Reviewerly/exHarmony huggingface dataset:
A Kind of Quaternary Sequences of Period <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> and Their Linear Complexity

Sequences with high linear complexity have wide applications in cryptography. In this paper, a new class of quaternary sequences over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> with period <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> is constructed using generalized cyclotomic classes. Results show that the linear complexity of these sequences attains the maximum.
Impact of Environmental Education with VR Equipment on Learning Performance and Environmental Identity

For the goals of environmental education, people should be encouraged to contact with the natural and increase the awareness and knowledge. Nowadays, immersive virtual reality (VR) devices provide people with a way to visualize the natural environment. Users can use it to create a real experience in the environment, not just the images they see. This empirical study suggests that an environmental education programs by VR and normal environmental films can be effective in helping to improving learning performance and environmental identity. The method of analysis is quantitative analysis, combined with qualitative. The experimental group used VR equipment to view VR video clips while the control group used a desktop computer to observe the same video clips. The subjects were given the same questionnaire after video viewing. The questionnaire included environmental knowledge related to video clips and a CNS scale. The results answered the research questions well and supported these hypotheses: (1) The use of VR equipment will affect the learning performance of the subjects. (2) The use of VR equipment could improve the environmental identity of the subjects. Compared with the control group, CNS scale in the experimental group showed a higher level of environmental identity.
A Discriminative Multi‐Channel Facial Shape (MCFS) Representation and Feature Extraction for 3D Human Faces

Abstract Building an effective representation for 3D face geometry is essential for face analysis tasks, that is, landmark detection, face recognition and reconstruction. This paper proposes to use a Multi‐Channel Facial Shape (MCFS) representation that consists of depth, hand‐engineered feature and attention maps to construct a 3D facial descriptor. And, a multi‐channel adjustment mechanism, named filtered squeeze and reversed excitation (FSRE), is proposed to re‐organize MCFS data. To assign a suitable weight for each channel, FSRE is able to learn the importance of each layer automatically in the training phase. MCFS and FSRE blocks collaborate together effectively to build a robust 3D facial shape representation, which has an excellent discriminative ability. Extensive experimental results, testing on both high‐resolution and low‐resolution face datasets, show that facial features extracted by our framework outperform existing methods. This representation is stable against occlusions, data corruptions, expressions and pose variations. Also, unlike traditional methods of 3D face feature extraction, which always take minutes to create 3D features, our system can run in real time.
A Proposed Impregnable 256-Bit Hash Producer

This paper proposes a robust 256-bit hash producer. The underlying motivation is to overcome security-related limitations of pseudo-random number generators embedded in Linux and Windows, which render these widely used operating systems vulnerable to security breaches. The proposed approach is called Hash Bits Producer-256 (HBP-256). It produces outputs that look like random to an adversary who is unaware of its internal state. In addition, it provides forward and backward security in case its internal state is known to an adversary at any particular time. HBP-256 offers such protection by providing the following capabilities. Firstly, output pseudorandomness. This is ensured by: (1) seeding and reseeding HBP-256 from three sources of entropy input, of which two are nondeterministic hardware sources; and (2) maintaining a high-level entropy input. Secondly, provide forward security. This is ensured by using two one-way irreversible functions: the Secure Hash Algorithm-256 (SHA-256) and SHA-512. Thirdly, provide backward security. This is ensured by: (1) maintaining cumulative entropy; (2) sustaining rapid refreshment rates for the entropy pools; and (3) preserving uniqueness of bits input to the SHA-512 at any particular time. Security analysis is conducted by exposing HBP-256 to external and internal attacks. Based on the security analysis, it is concluded that HBP-256's individual and collaborative defenses successfully address adversaries' attempts to compromise HBP-256 from outside as well as from within its boundary. HBP-256 meets the intended security requirements, and achieves its objective.
HopSAC: Frequency Hopping Parameter Estimation Based on Random Sample Consensus for Counter-Unmanned Aircraft Systems

