Reviewerly/exHarmony huggingface dataset:
Variance-Constrained Learning for Stochastic Graph Neural Networks

Stochastic graph neural networks (SGNNs) are information processing architectures that can learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, but this training comes with no guarantee about the deviation of particular output realizations around the optimal mean. To overcome this issue, we propose a learning strategy for SGNNs based on a variance constrained optimization problem, balancing the expected performance and the stochastic deviation. To handle the variance constraint in the stochastic optimization problem, training is undertaken in the dual domain. We propose an alternating primal-dual learning algorithm that updates the primal variable (SGNN parameters) with gradient descent and the dual variable with gradient ascent. We show the stochastic deviation is explicitly controlled through Chebyshev inequality and analyze the optimality loss induced by the primal-dual learning. Through numerical simulations, we observe a strong performance in expectation with a controllable deviation corroborating the theoretical findings.
DNANet: Dense Nested Attention Network for Single Image Dehazing

In this paper, we propose an innovative approach, called Dense Nested Attention Network (DNANet), to directly restore a clear image from a hazy image with a new topology of connection paths. Firstly, through dense nested connections from inside to outside, the DNANet can fuse both shallow and deep features from fine to coarse, then strengthen the feature propagation and reuse to a large extent. We use stacked dilated convolutions, as the basic operation, to alleviate the shortcomings of the traditional context information aggregation methods. Secondly, we examine the weakness of skipping connections by reasoning the existence of residual haze from the shallow to deep layers in the neural network. To address this problem, we use the attention mechanism to filter out the output of residual haze by capturing the information relations on the entire skip feature maps. Thirdly, we introduce an adjustable loss constraint on each block of the outermost nested structure to gather more accurate features. The result demonstrates that DNANet outperforms state-of-the-art methods by a large margin on the benchmark datasets in extensive experiments.
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy development of GNNs based software. GraphGallery is an easy-to-use platform that allows developers to automatically deploy GNNs even with less domain-specific knowledge. It offers a set of implementations of common GNN models based on mainstream deep learning frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be easily incorporated into the platform. Experiments demonstrate the reliability of implementations and superiority in fast coding. The official source code of GraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a demo video can be found at https://youtu.be/mv7Zs1YeaYo.
Comparision of Image Denoising using Convolutional Neural Network (CNN) with Traditional Method

Image Denoising plays a vital role in various applications in the present scenario like image segmentation, classification, restoration, etc., Image denoising will remove noise from the image and restore the image with high quality. Image has interrupted by noise through different ways like extrinsic (i.e., environment) or intrinsic (i.e., like sensors) conditions. There are different algorithms for image denoising. The proposed research work utilizes quantitative analysis, i.e., PSNR metric is considered for further comparison. In our proposed method, comparison of image denoising using convolutional neural network (CNN) model with general traditional method like wavelet based model. The analysis is done by adding Gaussian noise with different variance and calculate Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) for each images using convolutional neural network (CNN) and compared it with traditional wavelet based model. After the analysis, the final results show that the convolutional neural network (CNN) model gives better results when compared to wavelet based model. CNN method have higher PSNR and SNR value, when compared to wavelet based model.
iELMNet: An Application for Traffic Sign Recognition using CNN and ELM

Traffic Sign Recognition (TSR) is a crucial step for automated vehicles and driver assistance systems. Automated TSD in an extreme environment has always been challenging due to foggy, rainy, blurry, and cropping images. A real-time TSD model named improved Extreme Learning Machine Network (iELMNet) is proposed to tackle these challenges. Primary modules of iELMNet include: a) Custom DensNet; b) Accurate Anchor Prediction Model (A2PM); c) Scale Transformation (ST), and d) Extreme Learning Machine (ELM) classifier. Convolutional Neural Network (CNN) model improvises edges of traffic-signs using mapped images extracted from handcrafted features. A2PM removes the redundant features to improve efficiency. ST is utilized to allow the proposed technique for detecting these signs of variant sizes. ELM classifier tries to classify traffic signs robustly by minimizing the feature dimensions. The proposed model is evaluated over three publicly available datasets, i.e., CURE-TSR, TT100k, and GTSRB, and acquired 98.63%, 95.22%, and 99.45% precision, respectively. The output of proposed model demonstrates its competence and ability to implement it in a practical environment.
Data Security Based on Blockchain Digital Currency

