Reviewerly/exHarmony huggingface dataset:
Pedestrian Age and Gender Identification from Far View Images Using Convolutional Neural Network

Identification of several attributes of pedestrians such as age and gender from far view images collected from the surveillance camera has been an emerging research topic nowadays due to its growing importance and applications in several fields like identifying people, access control, security issues, etc. It is quite a difficult task to perform inference in pedestrian attributes on images which have poor resolutions since those are collected from the surveillance system. However, recognizing pedestrian’s age and gender have been used in so many important applications. In this paper, we addressed the recognition of pedestrian’s age and gender from far view images as a multi-label single class classification problem and proposed a customized convolutional neural network (CNN) model that achieves a good accuracy on seven pedestrian attributes regarding age and gender inference. We worked on PETA dataset which is the largest dataset of its kind and consists of far view images. As per comparison, our proposed model outperforms than other models with an accuracy of 85.13% on PETA dataset and 67.82% on the real dataset in terms of attribute recognition accuracy.
An Efficient Implementation of Low-Latency Two-Dimensional Gaussian Smoothing Filter using Approximate Carry-Save Adder

The two-dimensional Gaussian smoothing filter (2D-GSF) is one of the most useful techniques in image processing. Since the 2D-GSF requires high computational resources, its efficient design and implementation are critical in real-time processing purposes. Approximate computing is a new method that can be used to increase the performance of 2D Gaussian filter design with low computing overhead on field-programmable gate arrays (FPGAs). This study aims to provide a low-latency Gaussian filter architecture on FPGA such that it can be used in real-time processing applications. In this regard, accurate and approximate carry-save adders (CSAs) have been used in adder tree-based Gaussian filters. In our proposed method, we use two approximation steps: in the first step, we use an approximation structure named Speed–Power–Area–Accuracy for Gaussian filter design and in the second stage, we use approximate CSAs to convert adder-tree structures that are used in Gaussian filter, and as a result, we have significantly reduced the delay. The results of simulation and implementation show that the latency has reduced in a 3[Formula: see text] 3 2D-GSF architecture up to 22% using proposed accurate CSAs and 45% using proposed approximate CSAs, compared to existing Gaussian filters with an adder tree structure.
A Generalizable Framework for Domain-Specific Nonrigid Registration: Application to Cardiac Ultrasound

Many applications of nonrigid point set registration could benefit from a domain-specific model of allowed deformations. We observe that registration methods using mixture models optimize a differentiable log-likelihood function and are thus amenable to gradient-based optimization. In theory, this allows optimization of any transformations that are expressed as arbitrarily nested differentiable functions. In practice such optimization problems are readily handled with modern machine learning tools. We demonstrate, in experiments on synthetic data generated from a model of the left cardiac ventricle, that complex nested transformations can be robustly optimized using this approach. As a realistic application, we also use the method to propagate the model through an entire cardiac ultrasound sequence. We conclude that this approach, which works with both points and oriented points, provides an easily generalizable framework in which complex, application-specific transformation models may be constructed and optimized.
A characterization of the exponential symmetric Sheffer sequences

A polynomial sequence (sn(x))n∈N is symmetric, or self-dual, if sn(m)=sm(n) for all m,n=0,1,2,…. In this paper, we give the characterization of the exponential symmetric Sheffer sequences with weight sequences, and show that all the symmetric Sheffer sequences with weight (λn), (⟨λ⟩n) or ((λ)n) are in fact constant multiples of the Charlier polynomial sequence, the Meixner polynomial sequence, or the Krawtchouk polynomial sequence, respectively, where ⟨λ⟩n are the rising factorials, and (λ)n are the falling factorials.
Design and Implementation of multi-dimensional History Playback System Based on IoT Data

