Reviewerly/exHarmony huggingface dataset:
Non-classical properties for SU(1, 1) pair coherent states

Abstract A new type of pair quantum states is introduced. Which can be considered as pair Barut-Girardello coherent states. It is an eigenstate of the operators <?CDATA ${K}_{-}^{{ab}}={K}_{-}^{a}{K}_{-}^{b}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> </mml:mrow> <mml:mrow> <mml:mi mathvariant="italic">ab</mml:mi> </mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> </mml:msubsup> <mml:msubsup> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> and <?CDATA ${K}_{3}^{a}-{K}_{3}^{b}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> </mml:msubsup> <mml:mo>−</mml:mo> <mml:msubsup> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> . We construct these eigenstates and generation scheme is proposed in terms of two mode state described in terms of su (1, 1) Lie algebra , We employ the second-order correlation function to discuss some non-classical properties,and violations of Cauchy-Schwarz inequalities. The phenomenon of squeezing is examined , squeezing is clear and Q-functions support that. Finally the phase distribution in the framework of an appropriate Pegg and Barnett formalism is considered and discussed.
An Approach to Achieve Compression Along with Security via a User Assigned Security Key with Possible Lossless Compression

Although the current version of this research work might not be fool-proof as this kind of works are currently being researched upon and yet to be successfully implemented at large. Popular software such as WinRAR makes use of AES to encrypt the data and also compresses it at the same time but only one algorithm is used to compress the data [1]. It is sometimes seen that using different lossless compression algorithms multiple times can achieve a better compression ratio and may even achieve better security [2]. Our plan is to achieve such kind of compression ratios while achieving confidentiality with a model that we have devised ourselves.
Environmental Indicators through Artificial Neural Networks

Abstract Indicators are the most important management tool for environmental monitoring. Environmental indicators condense the information and simplify the approach to environmental phenomena, which are often complex, and makes them very useful for communication. The usefulness of these indicators consists of providing relevant information, summarized in the form of concise and illustrative statements for decision making, both for the organization’s management and for the rest of the members. The prediction of limit values, together with the potentialities offered by the recommendation system based on ontology make this system a powerful tool for supporting decision-making in the Environmental Management process with a wide possibility of generalization in the business sector.
AR Tracking with Hybrid, Agnostic And Browser Based Approach

Mobile platform tools are desirable when it comes to practical augmented reality applications. With the convenience and portability that the form factor has to offer, it lays an ideal basic foundation for a feasible use case in industry and commercial applications. Here, we present a novel approach of using the monocular Simultaneous Localization and Mapping (SLAM) information provided by a Cross-Reality (XR) device to augment the linked 3D CAD models. The main objective is to use the tracking technology for an augmented and mixed reality experience by tracking a 3D model and superimposing its respective 3D CAD model data over the images we receive from the camera feed of the XR device without any scene preparation (e.g markers or feature maps). The intent is to conduct a visual analysis and evaluations based on the intrinsic and extrinsic of the model in the visualization system that instant3Dhub has to offer. To achieve this we make use of the Apple's ARKit to obtain the images, sensor data and SLAM heuristic of client XR device, remote marker-less model based 3D object tracking from monocular RGB image data and hybrid client server architecture. Our approach is agnostic of any SLAM system or Augmented Reality (AR) framework. We make use of the Apple's ARKit because of the its ease of use, affordability, stability and maturity as a platform and as an integrated system.
Linguaconflictological Research of Internet Appeal

The present research features the genre of internet appeal as a dialogic conflict process. The paper focuses on the internet appeal as an element of conflict communication and a form of open dialogue between the state and society. The linguaconflictological approach was determined by communication strategies and intentional attitudes of the sender and recipient of the internet appeal. The authors proved that the internet appeal is a special sub-genre of the complaint and explained how conflict manifests itself in this genre. The analysis of strategies and tactics revealed conceptual ideas of internet users who applied for help to officials institutions. The empirical basis of research was obtained from the internet page of the Governor of Kemerovo region. The analysis showed the effect of various factors on speech strategies and tactics and revealed communicative intentions. The internet appeal is characterized by a set of specific strategies and tactics of conflict communication, determined by the gap between the intentional attitudes of the sender and the recipient.
INTEGRATIVE APPROACH TO DEVELOPING PROFESSIONAL COMPETENCES OF MASTER’S DEGREE STUDENTS OF “ADVERTISING AND PUBLIC RELATIONS” DIRECTION UNDER CONDITIONS OF MULTICULTURAL DIGITAL COMMUNICATION

