Reviewerly/exHarmony huggingface dataset:
Learning the Dynamics of Visual Relational Reasoning via Reinforced Path Routing

Reasoning is a dynamic process. In cognitive theories, the dynamics of reasoning refers to reasoning states over time after successive state transitions. Modeling the cognitive dynamics is of utmost importance to simulate human reasoning capability. In this paper, we propose to learn the reasoning dynamics of visual relational reasoning by casting it as a path routing task. We present a reinforced path routing method that represents an input image via a structured visual graph and introduces a reinforcement learning based model to explore paths (sequences of nodes) over the graph based on an input sentence to infer reasoning results. By exploring such paths, the proposed method represents reasoning states clearly and characterizes state transitions explicitly to fully model the reasoning dynamics for accurate and transparent visual relational reasoning. Extensive experiments on referring expression comprehension and visual question answering demonstrate the effectiveness of our method.
Self-Supervised Graph Neural Networks via Diverse and Interactive Message Passing

By interpreting Graph Neural Networks (GNNs) as the message passing from the spatial perspective, their success is attributed to Laplacian smoothing. However, it also leads to serious over-smoothing issue by stacking many layers. Recently, many efforts have been paid to overcome this issue in semi-supervised learning. Unfortunately, it is more serious in unsupervised node representation learning task due to the lack of supervision information. Thus, most of the unsupervised or self-supervised GNNs often employ \textit{one-layer GCN} as the encoder. Essentially, the over-smoothing issue is caused by the over-simplification of the existing message passing, which possesses two intrinsic limits: blind message and uniform passing. In this paper, a novel Diverse and Interactive Message Passing (DIMP) is proposed for self-supervised learning by overcoming these limits. Firstly, to prevent the message from blindness and make it interactive between two connected nodes, the message is determined by both the two connected nodes instead of the attributes of one node. Secondly, to prevent the passing from uniformness and make it diverse over different attribute channels, different propagation weights are assigned to different elements in the message. To this end, a natural implementation of the message in DIMP is the element-wise product of the representations of two connected nodes. From the perspective of numerical optimization, the proposed DIMP is equivalent to performing an overlapping community detection via expectation-maximization (EM). Both the objective function of the community detection and the convergence of EM algorithm guarantee that DMIP can prevent from over-smoothing issue. Extensive evaluations on node-level and graph-level tasks demonstrate the superiority of DIMP on improving performance and overcoming over-smoothing issue.
Ensemble of Convolution Neural Networks for Improving Automatic Modulation Classification Performance

This paper investigates convolutional neural networks (CNN) to classify 26 types of signal modulation under the influence of five different fading channels and Gausian noise with SNR from -20 dB to +18 dB. Specifically, five CNN models, including ResNet18, SqueezeNet, GoogleNet, MobileNet, and RepVGG, are taken into account for a accuracy competition to discover the best one. As a result, the SqueezeNet model achieves the highest accuracy of 97.5% for the SNR value of +8~dB. Based on the evaluation results of the single models, we propose an ensemble learning approach, which integrate some robust networks to improve classification accuracy. The numerical results show that ensemble learning can improve the automatic modulation classification accuracy compared to those single models. Specifically, the ensemble learning model gains the accuracy of 52.7% at the SNR of -20 dB and 77% at the SNR of -2 dB. In addition, three types of ensemble methods are considered for analysis and comparison. Consequently, the weighted ensemble provides a better performance in terms of accuracy than unweighted one.
Interpreting Gender Bias in Neural Machine Translation: Multilingual Architecture Matters

Multilingual neural machine translation architectures mainly differ in the number of sharing modules and parameters applied among languages. In this paper, and from an algorithmic perspective, we explore whether the chosen architecture, when trained with the same data, influences the level of gender bias. Experiments conducted in three language pairs show that language-specific encoder-decoders exhibit less bias than the shared architecture. We propose two methods for interpreting and studying gender bias in machine translation based on source embeddings and attention. Our analysis shows that, in the language-specific case, the embeddings encode more gender information, and their attention is more diverted. Both behaviors help in mitigating gender bias.
Large-Neighbourhood Search for Optimisation in Answer-Set Solving

