Reviewerly/exHarmony huggingface dataset:
The Tasks of Forensic Analysis of Vehicle Identifcation Markings

Special considerations and objectives of the forensic analysis of vehicle identifcation markingsare contemplated. The need to single out the classifcation tasks of such analysis into a separate form along with diagnostic and identifcation ones is corroborated. The issues of establishing original vehicle identifcation markings when conducting investigative actions that are beyond the scope of forensic examination are examined, and non-conventional methods of calculating the check digit of vehicle identifcation numbers that are manufactured in the United States or intended for sale in the United States market are described.
Texting With Students

Text messaging has become a standard form of communication between students. However, how texting can be used in higher education as a pedagogical practice has not been fully explored or articulated. This chapter provides critical insight into the value of text messaging as formal and informal communication both between faculty and students and also messaging led by students. Juxtaposing literature on the use of texting in educational environments with practical examples of university teaching in the United States and post-Soviet Kazakhstan, the chapter outlines challenges, benefits, and suggested methods of texting with students in higher education. Framed within concepts of 21st-century learning, multilingualism, and multiliteracies, the author suggests clear benefits for utilizing technology that students commonly use, while also creating an environment valuing students' changing modes of communication which puts less pressure on the traditional academic discussion, and gives a voice to the individual.
Program algebra for quantitative information flow

Models for quantitative information flow traditionally assume that the secret, once set, never changes. More recently, however, Hidden Markov Models (HMM's) have been used to describe program features that include both state updates and information flow, thus supporting more realistic contexts where secrets can indeed be refreshed. In this paper we explore HMM's further, with the aim of bringing algebraic concepts to bear in the analysis of confidentiality properties of programs. Of particular importance is the idea that local reasoning about program fragments should remain sound even when those same fragments are executed within a larger system. We show how to extend the basic HMM model to incorporate this core idea within an algebraic setting and, in so doing, show how it is related to established notion about privacy and correlated data sets in statistical databases. Using our algebra for an HMM-style model we show how to describe and prove some foundational properties of quantitative information flow.
Computational Analysis of Differences in Indian and American Poetry

Poetry is a verbal art which is motivating the human race from centuries. Indian authors’ English poetry has its own signature in the world poetry. Though it has great significance when compared with Western poetry, very little work has been done in the areas of authorship affinities, classification, style similarity of poets, and comparative studies. In this work, we investigated style and semantic differences between Indian and Western poetry and also compared the poetry in terms of variation in the usage of words and stylistic features such as orthographic, syntactic, and phonetic features. To capture style and variation differences, we considered 84 style features that cover structural, syntactical, sound devices of poetry and computed TF-IDF values using a bag of word method; then, we computed cumulative TF-IDF value of each word across all poems and arranged the values in decreasing order of their cumulative TF-IDF value. Later, we applied ranks and used PCA, LSA, and Spearman correlation to find variance in usage of words by Indian authors’ English poetry and Western poetry and style differences. We observed 40% semantic difference and 30% style difference between Indian authors’ English poetry and Western poetry. Our comparative analysis says that the features that work well with one country poetry may not be necessary to perform well with other country poetry.
Optimality in quantum data compression using dynamical entropy

In this article we study lossless compression of strings of pure quantum states of indeterminate-length quantum codes which were introduced by Schumacher and Westmoreland. Past work has assumed that the strings of quantum data are prepared to be encoded in an independent and identically distributed way. We introduce the notion of quantum stochastic ensembles, allowing us to consider strings of quantum states prepared in a more general way. For any identically distributed quantum stochastic ensemble we define an associated quantum Markov chain and prove that the optimal average codeword length via lossless coding is equal to the quantum dynamical entropy of the associated quantum Markov chain.
Anomaly analysis based on meta-subspace approach for sentiment classification

Anomaly detection in sentiment mining refers to detecting user’s abnormal sentiment patterns in a large collection of sentiment data. The anomalies detected may be due to rapid sentiment changes that are hidden in a huge amount of text. The anomaly of sentiment data sources is a foremost factor in affecting the efficiency of sentiment classification methods. Thus, analyzing sentiment data to identify abnormal sentiment patterns in a timely manner is a valuable topic of research. In this work, it is analyzed how anomaly detection and elimination can aid sentiment classification and hence enhance sentiment mining. This paper proposes a model that combines the proposed anomaly detection method with meta-classification method to detect and eliminate anomalies and classify user’s sentiments. This paper also focuses on identifying the optimum percentage of data to be eliminated as anomalies after detection, so as to perform sentiment classification effectively on movie review data. The results exhibit the capabilities of the proposed method and offer better insight into this area of research.
Towards the Formalization of Contradictions in a Legal Multiagent System

