Reviewerly/exHarmony huggingface dataset:
Virtual and Augmented Reality Animals in Smart and Playful Cities: (Invited Paper)

Our future urban environments are smart. Sensors and actuators are embedded in these environments and their inhabitants. We have an Internet of Things, where the `Things' include objects, cars, tools, buildings, street furniture, and whatever can be equipped with sensors and actuators, including human and non-human animals. Augmented humans and augmented animals have their senses extended with digital technology. Their smart wearables connected with the smart environment make humans and animals smarter. Rather than on living animals, in this survey paper we focus on non-living virtual and augmented reality non-human animals that will inhabit our smart and playable urban environments. They will co-exist with robotic animals and (digitally augmented) humans and non-human animals. We include observations on augmented humans interacting with virtual and augmented reality animals. The paper is meant to raise awareness for the possibilities of augmented reality to introduce virtual animals for social, entertainment, and educational reasons.
An Efficient Makespan Reducing Task Scheduling Algorithm in Cloud Computing Environment

The current trends in task scheduling problems in cloud computing are moving toward the optimization of task execution time with the invention of novel approaches for heterogeneous environments. This article aims to decrease the makespan time of the scheduling in cloud computing environment. The article introduces an approach for efficient task scheduling of the diversified machines used in cloud to minimize the makespan time. The proposed algorithm was checked with the Braun benchmark dataset. The experimental results demonstrate that the proposed algorithm minimizes the overall makespan up to 11.87% as compared to the other recent implemented algorithms.
Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics

A robot's ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing such transparency in decision making is particularly challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning algorithms. Towards addressing this challenge, our architecture couples the complementary strengths of non-monotonic logical reasoning with incomplete commonsense domain knowledge, deep learning, and inductive learning. During reasoning and learning, the architecture enables a robot to provide on-demand explanations of its decisions, beliefs, and the outcomes of hypothetical actions, in the form of relational descriptions of relevant domain objects, attributes, and actions. The architecture's capabilities are illustrated and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects. Experimental results indicate the ability to reliably acquire and merge new information about the domain in the form of constraints, and to provide accurate explanations in the presence of noisy sensing and actuation.
3D shape and strain measurement of a thin-walled elastic cylinder using fringe projection profilometry

This paper describes an optical measurement system that is capable of simultaneously measuring a three-dimensional shape and circumferential train in a thin-walled cylinder. We used the fringe projection technique to obtain a three-dimensional shape and out-of-plane displacements, which were then associated with out-of-plane strain. Although 360 deg profilometry is not new, we propose an experimental setup that requires only one camera and one projector, since two mirrors are located at 120 deg with respect to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>y</mml:mi> <mml:mspace width="negativethinmathspace" /> <mml:mo>−<!-- − --></mml:mo> <mml:mspace width="negativethinmathspace" /> <mml:mi>z</mml:mi> </mml:math> plane of the reference system to obtain three distinct, complementary views of the sample studied. A sequence of images of the fringe pattern was captured and processed using the phase-shifting method, while the out-of-plane data were determined by comparing the specimen before and after deformation. A latex tube filled with liquid was used to perform mechanical testing. The cylinder was subjected to internal hydrostatic pressure that caused deformation. Experimental results were compared with analytical solutions, and the results show that out-of-plane strains can be accurately measured using the proposed method.
Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However, this principle is often invalid in real Internet environment, which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet, a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly, we use the probes to measure the known landmarks to obtain their delay vectors, and cluster landmarks using them.Secondly, the landmarks are clustered again by their latitude and longitude, and the intersection of these two clustering results is taken to form training sets.Thirdly, we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally, we determine one of the neural networks for the target by the delay similarity and relative hop counts, and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering, the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong, Shanghai, Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation, and comparing with existing typical streetlevel geolocation algorithms, the proposed algorithm improves the geolocation reliability significantly.
A New Neural Network with Genetic Algorithm in Searching Nonlinear Function Extremum