The article actualizes the problem of developing professional competences in informational educational environment under the conditions of multicultural space. The author describes peculiarities of educational process at the Department of Advertising and Public Relations of M.K. Ammosov North-Eastern Federal University under the conditions of education digitalization. The paper justifies the necessity of the integrative approach, identifies and characterizes active learning technologies providing realization of the integrative approach. Problem-based informational teaching is considered the most productive one.
Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree

Despite the potential of association rules to extract knowledge in datasets, the selection of valuable rules has been the specialists’ big challenge because of the great number of rules generated by association rules’ algorithms. Unfortunately, for Species Distribution Modeling, researchers and analysts alike has been discourage regarding the efficacy and relative merits to the studies due to the conflicting of results. In this case study, we integrate geoprocessing techniques with association rules to analyze the potential distribution of the charcoal tree species of Amazon, Tachigali Subvelutina. This integration allowed the exploration of a new method to select valuable association rules based not only by frequent item sets, like confidence, support and lift. But also considering the geospatial distribution of the species occurrences. The application of one more dimensional to the measures of the frequent item sets provides a more effective understanding and adequate amount of relevance to represent significant association rules. Further, this new approach can support experts to: identify effective rules, i.e., rules that can be effective used with regression techniques to predict species in geospatial environments; identify variables that play a decisive role to discover the occurrence of species; and evaluate the effectivity of the datasets used for the study to answer the specified scientific questions.
Adaptive Evolution of Finite State Machines for the Tartarus Problem

Genetic algorithms can be used to evolve finite state machines for problems that require a large number of states and transitions. Tartarus problem is such a problem in which the purpose is to push the boxes towards the walls of a six by six grid using a bulldozer that can only sense its 8-neighbourhood. The bulldozer can rotate left, right, or move forward, each taking a single move out of its initial 80 moves. The result is scored by the number of boxes that are against a wall when the bulldozer is out of moves. Several approaches have been proposed, with genetic algorithms being the most common. We are proposing a representation of the problem using varying number of states and adaptive modification of the mutation parameter to decrease the probability of the population getting stuck at a local minima. Our results show improvement over the application of the genetic algorithm without parameter modification and dependency on the number states and the size of the population.
GAN-based Gaussian Mixture Model Responsibility Learning

Mixture model (MM) is a probabilistic framework allows us to define dataset containing K different modes. When each of the modes is associated with a Gaussian distribution, we refer to it as Gaussian MM or GMM. Given a data point x, a GMM may assume the existence of a random index k ϵ {1, ..., K} identifying which Gaussian the particular data is associated with. In a traditional GMM paradigm, it is straightforward to compute in closed-form, the conditional likelihood p(x|k, θ) as well as the responsibility probability p(k| x, θ) describing the distribution weights for each data. Computing the responsibility allows us to retrieve many important statistics of the overall dataset, including the weights of each of the modes/clusters. Modern large datasets are often containing multiple unlabelled modes, such as paintings dataset may contain several styles; fashion images containing several unlabelled categories. In its raw representation, the Euclidean distances between the data (e.g., images) do not allow them to form mixtures naturally, nor it's feasible to compute responsibility distribution analytically, making GMM unable to apply. In this paper, we utilize the generative adversarial network (GAN) framework to achieve a plausible alternative method to compute these probabilities. The key insight is that we compute them at the data's latent space z instead of x. However, this process of z → x is irreversible under GAN which renders the computation of responsibility p(k|x, θ) infeasible. Our paper proposed a novel method to solve it by using a socalled posterior consistency module (PCM). PCM acts like a GAN, except its generator C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PCM</sub> does not output the data, but instead it outputs a distribution to approximate p(k|x, θ). The entire network is trained in an “end-to-end” fashion. Trough these techniques, it allows us to model the dataset of very complex structure using GMM and subsequently to discover interesting properties of an unsupervised dataset, including its segments, as well as generating new “out-distribution” data by smooth linear interpolation across any combinations of the modes in a completely unsupervised manner.
Point cloud deep learning for multiple object pose estimation

