Reviewerly/exHarmony huggingface dataset:
Optimal strategies in the fighting fantasy gaming system: Influencing stochastic dynamics by gambling with limited resource

In many games and other processes, participants can choose to intervene in some way that does not follow the usual progress of the game (for example, cheating at cards, or spying on rivals) which may provide benefits, but also possibly incur substantial costs. Here, repeated interventions may be more likely to incur negative outcomes – for example, as the chance of getting caught increases. How to optimally employ these risky interventions, trading off potential advantages and disadvantages, can then be challenging to identify. Here, we study such a game, taken from the popular ‘Fighting Fantasy’ gamebook series. This stochastic game involves a series of rounds, each of which may be won or lost. Each round, a unit of limited resource (‘luck’) may be spent on a gamble to amplify benefits from a win or to mitigate deficits from a loss. However, the success of this gamble depends on the number of units of remaining resource, and if the gamble is unsuccessful, benefits are reduced and deficits increased. By choosing to expending resource, a player thus has diminishing probability of positive return, as in the cheating and espionage examples above. We characterise the dynamics of this system using stochastic analysis and dynamic programming, solve the Bellman equation for the complete system with diminishing returns, and identify the optimal strategy for any given state during the game. We use classification tools to distil general principles for this and related problems, informing resource allocation problems with diminishing returns in stochastic decision theory.
Licensing Requirement for the Presence of an Educational Organization in the State or the Involvement of Teachers on Another Legal Basis

In accordance with Article 46 of the Federal Law of December 29, 2012 No. 273-FZ (as amended on December 2, 2019) “On Education in the Russian Federation”, persons with secondary vocational or higher education and meeting qualification requirements are entitled to engage in pedagogical activities, indicated in the qualification handbooks, and (or) professional standards. Decree of the Government of the Russian Federation of October 28, 2013 No. 966 (as amended on November 29, 2018) “On the licensing of educational activities” established that in the implementation of educational activities, an educational organization should have in staff or engage teachers with professional education on other legal grounds with appropriate qualifications, with the work experience necessary for the implementation of educational activities under the implemented educational programs, and relevant the requirements of Article 46 of the Federal Law “On Education in the Russian Federation”, as well as the requirements of federal state educational standards, federal state requirements and (or) educational standards. The article describes the legal framework for the application of professional standards at the level of an educational organization and examines life situations regarding the application of qualification requirements.
Classification of Driving Behavior Events Utilizing Kinematic Classification and Machine Learning for Down Sampled Time Series Data

The proliferation of connected cars globally has the potential to produce torrents of Big Data that will enable improvements in driver safety, new location based services, improvements in vehicle quality, and optimized vehicle designs. One aspect of connected car data involves driving behavior data and its use for Usage Based Insurance (UBI). UBI has become one of the most widely used applications of driving behavior data. Currently, the transmission and processing of high frequency driving behavior data from the connected car to the cloud is limited by wireless data costs and in-vehicle hardware complexity. To alleviate these issues, we detail the development of a machine learning framework utilizing a kinematic classification methodology applied to down sampled time series vehicle data sets for accurate imputation of driving behavior events in UBI applications. The down-sampled data, consisting of 5 second frames with data fields of timestamp, vehicle speed, and vehicle acceleration is classified into unique kinematic clusters to standardize any driving behavior data distributions. Subsequently, machine learning is used to impute harsh driving events for each 5 second frame in select kinematic clusters. This novel machine learning methodology reduced data set sizes by 75%, utilized a limited set of five attributes, and achieved an average precision and recall of 84.5% and 63.5% for two distinct connected car data sets with 1/5 Hz down-sampled data.
Term Ordering-Based Query Expansion Technique for Hindi-English CLIR System

The quality of retrieval documents in CLIR is often poor compared to IR system due to (1) query mismatching, (2) multiple representations of query terms, and (3) un-translated query terms. The inappropriate translation may lead to poor quality of results. Hence, automated query translation is performed using the back-translation approach for improvement of query translation. This chapter mainly focuses on query expansion (Q.E) and proposes an algorithm to address the drift query issue for Hindi-English CLIR. The system uses FIRE datasets and a set of 50 queries of Hindi language for evaluation. The purpose of a term ordering-based algorithm is to resolve the drift query issue in Q.E. The result shows that the relevancy of Hindi-English CLIR is improved by performing Q.E. using a term ordering-based algorithm. The outcome achieved 60.18% accuracy of results where Q.E has been performed using a term ordering based algorithm, whereas the result of Q.E without a term ordering-based algorithm stands at 57.46%.
A Model for the Progressive Visualization of Multidimensional Data Structure