As existing legal systems expand and become more complex, the search of effective ways to model them becomes increasingly important in the area of Computational Law. We chose normative multiagent systems (NMAS) to study the interaction between a legal framework, a set of agents and the fundamental theories that regulate the legal dynamics of a society. Using this approach in conjunction with Computational Tree Logic (CTL), we determined that there is an increasing possibility of encountering contradictions inside complex legal systems, which in turn could undermine their purposes. We consider that contradictions are the fundamental units to take into account when diagnosing a legal system, and we classify them into several kinds according to the system structure they affect. Finally, we point to the direction of research needed for the development of intelligent agents capable of targeting contradictions.
Multiple Agents Representation Using Motion Fields

Providing reliable descriptions of the agents in a video scene is an essential task in many applications, such as surveillance. However, most works focus solely on the characterization of pedestrians, which is not sufficient to describe complex scenes, where a variety of vehicles (e.g., bikes and cars) are also present. In this work we address this limitation and propose a framework based on switching motion fields to efficiently characterize the different agents in a scene. Our method achieves a balanced accuracy of 91.9% on the identification of bikers and pedestrian classes on three challenging scenarios, and provides comprehensive information about their behaviors.
New Equivalence Tests for Approximate Independence in Contingency Tables

We introduce new equivalence tests for approximate independence in two-way contingency tables. The critical values are calculated asymptotically. The finite sample performance of the tests is improved by means of the bootstrap. An estimator of boundary points is developed to make the bootstrap based tests statistically efficient and computationally feasible. We compare the performance of the proposed tests for different table sizes by simulation. Then we apply the tests to real data sets.
A two stages algorithm for feature selection based on feature score and genetic algorithms

Feature selection is an effective approach for solving the curse of dimensionality. Evolutionary computation, such as genetic algorithms, are extensively applied into feature selection. However, with the available algorithms, features aren’t screened before evolutionary computation starts and all o f them are equal in status during the process of evolutionary computation. In this paper, a new algorithm that screens features before evolutionary computation starts, and makes full use of the screened ones during the process of evolutionary computation is proposed. In detail, important and useful features are found by scoring all features, and endowed with privileges in obtaining advantages comparing to other features during the forthcoming process of evolutionary computation, which is the first stage of our proposed algorithm. As for the second stage, we design a genetic algorithm with multiple sub populations, in which each sub population corresponds to a combination of important and useful features, and a competition mechanism between sub populations is introduced. As a result, important and useful features are further sufficiently used and extensively explored compared to the available algorithms, hence classification accuracies are increased. Experiments are performed with 8 datasets comparing to 11 state-of-the-art algorithms to validate our proposed algorithm. And the results show that our proposed algorithm outperforms the 11 state-of-the-art algorithms.
Evolutionary Multi-objective Training Set Selection of Data Instances and Augmentations for Vocal Detection

The size of publicly available music data sets has grown significantly in recent years, which allows training better classification models. However, training on large data sets is time-intensive and cumbersome, and some training instances might be unrepresentative and thus hurt classification performance regardless of the used model. On the other hand, it is often beneficial to extend the original training data with augmentations, but only if they are carefully chosen. Therefore, identifying a "smart" selection of training instances should improve performance. In this paper, we introduce a novel, multi-objective framework for training set selection with the target to simultaneously minimise the number of training instances and the classification error. Experimentally, we apply our method to vocal activity detection on a multi-track database extended with various audio augmentations for accompaniment and vocals. Results show that our approach is very effective at reducing classification error on a separate validation set, and that the resulting training set selections either reduce classification error or require only a small fraction of training instances for comparable performance.
Visual Cognitive CAPTCHA Based on Expert Knowledge

This paper will describe new approaches for creation of visual authentication protocols, which use cognitive CAPTCHA solutions. Proposed codes will create a new class of hybrid and image CAPTCHAs, which require special skills and knowledge for fast and secure personal authentication. Authentication protocols can be oriented especially to allow access for trusted group of expert persons, based of theirs expertise and knowledge connected with particular area or discipline. For new authentication protocols several possible examples of applications will be presented especially implemented in various computer networks and infrastructures.
Uranium Mineralization in Leucogranite of Mastipuram Area, Wanaparti District, Telangana — A New Find