6D pose estimation is an important task in the research field of robot vision. Pose estimation is used in picking task in industrial scene. the previous study shows that the pose estimation can be performed by deep learning if the scene is only a single object. In industrial applications, it is necessary to perform tasks in scenes multiple objects are placed in disorder. In multiple objects scene, each object must be identified from the scene, and pose estimation performed separately. In this paper we design a neural network that estimates multiple objects at the same time. Using an object clustering module, the network became possible to learn scenes of multiple objects.
DIDACTICS OF USING INTELLIGENT CONTROL SYSTEMS OF THE INTERNET OF THINGS IN THE PROJECT ACTIVITY OF STUDENTS

The article discusses the issues of teaching the basics of intelligent control systems of the Internet of Things in a school informatics course using modern information and communication technologies. Methodical recommendations for training on this theme in the elective course "The basics of intelligent control systems" using smart devices for smart home and the Internet of Things (Arduino, Raspberry Pi) are given.
Entrevistas orais com pilotos do Correio Aéreo Nacional: estudo de caso

The objective is to investigate the social function of archives as a place of memories, functional actions of information professionals in dealing with archival documentation and the relations between the institutions and the surrounding society, exposing interviews as oral documents. Archival aspects, their evolution, tasks, models, governmental functions in the preservation of oral memories of professionals were emphasized. We sought to clarify the importance that the institution should give to the place of memories in the professional context of archives and the importance of oral interviews as enabling the policies of integration of the Brazilian state for the history of the country. It was based on a case study that contributed to the research of the master and the bibliography written by the military, composed by speeches, documents, magazines, memoirs, interviews and monographs for the elaboration of this article. To support the theoretical foundation, we resorted to authors who perform an analysis of the various archival aspects. This research is justified by defending the importance of the archives in the oral preservation of interviews of professionals who worked in Brazilian aviation, because the memory of their work acts should be preserved and stimulated by the institution. The unprecedentedness of this interview research is explained by the extent of the original research, showing basic technique used to collect oral information.
The pragmatics of text messaging making meaning in messages

In The Pragmatics of Text Messaging: Making Meaning in Messages Michelle A. McSweeney seeks to explore the meaning to be found in the labyrinth of text messaging language, and offers explanations b...
An Efficient Application Traffic Signature Generation System

Application traffic signatures are byte subsequences or behaviors (such as packet sizes and interval times) within traffic that can distinguish which application is contributing to the network traffic, application traffic signatures form the building blocks of many constructions of deep packet analysis rules in numerous areas, such as network management, measurement, and even security systems. Under the pressure of the continual appearance of new applications and their frequent updates, how to efficiently and accurately extract signatures from network traffic becomes a more challenging issue. Although several generating methods have been proposed, because of the problems of efficiency, robustness, and refinement, the application of these methods in real network environments still has limitations. Existing CS (Common Subsequence) based approaches are ineffective in generating signatures from network traffic, especially when the network traffic is massive. In this paper, we propose ESGS, an efficient system to extracts signatures from application traffic traces. ESGS base on the Latent Dirichlet Allocation (LDA) and a modified sequence pattern algorithm. First, we use a semantic analysis algorithm based on the LDA to select the candidate packet from the traffic traces according to the semantic information of the packet and refine the traffic traces. Then, we use a modified sequence pattern algorithm to generate signatures in the filtered traffic trace. We compare ESGS with several existing generating methods via evaluation on real-world application traffic traces. The result shows that ESGS can generate application traffic signatures significantly faster, and the signatures perform high accuracy. In addition, this method can effectively reduce the input traffic of signature generation systems such as Sigbox, and significantly improve the efficiency of signature generation while having a little impact on accuracy.
Adapting Agile Practices to Security Context – Practitioners’ Perspective

In this paper we explore the problem of introducing agile practices to projects dealing with systems with high security requirements. We also propose an approach based on AgileSafe method and OWASP ASVS guidelines, that could support such introduction. What is more, we present the results of two surveys aimed at analyzing IT practitioners’ views on applying agile methods to security reliant systems as well as evaluating the set of agile security-oriented practices which are a part of the proposed approach. This paper is an extended version of the paper “Security-oriented agile approach with AgileSafe and OWASP ASVS” that was published as a part of LASD 2019 conference proceedings [36].
Improving Collaborative Filtering Approach by Leveraging Opposite Users