Abstract In order to more accurate for nonlinear function extreme, this paper used improved particle swarm optimization neural network combining with genetic algorithm method to solve the problem. In view of the particle swarm optimization algorithm is easy to appear “premature” faults, introducing the adaptive threshold, initializing particles if they were under the constraint conditions, making particles jump out to the optimal value of the position in previous search. Through the experiment, contrasts to the genetic neural network algorithm and traditional BP neural network, this method is faster in convergence and has the smallest prediction error. Finally, combining with genetic algorithm, calculating the extreme value of nonlinear function by using the above three kinds of neural network trained forecast as an individual output fitness value. The adaptive particle swarm optimization neural network proves the most close to the theoretical calculation. It shows that the method is effective.
A Bilingual Speech Synthesis System of Standard Malay and Indonesian Based on HMM-DNN

This paper designs and implements a Standard Malay and Indonesian bilingual speech synthesis system based on HMM-DNN. Standard Malay and Indonesian have high homology and relatively few electronic language resources. We combine the corpus of these two similar languages and introduce speaker codes to examine bilingual speech synthesis system based on HMM-DNN, and compare it with the monolingual speech synthesis system. The methods used include: sharing synthesis units between Standard Malay and Indonesian, designing unified context attributes and question set, speaker adaptive training with speech corpus of these two languages, and synthesizing speech using speaker-dependent Standard Malay and Indonesian acoustic models. Experiments show that the quality of the Malay speech and Indonesian speech synthesized by the system is not only superior to the traditional Hidden Markov Model (HMM)-based bilingual speech synthesis system, but also superior to the HMM-DNN-based monolingual speech synthesis system.
Tight Semi-nonnegative Matrix Factorization

The nonnegative matrix factorization is a widely used, flexible matrix decomposition, finding applications in biology, image and signal processing and information retrieval, among other areas. Here we present a related matrix factorization. A multi-objective optimization problem finds conical combinations of templates that approximate a given data matrix. The templates are chosen so that as far as possible only the initial data set can be represented this way. However, the templates are not required to be nonnegative nor convex combinations of the original data.
Hybrid Model with Word2vector in Information Retrieval Ranking

People have realized the importance of finding and archiving information with the computer advents for thousands of years, and storing of large amount of information became possible. It is actually not related to the fetching of the documents, it informs the user on the whereabouts and existence of the documents. In this paper, hybrid model has been used in which the document is classified using the support vector machine (SVM) classifier, and after the condition is applied, if it is satisfied, the extraction of the matched paragraph and the sentence is responsible for the generation of relevant answer. The knowledge base gets updated if condition does not match, and new updated answer will be generated. Finally, the best answer is displayed after ranking by using the PSO optimization. Word2vector is applied for feature extraction. In this paper, comparison of RankSVM, RankPSO and RankHSVM + PSO for the implementation of IR ranking is considered. Here, first SVM is used as a classifier for dividing most relevant and non-relevant results, and afterward PSO is used for the optimization of the result means extraction of the best answer or document. Selection of appropriate parameters is difficult in case of simple SVM, but for the ranking of the answers it gives potential solutions. PSO is used for optimization which has global search capability and is easy to implement and thus to optimize the ranking of document retrieval. We propose the RankHSVM + PSO model to find the fitness function. This technique improves the performance of the system as comparative to other techniques. The result shows that the algorithm applied here improves the value of performance evaluation by 4–5%. TREC 2004 QA DATA dataset is used which contains my datasets. It has a question answering track since 1999. The task was defined in each track. Retrieval of true equivalent test collection for standard retrieval is an open problem. In a retrieval test collection, the unit that is judged the document has a unique identifier.
We don't Need Thousand Proposals: Single Shot Actor-Action Detection in Videos

