evaluates GP’s competitiveness in business data science-driven applications and suggests the necessary steps to increase its reach, impact and competitiveness. First, the key business needs for Data Science are identified and discussed, followed by an analysis of the competitive landscape and popularity of Data Science methods. The competitive advantages and weaknesses of GP as well its impressive application record are reviewed. Two business applications with high value creation—inferential sensors and nonlinear business forecasting—are identified and described. The recommended action items to increase competitive presence of GP in Data Science business applications include: develop a successful marketing strategy toward statistical, machine/deep learning, and business communities; broaden application areas; improve professional development tools; and increase GP visibility and teaching in Data Science classes.
Lightweight Random Number Generation for Elliptic Curve Cryptography for Use in IoT

This white paper proposes a hybrid approach to elliptic curve cryptography (ECC) in IoT devices that today requires an increased level of security. This crypto-system makes use of key exchange algorithm to establish a secret key for future communication. Such algorithms rely on the generation of private keys by each node which becomes a challenge for these devices that are resource constrained in terms of power and memory. The solution proposed here is to make use of a lightweight random number generator to be used as private key in ECC- Elliptical Curve Cryptography Hellman system. This would substantially bring down computational costs and thus save resources for they are allocated in the first place.
Pin-Code Authentication by Local Proximity Based Touchstroke Classifier

Most of the recently released touchscreen devices enable fingerprint authentication; while pin-codes connected to the fingerprints are still in common usage. Among various biometric feature extractors, such as time-based, frequency-based or even pressure-based, the most reliable and implementable with mathematical infrastructure is location-based approach for enhancing security of touchscreen devices. Therefore, in this paper, we propose fundamentals of a novel feature extraction protocol to strengthen pin-codes by calculating local proximity of the touches on the screen without long training sessions. We presented a fuzzy-like area methodology for finding outputs for each input and also conducted experiments to show the discretization of the outputs per real and fraud attempts.
Notice of Violation of IEEE Publication Principles: Channel Decoding Based on Complex-valued Convolutional Neural Networks

Notice of Violation of IEEE Publication Principles<br>BR> “Channel Decoding Based on Complex-valued Convolutional Neural Networks” <br>by Lun Li, Guanghui Yu, Jin Xu, and Liguang Li <br> in the Proceedings of the 2nd 6G Wireless Summit (6G SUMMIT), March 2020 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This article contains significant portions of text from the article cited below that were paraphrased without attribution. <br><br> “Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks” <br> by Fei Liang, Cong Shen, and Feng Wu <br> in the Proceedings of the IEEE International Conference on Communications (ICC), May 2018 <br><br> <br/> Inspired by the recent outcomes in deep learning, we propose a novel decoding architecture which concatenates a complex-valued convolutional neural network (CCNN) with a belief propagation (BP) decoder for combating correlated noise in the channel. The CCNN can exploit the complex noise correlation and yield a more accurate estimation of the channel noise. Depressing the influence of channel noise via the proposed architecture, the BP decoder can obtain better decoding performances. Furthermore, extensive experiments are carried out to analyze and verify performances of the proposed framework.
Evidence Fusion Method Based on Evidence Trust and Exponential Weighting

In order to solve the problems of unreasonable fusion results of high conflict evidence and ineffectiveness of coefficient weighting in classical evidence theory, a method of evidence fusion based on evidence trust degree and exponential weighting is proposed. Firstly, the fusion factor is constructed based on probability distribution function and evidence trust degree, then the fusion factor is exponentially weighted by evidence weight, and then the evidence fusion rule based on fusion factor is constructed. The results show that this method can effectively solve the problems of unreasonable fusion results of high conflict evidence and ineffectiveness of coefficient weighting. It shows that the new fusion method are more reasonable, which provides a new idea and method for solving the problems in evidence theory.
INFLUENCE OF THE DUAL SYSTEM OF PROFESSIONAL EDUCATION ON THE QUALITY OF TRAINING OF STUDENTS IN VOCATIONAL EDUCATION