This paper presents a model for the progressive visualization and exploration of the structure of large datasets. That is, an abstraction on different components and relations which provide means for constructing a visual representation of a dataset's structure, with continuous system feedback and enabled user interactions for computational steering, in spite of size. In this context, the structure of a dataset is regarded as the distance or neighborhood relationships among its data points. Size, on the other hand, is defined in terms of the number of data points. To prove the validity of the model, a proof-of-concept was developed as a Visual Analytics library for Apache Zeppelin and Apache Spark. Moreover, nine user studies where carried in order to assess the usability of the library. The results from the user studies show that the library is useful for visualizing and understanding the emerging cluster patterns, for identifying relevant features, and for estimating the number of clusters k.
Chapter 3. Applying criteria of spontaneous Hebrew speech segmentation to English

Taking prosody to be the leading component in speech segmentation, this chapter attempts to transfer segmentation methodologies from Hebrew to English spontaneous speech. Following a process of segmentation by perception of two English chunks a detailed acoustic analysis has been conducted, using acoustic criteria that have been found meaningful for similar analyses of Hebrew, as detailed in my chapter for Part I of this volume, “The Basic Unit of Spoken Language and the Interface Between Prosody, Discourse and Syntax: A View from Spontaneous Spoken Hebrew”. This process has produced suggestive results. Further analysis into the interface of prosody with discourse has also been found meaningful. Some terminological issues are discussed as well.
Clique Displacement: A New Layout Technique

A graph allows the representation of data in a comprehensible way either for visualizing clusters in a large data set or analyzing traffic from network devices represented as nodes and links. It has always been a good mode for understanding a problem and subsequently analyzing the solution. A graph data structure is a good choice to represent data if it can be converted in the form of nodes and edges. There are different types of problems in various fields which require a graphical method to understand the problem, and so there are several approaches for doing it. On similar lines, this paper proposes a new visualization technique for a dense graph. It is evident that existing visualization techniques, such as force-directed placement would not give optimal results for a dense graph. The proposed work is interesting and has several potential applications such as in analyzing graphs for Social Networks, Biological applications, etc. There are many graph layout techniques available, and it has been studied for years. Every year some new method is proposed, or an older one is improved. This is because of the exponentially increasing data, which requires a better layout technique for representation. Today, graph is used in different research areas due to its simplicity in the way of representing the information. A graphical representation is always good for understanding the information easily. When these graphs become dense, the overlapping of edges in the graph also increases by a certain amount, and it becomes difficult to understand the graph or tough to visualize the graph. For a better understanding of dense graph, this paper proposes a solution by dividing the original graph into sub-graphs forming cliques. These cliques are then aligned in special positions to overcome the confusion between the connectivity of nodes. This idea considerably solves the visualization problem with dense networks, and the results show a better visual representation.
Computer Aided Higher Mathematics Teaching Based on the WEB Campus Network

With the rapid development of the information technology, the application of computers in the field of the education makes computers rapidly become the most promising teaching media and teaching management tool. In order to promote the development of our education and improve the teaching level, the development of computers in the field of our education has also received the attention of educators. The computer-aided instruction (CAI) is a new technology that applies the computer technology to the field of education. In this form of teaching, computers present the teaching objectives and the teaching priorities and difficulties through various kinds of the teaching software, record students’ learning situations, control students’ learning progress and other information, and complete certain learning and teaching tasks. The establishment of a Web-based CAI system fundamentally changes the basic shortcomings of the poor resource sharing and the inadequate interaction in the traditional teaching methods, and provides more advanced technical means for our teaching.
Botnet Host Detection Based on Heartbeat Association

As a common means of communication, heartbeat is often used by the network applications. Hosts with the same heartbeat tend to have the same applications and thus share the homogenous vulnerabilities. Based on the detected heartbeat, the paper designs the heartbeat network, the heartbeat associated graph and an attribute propagation algorithm based on the heartbeat associated graph. The paper takes the distributed denial of service (DDoS) malicious host information provided by the intrusion detection system (IDS) deployed on the boundary of China education and research network (CERNET) Nanjing master node network as attribution, and constructs the associated graph based on the user datagram protocol (UDP) heartbeat detection result at the same location. The attribute propagation algorithm was tested for 17 days. And The result shows that the method can effectively detect DDoS malicious hosts that are not located by IDS.
Context-Aware Personalized Mobile Learning