While Answer-Set Programming (ASP) is a prominent approach to declarative problem solving, optimisation problems can still be a challenge for it. Large-Neighbourhood Search (LNS) is a metaheuristic for optimisation where parts of a solution are alternately destroyed and reconstructed that has high but untapped potential for ASP solving. We present a framework for LNS optimisation in answer-set solving, in which neighbourhoods can be specified either declaratively as part of the ASP encoding, or automatically generated by code. To effectively explore different neighbourhoods, we focus on multi-shot solving as it allows to avoid program regrounding. We illustrate the framework on different optimisation problems, some of which are notoriously difficult, including shift planning and a parallel machine scheduling problem from semi-conductor production which demonstrate the effectiveness of the LNS approach.
AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract)

Federated learning (FL) performs the global model updating in a synchronous manner in that the FL server waits for a specific number of local models from distributed devices before computing and sharing a new global model. We propose asynchronous federated learning (AsyncFL), which allows each client to continuously upload its model based on its capabilities and the FL server to determine when to asynchronously update and broadcast the global model. The asynchronous model aggregation at the FL server is performed by the Boyer–Moore majority voting algorithm for the k-bit quantized weight values. The proposed FL can speed up the convergence of the global model learning early in the FL process and reduce data exchange once the model is converged.
Break the Blackbox! Desensitize Intra-domain Information for Inter-domain Routing

Along with the ever-increasing amount of data generated from edge networks, cross domain (also known as Autonomous Systems, AS) transmission problem has attracted more and more attention. As mature and widely used inter-domain routing protocols, BGP-based solutions often use the number of domains (i.e. AS hops) of each path to make inter-domain routing decisions, which is simple and effective, but usually can not get the optimal routing results due to the lack of real state/information within ASes. These protocols choose the path with less AS hops as the forwarding path, even if the total latency or cost of the domains on this path is higher. While to solve this problem, directly access to intra-domain information as the assistance to make routing decisions is impractical due to data privacy.In this paper, we propose DIT, which makes near-optimal inter-domain routing decisions with desensitized intra-domain information. To do so, we design a homomorphic encrypted-based private number comparison scheme to export intra-domain information securely and thus assist in routing decisions. We conduct a series of experiments according to five real network topologies with nearly 900 simulated flows, and the results show that DIT reduces the number of forwarding hops by about 45% in average and reduces flow completion time by about 60%.
The combined model for software development effort estimation using polynomial regression for heterogeneous projects

Subject matter: Estimating the software work is a crucial job of persons participating in software project management. The difficulty in predicting effort is compounded by the fact that software development is always changing. In the past, researchers used one form of development methodology in their work to estimate effort and time. Estimations of the software projects are estimated with different size matrices. The lines of code, story point and use case point are required for the estimation using algorithmic models for procedural, agile, and object-oriented development approaches. Currently, the companies use these three types of size matrices for estimating projects. Not any one model present estimates the effort for different development approaches with different size metrics. This paper proposes a combined software estimation model for three types of development methodologies with regression analysis. The estimation can be done with the proposed model for a software project developed using the procedural, agile, and object-oriented approach. Method: The input for the model is the size of the software, such as lines of code, story point, and use case point. The model is developed using the polynomial regression. The model is developed with the four constant parameters that are based on the procedural, agile, and object-oriented projects. A dataset of python projects for procedural, zia dataset for agile, company dataset for object-oriented methodology is used to propose the model. Conclusion: The effort is predicted for the procedural, agile, and object-oriented projects with the polynomial regression model and compare the results to existing models to validate the work. The R2 is used to measure accuracy and the MMRE is used to determine error. The accuracy of the proposed model was higher than 90% and the error was found to be less than 0.05. The results are compared with case-based reasoning and an ensemble model for the procedural approach, linear regression and Bayesian network for the agile approach, and linear and log-linear regression for object-oriented approach. The minimum error and maximum accuracy is achieved compared to these techniques.
Backend Ensemble for Speaker Verification and Spoofing Countermeasure

