Reviewerly/exHarmony huggingface dataset:
Guided Convolutional Network

Low-level handcrafted features (e.g., edge and saliency) dominate the design of traditional algorithms, and endow themselves the effective capability of dealing with simple classification problems. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Guided Convolutional Networks (GCNs), using low-level handcrafted features to guide the training process of the DCNNs, which can be used in the following vision tasks. Furthermore, signature structure is also investigated with saliency information as a basic block to help the network to be slim. With the modulated binary convolutional way, the memory of our small network is reduced by 132 theoretically. Experiments also demonstrate GCNs have comparable results in presicion compared with state-of-the-art networks such as Wide-ResNet (WRN) while reducing the network dramatically.
Quality of Context for VANETs: QoC Metrics for Connectivity in VANETs

Context describes the status of a system and of the environment in which it operates. In context-aware systems, contextual information is obtained by monitoring the environment with various sensors. Applied to a vehicular network, the context describes the state of the network, considering its various components and requirements for running applications. The context is used by the network to decide about routing, about fault correction and about recovery and the adequacy of services. This adapts the network to changes that occur in the environment. Due to the high dynamicity of the network, context information changes frequently, and old information should no longer be used for system decisions. It is therefore important that the information collected by the context is presented, along with indications of validity time, adding quality to this information. The validity time of context information is one of the parameters that characterize Quality of Context (QoC). In this article we present a context definition for vehicular networks and QoC parameters for this context. In particular, as QoC parameters we present the validity of the status of communication between pairs of vehicles, the timeliness of this context information, and the confidence, which indicates the reliability of the context. We programmed the context conditions into a simulator to generate results. The results show the efficiency of the context and the QoS metrics, and that these metrics are applicable and provide relative reliability to the context.
The punctured Dodecacode is uniformly packed

We show that puncturing the dodecacode, a nonlinear self-dual additive quaternary code of length 12, leads to a uniformly packed code of distance 5. The coset graph of this code is a diameter-3 distance-regular graph on 1024 vertices, with the intersection array {33,30,15;1,2,15}. We discuss the properties of the new distance-regular graph and related quaternary and binary completely-regular codes.
Second-Generation Rearview Mirror

We propose a system which synchronizes mirror of a vehicle with respect to driver’s face. This system eliminates manual adjustment of mirror. It will save time and provide a comfortable driving experience. It can be implemented in both motorbikes and cars. In this methodology, we divide the application into two major parts. Step one is the main application which gets the image of the driver and through image processing, face and eye coordinate is determined. After determining these points in the main image, angles are calculated and sent to the second part. In the second part, a microcontrolled servomotor is associated with the vehicle mirror. Microcontroller revises the angle-related data and controls the servomotor.
Modeling a Monitoring System for TB Patients Using RFID Technique with SMS Access

The Sustainable Development Goal has much higher and more determined health-related goals and targets than did the Millennium Development Goal. Policy development in the battle beside tuberculosis requires a plan to control infectious disease tuberculosis. Improvement of a locally targeted data response for tuberculosis will need a large deal to reconfigure existing systems which could be extra experimental data to calculate the use of correct approaches. We had projected an elegant attending tag with radio-frequency identification (RFID) technique and SMS access headed for avoiding the tuberculosis patient. During the RFID system, it contains the patient’s details and parent’s details. The patient needs to show this smart attending card to the healthcare coworker. The healthcare coworker will provide the smart attending card on the RFID machine. If the patient is found authentic, then the doctor starts treatment according to the disease. This smart attending card contains the attendance information of the TB patients. All these information are straightforwardly sent through SMS access to the management of the hospital and patient’s guardian.
A Shallow Parsing Approach to Natural Language Queries of a Database

The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language.Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked.This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable of being used to search a relational database.The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns.The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language.Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.
Image denoising based on non-subsampled shearlet transform use non-local means and hard threshold

In this paper, we propose a new method for image denoising. The new method based on non-subsampled shearlet (NSST), non-local means (NLM) and hard threshold. The method splits a noised image into three parts: low frequency sub-band, band-pass sub-band, high frequency sub-band. NLM filter is used in low frequency sub-band and high frequency sub-band to remove noise after inverse NSST. The hard threshold is applied to inhibit the noise in the band-pass sub-band. Finally merge the images to get the denoised image. Experimental results on greyscale images indicate that the proposed approach is competitive with respect to peak signal to noise ratio and structural similarity index measure with several state-of-the-art algorithms especially at low noise levels.
A Framework for Performing Prediction and Classification Using Machine Learning

