Reviewerly/exHarmony huggingface dataset:
A Linear Sub-Structure with Co-Variance Shift for Image Captioning

Automatic description of image has attracted many researchers in the field of computer vision for captioning the image in artificial intelligence which connects with Natural Language Processing. Exact generation of captions to image is necessary but it lacks due to Gradient Diminishing problem, LSTM can overcome this problem by fusing local and global characteristics of image and text that generates sequenced word prediction for accurate image captioning. We consider Flickr 8k data-set which consists of text as descriptions of images. The use of GLoVe embedding helps for the word representation to consider the global and local features of images which finds distance with Euclidean to understand the relationship between words in vector space. Inception V3 architecture which is pretrained on ImageNet used to extract image features of different objects in scenes. We propose Linear Sub-Structure that helps to generate sequenced order of words for captioning by understanding relationship between words. For extracting image features considers co-variance shift which mainly concentrates on moving parts of the image to generate accurate description of the image to maintain a semantic visual grammar relationship between the predicted text for image as the caption, the proposed model evaluated with the help of BLEU score which achieves state of art model in our work while compared with others that has greater than 81% of accuracy.
Research and application of interactive teaching music intelligent system based on artificial intelligence

With the development of quality education and information technology, new computer technology is continuously added in the field of music education. Computer technology assists students in music teaching, which can cultivate students' independent learning ability and strengthen students' enthusiasm for learning music. This paper studies the interactive teaching music intelligence system based on artificial intelligence, and proposes a music learning model based on RBF algorithm. The addition of artificial intelligence to music teaching can improve students' inquiry ability, give play to the leading role of teachers, and make music teaching in the music classroom complementary and let students become the subject of teaching. Artificial intelligence technology combined with music teaching, building a neuron model and RBF algorithm, makes the music classroom interesting and vivid. Artificial intelligence technology provides a better teaching method for music teaching, which has far-reaching significance for music teaching.
Path Planning for Optimal Coverage of Areas with Nonuniform Importance

Coverage of an inaccessible or challenging region with potential health and safety hazards, such as in a volcanic region, is difficult yet crucial from scientific and meteorological perspectives. Areas contained within the region often provide valuable information of varying importance. We present an algorithm to optimally cover a volcanic region in Hawai`i with an unmanned aerial vehicle (UAV). The target region is assigned with a nonuniform coverage importance score distribution. For a specified battery capacity of the UAV, the optimization problem seeks the path that maximizes the total coverage area and the accumulated importance score while penalizing the revisiting of the same area. Trajectories are generated offline for the UAV based on the available power and coverage information map. The optimal trajectory minimizes the unspent battery power while enforcing that the UAV returns to its starting location. This multi-objective optimization problem is solved by using sequential quadratic programming. The details of the competitive optimization problem are discussed along with the analysis and simulation results to demonstrate the applicability of the proposed algorithm.
Determining Ground-State Phase Diagrams on Quantum Computers via a Generalized Application of Adiabatic State Preparation

Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of the competing states close to the level crossings. On the other hand, classical computers are limited to small system sizes, which quantum computers may help overcome. In this work, we use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. This methodology is illustrated by examining the ground states of the XY model with a magnetic field in the z-direction in one dimension. We are able to calculate an accurate phase diagram on both two- and three-site systems using IBM quantum machines.
Simple Local Attentions Remain Competitive for Long-Context Tasks

Wenhan Xiong, Barlas Oguz, Anchit Gupta, Xilun Chen, Diana Liskovich, Omer Levy, Scott Yih, Yashar Mehdad. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Revisiting Knowledge Distillation for Image Captioning

Knowledge Distillation (KD) [], as an effective technique for model compression and improving a model's performance, has been widely studied and adopted. However, most previous researches focus on image classification and few on sequence generation (such as Neural Machine Translation). We also note that few works for image captioning have incorporated KD, but they mainly treat it as a training trick. In contrast, we thoroughly investigate KD in the context of the image captioning task by conducting a series of experiments in this work. Specifically, we first apply the standard word-level KD to the image captioning model and explore cross-model distillation and self-distillation. We find that self-distillation is a practical choice that can achieve competitive performance while without spending time on choosing teacher's architecture. Inspired by the sequence-level distillation for Neural Machine Translation (NMT) [], we secondly adopt and modify it for image captioning and observe that competitive performance can be obtained using only one-fifth of resources and the speed of inference can be significantly improved by eliminating the need for beam search at the cost of slight performance degradation. Inspired by distilling BERT [] for NMT, we finally try to distill VL-BERT [] to make the captioning model look ahead by leveraging its bidirectional nature.
Two-photon sideband interaction in a driven quantum Rabi model: Quantitative discussions with derived longitudinal drives and beyond the rotating wave approximation

