Reviewerly/exHarmony huggingface dataset:
Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration

Motion averaging has recently been introduced as an effective means to tackle the registration of multi-view range scans. This approach can view parts of pair-wise motions with high reliability as an input to estimate the global motions for a multi-view registration. However, reliable pair-wise motions are not easy to confirm in most practical applications without prior knowledge. In this paper, we propose an adaptive low-rank sparse (LRS) weighted motion averaging method for a robust and accurate multi-view registration, which can directly reconstruct high-quality 3D shape models from a set of unordered range scans. Specifically, we first introduce LRS matrix decomposition to automatically compute the initial global motions. The LRS matrix decomposition can recover the initial global models through the full exploration of a set of pair-wise motions. Subsequently, we extend the motion averaging with an adaptive weight computation by developing an optimization strategy using the Lagrange multiplier method, which can adaptively compute the weights of the reliability for each pair-wise relative motion. Accordingly, the proposed method can recover accurate and robust global motions in a set of iterations through weighted motion averaging. Experimental results on several public datasets demonstrate the excellent performance of the proposed method in comparison with state-of-the-art multi-view registration and 3D scene reconstruction.
Journey to the center of the linear ordering problem

A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the escape technique play a key role in the performance of these algorithms. Of course, a specific strategy has a different effect on distinct problems or instances. In this paper, we focus on a permutation-based combinatorial optimization problem: the linear ordering problem. We provide a theoretical landscape analysis for the adjacent swap, the swap and the insert neighborhoods. By making connections to other different problems found in the Combinatorics field, we prove that there are some moves in the local optima that will necessarily return a worse or equal solution. The number of these non-better solutions that could be avoided by the escape techniques is considerably large with respect to the number of neighbors. This is a valuable information that can be included in any of those algorithms designed to escape from the local optima, increasing their efficiency.
Face video Super Resolution using Deep Convolutional Neural Network

In video surveillance, low resolution in face recognition is a major problem. Various Super Resolution (SR) approaches are introduced to perform the high resolution of face video recognition from low resolution videos. However, enhancing the resolution of face videos and reconstructing the high frequency data is a major problem in research area. Therefore, an effective face video Super Resolution method-based on Deep Convolutional Neural Network (Deep CNN) is introduced in this paper to achieve the face resolution effectively. Initially, the input video collected from the database is passed into the frame extraction stage, where the video frames are extracted and the face detection is carried out using the Viola Jones algorithm. Moreover, the detected image frame is processed by the Deep Convolutional Neural Network (Deep CNN) to enhance the image resolution. Deep CNN is highly effective in performing super resolution in face videos. However, the proposed Deep Convolutional Neural Network attains better performance using the metrics, like Second Derivative like Measure of Enhancement (SDME) as 0.9743 using video-1, and Feature SIMilarity index (FSIM) as 53.843 for video-4, respectively.
Unsupervised Remote Sensing Image Super-Resolution Method Based on Adaptive Domain Distance Measurement Network

Compared with supervised learning, unsupervised learning is more practical; however, the associated training process is more difficult and complex. To solve the problems of unstable training and insufficient diversity of generative adversarial networks (GAN), which are widely used to realize unsupervised learning, we propose a novel unsupervised remote sensing image super-resolution method based on a reverse generating network module and the adaptive domain distance measurement network. The discriminant network of GAN is considered as a tool to measure a certain image attribute instead of the original GAN binary classification network. Furthermore, the adaptive domain distance measurement network is used to back feed the information of a high-resolution image to guide the optimization of the generating network. The results of experiments performed on various datasets demonstrate the effectiveness of the proposed method.
Agent-Based Crowd Discussion Support System and Its Societal Experiments

Crowd-scale discussion platforms are receiving great attention as potential next-generation methods for democratic citizen platforms [1, 2, 5, 8]. One of the studies clarified the critical problem faced by human facilitators caused by the difficulty of facilitating large-scale online discussions. In order to address this issue, we implement an automated facilitation agent to manage crowd-scale online discussions. An automated facilitator agent extracts the discussion structure from the texts posted in discussions by people. We conduct large-scale social experiments with several cities including Nagoya city in Japan, and the Kabul city in Afganistan. In this demonstration, we present our current implementation of D-agree, a crowd-scale discussion support system based on an automated facilitation agent, and some results on social experiments.
A Fast Copy-Move Forgery Detection Using Global and Local Features