Collaborative filtering is a widely used recommendation approach that aims to predict for a target user the most appropriate items. This approach uses the ratings given by users who share similar tastes and preferences to predict ratings for items that haven’t been rated yet. Despite its simplicity and justifiability, CF approach stills suffering from several drawbacks and problems, including sparsity, gray sheep and scalability. These problems affect the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user ┐X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques.
An Approach to Analyze Vulnerability of Information Flow in Software Architecture

Current research on software vulnerability analysis mostly focus on source codes or executable programs. But these methods can only be applied after software is completely developed when source codes are available. This may lead to high costs and tremendous difficulties in software revision. On the other hand, as an important product of software design phase, architecture can depict not only the static structure of software, but also the information flow due to interaction of components. Architecture is crucial in determining the quality of software. As a result, by locating the architecture-level information flow that violates security policies, vulnerabilities can be found and fixed in the early phase of software development cycle when revision is easier with lower cost. In this paper, an approach for analyzing information flow vulnerability in software architecture is proposed. First, the concept of information flow vulnerability in software architecture is elaborated. Corresponding security policies are proposed. Then, a method for constructing service invocation diagrams based on graph theory is proposed, which can depict information flow in software architecture. Moreover, an algorithm for vulnerability determination is designed to locate architecture-level vulnerabilities. Finally, a case study is provided, which verifies the effectiveness and feasibility of the proposed methods.
Optimization Strategies for Myopic and Forecasted Divergence-Based Sensor Tasking Objectives

In light of the ever increasing value of space assets operating in the presence of a continuously growing population of space objects in orbit about Earth, space situational awareness is becoming more crucial than ever. The process of tracking and maintaining estimates for the numerous assets and space objects is a complex and highly demanding problem, as it requires robust state estimates in the presence of sparse data. As the number of space objects greatly outweighs the number of available sensors, a sensor tasking policy is necessary to properly utilize the available sensor resources in a way that ensures targets are observed regularly in order to maintain state estimates. Previous research has established the efficacy of both myopic and forecasted divergence measures as sensor tasking objectives; however, their implementations can require significant computational effort when generating large sensor schedules. This work studies differing optimization strategies to provide different mentalities of sensor schedule optimization, with either run-time or schedule quality being the emphasis. Properties of the objective functions are leveraged to provide guarantees on the level of optimality for greedy solutions, while a convex closure form is utilized to provide a more robust, albeit slower, optimization approach. The results illustrate a trade-off between performance and schedule generation time and provide a relative comparison between the more computational demanding solutions and their more inexpensive counterparts.
Auto-Tuning Online POMDPs for Multi-Object Search in Uncertain Environments

Future exploration missions will require mobile robots to autonomously solve the challenging problem of searching for and localizing multiple objects of interest in uncertain environments. Such problems can be posed as partially observable Markov decision processes (POMDPs), which require approximations to obtain practically implementable policies for sequential decision making under uncertainty. Although online solvers such as Partially Observable Monte Carlo Planning (POMCP) have recently attracted much interest due to their fast anytime closed-loop capabilities, they require extensive hyperparameter tuning before implementation to achieve acceptable performance. However, in missions where robots cannot receive any external aid for long periods of time, they must be able to adjust and adapt hyperparameters to varying operating conditions and environmental contexts. In this paper, we cast this hyperparameter selection problem for search-tree based online solvers a Multi-Armed Bandit (MAB). Our contributions for this problem are as follows. First of all, we evaluate which existing MAB policy approximation strategies are suitable for this problem and examine the definition of utility function for MAB auto-tuning. Then, the performance of auto-tuning robot is evaluated in a simple baseline fully observable Markov Decision Process (MDP) single goal navigation problem using Monte Carlo Tree Search policy approximation. It is shown that the performance of the robot using auto-tuned MCTS planner is shown to be almost the same as the best theoretical performance produced by offline value iteration. Next, the auto-tuning robot is evaluated in a simulated fully observable multi-goal navigation problem in more complex environments, showing that the auto-tuning robot performs equally/better than an offline tuned fixed hyperparameter robot. Finally, the auto-tuning robot is evaluated in simulation for a multi-object search problem in uncertain environment where locations and number of objects are unknown. The results validate that the proposed tree-search auto-tuning process works even in partially observable environments.