In this paper, we analytically and numerically study the sideband interaction dynamics of the driven quantum Rabi model (QRM). We focus in particular on the conditions when the external transverse drive fields induce first-order sideband interactions. Inducing sideband interactions between two different systems is an essential technique for various physical models, including the QRM. However, despite its importance, a precise analytical study has not been reported yet that successfully explains the sideband interaction rates in a driven QRM applicable for all system parameter configurations. In our paper, we analytically derive the sideband interaction rates based on second-order perturbation theory, not relying on the rotating wave approximation (RWA). Our formula are valid for all ranges of drive frequencies and system's parameters. Our analytical derived formula agrees well with the numerical results in a regime of moderate drive amplitudes. Interestingly, we have found a nontrivial longitudinal drive effect derived from the transverse drive Hamiltonian. This accounts for significant corrections to the sideband interaction rates that are expected without considering the derived longitudinal effect. Using this approach, one can precisely estimate the sideband interaction rates in the QRM not confining themselves within specific parameter regimes for moderate drive amplitudes. This provides important contributions for quantitatively understanding experiments described by the driven QRM.
Design and deep entanglements

research-article Open Access Share on Design and deep entanglements Authors: Jörgen Rahm-Skågeby Stockholm University Stockholm UniversityView Profile , Lina Rahm KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 72–76https://doi.org/10.1145/3502279Online:06 January 2022Publication History 0citation276DownloadsMetricsTotal Citations0Total Downloads276Last 12 Months276Last 6 weeks54 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Lode Encoder: AI-constrained co-creativity

We present Lode Encoder, a gamified mixed-initiative level creation system for the classic platform-puzzle game Lode Runner. The system is built around several autoencoders which are trained on sets of Lode Runner levels. When fed with the user's design, each autoencoder produces a version of that design which is closer in style to the levels that it was trained on. The Lode Encoder interface allows the user to build and edit levels through 'painting' from the suggestions provided by the autoencoders. Crucially, in order to encourage designers to explore new possibilities, the system does not include more traditional editing tools. We report on the system design and training procedure, as well as on the evolution of the system itself and user tests.
Adaptive CL-BFGS Algorithms for Complex-Valued Neural Networks

Complex-valued limited-memory BFGS (CL-BFGS) algorithm is efficient for the training of complex-valued neural networks (CVNNs). As an important parameter, the memory size represents the number of saved vector pairs and would essentially affect the performance of the algorithm. However, the determination of a suitable memory size for the CL-BFGS algorithm remains challenging. To deal with this issue, an adaptive method is proposed in which the memory size is allowed to vary during the iteration process. Basically, at each iteration, with the help of multistep quasi-Newton method, an appropriate memory size is chosen from a variable set {1,2, ... , M} by approximating complex Hessian matrix as close as possible. To reduce the computational complexity and ensure desired performance, the upper bound M is adjustable according to the moving average of memory sizes found in previous iterations. The proposed adaptive CL-BFGS (ACL-BFGS) algorithm can be efficiently applied for the training of CVNNs. Moreover, it is suggested to take multiple memory sizes to construct the search direction, which further improves the performance of the ACL-BFGS algorithm. Experimental results on some benchmark problems including the pattern classification, complex function approximation, and nonlinear channel equalization problems are given to illustrate the advantages of the developed algorithms over some previous ones.
Fregel: a functional domain-specific language for vertex-centric large-scale graph processing

Abstract The vertex-centric programming model is now widely used for processing large graphs. User-defined vertex programs are executed in parallel over every vertex of a graph, but the imperative and explicit message-passing style of existing systems makes defining a vertex program unintuitive and difficult. This article presents Fregel, a purely functional domain-specific language for processing large graphs and describes its model, design, and implementation. Fregel is a subset of Haskell, so Haskell tools can be used to test and debug Fregel programs. The vertex-centric computation is abstracted using compositional programming that uses second-order functions on graphs provided by Fregel. A Fregel program can be compiled into imperative programs for use in the Giraph and Pregel+ vertex-centric frameworks. Fregel’s functional nature without side effects enables various transformations and optimizations during the compilation process. Thus, the programmer is freed from the burden of program optimization, which is manually done for existing imperative systems. Experimental results for typical examples demonstrated that the compiled code can be executed with reasonable and promising performance.
Physical-layer Secure Key Distribution Scheme Based on Phase Noise in PSK/QNSC

A secure key distribution scheme with phase noise resisting the beam splitting attack in PSK/QNSC is proposed. The highest key consistency rate 98.4% and key generation rate 70.41 Mbit/s are demonstrated over 50 km SSMF.
CBIR for urban building using A-KAZE features

Content based image retrieval systems are an active research topic in the Computer Vision domain. These systems are important when dealing with urban environment augmented reality applications like building landmarks recognition, tourism storytelling, cultural heritage and so on. In this paper we propose a content-based image retrieval system that handles recognizing buildings from an urban scenario using only visual cues. The system use a Bag of Features feature descriptor framework and, for extracting points of interest, image features. The proposed system is a natural step forward as Bag of Features or A-KAZE both have shown good results in other applications. To evaluate our proposed system, we have used the popular dataset, ZuBuD, where we obtained a 99% accuracy in detection.
Novel Active Learning Framework for Anomaly Detection in Aviation with Expert in the Loop