Numerous image tampering techniques are in use for modifying the content of an image. Due to which the entire information present in image may mislead the person. Hence image forgery detection is most prime branch in digital forensic. In this paper, a new block based image hashing technique is proposed by using global and local feature extraction for detection of tampering in an image. The global features are extracted by using Zernike moments and local features are extracted by using SIFT descriptors. The extracted Zernike moments gives information about luminance and chrominance components of an image. The local feature from image are extracted which are invariant changes in scaling, rotation and illumination. The proposed algorithm gives fast and accurate result for detection of copy-move attack. Experimental results show that proposed method is highly sensitive to tampering in small regions with success rate of 95%.
Integrated Parallel System for Audio Conferencing Voice Transcription and Speaker Identification

In response to the request from a well-known international financial corporation, an integrated system prototype was architected and implemented to automatically record corporate audio conferencing, transcribe the recordings to text while identifying speakers, and compile the transcription and identification results into text-based meeting minutes, which then gets sent as meeting summary email attachments as well as saved into a meeting management database. Three technology focuses of this integrated system are discussed in this paper 1) Selection of a 3rd-party audio transcription and identification API (Audio API) through prototyping, factor comparison, and considering the existing technology environment at the corporation. 2) Optimize the adoption of the selected Audio API based on knowledge of Natural Language Process (NLP) methods. 3) Support asynchronous scheduling and processing of concurrent meetings using parallel computing architecture methods. The completed system was evaluated and shown to have met all the requirements from the corporation, perform well in audio language intelligent processing and multi-threaded parallel execution. With further enhancements, we foresee this system solution has good commercial values and has potential to be adopted widely among other businesses.
Multi-scale orientation estimation using higher order Riesz transforms

In this paper, we unify the Riesz transform with the Teager–Kaiser energy operator (TKEO) and monogenic multi-resolution analysis in comparison to conventional gradient and Riesz transform-based approaches. We show that benefits of our approach maintain the following aspects of different known estimators: the stability of mixed (higher) order of derivatives, like given in TKEO, the all-pass filter characteristics featuring the Riesz transform and the scale-based analysis. Furthermore, we demonstrate the ability of the considered technique for extracting structural defects at different scales, estimating orientation or being used for 2D demodulation of amplitude- and phase modulated signals, like interferometric fringe patterns. Additionally, the abilities of orientation estimation of 3D data is demonstrated. From the viewpoint of mathematical analysis, we will emphasize the connection to monogenic higher dimensional signals, Clifford frames, and monogenic multi-resolution signal analysis. Furthermore, we will discuss higher order Riesz transform.
Trans-professionalism as a predictor of adaptation of a person to digital professional future

In the modern world, which follows the path of digitalization, there is a demand for professionals who are capable and ready to adapt to the rapidly changing conditions of the socio-professional environment - transprofessionals. The purpose of the research was to study the educability of teachers as a key construct of transprofessionalism and to describe the psychological peculiarities of trans-professional competence “capable and motivated to continue learning throughout life, to independently master new professional knowledge and skills” as a predictor of personality adaptation to a digital professional future on the example of preschool teachers. Research methods: theoretical analysis of scientific sources (generalization, analysis, synthesis, classification), empirical method (interrogative), methods of mathematical data processing (descriptive analysis, Kruskal-Wallis H-test), interpretation methods. Statistically significant differences were found among teachers of preschool education with different ages in standards of the expression of general reflexivity (at the trend standard), self-esteem and performance, and depending on the length of service - in standard of introjected motivation. The obtained data demonstrate a sufficient standard of expression of indicators of the cognitive-mnemonic component among teachers of the preschool education system, while differences in the expression of particular elements of the organizational, motivational and volitional components of learning are revealed. This article is the first in a series of researches devoted to solving the scientific problem of determining the structure and evaluation of trans-professional competencies. The materials may be useful for workers of the socionomic sphere in relation to forecasting and adaptation workers to a digital professional future.
Logic-Independent Proof Search in Logical Frameworks

Logical frameworks like LF allow to specify the syntax and (natural deduction) inference rules for syntax/proof-checking a wide variety of logical systems. A crucial feature that is missing for prototyping logics is a way to specify basic proof automation. We try to alleviate this problem by generating $$\lambda $$ Prolog (ELPI) inference predicates from logic specifications and controlling them by logic-independent helper predicates that encapsulate the prover characteristics. We show the feasibility of the approach with three experiments: We directly automate ND calculi, we generate tableau theorem provers and model generators.
Calibration of underwater fisheye camera with multi-frequency heterodyne technique based on light field