Blockchain is a distributed shared ledger and database. Because of its unforgeable, traceable, open, and transparent, and collective maintenance features, it has laid a solid foundation of “trust”. At the same time, the blockchain also brings hidden dangers of data security. How to protect the data security of digital currency in blockchain transactions is the direction of this article. This article first analyzes the basic structure of the blockchain, and then takes Bitcoin in the digital currency as an example to introduce its technical principles and implementation process. Finally, the PBFT algorithm is proposed, which can search for the application of encryption in the blockchain consensus mechanism. The experimental data verification proves the future application prospects of blockchain-based digital currency using PBFT algorithm to protect data security.
Indian Sign Language Alphabets Recognition from Static Images Using Correlation-Coefficient Algorithm with Neuro-Fuzzy Approach

Sign Language is a language used by mute people to communicate with other person by expressing their thoughts in the form of signs. Signs are expressed by hand and face movement. This paper describes proposed methodology for design and implementation of Alphabets recognition system of Indian sign language where static images of hand gesture are used as an input. Methodology includes various image processing techniques to remove unnecessary noise and to make images smooth. This Paper describe Correlation-coefficient algorithm which has been used for feature extraction and Neuro-fuzzy algorithm which has been applied as recognition algorithm. Proposed technique has been implemented on database of 100 images for testing. Implementation has been done on Matlab. Average accuracy of proposed methodology is 92.30%.
Matching Conservation-Restoration Trajectories: An Ontology-Based Approach

The context of our work is a project at the French National Library (BnF), which aims at designing a decision support system for conservation experts. The goal of this system is to analyze the conservation history of documents in order to enable reliable predictions on their physical state. The present work is the first step towards such system. We propose a representation of a document conservation history as a conservation-restoration trajectory, and we specify its different types of events. We also propose a trajectory matching process which computes a similarity score between two conservation-restoration trajectories taking into account the terminological heterogeneity of the events. We introduce an ontological model validated by domain experts which will be used during the pairwise comparison of events in two trajectories. Finally, we present experiments showing the effectiveness of our approach.
A public key encryption scheme based on a new variant of LWE with small cipher size

The lattice cryptosystem is considered to be able to resist the attacks of quantum computers. Lattice-based Public Key Encryption (PKE) schemes have attracted the interest of many researchers. In lattice-based cryptography, Learning With Errors (LWE) problem is a hard problem usually used to construct PKE scheme. To ensure the correctness of decryption, LWE-based schemes have a large ciphertext size. This makes these encryption schemes not practical enough when the communication bandwidth is limited. We propose a new variant of LWE, named Learning With Modulus (LWM) and prove that the new problem can be reduced from LWE problem. The proof idea of our reduction is similar to the reduction of LWR problem. We also construct a new PKE scheme based on the proposed LWM and LWE, which has small ciphertext size. For a 128 bits plaintext, the ciphertext size of our scheme is 53.57% of Lindner–Peikert’s (LP) scheme under the same security level. We use python to test the performance of our scheme. The results show that our scheme is only about 0.015 ms slower than LP in the decryption. The performance of our scheme for generating keys and encrypting messages is similar to LP.
Developing VR content for digital career guidance in the context of the pandemic

This paper presents our reflection on adapting our methodology for developing training content for young jobseekers to new influencing factors. The development process centers around small development teams working incrementally to expand a suit of VR applications being used for career guidance, so-called `immersive job taste'. COVID-19 measures have already impacted our ability to carry out user evaluations. Our process can be negatively affected by a headset vendor's new policy. This paper `s contribution is a perspective on the lessons learned and challenges of creating content for a set of VR-based training and career guidance apps targeted at young job seekers, as a part of Virtual Internship project.
SEQ-CPC : Sequential Contrastive Predictive Coding for Automatic Speech Recognition

Inspired by the contrastive predictive coding (CPC), we propose a feature representation scheme for automatic speech recognition (ASR), which encodes sequential dependency information from raw audio signals. Following the original CPC, for a given frame, mutual information (MI) lower bound is maximized between historical context and future prediction. While computing the MI lower bound, based on original CPC, we develop the sequential CPC (SEQ-CPC), which takes the sequential information between frames into consideration. Since speech frames are not independent events, incorporating sequential information leads to better recognition performance. Experimental results on WSJ corpus show that SEQ-CPC achieves the best performance than CPC and NCE which is the contrastive objective used in wav2vec.
Unsupervised Common Particular Object Discovery and Localization by Analyzing a Match Graph