Small unmanned aircraft systems (UAS), commonly known as drones and widely used in recreational and commercial applications, have caused alarming concerns of public safety and homeland security due to frequently reported unauthorized UAS incidents in recent years. To effectively disable potential threats from frequency hopping drones and controllers, the counter attack of Counter-UAS (CUAS) systems typically require parameter estimation of the frequency hopping signals with high precision and low complexity for real-time responses. Therefore, a model parameter estimation method to meet all these requirements becomes a challenge for CUAS systems. In this paper, a novel hopping parameter estimation method based on random sample consensus called HopSAC is proposed to conquer this challenge. Given a small set of samples, HopSAC estimates the parameters of linear frequency hopping sequence and achieves high multiple target detection performance with low implementation complexity that can be realized in real time. Simulation results show that the proposed HopSAC significantly outperforms linear Least Squares method in achieving exceptional accuracy of model parameter estimation under the impact of gross errors, timing errors, and multiple UAS targets.
A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality

Stochastic mirror descent (SMD) is a fairly new family of algorithms that has recently found a wide range of applications in optimization, machine learning, and control. It can be considered a generalization of the classical stochastic gradient algorithm (SGD), where instead of updating the weight vector along the negative direction of the stochastic gradient, the update is performed in a "mirror domain" defined by the gradient of a (strictly convex) potential function. This potential function, and the mirror domain it yields, provides considerable flexibility in the algorithm compared to SGD. While many properties of SMD have already been obtained in the literature, in this paper we exhibit a new interpretation of SMD, namely that it is a risk-sensitive optimal estimator when the unknown weight vector and additive noise are non-Gaussian and belong to the exponential family of distributions. The analysis also suggests a modified version of SMD, which we refer to as symmetric SMD (SSMD). The proofs rely on some simple properties of Bregman divergence, which allow us to extend results from quadratics and Gaussians to certain convex functions and exponential families in a rather seamless way.
Evaluation of analogical analysis techniques in interpretation of lineaments and litho-boundaries using Landsat 7 ETM+ imagery of western Jahajpur belt, Bhilwara, Rajasthan, India

Broader capability of synoptic view of remote sensing image helps solving many geological enigmas because of limitations of conventional geology investigation to map the structural features such as lineaments because of manual and mapping error. To correct these types of error using remote sensing technology, which have better capability to map or delineate the structural features. The lithological map on the characteristics of rocks, lithotype, association and mineral assemblage for example ground water investigation on the basis of lineaments network. The main rocks of Jahajpur group are associated with dolomite, phyllite and quartzite. Demarcation of litho boundaries in this area is a challenging task because of minute variations in place to place according to SW-NE trending ridge. This research work carried out for analogical interpretation of lithobounderies and lineament through Landsat 7 ETM+ multispectral satellite on Eradas Imagine and Arc GIS software. There are various shorter lineaments are visually interpreted other than long lineaments.
Protecting Security-Sensitive Data Using Program Transformation and Intel SGX

Cloud computing allows clients uploading their sensitive data to the public cloud and perform sensitive computations in those untrusted areas, which drives to possible violations to the confidentiality of client sensitive data. By leveraging the program transformation and the Intel Software Guard Extension (SGX) technology, our proposed solution hides the security-sensitive statements inside an SGX enclave. Some former works have shown that most applications can run in their entirety inside trusted areas such as SGX enclaves, and that leads to a large trusted computing base (TCB). As a result, we analyze a case study in which we partition an application and use an SGX enclave to protect only security-sensitive statements, thus obtaining a smaller TCB. In this paper, we describe our case study that secures applications written in Java using Intel SGX technology. We analyzed our proposed solution using concrete examples to show how the confidentiality of security-sensitive variables is protected.
NLP for Clinical Data Analysis

This work focuses on the topic of natural language processing for clinical data analysis. In a world where information is being generated at an exponential rate, the need for this information handling and management finds wide attention. The majority of the data being generated is in the form of unstructured data. The processing of structured information is relatively easier as compared to semi-structured or unstructured data. In the case of clinical data, the larger chunk is in unstructured form like the patient's case study and history. This chapter will provide a deeper insight into this class of data and will provide various solutions to how this data can be interpreted and represented for better healthcare of the common masses. In this chapter, the authors discuss a generic system developed for unstructured data handling: Natural Language Information Interpretation and Representation System (NLIIRS).
Application Research of Lightning Monitoring Data in Regional Lightning Disaster Risk Assessment