We propose SSA2D, a simple yet effective end-to-end deep network for actor-action detection in videos. The existing methods take a top-down approach based on region-proposals (RPN), where the action is estimated based on the detected proposals followed by post-processing such as non-maximal suppression. While effective in terms of performance, these methods pose limitations in scalability for dense video scenes with a high memory requirement for thousands of proposals. We propose to solve this problem from a different perspective where we don't need any proposals. SSA2D is a unified network, which performs pixel level joint actor-action detection in a single-shot, where every pixel of the detected actor is assigned an action label. SSA2D has two main advantages: 1) It is a fully convolutional network which does not require any proposals and post-processing making it memory as well as time efficient, 2) It is easily scalable to dense video scenes as its memory requirement is independent of the number of actors present in the scene. We evaluate the proposed method on the Actor-Action dataset (A2D) and Video Object Relation (VidOR) dataset, demonstrating its effectiveness in multiple actors and action detection in a video. SSA2D is 11x faster during inference with comparable (sometimes better) performance and fewer network parameters when compared with the prior works. Code available at https://github.com/aayushjr/ssa2d.
Tripartite quantum operation sharing with six-qubit highly entangled state

A three-party scheme for sharing an arbitrary single-qubit operation on a distant target qubit is proposed by first utilizing a six-qubit genuinely entangled state presented by [Borras et al., J. Phys. A 40, 13407 (2007)]. The security of the scheme is simply analyzed and ensured. The essential role which the state in the given qubit distribution plays in the QOS task is revealed. The important features including the sharing determinacy and the sharer symmetry are identified. Moreover, the experimental implementation feasibility of the scheme is discussed and confirmed.
Minimum unbiased risk estimate based 2DPCA for color image denoising

Low-rank approximation of matrices plays an important role in many application scenarios, including image denoising. This paper introduces a new low-rank approximation method named minimum unbiased risk estimate formulation of 2DPCA (MURE-2DPCA). MURE-2DPCA starts by considering the problem of estimating noise-free matrices from observations, and can exhibit robustness to outliers. In the case of a single data matrix constructed with Gaussian vectors, we find that the optimal dimension of the principal subspace can be determined automatically from the data itself. Based on MURE-2DPCA, a three-step algorithm is developed for color image denoising. Experiments demonstrate the ability and efficiency of our algorithm in achieving the denoising task.
Bhutanese Sign Language Alphabets Recognition Using Convolutional Neural Network

The emerging communication difficulty between deaf people and the public is the increasing concern in Bhutan. The government of Bhutan and the deaf institute are prioritizing the issue by urging people to learn sign language but learning Bhutanese Sign Language (BSL) is difficult. However, the progress in computer vision has allowed the development of gesture recognition systems. The purpose of this study was to introduce technology to the BSL by developing the alphabets recognition system. This paper presents a hand-shaped Dzongkha alphabets dataset created from 1200 videos recorded from 40 people from 5 countries. Using the Convolutional Neural Network (CNN) inspired by the VGGNet architecture, the images were trained by fine-tuning the different parameters to give the best performance of the system. The proposed method obtained the training and validation accuracy of 98.27% and 99.72% respectively.
IOT Model - UV based system for Sanitization of package surfaces

Abstract Global issue corona pandemic and lock down has stopped the human life or disturbed. But this situation will not continue for longer period. Normality will take place and life smoothen, but people will fear at workplace due to unknown factors of the corona pandemic, they will be in a dwell mind situation whether to touch the things or not. In this research to mitigate or avoid such situation to think develop IOT devices along with a programmable-Ultra-Violet (UV) based system for sanitization of package surfaces. In this paper, we proposed an emphasized artificial intelligence based IOT - UV system for Sanitization of package surfaces, also developed novel algorithm for the IOT-UV based devices to enhance the efficiency of the disinfection devices in the context of low energy consumption. In order to evaluate performance of IOT-UV will be more efficient than Programmable-UV light in the context of low energy consumption.
Efficient foreground object segmentation from video by Probability Weighted Moments