In the Internet of Things(IoT) monitoring system, there are various devices for monitoring the target's movement trajectory and behavior, the raw data of these devices has accumulated over time to form huge historical data. A historical review of monitoring targets requires a system that presents the target's historical data in a visual form. The existing system has realized the storage of target historical data, but there is still a lack in the playback of historical data. The system lacks the visual playback of the target multi-device historical monitoring data, that is difficult to comprehensively reflect the behavior information and movement status of the target during the playback period. The system lacks dynamic presentation of the target's historical motion trajectory, that is difficult to visually display the target's historical position at different times during playback, and it is impossible to analyze the historical behavior trend of the target. This paper designs and implements a historical data playback system that supports the display of target multi-dimensional information and the dynamic presentation of target track to solve the above problems. A lightweight web application framework Express is used to build a website. Based on GIS(Geographic Information System) technology, an incremental update method for fast and dynamic rendering of the target track is proposed. Combined with the component technology, the target history multidimensional information visualization is presented. Test results show that the system can complete multi-dimensional playback of historical IoT monitoring data and rapid dynamic presentation of target tracks.
Patterns of maximally entangled states within the algebra of biquaternions

Abstract This paper proposes a description of maximally entangled bipartite states within the algebra of biquaternions– <?CDATA ${\mathbb{C}}\otimes {\mathbb{H}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="double-struck">C</mml:mi> <mml:mo>⨂</mml:mo> <mml:mi mathvariant="double-struck">H</mml:mi> </mml:math> . We assume that a bipartite entanglement is created in a process of splitting one particle into a pair of two indiscernible, yet not identical particles. As a result, we describe an entangled correlation by the use of a division relation and obtain twelve forms of biquaternions, representing pure maximally entangled states. Additionally, we obtain other patterns, describing mixed entangled states. Finally, we show that there are no other maximally entangled states in <?CDATA ${\mathbb{C}}\otimes {\mathbb{H}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="double-struck">C</mml:mi> <mml:mo>⨂</mml:mo> <mml:mi mathvariant="double-struck">H</mml:mi> </mml:math> than those presented in this work.
The Prediction of Internet Technology Utilization within Learning Environments

Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.
Tracks from hell — When finding a proof may be easier than checking it

We consider the popular smartphone game Trainyard: a puzzle game that requires the player to lay down tracks in order to route colored trains from departure stations to suitable arrival stations. While it is already known [Almanza et al., FUN 2016] that the problem of finding a solution to a given Trainyard instance (i.e., game level) is NP-hard, determining the computational complexity of checking whether a candidate solution (i.e., a track layout) solves the level was left as an open problem. In this paper we prove that this verification problem is PSPACE-complete, thus implying that Trainyard players might not only have a hard time finding solutions to a given level, but they might even be unable to efficiently recognize them.
A Novel Procedure for Font Recognition through Deep Learning

In this paper, a novel font recognition system, which works through deep learning is introduced. This novel recognition procedure is based on letter by letter font classification through Convolutional Neural Networks (CNN). The font that is statistically recognized more frequently than the others is reported as the font of the entire studied text. Knowing the font of an image document can enhance the initial letter recognition in a feedback loop. Alternatively, the recognized font can be used as a prior knowledge, to enhance the performance of another Optical Character Recognition (OCR) system.
What Is A Bezel On A Phone

Youve probably heard the term bezel before but do you actually know what it means. I will explain what is a bezel on a phone and I will tell you ...
Design of Sampling Clock Based on High-Speed Time-Interleaved Sampling System

In order to meet the stringent requirements of high speed time-interleaved sampling for clock, we take a low phase noise clock chip AD9522 with an internal integrated 2 GHz VCO (voltage controlled oscillator) to provide four sampling clocks with a phase difference of 90° and a frequency of 250 MHz for four alternately sampled analog-to-digital converters. Based on the introduction of the characteristics of AD9522, the design of the sampling clock circuit and the calculation of the parameters of the loop filter are described in detail. The relevant software simulation is used to show that the design meets the system requirements.
Research and Implementation on an Efficient Public Key Encryption Algorithm with Keyword Search Scheme

With the rapid development of network storage service, a number of companies and individuals have stored data on a third-party server. Encryption is an effective means of protecting the confidentiality and privacy of data, but retrieval on the encrypted data is a very difficult task. Thus, searchable encryption has become a hot topic in recent years. The paper first introduces the existing searchable encryption algorithms. Then studies the new PEKS scheme (NPEKS) and analyzes its performance and efficiency. In the end, based on NPEKS, introduced attribute encryption, designed a scheme which is suitable for corporate cloud storage environment. This scheme not only has the advantages of simplicity and efficiency, but also can realize the secret retrieval of the third-party data. Experiments show that comparing with existing PEKS schemes and other improved schemes, this scheme has the advantages of simplicity and high efficiency. In addition, its security is the same as existing PEKS schemes.
Study on Mining Method of Network Fraud Gang Based on Big Data Analysis of Microblog