The article presents the results of theoretical and experimental research of a practice-oriented model of the dual system of vocational education. The study is based on the research of the implementation of dual education abroad, and on the analysis of systems of practice-oriented vocational education in the Russian Federation and the Republic of Kazakhstan. The distinctive and structural features of the dual system of vocational education are considered. To ensure the quality of vocational education of college students a practice-oriented model of dual education was developed. The model was created within the framework of the authors’ concept based on the requirements of the system, activity and acmeological approaches. The main research methods were the methods of analysis of the existing system of dual education, modeling of the process of professional training of college students, experiment, survey methods and expert evaluation. Dual vocational education is considered as a system that provides a consistent implementation of the pre-planned process of vocational education and the development of students in the conditions of theoretical training in the institution of vocational education and industrial training at the enterprise. The article presents the refined features of the concepts of “dual education” and “dual system of vocational education”. The main results of the authors are a practiceoriented model of dual education of students in vocational education institutions and the criteria for its effectiveness. It is proved that professional training of vocational education students in the system of dual education provides the increase of education quality and raises the level of personality development of students. The system of dual education guarantees the quality preparation of specialists according to the social order and Federal Educational Standards. The article presents experimental data confirming that the organization of the process of professional training of college students within the practice-oriented model ensures the transition of students to higher levels of quality of educational activity of the organization of vocational training and industrial activity of the enterprise. The results obtained expand the understanding of the role and features of the dual system of vocational education; determine the ways and forms of its implementation. The developed practice- oriented model of dual education is a significant contribution to solving the problems of ensuring the quality of vocational training of students of vocational education.
Video Folding: Increased Framerate for Semi-Repetitive Sequences

We introduce a technique to synthetically increase the framerate of semi-repetitive videos (i.e., videos of motion that repeats but not in an identical fashion) to aid in visualization. By reordering and combining frames from all repetitions, we produce a single non-repetitive sequence with much higher temporal resolution. Then, we use a novel frame warping technique based on a dense corrective flow to counteract differences between repetitions. The resulting video maintains smoothness of motion and additionally allows for seamless, infinite looping. We demonstrate the effectiveness of the proposed solution both quantitatively, by measuring the improvement over existing methods, and qualitatively, by performing a user evaluation and providing several examples in the article and accompanying video.
Information culture and media literacy in the structure of the library cultural and educational activities

Due to the active expansion of the media environment, convergent processes occurring in the modern socio-cultural space, there is a need to create information and educational platforms on forming information culture of population and, in particular, school-age children. The article describes the results of a study on creating favorable cultural and educational conditions for organizing systematic work on information culture and media literacy formation for children of middle and high school age in a library. The goal of the study is to build a universal model of the educational program with its subsequent implementation in the library environment. Its tasks were: 1) to make experiments on testing interactive lessons on the course «Information Culture in the Study of the Native Land Literature», developed according to the proposed universal model; 2) to analyze the results of experimental work; 3) to design the development program for the Information and Digital Culture Center of the Sakhalin Regional Universal Scientific Library for it to form information culture and media literacy for various population groups, in particular, the young generation. The article assesses the significance of the problem, describes the research parameters, and presents the results of testing the course "Information culture in the study of literature of the native land». The development program of the center for information and digital culture of the Sakhalin Regional Universal Scientific Library was made, designed to solve the problem of forming the information culture of the younger generation.
Ocean energy data learning from big data

Nowadays big data play an important role in the field of electrical energy systems. An ocean energy system is one part of the electrical energy generation system. In this chapter we discuss ocean energy data assessment through different big data environments. The prefeasibility assessment of an ocean energy system is done through a Hadoop environment, which assesses the site location and primary levels of technical and financial parameters. Energy aware resource allocation and energy aware cluster node management is also done in this chapter. At the end of the chapter an assessment of ocean energy data is done through the NoSQL and MapReduce algorithms.
Performance Comparison of Spline Curves and Chebyshev Polynomials for Managing Keys in MANETs

The dynamic and unstable nature of Mobile Ad hoc Networks (MANETs) does not support the existence of a centralized key server to govern keys. There exist two hierarchical key management techniques which are based on Polynomial Interpolation methods i.e. Lagrange's Polynomial interpolation and Curve fitting method. Both these techniques require high computational costs for higher-order polynomials as well as they suffer from Runge's problem. So, other polynomial interpolation methods were tried. In this work, key management is implemented using Spline Curves and Chebyshev Polynomials interpolation method and simulated in various settings. The key shares by Security Association Members (SAMs) are generated, distributed and secret key is built using any of the two polynomial interpolation methods. It is analyzed from the simulation results that the power and memory consumption in MANETs is decreased by using these methods. Spline Curves and Chebyshev polynomials both are more accurate, secure and stable as they not only provide security but they also impose no restriction to the order of the Polynomial. Hence, the key management schemes using these methods provide better cryptography. The results of their comparison have been analysed on various parameters. The significant property of Chebyshev polynomials is its recursive nature which outperforms it over Spline Curves.
CPGAN: Curve Clustering Architecture Based on Projected Latent Vector of Generative Adversarial Network