With constant rise in data globally, making timely and accurate decisions based on this voluminous data is essential for corporates and industries. Data analytics and machine learning algorithms can help us perform classification and prediction on such data. Our system eliminates the need for multiple systems as it provides a generalized platform that helps technically sound people work on imported datasets from varied sources. Beyond giving the user a default graphical output, choice of other types of visualizations are recommended. Furthermore, it provides actionable insights based on the parameters chosen from the dataset, consequently leading to better decisions. Additionally, the user can opt to export the displayed result into a file for later reference.
RAFTing Over on Geo-Diverse Spot markets

Raft is a networked service used to synchronize cloud workloads at different locations. It is widely used, especially by workloads that span geographically distributed sites. As these workloads grow, Raft's costs should grow proportionally. However, auto scaling approaches for Raft inflate costs by provisioning at all sites when one site exhausts its local resources. This paper presents a Raft implementation, i.e., gRaft, that enables precise auto scaling on spot markets. gRaft extends Raft with the following abstractions: (1) secretaries which relieve log processing from leader nodes and (2) observers which relieve read requests from followers. These abstractions are stateless, allowing for elastic auto scaling, even with unreliable spot instances. gRaft preserves strong consistency guarantees provided by Raft. We set up and tested gRaft with multiple auto scaling techniques using traces from Google. gRaft scales in resource footprint increments 5-7X smaller than Multi-Raft, the state of the art. Using spot instances, gRaft reduces costs by 84.5% compared to Multi-Raft. gRaft improves goodput of 95th-percentile SLO by 9X.
Neural Classifier with Statistic Information of User and Product for Sentiment Analysis

Sentiment analysis models based on neural network architecture have achieved promising results. Some works bring improvement to these neural models via taking user and product into account. However, the way of utilizing significant role user and product by now is limited to embed them into vectors on word or semantic level, and ignore statistic information carried by them such as all the marks given by one user. In this paper, we propose a novel neural classifier, which extracts and feeds statistic information carried by user and product to neural networks. Our proposed method can utilize user preference and product characteristics so as to yield excellent performance on sentiment analysis. To fully evaluate the efficiency of our model, we conduct experiment on three popular sentiment datasets: IMDB, Yelp13 and Yelp14. And the experiment results show that our model achieves state-of-the-art on all three datasets .
Overview on Security Concerns Associated in Cloud Computing

Cloud computing is a state of the art in computational paradigm, adapting the way for offering software, hardware design, and procurements. Counter-measures vulnerabilities and threats in the cloud are well documented, yet incapable of addressing security problems which may exacerbate the service quality and customer’s data privacy. In this study, the basic topographies of the cloud computing, security problems, threats, and their solutions are discussed. Moreover, this paper has compared various interference detection and prevention computations that are based upon recent researches on end-to-end encrypting for accessing data that are utilized to address the security issues. Furthermore, this paper highlights symmetric key encryption that is considered as extremely secure, along with asymmetric key encryption, relatively a new method.
Deep Generative Model with Supervised Latent Space for Text Classification

Recent advances in applying deep neural networks to Bayesian Modelling, sparked resurgence of in- terest in Variational Methods. Notably, the main contribution in this area is Reparametrization Trick introduced in [1] and [2]. VAE model [1], is unsupervised and therefore its application to classification is not optimal. In this work, we research the possibility to extend the model to supervised case. We first start with the model known as Supervised Variational Autoencoder that is researched in the literature in various forms [3] and [4]. We then modify objective function in such a way, that latent space can be better fitted to multiclass problem. Finally, we introduce a new method that uses information about classes to modify latent space, so it even better reflects differences between classes. All of this, will use only two dimensions. We will show, that mainstream classifiers applied to such a space, achieve significantly better performance than if applied to original datasets and VAE generated data. We also show, how our novel approach can be used to calculate better classification score, and how it can be used to generate data for a given class.
String Indexing with Compressed Patterns