Segmentation of foreground objects is an important issue in computer vision. Since, there exist no predefined classes for unsupervised learning, which makes it computationally expensive. Also, the contrast of foreground to background needs to be tackled effectively. To overcome the aforementioned problems, in this paper an efficient approach named Fused Probability weighted moments and Principal component analysis Video Object Segmentation (FPPVOS), is proposed by the fusion of Probability Weighted Moments (PWM) with Principal Component Analysis (PCA). FPPVOS significantly reduces the computational cost by taking into account the intra-class variability due to its less sensitivity towards the outliers and low sample variability. Theoretically, this fusion ensures the efficiency of the unsupervised learning where classification is assured, even though if no grouping or labels of the objects are available. The results are evaluated on the standard YouTube-Objects dataset. We achieved the preeminent performance by using FPPVOS while solving the problems of object separation and segmentation from its background in a reasonable time interval comparative to the state-of-the-art methods.
Measuring the correlation between digital media usage and students’ perceived writing ability: Are they related?

The purpose of our correlational, quantitative study was to determine if time spent using digital media (i.e. text messaging and social media) influences students’ media writing self-perceptions (MWSPs). We measured students’ perceived writing ability using the MWSP scale and their time spent using digital media with the social networking time use scale (SONTUS). Correlations between students’ MWSP scores and SONTUS scores were statistically insignificant, suggesting that time spent using digital media does not negatively influence their perceived writing abilities. However, results from further analyses indicated that as students’ social media use increased, so did their ability to recognise the difference between writing for social media and writing for professional publications. We also found that the more students text the more they use social media and vice versa. We present directions for future research and practice.
Small weight code words arising from the incidence of points and hyperplanes in $$\text {PG}(n,q)$$

Let $C_{n-1}(n,q)$ be the code arising from the incidence of points and hyperplanes in the Desarguesian projective space PG($n,q$). Recently, Polverino and Zullo proved that within this code, all non-zero code words of weight at most $2q^{n-1}$ are scalar multiples of either the incidence vector of one hyperplane, or the difference of the incidence vectors of two distinct hyperplanes. We improve this result, proving that when $q>17$ and $q\notin\{25,27,29,31,32,49,121\}$, all code words of weight at most $(4q-\sqrt{8q}-\frac{33}{2})q^{n-2}$ are linear combinations of incidence vectors of hyperplanes through a fixed $(n-3)$-space. Depending on the omitted value for $q$, we can lower the bound on the weight of $c$ to obtain the same results.
Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos

This paper focuses on weakly-supervised action alignment, where only the ordered sequence of video-level actions is available for training. We propose a novel Duration Network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which captures a short temporal window of the video and learns to predict the remaining duration of a given action at any point in time with a level of granularity based on the type of that action. Further, we introduce a Segment-Level Beam Search to obtain the best alignment, that maximizes our posterior probability. Segment-Level Beam Search efficiently aligns actions by considering only a selected set of frames that have more confident predictions. The experimental results show that our alignments for long videos are more robust than existing models. Moreover, the proposed method achieves state of the art results in certain cases on the popular Breakfast and Hollywood Extended datasets.
Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications

The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.
Influences of Artificial Speciation on Morphological Robot Evolution

One key challenge in Evolutionary Robotics (ER) is to evolve morphology and controllers of robots. Most experiments in the field converge rapidly to a single solution for the entire population. Early convergence results in a premature loss of diversity, which creates inconsistent results across multiple runs, sometimes converging to a local optimum. In Nature we can observe the opposite behavior: the more time passes, the more life becomes increasingly diverse. The increasing diversity is correlated to the formation of new species, which is catalyzed by reproductive isolation caused by physical or behavioral separation. Inspired by natural evolution, in this paper we apply artificial speciation based on morphological traits to an ER system. Individuals are forced to crossover only with individuals within the same species and a protection mechanism is applied to newly created species. In our experiments, we demonstrate that this speciation mechanism, inspired by NEAT, can evolve a population rich of many coexisting individuals, differing both in morphology and behavior.