Increasing usage of mobile apps encourages the mobile application developers to add more services through specialized mobile applications. One of such service is m-leaning (mobile learning) application. Learning through mobile provides unique experience to the learners to learn course/subject at anywhere and at any time. To increase the learning interest and success of learning is relying on the learners need and the learning environment. This requirement brings new research direction called context-aware personalized learning. This paper proposes the new m-learning system which organizes the course content, accommodating the dynamic nature of learner’s preferences and state. Learners’ preferences are identified through ILS (index of learning styles) test and subsequently updated based on learners activity log using apriori algorithm. The system finally will adopt the content based on the learner’s environment context such as network and surrounding light.
Fundamentals for the Automation of Information Processing in the Identification of Chemical-Technological Systems

The theoretical and methodological aspects of the formalization of an applied task of studying chemical-technological systems in an automated mode under uncertainty to create promising scientific and technical developments are considered. Information processing is carried out by using fuzzy mathematics. Fuzzy numbers are proposed to formalize the source data. This approach has led to dimensionless relative values, which, allow us to build aggregate estimates of a set of criteria, under conditions of multicriteriality. This was the basis for the algorithmization of the decision support procedure when choosing the optimal composition of the polymer composition. As an example, the proposed task of selecting and identifying functional additives to create the optimal polymer composite material in the conditions of technological and informational constraints. Based on the classification of additives, a training set was created, which allowed to generate data for building a decisive rule for identifying the level of activity of the additive.
Secret Sharing Based BLMQ Signature Generation

BLMQ is an identity based signature scheme named with the initial letters of the surnames of its proposers, Paulo S.L.M. Barreto, Benoit Libert, Noel McCullagh, and Jean-Jacques Quisquater. It can be used in various applications and systems. However when applied in an end device without dedicated cryptographic hardware, such as a mobile phone, the identity based private key is stored in the permanent storage and used in the volatile memory, which suffers from potential key leakage. When applied in a resource constrained end device, such as an industrial sensor or actuator, the end device cannot afford to perform the complicated signature computation of the BLMQ scheme. To address these issues, this paper proposes a number of secret sharing based BLMQ signature generation schemes, with which an end device shares the secret of a private key with the dedicated cryptographic devices or systems with strong security protection. The end device and the dedicated cryptographic devices or systems collaborate to generate the signature with their respective shares when signing a message. The leakage of the secret share in the end device does not lead to the compromise of the whole private key. Further with the proposed schemes, a resource constrained or limited end device can leverage the dedicated cryptographic devices or systems to perform complex signature computations. The proposed schemes have a promising application prospect.
Parameter Continuation Methods for the Optimization of Deep Neural Networks

There are many extant methods for approximating the solutions of non-convex optimization problems arising in deep neural networks, including stochastic gradient descent, RMSProp, AdaGrad, and ADAM. In this paper, we propose a novel training strategy for deep neural networks based on ideas from numerical parameter continuation methods. Parameter continuation methods have a long history in many application domains such as bifurcation analysis and the study of systems of differential equations. However, as far as we are aware, such powerful methods have seen relatively limited use in the optimization of deep neural networks. Specifically, herein we derive a homotopy formulation of standard activation functions. Such a derivation allows one to decompose the optimization of deep neural networks into a sequence of optimization problems, each of which is armed with a good initial guess based upon the solution of the previous problem. The whole process is initiated by using a closed-form solution for the first of these problems provided by the homotopy formulation. Intuitively, there is a deep connection between our homotopy techniques and many ideas used in transfer and curriculum learning. However, our proposed methods leverage decades of theoretical and computational work in parameter continuation methods and can be viewed as an initial bridge between those techniques and deep neural networks. In particular, we propose a method that we call Natural Parameter Adaption Continuation with Secant approximation (NPACS). This method provides an effective optimization technique that uses standard algorithms such as ADAM in a novel way to achieve faster and more stable convergence. We demonstrate the effectiveness of our method on standard benchmark problems, and we compute local minima more quickly and with lower train and test loss values than current state-of-the-art techniques in a majority of cases.
Extended object tracking using random matrix with converted measurements

The random matrix approach for extended object tracking (EOT) is appealing. This approach assumes that the measurements are linear in the kinematic state and measurement noise. In many practical applications, however, this linear condition cannot always be satisfied. First, this study derives a linearised measurement model. The model first employs a decorrelated unbiased technique to convert the non-linear measurements into Cartesian coordinates. Then, due to the property of the random matrix as the covariance of the extension noise, the model reformulates the likelihood function by calculating a product of two multivariate Gaussian distributions. The proposed linearised measurement model can be incorporated into existing random matrix approaches. Secondly, to describe a more complicated dynamics without the restriction of the existing random matrix framework, the authors propose a variational Bayesian (VB) approach for EOT. The VB approach minimises the Kullback–Leibler divergence between the true and approximate posterior density to obtain a convergent solution. The effectiveness of the proposed linearised model and the VB approach is illustrated by simulation results.
The Optimized Classification of Mammograms Based on the Antlion Technique