As social media plays an increasingly important role in people's lives, microblog has become a very popular social platform in Chin. While microblog brings convenience to people in information transmission, it also brings the problem of many criminals using it to commit fraud., Fraud gangs use microblog to set language traps to defraud others of money and profit. In this regard, the sociogram is used to represent the social network of microblog. The network is a directed graph, with nodes representing microblog users and connecting lines representing the propagation path of microblog, so as to connect the initiation node of microblog and the forwarding node of microblog. In addition, this article will analyze the methods used by fraud groups through in-depth analysis of social networks and the use of data mining techniques. In the following paper, the structural composition and detailed characteristics of the microblog fraud group will be analyzed in detail. It looks for potential fraud groups on microblog to help users identify scams and avoid being tricked.
The assessment instrument product of macrame technique in textile craft course

Abstract The assessment instrument product of macrame technique in textile craft courses is an instrument that is arranged systematically to determine product accuracy based on established criteria and indicators. The purpose of this study was to make a assessment instrument for evaluating macrame technique products in textile craft courses that can be used as a reference by educators in assessing student work in the form products of macrame technique. Quantitative research methods with research and development (R&amp;D) models through preliminary study stages, the design products of macrame technique, production and manufacturing of macrame product valuation tools, validation, revision, and analysis. The results of the validation stage products of macrame technique valuation the assessment instrument show that the “Very Eligible” is used, judging by the evaluation given by the evaluation experts and material experts with an average percentage given of 100% of the evaluation experts and 98, 21% of material experts include six indicators, namely types of macrame motifs, function of macrame products, design elements, design principles, use of materials, and completion techniques. The assessment instrument product of macrame technique that have been made can be used as a reference in assessing student work in the form product of macrame technique.
Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

Summary Cloud‐based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses dealing with confidential big data (e.g., criminal reports) are reluctant to fully embrace such cloud services due to valid data privacy concerns. Solutions based on client‐side encryption have been developed to mitigate these concerns. Nonetheless, such solutions hinder data processing, especially, data clustering, which is pivotal in applications such as real‐time search on large corpora (e.g., big datasets). To cluster encrypted big data, we propose privacy‐preserving clustering schemes, called ClusPr, for three forms of unstructured datasets, namely static, semi‐dynamic, and dynamic. ClusPr functions based on statistical characteristics of the datasets to: (A) determine the suitable number of clusters; (B) populate the clusters with topically relevant tokens; and (C) adapt the cluster set based on the dynamism of the underlying dataset. Experimental results, obtained from evaluating ClusPr against other schemes in the literature, on three different test datasets demonstrate between and improvement on the cluster coherency. Moreover, we notice that employing ClusPr within a privacy‐preserving enterprise search system can reduce the search time by up to , while improving the search accuracy by up to .
Exploratory Data Analysis Using Supervised Learning Techniques on Credit Card Default Data Set in Machine Learning

The objective of this paper is to analyze the various Supervised Learning Techniques in Machine Learning for predicting Credit card defaulters. Supervised Learning Technique can be applied whenever the sample Training set has input with corresponding output data. By applying the Supervised Technique a data model is created based on the input and output. For new data, the respective output can be predicted by applying the data model. The supervised technique which gives best accuracy for credit card data may not give best result for House price prediction or any other dataset, and hence there is no one best machine learning technique as the accuracy of the predicted new output is purely based on the type of the problem and data. Based on the data distribution and relationship among the data, the supervised learning technique or algorithm will give good result.
The Impact of Constructors on the Validity of Class Cohesion Metrics

Class cohesion is a measure of the degree to which a class's inner elements, like methods and attributes, are bound or related to one another. There have been over thirty different formulas proposed in order to calculate the metric. None of them are explicitly designed to deal with constructors in any different way than with regular methods-they simply treat them as identical entities. However, as many object-oriented theorists say, constructors play a very specific role in object life-cycle. In the scope of this empirical research, five different formulas were implemented in two ways: including constructors and excluding them. Then both set of formulas were applied to the same set of 1000 mid-size open source Java projects. The results obtained demonstrated how much of a distraction constructors were bringing into metric calculations.
KSR: Knowledge-based Sequential News Recommendation System

Abstract With the popularity of the Internet, some online news reading habits have gradually replaced traditional media devices. People can see a variety of news on mobile phones or web pages. The overloaded information makes it impossible for users to quickly get the news they want to see, so the existence of a news recommendation system is necessary. However, unlike other fields, the recommendation of news is time-sensitive, and the problem of sparse data makes traditional collaborative filtering algorithms invalid. There are many entities in the news, and the knowledge graph is a collection of a large number of entities. In this paper, we propose a KSR model (Knowledge-based Sequential Recommendation) which uses the knowledge graph as side information to enrich the feature representation of the news to calculate the user’s probability to click the forecasted news. KSR uses the knowledge graph to represent the entities in the news and uses a recurrent neural network to capture the sequential relationships in the news data to enrich the feature representation of the news. Because the different news that user has clicked have different relations with the news to be predicted, the attention model is introduced to calculate the weights. Finally, we conduct experiments on the existing dataset and results prove the efficacy of KSR over several baselines. Different contrast experiments also prove the effectiveness of each module of the model.
Vehicle Pose Detection and Application Based on Grille Net

With the rapid development of technology, autonomous driving technology has also received widespread attention. Among them, the vehicle's pose information provides important information for automatic driving decision and vehicle active safety. To solve this problem, this paper proposes to use the deep learning method to improve the traditional pose network and use the random area connection network to reduce the difficulty of vehicle orientation detection. In order to improve the performance of the random area connection network, this paper innovatively proposes raster convolution and designs a grid network based on raster convolution. Experiments were performed on the famous autopilot data set KITTI, achieving an average angular similarity of 89.12% and an average similarity of 87.32%. The experimental results show that compared with the traditional pose detection method, the proposed method has better robustness and detection effect.
Selected poster presentations from the British Association of Applied Linguistics conference, Manchester Metropolitan University, UK, August 2019

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Using Convolutional Network Visualisation to Determine the Most Significant Pixels

Over the last years, Deep Neural Network models have been recognized as successful in solving many complex problems. However, these methods are mostly focused on the efficiency of final results and rarely provide sufficient evidence and details on factors that contribute to their outcomes This is why a growing demand for analysis techniques appeared. Thanks to visualisation techniques we can if network works as expected or even improve output of given model if possible. Moreover we can use these methods as optimization technique to boost network’s performance but pruning less important neurons. Finally, if we know how a given model works we can prepare a disruption to its work process. This paper shows how we can combine Class Activation Map with feature map to determine a few of the most contributing pixels for given input and modify them to perform an adversarial attack.
Lyndon White, Roberto Togneri, Wei Liu and Mohammed Bennamoun, Neural Representations of Natural Language. Singapore, Springer, 2019. XIV + 122 pages, ISBN:9789811300615

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
How to Record Audio From Your Computer With Audacity

Are you new to Audacity? This article covers the tutorial on how Audacity records computer audio. Audacity audio recorder is a software ...
Improving Multi-turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting

Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters. However, in practice, the negative samples of training data are usually selected from an unannotated conversation data set at random. The generated training data is likely to contain noise and affect the performance of the response selection models. To address this difficulty, we consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals and reduce the influence of noisy data. More specially, we consider a main-complementary task pair. The main task (i.e., our focus) selects the correct response given the last utterance and context, and the complementary task selects the last utterance given the response and context. The key point is that the output of the complementary task is used to set instance weights for the main task. We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets. We also investigate the variant of our approach in multiple aspects, and the results have verified the effectiveness of our approach.
Nonlinear Regression Analysis Using Multi-verse Optimizer

Regression analysis is an important machine learning task used for predictive analytic in business, sports analysis, etc. In regression analysis, optimization algorithms play a significant role in searching the coefficients in the regression model. In this paper, nonlinear regression analysis using a recently developed metaheuristic multi-verse optimizer (MVO) is proposed. The proposed method is applied to 10 well-known benchmark nonlinear regression problems. A comparative study has been conducted with particle swarm optimizer (PSO). The experimental results demonstrate that the proposed method statistically outperforms the PSO algorithm.