The NNW-SSE trending leucogranites in and around Mastipuram, Wanaparthi district, Telangana are intrusive into Peninsular Gneissic complex. The leucogranite is metaluminous to mildly peraluminous, alkali-feldspar granite with I-type signatures, probably formed from a fractionated melt involving crustal component. Magmatic and hydrothermal contributions are supported by wide range of U/Th ratios in mineralized samples, which also show Na-enrichment as compared to non-mineralized leucogranites. Average U and Th contents and U/Th ratio in mineralized leucogranite is 541ppm, 147 ppm and 2.85, respectively. Main ore mineral is uraninite and its altered form - gummite, while secondary uranium minerals such as schoepite, uranophane and haiweeite are also identified in leucogranite by XRD. Reconnoitory drilling of ten boreholes have confirmed sub-surface continuation of uranium mineralization with varying width of 1m to 31m at vertical depth of about 50m. Thus, the leucogranites bordering Gadwal schist belt around Mastipuram forms a very potential target.
A Dynamic Object Detection In Real-World Scenarios

The object recognition is one of the most challenging tasks in computer vision, especially in the case of real-time robotic object recognition scenes where it is difficult to predefine an object and its location.Hena, Kausar To address this challenge, we propose an object detection method that can be adaptive to learn objects independent of the environment, by enhancing the relevant features of the object and by suppressing the other irrelevant feature. The proposed method has been modeled to learn the association of features from the given training dataset.Amudha, J. Using dynamic evolution of neuro-fuzzy inference system (DENFIS) model has been used to generate number of rules from the cluster formed from the dataset. The validation of the model has been carried on various datasets created from the real-world scenario.Aarthi, R. The system is capable of locating the target regardless of scale, illumination variance, and background.
The Application of Intelligent Database for Modern Information Management

With the capacity to significantly enhance the precision and effectiveness of information organization, a computer database plays a critical part in information management. Business units at present need more information and technical data for defending against business rivals. The database management system has a major role in this solving this problem for businesses. Still, conventional database management systems typically fail to proficiently deal with the vast amount of data and information, this creating the information anxiety obstacle. Accordingly, this paper suggests an integration approach for the creation of an intelligent database system by combining artificial intelligence and a database system. The knowledge modeling of the system has three levels of construct in the recommended approach. Also detailed in this work is a case study of the integration database system for gathering research data. The integration database system achieves better effectiveness than general database systems through various advantages, as evidenced by the results.
Coupled s‐excess HMM for vessel border tracking and segmentation

Abstract In this paper, we present a novel image segmentation technique, based on hidden Markov model (HMM), which we then apply to simultaneously segment interior and exterior walls of fluorescent confocal images of lymphatic vessels. Our proposed method achieves this by tracking hidden states, which are used to indicate the locations of both the inner and outer wall borders throughout the sequence of images. We parameterize these vessel borders using radial basis functions (RBFs), thus enabling us to minimize the number of points we need to track as we progress through multiple layers and therefore reduce computational complexity. Information about each border is detected using patch‐wise convolutional neural networks (CNN). We use the softmax function to infer the emission probability and use a proposed new training algorithm based on s‐excess optimization to learn the transition probability. We also introduce a new optimization method to determine the optimum sequence of the hidden states. Thus, we transform the segmentation problem into one that minimizes an s‐excess graph cut, where each hidden state is represented as a graph node and the weight of these nodes are defined by their emission probabilities. The transition probabilities are used to define relationships between neighboring nodes in the constructed graph. We compare our proposed method to the Viterbi and Baum–Welch algorithms. Both qualitative and quantitative analysis show superior performance of the proposed methods.
Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis

In the past 20 years, many swarm intelligence, evolutionary, as well as nature-inspired algorithms have been developed in order to solve many real life problems in several fields. Compared to the earlier algorithms, it has been a recent trend to develop a new optimization algorithm for solving any problem. However, it is worthy to note that any novel algorithm may not be able to solve all diverse real domain problems. Recently, a new algorithm based on the concept of the human kidney was developed and is able to optimize many standard functions. In this paper, an attempt has been made to integrate the kidney-inspired optimization and the fuzzy c-means algorithm to solve nonlinear problems of data mining. It has always been a challenge for researchers to solve the data clustering problem, as they are highly nonlinear in nature. So, hybridization of two efficient algorithms may result in a competitive solution. Inspired by this, this paper proposes a hybrid method of two intelligent techniques to solve the clustering problem, particularly in biomedical data. The experimental results proved that the proposed method efficiently outperforms the other compared methods.
Multichannel Filters for Wireless Networks: Lookup-Table-Based Efficient Implementation