Although the unsupervised discovery and localization of common objects from within a set of images has received considerable attention, the difficulty of this task means that current methods are not sufficiently accurate. This paper describes an unsupervised method that more accurately discovers and localizes common particular objects within a set of images. First, our method constructs a match graph that precisely represents the relationship among particular object proposals extracted from images. The match graph necessarily contains communities, i.e., subgraphs where object proposals with identical subjects are densely connected by edges, indicating matches between local image features in the proposals. Our method determines the common particular objects by scoring the object proposals using a novel node centrality measure for graphs with a community structure, which we call the restrained random-walk centrality. Experiments demonstrate that our method is considerably more accurate than previous approaches on a particular object image set.
Hand Gesture Recognition Based on Shape Context Analysis

The technological advancement is evolving the human-computer interaction (HCI). The goal is to ameliorate the HCI to a level where computers can be interacted in a natural way. It is a demanding aim and keeps the contemporary HCI systems complex and challenging. This paper aims to develop an effective hand gesture identification piloted HCI. It is realizable by three stages of preprocessing, features extraction and classification. The system functionality is studied by using a colored images database. Each incoming instance presents a hand gesture. Firstly it is subtracted from the background template to focus on the intended hand gesture. Afterward the subtracted image is enhanced and then converted into the grayscale one which is then thresholded by converting it in a binary image. This segmented version is further enhanced by using the morphological filters. The features are extracted by using the grayscale pixel values and shape context analysis (SC). Gestures are automatically recognized by using the k-Nearest Neighbor (k-NN) classification algorithm. The system achieves 83.3% of gesture recognition precision. The classification decisions are conveyed to the front-end embedded controller for systematic actuations and actions.
Photovoltaic Power Forecasting Using Recurrent Neural Network Based On Bayesian Regularization Algorithm

Photovoltaic (PV) is a generator that utilizes solar energy into electrical energy. On-grid photovoltaic is the system can reduce electricity bills, besides that the electricity produced is environmentally friendly and free emission. PV power output intermittent depending on weather conditions. Therefore, this research will predict the power output PV one day ahead using Recurrent Neural Network (RNN) method with Bayesian Regularization Algorithm because it can solve problems regarding prediction, classification, and energy management. The measure of accuracy error from the simulation result in this study is calculated using Mean Absolute Percentage Error (MAPE). The PV power forecasting accuracy using RNN method is compared with actual data. The amount of load power that PV cannot fulfill will later be back up by the grid. The prediction of PV power using RNN method with 4 neuron hidden layers and learning rate 0.01 resulted in the best MAPE value of 2,2784 %. Based on the results, PV power forecasting output using the RNN method with historical data can be applied to determine the amount of PV power for day ahead.
The Effects of a Stressful Physical Environment During Virtual Reality Height Exposure

Virtual reality height exposure is a reliable method of inducing stress with low variance across age and demographics. As the virtual environment's quality of rendering fidelity increases dramatically, it leads to the neglect or simplification of the physical environment. This paper presents an experiment that explored the effects of an elevated physical platform with a virtually heightened environment to induce stress. Fifteen participants experienced four different conditions of varying physical and virtual heights. Participants reported significantly higher stress level when physically elevated regardless of the virtual height, which suggests that the inherent elevation will induce more stress within participants.
DAP-Sketch: An accurate and effective network measurement sketch with Deterministic Admission Policy

Accurate measurement of network traffic is an essential part of current network management tasks. Traditional measurement methods based on counter and sketch respectively focus on the large flow detection and all flows queries, and the measurement accuracy is limited by the uncertainty in the large flow detection algorithm. Therefore, we design an asymmetric measurement architecture named DAP-Sketch. By using different data structures to measure large and small flows separately, DAP-Sketch provides the ability to query the approximate sizes of small flows while ensuring large flow detection. We propose a Deterministic Admission Policy (DAP) to dynamically distinguish the large and small flows, which effectively improves the accuracy of large flow detection and reduces the demand for storage resources. To improve the usability and universality of DAP, we put forward a d-Length DAP which applies local optimality instead of global optimality and makes our algorithm easy to implement. Furthermore, two optimization strategies with adaptive parameter adjustment are also designed in terms of the changes in memory space and traffic characteristics. Experimental results show that DAP-Sketch can outperform the best of the five typical measurement methods up to 59.3 times, decrease the requirements of network equipment resources, and achieve highly-precise and low-overhead network traffic measurement.
Analysis of X-Vectors for Low-Resource Speech Recognition