Anomaly detection in commercial aviation is an extremely challenging yet crucial task. Accurately detecting operationally significant anomalies in operational data can save civilian lives and/or result in significant savings in aircraft/maintenance cost. The current practice uses manually tuned rule-based mechanisms to flag exceedances from pre-defined safety boundaries. However, this system cannot identify unknown risks and emerging vulnerabilities. Recently, innovative approaches based on data science and machine learning have been utilized to automate anomaly detection. However, there are limits to their applicability in the field of aviation due to several fundamental challenges: (1) Properly reviewed data is scarce in aviation and, as a result, supervised learning approaches cannot reach optimal performance. (2) Operationally significant anomalies do not coincide with statistically significant ones and, as a result, unsupervised learning approaches fail to provide reliable and robust performance. In this paper, we propose SALAD, a Semi-supervised Active Learning framework for Anomaly Detection, which detects operationally significant anomalies in flight operational quality assurance data. The developed framework works with vast amounts of unlabeled data as well as a small quantity of labeled data reviewed by subject matter experts (SMEs) to reliably identify safety anomalies in flight operations. Moreover, the model’s active learning strategy allows it to detect unknown risks and anomalies that might emerge in the system earlier with the help of SME reviews. We validate performance of the SALAD framework and its components with a real-world case study of multi-class anomaly detection during the approach to landing of commercial aircraft. We specifically show that the proposed framework reaches reliable anomaly-detection accuracy when only one percent of the data is reviewed and labeled and can identify unknown anomalies effectively.
A novel partitioned matrix‐based parameter update method embedded in variational Bayesian for underwater positioning

Abstract In order to meet the requirements of the high-precision positioning for the autonomous underwater vehicles (AUVs) in the complex and time-varying marine environments, a novel partitioned matrix-based parameter update method embedded in variational Bayesian (PMPU-VB) is proposed to deal with the positioning accuracy problem caused by the inaccurate predicted error covariance and measurement noise matrices. By employing the variational Bayesian (VB) method, the accurate predicted error covariance matrix and the measurement noise matrix can be obtained. Subsequently, the PMPU-VB, which employs the accurate predicted error covariance matrix and measurement noise matrix, is used as a substitute of the traditional Gaussian filtering (GF) algorithm, to update the probability density function (PDF) of the state vector. The state vector is defined to follow the Gaussian distribution, and the parameters of the Gaussian distribution are deduced by using the proposed partitioned matrix-based parameter update method. Finally, the accurate position information of the AUV can be obtained. Therefore, the more precise state vector and the error covariance matrix are acquired. The experiments results illustrate that the PMPU-VB has higher estimate accuracy, better stability and robustness than other comparison algorithms.
English-Manipuri Machine Translation: An empirical study of different Supervised and Unsupervised Methods

English-Manipuri MT is a challenging task because of the limited availability of parallel data and language divergences between English and Manipuri. This work investigates the English-Manipuri MT with different supervised and unsupervised methods of SMT and NMT. In the supervised approach, we have experimented on Phrase-based SMT and Transformer-based NMT. Furthermore, unsupervised methods are also examined, namely Unsupervised NMT(Artetxe et al.), Unsupervised SMT(Lample et al.), XLM(Cross-lingual Language Model Pretraining), and MASS(Masked Sequence to Sequence Pretraining). Common practices for improving low resource Machine Translation like Back-translation and Self-Training are also tried. Detailed error analysis and the challenges faced in the English-Manipuri MT system are also presented in this paper. The paper also explains how a new corpus of 74K parallel English-Manipuri sentences is constructed. This is the largest English-Manipuri parallel corpus reported till date.
A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios

Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.
Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm

Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.
Basic concepts and stages of research development on artificial intelligence

The article describes artificial intelligence, which is now widely used and developed in the field of information technology and is a part of our life. The basic concepts and classifications of artificial intelligence, its features, areas of research, analytical data on the stages of development are described. At the same time, the role and functions of artificial intelligence in intelligent systems, as well as approaches in the field of application are presented.
Cost-Optimized Video Transfer using Real-Time Super Resolution Convolutional Neural Networks

The explosion of video generation and consumption, coupled with an inadequate rise in network bandwidth has led to network delays and decreased Quality of Experience, limiting the opportunities to tap into the full potential of video data. These deficiencies in network resources with a shift to cloud computing models have resulted in the need to revisit the overall mechanism for video transfer and storage of videos between edge devices and the cloud. We propose a novel multi-scale real-time super-resolution convolutional neural network to achieve the composite task of optimizing the entire cost of video transfer with minimal loss of quality that can be used for any application involving the transfer of video data. To achieve this, we develop a cost-optimized video transfer system that optimizes the metrics of video transfer to give the best quality video output, given the user budget. The model makes use of Convolution blocks for extracting features and output creation with multiple sub-pixel convolutions in a novel structure. For upscaling to full High Definition video at 30 fps, the model successfully retained the frame rate while the system achieved savings in transfer time and bandwidth usage. This model has been trained on surveillance videos (VIRAT dataset), but consistent results were obtained during testing even on feature films and sports videos which demonstrates its content invariance. The evaluation of our approach averaged over 376 videos, yielded meager quality losses of 8%, measured by a novel non-referential quality metric, also proposed in this paper. Additionally, average network bandwidth savings of 80% and average video transfer time reduction of 52% were achieved.