Given a string S of length n , the classic string indexing problem is to preprocess S into a compact data structure that supports efficient subsequent pattern queries. In this article, we consider the basic variant where the pattern is given in compressed form and the goal is to achieve query time that is fast in terms of the compressed size of the pattern. This captures the common client-server scenario, where a client submits a query and communicates it in compressed form to a server. Instead of the server decompressing the query before processing it, we consider how to efficiently process the compressed query directly. Our main result is a novel linear space data structure that achieves near-optimal query time for patterns compressed with the classic Lempel-Ziv 1977 (LZ77) compression scheme. Along the way, we develop several data structural techniques of independent interest, including a novel data structure that compactly encodes all LZ77 compressed suffixes of a string in linear space and a general decomposition of tries that reduces the search time from logarithmic in the size of the trie to logarithmic in the length of the pattern.
Parallel Evolutionary Algorithm in Scheduling Work Packages to Minimize Duration of Software Project Management

Software project management problem mainly includes resources allocation and work packages scheduling. This paper presents an approach to Search Based Software Project Management based on parallel implementation of evolutionary algorithm on GPU. We redesigned evolutionary algorithm to cater for the purpose of parallel programming. Our approach aims to parallelize the genetic operators including: crossover, mutation and evaluation in the evolution process to achieve faster execution. To evaluate our approach, we conducted a “proof of concept” empirical study, using data from three real-world software projects. Both sequential and parallel version of a conventional single objective evolutionary algorithm are implemented. The sequential version is based on common programming approach using C++, and the parallel version is based on GPGPU programming approach using CUDA. Results indicate that even a relatively cheap graphic card (GeForce GTX 970) can speed up the optimization process significantly. We believe that deploy parallel evolutionary algorithm based on GPU may fit many applications for other software project management problems, since software projects often have complex inter-related work packages and resources, and are typically characterized by large scale problems which optimization process ought to be accelerated by parallelism.
Optimizing the Requirement Engineering Process: A Case Study of I/O List Management in Integrated Automation Systems

Abstract Integrated Automation Systems (IAS) suppliers in the maritime sector have been facing the cost pressure to deliver projects over the years, while the system complexity rapidly increases. How to improve the IAS delivery process to assert the project margins thus has become a critical issue. The IAS delivery process is mainly related to design and production activities of the software activities. For instance, the requirements documentation and Input/Output list (I/O list) management during the software design and engineering phases tend to be recurring reasons for delays in the IAS production. Thus, this paper focuses on how to optimize the requirements engineering (RE) process related to I/O list management in the IAS production. A real‐life case study is conducted from both the customer (i.e. Shipyard) and supplier (i.e. The case company ) perspectives to investigate I/O list management in the RE process of the IAS production. It is found that late design changes to the I/O list are the main reasons for delays in the IAS production; the lack of standardization, documentation, inconsistency between customer and supplier milestones as well as the I/O list ownership by customers are the important root causes. Based on the case data, the solution in resolving those root causes is derived for reduced man‐hours in the RE process of the IAS production.
Surpassing Traditional Image-Colorization Problems with Conditional Generative Adversarial Networks

Color helps to understand the semantic information of the image more accurately and reveals a lot more details which grayscale images cannot. By looking at an image, humans can automatically segment different objects present in an image making it easier for us to color an image. We propose a completely automated system to colorize grayscale images which learns to segment and color images in a realistic manner. We leverage the recent advancements in deep learning, Generative Adversarial Networks and improved cost functions, to overcome the problems of traditional Convolutional Neural Networks with image colorization. Given the unconstrained nature of the problem, we propose this algorithm to make a colorization model that achieves realistic colorizations. We have experimented different deep network architectures with various training algorithms and cost functions to come up with this network where we can clearly see realistic colors for given gray scale image and differentiate the characteristics of generative adversarial network from a traditional convolutional neural network.
On the Complexity of the 3XORSUM Problem