This paper describes the NPU system submitted to Spoofing Aware Speaker Verification Challenge 2022. We particularly focus on the \textit{backend ensemble} for speaker verification and spoofing countermeasure from three aspects. Firstly, besides simple concatenation, we propose circulant matrix transformation and stacking for speaker embeddings and countermeasure embeddings. With the stacking operation of newly-defined circulant embeddings, we almost explore all the possible interactions between speaker embeddings and countermeasure embeddings. Secondly, we attempt different convolution neural networks to selectively fuse the embeddings' salient regions into channels with convolution kernels. Finally, we design parallel attention in 1D convolution neural networks to learn the global correlation in channel dimensions as well as to learn the important parts in feature dimensions. Meanwhile, we embed squeeze-and-excitation attention in 2D convolutional neural networks to learn the global dependence among speaker embeddings and countermeasure embeddings. Experimental results demonstrate that all the above methods are effective. After fusion of four well-trained models enhanced by the mentioned methods, the best SASV-EER, SPF-EER and SV-EER we achieve are 0.559\%, 0.354\% and 0.857\% on the evaluation set respectively. Together with the above contributions, our submission system achieves the fifth place in this challenge.
Array Camera Image Fusion using Physics-Aware Transformers

We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure. We also demonstrate a scalable solution for synthetic training data generation for the transformer using open-source computer graphics software. We demonstrate image synthesis on arrays with diverse spectral responses, instantaneous field of view and frame rate.
Impact of homodyne receiver bandwidth and signal modulation patterns on the continuous-variable quantum key distribution

In continuous-variable quantum key distribution (CV-QKD), the key information are encoded on quadratures of the optical field, which are measured via balanced homodyne detector (BHD). The bandwidth of the BHD is one of key parameters for precise characterization of quantum states. We establish a theoretical model to analyze the impact of the BHD bandwidth and signal modulation patterns on the channel parameters estimation of CV-QKD systems. Based on the proposed model, the secure key rate of a practical CV-QKD system under different BHD bandwidths and signal modulation patterns are investigated. Our results show that insufficient BHD bandwidth will result in wrong estimate of the transmission loss and excess noise, which significantly affects the performance of CV-QKD systems. Given the BHD bandwidth, there exists an optimal signal repetition rate that maximizes the secure key rate. The BHD bandwidth requirement of the QKD system increases with the transmission distance for large duty cycle pulse. Furthermore, the root raised-cosine pulse signal modulation performs better than the square pulse signal modulation in general.
Deep Multimodal Detection in Reduced Visibility Using Thermal Depth Estimation for Autonomous Driving

Recently, the rapid development of convolutional neural networks (CNN) has consistently improved object detection performance using CNN and has naturally been implemented in autonomous driving due to its operational potential in real-time. Detecting moving targets to realize autonomous driving is an essential task for the safety of drivers and pedestrians, and CNN-based moving target detectors have shown stable performance in fair weather. However, there is a considerable drop in detection performance during poor weather conditions like hazy or foggy situations due to particles in the atmosphere. To ensure stable moving object detection, an image restoration process with haze removal must be accompanied. Therefore, this paper proposes an image dehazing network that estimates the current weather conditions and removes haze using the haze level to improve the detection performance under poor weather conditions due to haze and low visibility. Combined with the thermal image, the restored image is assigned to the two You Only Look Once (YOLO) object detectors, respectively, which detect moving targets independently and improve object detection performance using late fusion. The proposed model showed improved dehazing performance compared with the existing image dehazing models and has proved that images taken under foggy conditions, the poorest weather for autonomous driving, can be restored to normal images. Through the fusion of the RGB image restored by the proposed image dehazing network with thermal images, the proposed model improved the detection accuracy by up to 22% or above in a dense haze environment like fog compared with models using existing image dehazing techniques.
Towards Automated Sign Language Production: A Pipeline for Creating Inclusive Virtual Humans