Although Generative Adversarial Network(GAN) has obtained remarkable achievements in the image analysis and generation, its exploration in GAN-based curve clustering is still limited. The latent space of curve data is often used for clustering. However, the distance geometry in the latent space does not reflect the inherent clusters. In this paper, we propose CPGAN(Curve Clustering Architecture based on Projected Latent Vector of Generative Adversarial Network) for the clustering of curve dataset. Firstly, a novel GAN network structure, which utilizes a projector P (composed of the transposed convolutional network) to reconstruct the latent space of curve data, is proposed. CPGAN utilizes the concatenation of discrete code and Gaussian noise as a latent vector to preserve the implicit signal and structure of the cluster. Secondly, the loss function with two regularizations for CPGAN is proposed to guarantee the robustness and effectiveness of the model. Based on these, the jointly trained projector P is used to participate in the clustering process, while the generator can be used in the generating process. Finally, the spectral dataset from the LAMOST survey, the UCI dataset, and the UCR dataset are used as experimental data to evaluate clustering performance, the robustness of CPGAN, and further application on anomalous detection. CPGAN presents higher results than other methods.
Research on Uyghur Pattern Matching Based on Syllable Features

Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm.
Tricking Johnny into Granting Web Permissions

We studied the web permission API dialog box in popular mobile and desktop browsers, and found that it typically lacks measures to protect users from unwittingly granting web permission when clicking too fast.
Resetting Threshold Values for Improving Facial Expression Recognition Accuracy

When facial expression recognition is performed using a counter propagation network and fuzzy adaptive resonance theory, additional facial expression pattern learning is also performed based on the feature space of facial expression generated by the initial learning. It is necessary to carefully judge whether or not to perform this additional learning. In this study, appropriate provisional threshold values were investigated for this purpose.
Learning Affective Language and Its Application

Affective in natural language refers to aspects of language used to express opinions, emotions, and beliefs. There are numerous natural language processing applications for which affective analysis is relevant, including online chat, news comment, predicting of the stock market and tracking customers' emotion states. The goal of this work is learning affective language from corpora and using this knowledge for affective analysis. Clues of affective are generated and tested, including unique words, collocations based on dependency grammar, and composition feature using distributional semantic models. The clues, generated from different data sets using different procedures, then the clues are used to perform affective analysis to demonstrate the utility of the knowledge acquired in this paper.
Relation Extraction across sentences using Bi-directional Long Short Term Memory Networks

Most of the past work on relation extraction(RE) has focused on identifying relationships between entities within a sentence. Nowadays, most of the research in the field of RE has got interested in relation extraction between entity pairs across sentence boundaries. This paper proposes a Bi-directional LSTM model for for inter-sentential RE. Experimental results show that the proposed Bi-LSTM model can achieve better results on relation classification by capturing the information hidden in long-distance relation patterns.
Quadratic Simulations of Merlin–Arthur Games

The known proofs of MA ⊆ PP incur a quadratic overhead in the running time. We prove that this quadratic overhead is necessary for black-box simulations; in particular, we obtain an oracle relative to which MA-TIME ( t ) ⊈ P-TIME ( o ( t 2 )). We also show that 2-sided-error Merlin–Arthur games can be simulated by 1-sided-error Arthur–Merlin games with quadratic overhead. We also present a simple, query complexity based proof (provided by Mika Göös) that there is an oracle relative to which MA ⊈ NP BPP (which was previously known to hold by a proof using generics).
A New Approach to Keep the Privacy Information of the Signer in a Digital Signature Scheme

In modern applications, such as Electronic Voting, e-Health, e-Cash, there is a need that the validity of a signature should be verified by only one responsible person. This is opposite to the traditional digital signature scheme where anybody can verify a signature. There have been several solutions for this problem, the first one is we combine a signature scheme with an encryption scheme; the second one is to use the group signature; and the last one is to use the strong designated verifier signature scheme with the undeniable property. In this paper, we extend the traditional digital signature scheme to propose a new solution for the aforementioned problem. Our extension is in the sense that only a designated verifier (responsible person) can verify a signer’s signature, and if necessary (in case the signer refuses to admit his/her signature) the designated verifier without revealing his/her secret key is able to prove to anybody that the signer has actually generated the signature. The comparison between our proposed solution and the three existing solutions shows that our proposed solution is the best one in terms of both security and efficiency.
An Approach to Map Geography Mark-up Language Data to Resource Description Framework Schema

GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS.