Reviewerly/exHarmony huggingface dataset:
A Modified Graph Representation for Room Layout Matching using Spectral Embedding

Graph matching is efficient to retrieve similar layout from the huge size of architectural layout. Since floor plan matching using spectral embedding is popular and its computational time is only in seconds. However, the weakness of this method is the isomorphism of each floor plan that reduces accuracy in the matching process. In this paper, we propose a modified graph representation for room layout matching using spectral embedding. General graph representations of the floor plan assume the node as rooms and the edge as the connection between rooms. In addition, the spectral embedding aims to find the descriptor of each floor plan by ignoring the semantic of rooms. We also consider both room semantic that is the connection between the area outside and inside the room, and the structure of each layout. Moreover, we mathematically proved that adding an extra node can handle the isomorphism of a graph by eigenvalue testing. Our proposed method consists of three processes: i) floor plan extracting, ii) appended topology graph and iii) floor plan matching. The performance from our experiment shows that our proposed method can increase the matched accuracy from the conventional method for about 35.44 percent.
Dynamic Clustering of Stream Short Documents Using Evolutionary Word Relation Network

The explosive growth of web 2.0 applications (e.g., social networks, question answering forums and blogs) leads to continuous generation of short texts. Using clustering analysis to automatically categorize the stream short texts has been proved to be one of the critical unsupervised learning techniques. However, the unique attributes of short texts (e.g, few meaningful keywords, noisy features and lacking context) and the temporal dynamics of data in the stream challenge this task. To tackle the problem, in this paper, we propose a stream clustering algorithm EWNStream by exploring the Evolutionary Word relation Network. The word relation network is constructed with the aggregated word co-occurrence patterns from batch of short texts in the stream to overcome the sparse features of short text at document level. To cope with the temporal dynamics of data in the stream, the word relation network will be incrementally updated with the new arriving batches of data. The change of word relation network indicates the evolution of underlying clusters in the stream. Based on the evolutionary word relation network, we proposed a keyword group discovery strategy to extract the representative terms for the underlying short text clusters. The keyword groups are used as cluster centers to group the stream short texts. The experimental results on real-word Twitter dataset show that our method can achieve much better clustering accuracy and time efficiency.
Exploiting WiMAX for covert transmission of secret data over fading channel

Summary Worldwide Interoperability for Microwave Access (WiMAX) is the broadband wireless access technique that provides rapid broadband services to domestic and enterprise users. Owing to the broadcast characteristic of the WiMAX system, transmitted data security is crucial, particularly when the messages are confidential. In this work, exploiting of WiMAX to convey sensitive secret information is presented. In the first phase, we exploited conventional WiMAX (fast Fourier transform [FFT]‐WiMAX), and in the second phase, we propose a wavelet packet transform (WPT)–based WiMAX for covert communication. The quality evaluation of both covert transmission models (FFT‐WiMAX and WPT‐WiMAX) over the fading channel is done in this work. The experimental outcomes of the proposed WPT‐WiMAX covert transmission system reveal a significant enhancement in error rate (bit error rate) and peak signal‐to‐noise ratio for a given signal‐to‐noise ratio.
Tackling the Cloud Forensic Problem While Keeping Your Eye on the GDPR

If the cloud is just someone else's computer, securing forensic evidence in case of a breach can be tricky. A blockchain-based distributed ledger could contribute to solve this problem, provided the required forensic information finds its way onto the blockchain. In this paper we sketch how blockchain technology and smart contracts apply to various processing models and their respective forensic challenges, and highlight how an accountability-based approach will be instrumental to the overall acceptability of the solution.
Prominent Structures for Video Analysis and Editing

We present prominent structures in video, a representation of visually strong, spatially sparse and temporally stable structural units, for use in video analysis and editing. With a novel quality measurement of prominent structures in video, we develop a general framework for prominent structure computation, and an efficient hierarchical structure alignment algorithm between a pair of videos. The prominent structural unit map is proposed to encode both binary prominence guidance and numerical strength and geometry details for each video frame. Even though the detailed appearance of videos could be visually different, the proposed alignment algorithm can find matched prominent structure sub-volumes. Prominent structures in video support a wide range of video analysis and editing applications including graphic match-cut between successive videos, instant cut editing, finding transition portals from a video collection, structure-aware video re-ranking, visualizing human action differences, etc.
Harp and Electronics: Composition, Design, and Performance