Breast cancer is one of the main health issues for women. This disease can be cured only if detected at early stages. Digital mammography is used to detect the malignant cells at an early stage. This article designs a methodology to detect the malignant tumors. The methodology is comprised of preprocessing feature extraction by Gabor and Law's feature extraction, and feature reduction by ant-lion optimization as well as a classification step using a SVM classifier which is implemented on the live dataset prepared through the Rajindra Hospital Patiala along with MIAS and DDSM datasets. The results of proposed techniques have been compared with three states of art techniques SVM based classification without feature reduction, PSOWNN i.e. PSO based reduction with a neural network as a classifier and binary gray wolf-based feature reduction with SVM classifier. The performance analysis proves the significance of the technique.
User Identification Using Neural Network Based on Japanese Flick Input on Smartphone

This study tries to identify users based on the personality in the characteristics of single tap and flick input when they input Japanese on smartphone. So we develop a communication chat application for smartphone in order to extract 12 touch features from Japanese input operations. We perform two experiments to obtain training data and test data for 16 subjects using smartphones installed the application. For the obtained data, user identification using a neural network of TensorFlow results in the accuracy of 80.8%.
Face Classification Across Pose by Using Nonlinear Regression and Discriminatory Face Information

Face recognition across pose cripples with the issue of non-availability of few important facial features. Some of the facial key features undergo occlusion during pose variations. The sole application of linear regression model in face recognition across pose is unable to predict the occluded features from the remaining visible features. With the approach like discriminative elastic-net regularization (DENR), the training sample’s discriminatory information, is embedded into regularization term of the linear regression model. Classification is realized using least sqaure regression residuals. However, the existence of nonlinear mapping between frontal face and its counterpart pose limits the application of DENR. In this paper, discriminative elastic-net regularized nonlinear regression (DENRNLR) is proposed for face recognition across pose. DENRNLR learns discriminant analysis-based kernelized regression model constrained by elastic-net regularization. The effectiveness of the proposed approach is demonstrated on UMIST and AT&T face database.
L’evoluzione nell’uso delle voci in '-oni' dell’italiano

The paper analyses the diachronic evolution of Italian adverbs ending in -oni, e.g. “bocconiµ, “carponiµ, “cavalcioniµ, and a number of others referring to bodily movements and postures. Through a systematic analysis of all the occurrences of -oni adverbs in the main corpora of written Italian, the evolution of these forms is presented using a model of morphological description inspired by the prototype theory of Eleanor Rosch (1973) and adapted to the analysis of linguistic units in the works of Bybee & Eddington (2006) and Wilson (2009). This usage-based model allows us to consider the data relating to the first occurrence of the entries, to their frequency and semantic evolution. Furthermore, it allows us to explain some apparent anomalies of the forms, such as the existence of a variant (-one, e.g. “carpone/carponiµ) or the possible occurrence of these adverbs in bare form, in a PP introduced by a (“a carponiµ), or with reduplication (“carpon carponiµ).
Comparing Evolutionary Artificial Neural Networks from Second and Third Generations for Solving Supervised Classification Problems

Constituting nature-inspired computational systems, Artificial Neural Networks (ANNs) are generally classified into several generations depending on the features and capabilities of their neuron models. As generations develop, newer models of ANNs portrait more plausible properties than their predecessors, accounting for closer resemblance to biological neurons or for augmentations in their problem-solving abilities. Evolutionary Artificial Neural Networks (EANNs) is a paradigm to design ANNs involving Evolutionary Algorithms (EAs) to determine inherent aspects of the networks such as topology or parameterization, while prescinding—totally or partially—from expert proficiency. In this paper a comparison of the performance of evolutionary-designed ANNs from the second and third generations is made. An EA-based technique known as Grammatical Evolution (GE) is used to automatically design ANNs for solving supervised classification problems. Partially-connected three-layered feedforward topologies and synaptic connections for both types of considered ANNs are determined by the evolutionary process of GE; an explicit training task is not necessary. The proposed framework was tested on several well-known benchmark datasets, providing relevant and consistent results; accuracies exhibited by third-generation ANNs matched or bested those from second-generation ANNs. Furthermore, produced networks achieved a considerable reduction in the amount of existing synapses, as in comparison with equivalent fully-connected topologies, and a lower usage of traits from the input vector.