The paper presents a study of usability of x-vectors for adaptation of automatic speech recognition (ASR) systems. X-vectors are Neural Network (NN)-based speaker embeddings recently proposed in speaker recognition (SR). They quickly replaced common i-vectors and became new state-of-the-art technique. Here, the same approach is adopted for ASR with the hope of similar outcome. All experiments were done on ASR for the latest IARPA MATERIAL evaluation running on Pashto language. Over 1% absolute improvement was observed with x-vectors over traditional i-vectors, even when the x-vector extractor was not trained on target Pashto data.
A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.
Toward new creative services: a case study in building a virtual reality classroom in an academic library

Virtual Reality (VR) devices are now more accessible than ever before and academic libraries are well-positioned to provide democratized access and enhance library services with the technology. In this case study, faculty and staff of the J. Willard Marriott Library at the University of Utah transformed a traditional classroom into a dedicated VR classroom. The paper will cover five key points: a short literature review of VR trends and uses, a discussion of the methods used to identify a need and secure funding, highlights of the design process, preparation and installation of technology, and an initial look at how the VR classroom is being used today. The paper concludes with an overview of plans to study the effectiveness of the space as use increases.
Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition

Recently, several types of end-to-end speech recognition methods named transformer-transducer were introduced. According to those kinds of methods, transcription networks are generally modeled by transformer-based neural networks, while prediction networks could be modeled by either transformers or recurrent neural networks (RNN). In this paper, we propose novel multitask learning, joint optimization, and joint decoding methods for transformer-RNN-transducer systems. Our proposed methods have the main advantage in that the model can maintain information on the large text corpus eliminating the necessity of an external language model (LM). We prove their effectiveness by performing experiments utilizing the well-known ESPNET toolkit for the widely used Librispeech datasets. We also show that the proposed methods can reduce word error rate (WER) by 16.6 % and 13.3 % for test-clean and test-other datasets, respectively, without changing the overall model structure nor exploiting an external LM.
Bidirectional Focused Semantic Alignment Attention Network for Cross-Modal Retrieval

Cross-modal retrieval is a very challenging and significant task in intelligent understanding. Researchers have tried to capture modal semantic information through a weighted attention mechanism. Still, they cannot eliminate irrelevant semantic information's negative effects and cannot capture fine-grained modal semantic information. In order to further accurately capture the multi-modal semantic information, a bidirectional focused semantic alignment attention network (BFSAAN) is proposed to handle cross-modal retrieval tasks. Core ideas of BFSAAN are as follows: 1) Bidirectional focused attention mechanism is adopted to share modal semantic information, further eliminating the negative influence of irrelevant semantic information. 2) Strip pooling is applied to image and text modalities, a lightweight spatial attention mechanism to capture modal spatial semantic information. 3) Second-order covariance pooling is explored to obtain multi-modal semantic representation, capturing modal channel semantic information and achieving semantic alignment between image-text modalities. The experiment is executed in two standard cross-modal retrieval datasets (Flickr30K and MS COCO). The experimental design includes four aspects: performance comparison, ablation analysis, algorithm convergence, and visual analysis. Experimental results show that BFSAAN has better crossmodal retrieval performance.
Danesh: Interactive Tools for Understanding Procedural Content Generators

In order to advance the field of procedural content generation, and transfer knowledge from academic research to everyday use, we need to develop tools that make generative systems easier to understand and control. In this article, we introduce Danesh, a plugin to the unity game development environment, which helps provide a suite of tools that provide automation or analysis of different aspects of procedural generators. We describe here the features of Danesh, including automatic analysis of generated content, the visualization of generative spaces, automatic parameter discovery, and interface smoothing. We also provide reflections on our development of the tool so far.