In this paper, lightning distribution data is used to analyze the lightning distribution characteristics of the region. Based on this, a method of regional lightning disaster risk assessment is given, which builds a three-tier hierarchical structure model and analyzes each risk index which respectively from four aspects such as the lightning risk, the region risk, the hazard-affected bodies risk and defense risk, simultaneously applies the fuzzy comprehensive evaluation method to make predictive evaluation of the lightning risk grade in a region. Regional lightning disaster risk assessment method uses the fuzzy mathematics knowledge to analyze and calculate, calculate the membership degree matrix of each index, and the weight of each index is determined by the fuzzy judgment matrix sorting method, and makes fuzzy comprehensive evaluations about the three levels. Finally an actual project is evaluated using the regional lightning disaster risk assessment method. The practical application shows that, the method can give decision-making guidance for project location, classification of lightning risk and measures to avoid the thunder.
Hierarchical Text Classification using CNNs with Local Classification Per Parent Node Approach

In this paper, authors present the application of Convolutional Neural Networks (CNNs) for Hierarchical Text Classification (HTC) using Local Classification per Parent Node (LCPN) approach. A 20Newsgroup hierarchical training dataset with 3 hierarchical levels and more than 20 categories was used for training, evaluation and testing of the model. Several variations of hyperparameters settings are involved in the experiments such as batch sizes, embedding sizes, and number of training samples. The comparison of achieved results is performed against baseline and hierarchical models such as Support Vector Machine (SVM), Logistic Regression (LR), flat CNN and CNN with Local Classification per Node (LCN) approach. The results indicated that HTC using CNNs with LCPN approach outperformed the rest of selected methods by a margin between 5% - 15%.
Task-oriented Dialogue System Based on Reinforcement Learning

In this paper, we propose a task-oriented dialogue system based on reinforcement learning. The overall system is composed of three parts: natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG). And our model can interact with the database in real time and acquire effective information from it. For the DM part, reinforcement learning is applied. Specially, we adopt an improved double deep Q-learning (DQN) strategy. In that case, the DM agent can resist the environmental noise considerably. Besides, we put forward a joint model for NLU module, and the experiments on ATIS and Snips datasets have proved the effectiveness of the joint model. For the overall system, the experiments are conducted on a public movie-ticket booking dataset. The experimental results indicate that the proposed model has outperformed the traditional rule-based multi-turn dialogue system both on simulated and real users. Besides, the double-DQN agent has better performance for both objective and subject evaluation, which demonstrates the effectiveness and superiority of our proposed model.
Evaluation of High Dynamic Range Reduced-Reference Image Quality Assessment based on Spatial Features

This paper presents an objective image quality assessment method for High Dynamic Range (HDR) images with a partial access to some reference information. Our work focuses on developing Reduced-Reference (RR) data that can be used in image quality models for HDR. In this paper, we present results of using various simple features derived from spatial information. This method was evaluated by using the publicly available HDR image quality assessment database. The result shows a promising performance although there are room for improvement.
Learning Adaptive Downsampling Encoding for Online End-to-End Speech Recognition

Attention based encoder-decoder models have shown promising performance for various sequence-to-sequence problems. However, for speech recognition, the very long input speech consumes a lot of computation and memory resource when performing encoding and soft attention over the input sequence. While fixed-rate downsampling is usually employed to reduce the computation steps, it fails to consider the variable durations of phonemes. Motivated by this, we propose a differentiable adaptive downsampling approach which encodes the input sequence with a recurrent layer by keeping crucial frames and discarding redundant frames adaptively in real-time. Therefore, the proposed downsampling approach can dynamically generate input hidden representations and is suitable for online end-to-end speech recognition. Experiments show that our proposed method can reduce phone error rate (PER) by 7.0 % relative without loss of speed compared with fixed downsampling technique. In addition, the adaptive encoding makes the model robust to variable speed speech.
Artificial Neural Networks in Educational Process

In this paper an artificial neural networks for windows operation system are presented. Artificial neural networks are essential part of the machine learning algorithms. As they are more and more used in the commercial applications, it is desirable to introduce them in the educational process. To work with them on the Windows operating system, Anaconda environment provides simple and free of charge solution. After introduction to the topic, basic theory about artificial neural networks continues. Next is installation description of Anaconda environment. Subsequent chapter defines MNIST example in the Keras library. Conclusion is last chapter of this paper. Preview of experiment is done with MNIST dataset and included neural network.
Topography of the z-Plane Discretized by Quantizing the Coefficients of the Canonical Form of Recursive Digital Filter