This article outlines methods, considerations, and challenges for composing and performing with harp and electronics, investigated over the last five years. My background is in Irish traditional music and in experimental, contemporary, and computer music, as a composer, programmer, performer, and sound engineer. The harp, as a resonant and multiphonic instrument, presents specific challenges for the design of interfaces for computer sound control. I explain in detail my motives, approach, and decision-making in developing new technologies and music for harp and electronics. In particular, I present conclusions drawn from working with Camac’s MIDI harp, and I consider questions of rhythm and flexibility in electronics control that are particularly relevant to traditional dance musics. I conclude with my most recent work on the control of electronics via microphones and movement sensors to exploit an existing playing gesture. I highlight the importance of considering performance, composition, and technological questions in parallel.
Parallel Approach to Sliding Window Sums

Sliding window sums are widely used for string indexing, hashing, time series analysis and machine learning. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost. We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). Implementing the algorithm for the bioinformatics application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5 $$\times $$ .
A highly sensitive laser focus positioning method with sub-micrometre accuracy using coherent Fourier scatterometry

Abstract We report a novel method of focus determination with high sensitivity and submicrometre accuracy. The technique relies on the asymmetry in the scattered far field from a nanosphere located at the surface of interest. The out-of-focus displacement of the probing beam manifests itself in imbalance of the signal of the differential detector located at the far field. Up–down scanning of the focussed field renders an error S -curve with a linear region that is slightly bigger than the corresponding vectorial Rayleigh range. We experimentally show that the focus can be determined not only for a surface with high optical contrast, such as a silicon wafer, but also for a weakly reflecting surface, such as fused silica glass. Further, for the probing wavelength of 405 nm, three sizes of polystyrene latex spheres, namely 200, 100, and 50 nm in diameter, are tested. Higher sensitivity was obtained as the sphere diameter became smaller. However, due to the fact that the scattering cross-section decreases as the sixth power of the nanosphere diameter, we envision that further size reduction of the studied sphere would not contribute to a drastic improvement in sensitivity. We believe that the proposed method can find applications in bio/nano detection, micromachining, and optical disk applications.
Construction and Application of Information-Based Teaching Resources Sharing Platform Based on Deep Learning

Deep learning means that learners learn new content with critical thinking on the basis of understanding knowledge and integrate it into the original knowledge system to form in-depth insights. Deep learning emphasizes the autonomy, cooperation and exploration of learners' learning. To realize deep learning, learners need specialized and systematic learning resources. Based on the problems faced by the current information-based teaching and the concept of deep learning, this paper integrates resources of enterprises, society and schools, explores the construction of information-based teaching resource sharing platform based on deep learning, and provides learning support for independent inquiry learners. And also provides a reference to researchers engaged in information teaching in universities.
On Privacy of Socially Contagious Attributes

A common approach to protect user's privacy in data collection is to perform random perturbations on user's sensitive data before collection in a way that aggregated statistics can still be inferred without endangering individual secrets. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when sensitive attributes are subject to social contagion. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot train a classifier that achieves an area under the ROC curve (AUC) above 1-(1-δ)/(1+e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> ), if the dataset is perturbed using an (ε,δ)-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do substantially better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. Our method's superior performance is demonstrated through extensive experiments on synthetic and real-world networks.
Desaturated Probability Integral Transform for Normalizing Power System Measurements in Data-Driven Manipulation Detection

Data-driven solutions to detecting power system measurement manipulation rely on thorough understandings of the patterns behind the data. From a probabilistic viewpoint, power system measurements inherently follow multimodal distributions usually with heavy tails. Existing normalization methods, both linear and nonlinear, suffer from some drawbacks when being applied to this kind of distribution. Using inappropriately normalized data to train the detectors may undermine their pattern recognition capability. In this paper, a tradeoff between the linear and nonlinear normalization is investigated to propose a variant of the widely used probability integral transform (PIT) by introducing an approximate linear compensation. When being employed to normalize power system measurements, the socalled desaturated PIT can have a better fidelity towards the measurements collected under infrequent operation conditions. Experiments show that the proposed normalization method is promising in improving the detection performance.
Cybersickness and Evaluation of a Remediation System: A Pilot Study