Abstract In this paper, a novel approach is proposed to calibrate an underwater fisheye camera with the multi-frequency heterodyne technique based on the light field. First, an underwater fisheye camera imaging model based on the light field is established to demonstrate the multiple refractions of light in underwater propagation and the extraordinary imaging characteristics of the fisheye lens, which shows better conciseness and organization. Then, the calibration method applied in the underwater fisheye camera imaging system is proposed to determine the mapping of the light field and the pose of the underwater fisheye camera. The light field mapping of the fisheye lens is obtained with the assistance of the multi-frequency heterodyne technique. Finally, an underwater fisheye camera is developed and the experimental results are obtained to validate the correctness and feasibility of the proposed method. Our underwater imaging system proves capable of achieving higher accuracy in the context of underwater 3D measurement.
Recognition of Printed Odia Characters and Digits using Optimized Self-Organizing Map Network

Optical character recognition is a kind of report picture investigation where filtered images that contain either machine printed or composed by hand content commitment to an optical character recognition (OCR) programming and making an interpretation of it into an editable machine discernible computerized content organization. An effort has been made to build up OCR framework for Odia language for that we have taken Self Organizing Map Network (SOM). Research in the field of character recognition for Odia content faces challenges generally in view of its properties like nature, various content styles and setting subordinate conditions of characters and their circumstance with respect to the check. This paper shows the impact of various element extraction strategy based on structural features are utilized in Odia character recognition framework with very high recognition accuracy rate. In this paper we proposed a Self Organizing Map Network (SOM) developed with specific structural features like height, width, cross section, end points to get high recognition accuracy up to 97.55%.
Predictive Analysis of Manpower Requirements in Scrum Projects Using Regression Techniques

Flexible iterative development life cycle, adaptive nature and fast delivery has given Agile an upper edge as compared to all other software development frameworks. In the current industry scenario agile methods are gaining popularity, owing to its people centric approach, hence organizations are adopting agile development methodologies at a large scale. Agile projects work in self-organizing small collaborative teams. Team size varies according to the project requirement however, agile development focus on smaller team size. Supervised machine learning is applied in this study to provide optimum prediction model. All the available regression models in Matlab R2019b are used to predict number of team members required for an agile project. Iterations from five different open source projects are considered for this study. The results after training all the variants of each regression model, namely Linear Regression models, Support Vector Machine models, Tree models, Ensemble models and Gaussian Process Regression models are compared using Root Mean Square Error (RMSE) score and R-squared values. On the basis of evaluative and comprehensive analysis, the most significant model to predict manpower requirement for an agile project has been chosen.
Ethnographic Fictions

Ethnographically informed research that investigates people's lived experiences, emotions, attitudes and behaviours inevitably draws primarily on current and past situations. Design practice is naturally concerned with using research to inform the creation of products, services, and interventions that are intended to change the future. Speculative Design has a particular interest in future scenarios that address essential human attitudes, assumptions and concerns. This raises the question of whether alternative design research methods should be used for Speculative Design projects. This paper presents one such adaptation; Fictional Ethnography, that has been trialled in an educational workshop setting. The paper describes the rationale behind the approach, situates it in relation to previous work, and sets out intentions for developing the work further.
New search operators for node-depth based encoding

Node-Depth Based Encoding is a representation for Evolutionary Algorithms applied to problems modelled by trees, storing nodes and their respective depths in an appropriately ordered list. This representation was highlighted by the results obtained, whose mutation and recombination operators have low time complexity in relation to other representations for the same problems. This work proposes new search operators. A high locality mutation operator, having as its main differential the possibility of including any edge present in a graph and a recombination operator with the ability to generate solutions with as much heritability as possible considering the edges set in the solutions, making it possible to recombine any solutions in the population with the aim of improving search convergence. All proposed operators also allow for dealing with problems modelled by forests with many trees and complete or non-complete graphs, always generating feasible solutions. This study performed bias, locality and heritability investigations for proposed operators showing that they have adequate characteristics for dealing with high dimensionality problems. In order to evaluate the performance of proposed operators, the results of preliminary tests were obtained applying to a benchmark problem in the literature. The use of proposed operators resulted in faster convergence.
Enabling an IoT System of Systems through Auto Sound Synthesis in Silent Video with DNN