It is established that the zeros and poles of recursive digital filters with finite word length are elements of the set of algebraic numbers. Therefore, not every point of the unit circle of the z-plane can be the zero and/or pole of such digital filters. The position of admissible positions for zeros and poles depends on the degree of algebraic numbers and the length of the fractional part of the coefficients of the equivalent canonical structure of the corresponding order. The formation of the corresponding configurations is considered as a z-plane discretization due to quantization of the filter coefficients. The z-plane discretization for second-order filters has been well studied. The geometric locus of the corresponding algebraic numbers is a system of concentric circles, that is, plane algebraic curves of the second degree. This chapter confirms the hypothesis that for digital filters of the higher orders, zeros and poles are points of algebraic numbers of the higher degrees.
Tuition Single Classification using Decision Tree Method and C4.5

Tuition Single (UKT in Indonesia) is an operational cost measurement standard for universities in Indonesia which is applied by the government since 2013. This standard is enforced for every public university students who pass entrance university test. The problem with this standard is still found that there are students with tuition single category which does not fit with their condition, thus resulting in the incriminating of students with difficult economic situations. The usage of the decision tree and C4.5 algorithm for classification is able to process continuous data and build model efficiently with a large number of data. The input variables for tuition single classification are the income of the financier, the status of the children within the family, the taxable value of land and building, and the ownership of the vehicle. This work uses a multiclass variable. The output variables are group 1 to group 7. The accuracy of the system is 80.25% where the groups with the most students are group 3 and group 7. The decision tree built is also quite big considering the number of the variables and the variation of data used in each variable.
Content Based Hierarchical URL Classification with Convolutional Neural Networks

Classifying the URL into respective user segments helps the marketing researchers to identify the intent and the preferences of a user. The combined words in the URL along with the punctuations makes it very hard and challenging to classify. The proposed model and the architecture in this paper addresses the problem statement. The taxonomy under which the URLs are segmented follows a hierarchical pattern. We follow an ensemble nature of classifying the textual content into the respective branches, depending on the unique confidence values for each branch. With the growth of the digital work on the internet day by day, assigning a single category for a particular class of text will yield less meaning compared to assigning a hierarchical class which also helps the marketing and advertisement industry. We perform the classification of the text into a pre-defined hierarchical taxonomy using an approach that uses a combination of multiple neural networks to derive the results.
An Evolutionary Tool For Requirements and Design Crosscutting Concerns

To elevate a simple but important fashion to tolerate rapid changes in cross-cutting concerns in the requirements and design phases in multiple sizes of software development and maintenance tasks, Identification, Modularization, Design Composition Rule and Conflict Dissolution (IM-DeCRuD) approach was previously offered. This study delivered a tailored-design, prototype and constructed tool as a proof of concept of the proposed approach to IM-DeCRuD. The main attributes of the IMDeCRUD prototype are: requirements specification definition, requirements specification modification, requirements prioritization setting and graphics visualization of the representation generated using the Generic Modeling Environment (GME) tool. Java language was used as an interpreter to integrate the prototype functions. This research applied a library system as a simple case study to determine the importance of the IM-DeCRuD prototype. Ultimately, during the software development and evaluation activities, the prototype showed its ability for the tedious engineering process of requirements and design crosscutting concerns becoming more simpler.
Developing Dynamic Navigation Software for a Laser Guided Autonomous Mobile Robot

The demand for autonomous machines is becoming increasingly present in today's growing economy. Autonomous robots and machines can provide services that are physically or economically infeasible for humans alone. To fill this demand for useful and relatively inexpensive services, autonomous machines must be reliable and efficient. This paper is focused on developing a guidance software system method for autonomous mobile robots. A mobile robot using an integrated Wavefront algorithm and an advanced laser sensor was able to complete tasks in a dynamic environment with accuracy and speed. The integrated system was developed in a MATLAB virtual environment and was tested in the real world by using a Pioneer mobile robot attached with a Hokuyo laser. Many scenarios were conducted, and the results showed the navigation system integrated with the robot was able to efficiently and accurately reach its goal destination, while avoiding dynamic obstacles.