Many questions arise regarding the use of virtual reality (VR) in the naval military field. This is particularly the case from a human factors' perspective, where system's usability is supposed to guarantee user's performance. Cybersickness, resulting from a sensory conflict between visual and vestibular systems, is one of the major limitations to the development of VR. Cybersickness can lead to nausea, oculomotor discomfort and disorientation. The major aim of the current study was to evaluate the efficiency of a remediation system for cybersickness. This system is designed to help remove the sensory conflict, thanks to miniature LED screens placed in the head mounted display (HMD). 18 subjects were confronted with a dynamic environment in VR, equipped with HMDs. Different physiological variables were measured during immersion. Every subject showed effects of cybersickness, starting, on average, after eight minutes of exposure, even if the system may reduce symptoms under certain conditions.
Petrography, XRD Analysis and Identification of Talc Minerals near Chhabadiya Village of Jahajpur Region, Bhilwara, India through Hyperion Hyperspectral Remote Sensing Data

The larger synoptic view and contiguous channels arrangement of Hyperion hyperspectral remote sensing data enhance the minor spectral identification of earth's features such as minerals, atmospheric gasses, vegetation and so on. Hydrothermal alteration minerals mostly associated with vicinity of geological structural features such as lineaments and fractures. In this study Hyperion data is used for identification of hydrothermally altered minerals and alteration facies near Chhabadiya village of Jahajpur area, Bhilwara, Rajasthan. There are some minerals such as talc minerals identified through Hyperion imagery. The identified talc minerals correlated and evaluated through petrographic analysis, XRD analysis and spectroscopic analysis. The validation of identified minerals completed by field survey, field sample spectra and USGS spectral library talc mineral spectra. The conclusion is that Hyperion hyperspectral remote sensing data have capability to identify the minerals, mineral assemblage, alteration minerals and alteration facies.
A Clustering Based Approximate Algorithm for Mining Frequent Itemsets

We present an approximate algorithm for finding frequent itemsets. The main idea can be described as turning the problem of mining frequent itemsets into a clustering problem. More precisely, we first represent each transaction by a vector using one-hot encoding scheme. Then, by means of mini batch k-means, we group all transactions into a number of clusters. The center of each cluster can be assumed as a potential candidate for a frequent itemset. To test the validity of this assumption, we compute the support of itemsets represented by cluster centers. All clusters that do not meet the minimum support condition will be removed from the set of clusters. As our experiments show, this approximate algorithm can capture more than 90% of all frequent itemsets at a much faster rate than the competing algorithms. Moreover, we show that the execution time of our algorithm is linear.
Robust Detection of the Coordinate of a Solar Panel Using Deep Learning Algorithm

The objective of this paper is to develop a robust and intelligent algorithm for the detection of the coordinates of the solar panel or its array in the field. This goal is achieved by using a TensorFlow-based real-time custom object detector developed for a solar panel. Collecting the database consisting of labelled pictures of solar panels in different environments and lightings is done for training and testing the ML-based deep learning models. After several experimentations and testing, the coordinates of the solar panel is successfully detected.
Highly nonlinear functions over finite fields

We consider a generalisation of a conjecture by Patterson and Wiedemann from 1983 on the Hamming distance of a function from Fqn to Fq to the set of affine functions from Fqn to Fq. We prove the conjecture for each q such that the characteristic of Fq lies in a subset of the primes with density 1 and we prove the conjecture for all q by assuming the generalised Riemann hypothesis. Roughly speaking, we show the existence of functions for which the distance to the affine functions is maximised when n tends to infinity. This also determines the asymptotic behaviour of the covering radius of the [qn,n+1] Reed-Muller code over Fq and so answers a question raised by Leducq in 2013. Our results extend the case q=2, which was recently proved by the author and which corresponds to the original conjecture by Patterson and Wiedemann. Our proof combines evaluations of Gauss sums in the semiprimitive case, probabilistic arguments, and methods from discrepancy theory.
Chat bot Using API : Human To Machine Conversation