The 3XORSUM problem aims to find (x, y, z) such that x + y + z = 0 over {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , with each variable drawn from one of 3 randomly generated lists. In addition to being of interest in its own right, this problem has cryptographic applications including proof of stake methods in Blockchain.The 3XORSUM problem is also related to the integer 3SUM problem from theoretical computer science on which there is extensive recent literature. It is conjectured that the integer 3SUM problem has complexity $\tilde \Omega \left({{n^2}}\right)$ for lists of size O(n).Wagner [10] has presented an algorithm with complexity Õ(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d/3</sup> ) for finding a 4XORSUM solution (x + y + z + w = 0) with each variable drawn from one of 4 randomly generated lists of size O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d/3</sup> ) with members from {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> .We present an algorithm which solves the 3XORSUM problem for randomly generated binary vectors from {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> with time and memory complexity Õ(n) = Õ(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d/3</sup> ). This substantially improves results from [1], [8]. Our algorithm has applications to blockchains and other cryptographic problems.
A scheme for the sticky policy representation supporting secure cyber-threat intelligence analysis and sharing

Purpose This paper aims to propose a structured threat information expression (STIX)-based data representation for privacy-preserving data analysis to report format and semantics of specific data types and to represent sticky policies in the format of embedded human-readable data sharing agreements (DSAs). More specifically, the authors exploit and extend the STIX standard to represent in a structured way analysis-ready pieces of data and the attached privacy policies. Design/methodology/approach The whole scheme is designed to be completely compatible with the STIX 2.0 standard for cyber-threat intelligence (CTI) representation. The proposed scheme will be implemented in this work by defining the complete scheme for representing an email, which is more expressive than the standard one defined for STIX, designed specifically for spam email analysis. Findings Moreover, the paper provides a new scheme for general DSA representation that has been practically applied for the process of encoding specific attributes in different CTI reports. Research limitations/implications Because of the chosen approach, the research results may have limitations. Specifically, current practice for entity recognition has the limitation that was discovered during the research. However, its effect on process time was minimized and the way for improvement was proposed. Originality/value This paper has covered the existing gap including the lack of generality in DSA representation for privacy-preserving analysis of structured CTI. Therefore, the new model for DSA representation was introduced, as well as its practical implementation.
Improvement of Sleep Spindle Detection by Aggregation Techniques

The study focuses on automatic sleep spindle detection. Plenty of methods have been proposed in previous decades. However, there is still space for improvement. In this study, we investigate aggregation methods such as voting to achieve better results. We employ an unweighted model and two weighted voting models in which assigned weights represent reliability of automatic detectors. First weighted model utilizes supervised approach based on logistic regression. The second one applies unsupervised generative Bayesian model often used in crowdsourcing. Using the expectation maximization algorithm, we uncover hidden true labels and weighs of detectors. We test methods on the real world datasets. The aggregation method overcome single detectors on 10% on average in terms of F1. Moreover, a probabilistic explanation of weights could be used in applications for visual analysis.
Slice Sampling for Lattice Gaussian Distribution

Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain Monte Carlo (MCMC) is adopted to lattice Gaussian sampling. Firstly, the slice-based sampling algorithm is proposed to sample from lattice Gaussian distribution. Then, we demonstrate that the Markov chain arising from it is uniformly ergodic, namely, it converges exponentially fast to the stationary distribution. Moveover, the convergence rate of the underlying Markov chain is investigated, and we show the proposed slice sampling algorithm entails a better convergence performance than the independent Metropolis-Hastings-Klein (IMHK) sampling algorithm. Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement.
A Mobile Intelligent Reading Teaching Annotation System

In the era of the popularization of the intelligent mobile devices, the field of mobile learning is expanding constantly, which has gradually become one of the most important ways for people to obtain information. Education and digital reading are sustainably developing towards the direction of intelligence. How teachers auxiliary teaching as a guide and how students according to their interests and hobbies for personalized learning has become a pressing problem. Annotated reading can better stimulate students' thinking in reading as a reading method centered on active inquiry, so the project team proposed a mobile intelligent reading teaching annotation system. The combination of the intelligent mobile device and the web client can realize the whole reading teaching process, and implement intelligent recommendation of readings materials by data collection and analysis of students reading. Practice shows that the system can meet students' personalized learning needs to a certain extent, and realize the paperless operation at the same time. Using this system to standardize the annotation format can facilitate teachers' teaching and verification, and also improve the efficiency and teaching quality.