The Internet of Things has enabled a wide variety of new applications not possible only a short time ago. Sensing data found at the Edge of the network, close to the environment where people are found, is a critical component with many modern applications. Often restrictions at the device level or on the available bandwidth limit the ability to capture all locally available data required for processing and analysis. In this research, we present a novel method for extracting sound from video data where no original sound was present. Our novel method of sound synthesis first uses the image features output from a Convolutional Neural Network (CNN) to determine class prediction weights using an advanced Long Short Term Memory (LSTM) network. A Generative Adversarial Network (GAN) is then used to generate the representative sound of the predicted class for the input video sample. By combining the output of many Auto Sound Generators in a System of Systems framework, we show that new applications emerge that were never before possible.
3D Image Segmentation of MRI Prostate Based on a Pytorch Implementation of V-Net

Abstract Prostatitis is one of the common diseases in adult males. At present, the MRI prostate volumetric medical image analysis mainly relies on the radiologist’s naked eye localization recognition diagnosis, which is easy to be misdiagnosed due to the fatigue of the doctor. Although the traditional technology uses the Support Vector Machine (SVM) to identify the image, it exists. Manual design features are incomplete and detection accuracy is not high. Recently, with the popularity and breakthrough of convolutional neural networks in the field of computer vision and medical image analysis, the author uses the MRI Prostate MR Image Segmentation 2012 combined with the Vnet method to exploit the depth of open source. Learning framework Pytorch, to achieve 3D image segmentation. The experimental results show that the Dice coefficient of the model on the training set reaches 0.9238 that is, the similarity between the image after cutting and the original image of the original label is 0.9238, and the error value is only 6.42, which is fast and automatic and accurate for intelligent target area painting, thus facilitating the doctor. Diagnosing medical imaging work is conducive to alleviating the imbalance of the proportion of doctors and patients in China.
Person Following Mobile Robot Using Multiplexed Detection and Tracking

Helper robots are widely used at various places like airports and railway stations. This paper presents a pipeline to multiplex the tracking and detection of a person in dynamic environments using a stereo camera in real time. Recent developments in object detection using ConvNets have led to robust person detection. These deep convolutional neural networks generally fail to run with high frame rates on devices with less computing power. Trackers are also used to retain the identity of the target person as well as impose fewer constraints on hardware. A concept of multiplexed detection and tracking is introduced which makes the pipeline faster by many folds. TurtleBot-2 is used for prototyping the robot and tuning of the motion controller. Robot operating system (ROS) is used to set up communication between various nodes of the pipeline. The results found were comparable to recent state-of-the-art person followers and can be readily used in day to day life usages.
Analysis of Deep Learning Cyclical order for Prediction of Fresh Milk Production in Sumatera

Abstract Milk is one of the most preferred and most easily absorbed nutrients. This drink naturally contains important nutrients, such as vitamins A, D, B12, protein, calcium, magnesium, phosphorus, and zinc, and others. Milk is also beneficial for children and adults. Starting from maintaining healthy bones and teeth, as a source of energy, and so on. In Indonesia, especially on the island of Sumatra, fresh milk production is carried out by cattle and goat farmers in cooperation with milk companies. This research have a purpose is to predict the production of fresh milk on the island of Sumatra so that the local government, as well as cattle and goat breeders on the island of Sumatra, have benchmarks to further increase the production of fresh milk in their respective regions in the future. The method that will be used in this research is Deep Learning Cyclical order which is the development of ANN. The research data used were data on the production of fresh milk on the island of Sumatra in 2009-2018 sourced from the Indonesian Statistics Agency. This research will be analyzed using 3 network architecture models, namely 4-5-1, 4-10-1 and 4-5-10-1 with the best network model chosen is 4-5-10-1 with an accuracy level of 90% and MSE value of 0.0157179042. Based on this best model a prediction of fresh milk production in Sumatra will be carried out in 2019-2020.
Cyber Attacks and Security—A Critical Survey

We know that all live in the era of the internet where most of everyday task is done over the internet from online transaction to net banking. Since web is a global stage that connects people around the world everyone can access the resources provided by the internet from anywhere. This Internet is sometimes used by few people for criminal purposes like invading person’s privacy, scams, cyberbullying, etc. These acts are termed as cybercrime. This paper provides a survey of these cybercrimes which are performed using various attacking mechanisms in India.
A Multi-scale Embossed Map Authoring Tool for Indoor Environments

We introduce a multi-scale embossed map authoring tool (M-EMAT) that produces tactile maps of indoor environments from the building’s structural layout and its 3D-scanned interiors on demand. Our tool renders indoor tactile maps at different spatial scales, representing a building’s structure, a zoomed-in of a specific area, or an interior of a room. M-EMAT is very easy to use and produces accurate results even in the case of complex building layouts.