We present an efficient multichannel finite impulse response (FIR) filter that uses the lookup-table (LUT) method. The size of the LUT is reduced by a factor of 8 over conventional LUTs by using Booth recoding, odd-multiple storage, and antisymmetric-product coding (APC) methods. The proposed multichannel FIR filter architecture provides up to 35% reduction in the power-delay product (PDP) over the existing architectures. The proposed strategy could be very useful for applications relating to IEEE Standard 802.15.4.
Privacy Protocols

Security protocols enable secure communication over insecure channels. Privacy protocols enable private interactions over secure channels. Security protocols set up secure channels using cryptographic primitives. Privacy protocols set up private channels using secure channels. But just like some security protocols can be broken without breaking the underlying cryptography, some privacy protocols can be broken without breaking the underlying security. Such privacy attacks have been used to leverage e-commerce against targeted advertising from the outset; but their depth and scope became apparent only with the overwhelming advent of influence campaigns in politics. The blurred boundaries between privacy protocols and privacy attacks present a new challenge for protocol analysis. Or maybe they do not, as the novelty is often in the eye of the observer. Cathy Meadows spearheaded and steered our research in security protocols. The methods for analyzing privacy protocols arise directly from her work.
Network Deployments of Bitcoin Peers and Malicious Nodes Based on Darknet Sensor

Bitcoin depends on Peer-to-Peer (P2P) network in a major way and shares the connecting IP address list with the nearest peer. In addition, the blockchain which is the basic technology can be accessed by anyone, and the transaction stored in the block can be checked anytime. Recent research has reported that anonymity of such a bitcoin P2P network is low, regardless of whether peer uses the anonymizers like TOR to keep the anonymity. This fact shows the risk of the malicious users being able to use this public information without exception. However, when the malicious user is hiding behind the network and browsing public information, it is difficult to distinguish between a malicious user and a honest one, and it is a challenge to detect signs of hidden threats. In this research, we propose a data mining approach to analyze by combining two kinds of IP address distributions: Bitcoion peer and malicious node (not in the bitcoin network), in order to obtain characteristics of hidden users. As a result, we confirmed that the nodes, which matched the first 24 bits of the IP address in the bitcoin network peer, sent the packet to the darknet. The contribution of this paper is three-fold: (1) we employ a novel approach to analyze a bitcoin network using Darknet dataset, (2) we identify the malicious node in the same network as the honest peer, and (3) we clarify the network deployments of Bitcoin peers and malicious nodes.
Dynamics of indistinguishable free particles driven by a quantum bath

The time evolution of correlations for two indistinguishable free particles, in a one-dimensional lattice interacting with a quantum bath, is studied in the Born-Markov approximation. The analytical solution for the density matrix is obtained, for bosons and fermions, in the second quantization formalism. The time evolution of the negativity of indistinguishable particles is analyzed for quantum mixed states. Quantum correlations heavily depend on the selected bipartition; in return, a small difference between the statistics of the particles is noted, due to the Pauli exclusion principle. In the presence of dissipation, the negativity (a nonclassical correlation) shows a time-oscillatory behavior for a particular geometrical bipartition. The total probability of finding one particle in each subsystem is also calculated and shows consistent behavior.
Emotion Recognition by a Hybrid System Based on the Features of Distances and the Shapes of the Wrinkles

Abstract Emotion recognition is a key work of research area in brain computer interactions. With the increasing concerns about affective computing, emotion recognition has attracted more and more attention in the past decades. Focusing on geometric positions of key parts of the face and well detecting them is the best way to increase accuracy of emotion recognition systems and reach high classification rates. In this paper, we propose a hybrid system based on wavelet networks using 1D Fast Wavelet Transform. This system combines two approaches: the biometric distances approach where we propose a new technique to locate feature points and the wrinkles approach where we propose a new method to locate the wrinkles regions in the face. The classification rates given by experimental results show the effectiveness of our proposed approach compared to other methods.
Towards Distributed Runtime Monitoring with C++ Contracts

Design by Contract supports the specification of the intended behavior of a software component at its interfaces. Out of these specifications, runtime monitors can be generated. While those runtime monitors check the correct behavior of the software component, they are usually executed on the same processor as the monitored software and therefore prone against the same possible hardware failures.