Human to machine conversation is made by using a chatbot. Nowadays the use of Chatbots is very popular as large scale of applications especially in systems that provide an intelligence support to the user. In India a large number of people use railways to commute from one place to another. For the same reason, most of the people use irctc website to complete actions like ticket booking, checking PNR status, checking seat availability, checking train details from one place to another. As we know the irctc website is overwhelmed by the number of users visiting the site and hence results in slow response by the website, So to create an alternative to such railway actions, the users can use the chatbot. To the date, we have never seen a chatbot or such a thing that is user-friendly with providing railway information by considering the idea of chatbot, Users can request for the information in simple English language and more over the chatbot will also provide the data at their voice commands as well the chatbot will provide easy updation of data. This chatbot can be integrated with various platforms such as facebook, Google assistant, viber etc, So to conclude we can say that, using this chatbot our task of visiting multiple web pages to complete required railway requests can be saved with multiple user-friendly applications.
Scene Matching Method of Video Satellite Remote Sensing Image based on Deep Convolutional Neural Network

The problem of scene matching for video satellite remote sensing images is studied. The principle of image classification based on deep convolutional neural network is analyzed. It is pointed out that deep convolutional neural network can be used to extract the feature vector of an image. Based on this, a scene matching method based on deep convolutional neural network is proposed, which uses the feature vector extracted by deep convolutional neural network to match, and has good robustness to illumination, scale and viewing angle differences. The algorithm was verified by images shot by video satellites.
A Multi-Strategy Combined Grey Wolf Optimization Algorithm

The Grey Wolf Optimization (GWO) Algorithm is a heuristic evolutionary algorithm inspired by the hunting and leadership mechanisms of the natural grey wolf. However, its location update equation has the disadvantages of strong development ability and weak exploration ability. To improve these shortcomings, we propose the RWCA-GWO algorithm. It occurs inspired by the random walk strategy and enhances the exploration ability of the leading wolves. Inspired by the analytic hierarchy process, a procedure for setting the weights of α, β, and δ are proposed. Besides, to promote the global convergence speed of the GWO algorithm, the initial population is generated by the chaotic initialization method. The simulation experiments are carried out with 11 test functions. The results show that for most cases, the performance of RWCA-GWO algorithm is significantly better than the standard grey wolf algorithm and other improved grey wolf algorithms under the same maximum fitness function evaluation times.
Research on the Sustainable Development Model of Information Technology Literacy of Normal Students Based on Deep Learning Recommendation System

In recent years, deep learning has made breakthroughs in computer vision, speech recognition, natural language processing and recommendation systems. "Artificial Intelligence + Education" has been put on the agenda of our country. However, the level of information technology of primary and secondary school teachers in our country is still insufficient. There are many reasons for this problem. One of the important reasons that restrict the development of information technology ability of primary and secondary school teachers is the gap between the continuous learning of information technology and timely help of normal school students from learning to teaching, we propose to break this gap by using the model of recommendation system based on deep learning to improve the information technology ability of primary and secondary school teachers.
Adaptive Authentication to determine login attempt penalty from multiple input sources

Previously, Trust Engine component in Mi-UAP already provides security checking by having an adaptive authentication based on user attributes factor. However, we find out that the implementation is not enough to ensure the security of our system is well protected. We have come out with an initiative to provide another layer of security checking by implementing IP address checking module on the top of Trust Engine process. This module will identify a user location accessing the application. It then will define the user penalty if the IP address is from the penalty databases.
Doodle Recognition using machine learning for hearing and speech-impaired people

In this paper, we test the different classifiers used in machine learning and compare the different accuracies for the doodles which are obtained from Google's Quick Draw Dataset. The classifier with the best accuracy can be fed into an application for the hearing and speech impaired people through which the can be trained to draw their immediate needs, and the machine learning model could classify it into the correct category, so it would be easy for the people nearby to help them.
Single image rain removal with reusing original input squeeze‐and‐excitation network

In this study, the authors propose a novel network architecture to address the problem of removing rain streaks from single images. To strengthen the representational power of the network, they adopt the squeeze-and-excitation block in the network. Furthermore, they propose a new network connection called reusing original input (ROI). The ROI connection reuses the original input of the network and can provide more texture details of the background. These details can be useful for the restoration of the image after removing the rain streaks. Batch normalisation is applied to further improve the rain removal performance of the network. Despite the fact that the network is trained on synthetic data, experimental results show that the proposed network has a comparable performance on both synthetic images and real-world images to the state-of-the-art methods.
Crime in virtual reality: discussion

The following topics are dealt with: security of data; invasive software; organisational aspects; computer crime; personnel; text analysis; information retrieval; mobile computing; pattern classification; natural language processing.