In everyday life, Deaf People face barriers because information is often only available in spoken or written language. Producing sign language videos showing a human interpreter is often not feasible due to the amount of data required or because the information changes frequently. The ongoing AVASAG project addresses this issue by developing a 3D sign language avatar for the automatic translation of texts into sign language for public services. The avatar is trained using recordings of human interpreters translating text into sign language. For this purpose, we create a corpus with video and motion capture data and an annotation scheme that allows for real-time translation and subsequent correction without requiring to correct the animation frames manually. This paper presents the general translation pipeline focusing on innovative points, such as adjusting an existing annotation system to the specific requirements of sign language and making it usable to annotators from the Deaf communities.
Analysis of prerequisite relation in knowledge graph using ARM and MSMM: Focusing on problem evaluation data of K-12 math

EDUCAUSE is a higher education technology association and the largest community of IT leaders and professionals committed to advancing higher education.Technology, IT roles and responsibilities, and higher education are dynamically changing.Formed in 1998, EDUCAUSE supports those who lead, manage, and use information technology to anticipate and adapt to these changes, advancing strategic IT decision-making at every level within higher education.EDUCAUSE is a global nonprofit organization whose members include US and international higher education institutions, corporations, not-for-profit organizations, and K-12 institutions.With a community of more than 100,000 individuals at member organizations located around the world, EDUCAUSE encourages diversity in perspective, opinion, and representation.For more information, please visit educause.edu.
VQEG column: Recent contributions to ITU recommendations

Welcome to the second column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG). VQEG plays a major role in research and the development of standards on video quality and this column presents examples of recent contributions to International Telecommunication Union (ITU) recommendations, as well as ongoing contributions to recommendations to come in the near future. In addition, the formation of a new group within VQEG addressing Quality Assessment for Health Applications (QAH) has been announced.
"Inventing of Macrocyclic Formazan Compounds with Their Evaluation in Nano- Behavior in the Scanning Microscope and Chromatography"

Nagham Mahmood Aljamali* Author Affiliations Organic Chemistry, Synthetic Chemistry Field, Iraq Received: January 21, 2022 | Published: February 01, 2022 Corresponding author: Nagham Mahmood Aljamali, Organic Chemistry, Synthetic Chemistry Field, Iraq DOI: 10.26717/BJSTR.2022.41.006616
Anomaly Detection in Small-Scale Industrial and Household Appliances

Anomaly detection is concerned with identifying rare events/observations that differ substantially from the majority of the data. It is considered an important task in the energy sector to enable the identification of non-standard device conditions. The use of anomaly detection techniques in small-scale residential and industrial settings can provide useful insights about device health, maintenance requirements, and downtime, which in turn can lead to lower operating costs. There are numerous approaches for detecting anomalies in a range of application scenarios such as prescriptive appliance maintenance. This work reports on anomaly detection using a data set of fridge power consumption that operates on a near zero energy building scenario. We implement a variety of machine and deep learning algorithms and evaluate performances using multiple metrics. In the light of the present state of the art, the contribution of this work is the development of a inference pipeline that incorporates numerous methodologies and algorithms capable of producing high accuracy results for detecting appliance failures.
Utilization of XR Technology in Distance Collaborative Learning: A Systematic Review

Extended reality technology are raising high hopes among researchers for its use to overcome the difficulties associated with spatial separation in collaborative learning. In this work, a systematic review of the research literature was conducted on the use of extended reality (XR) technology to support distance collaborative learning. We searched Web of Science to collect relevant articles. The articles were manually screened using the inclusion and exclusion criteria which refer to PRISMA [23]. By examining the relevant articles closely, we show the advantages of using XR technology in collaborative learning, such as increasing engagement, increasing students’ interest, and facilitating student interactions. We then analyse the challenges in the application of XR, such as skill requirements for teachers and students, lack of accessibility, and technical issues. Finally, we put forward suggestions for the future development of XR-supported distance collaborative learning environments.
Mapping features to source code based on execution trace

One of the important activities in software maintenance is to understand the software system before implementing any change in the software. Mapping software features to their relevant source code is one aspect of software comprehension. In this paper, we propose a full dynamic feature location technique to map the source code to the relevant functional features. The proposed solution aims to use execution trace to do the required mapping. The developer first uses a scenario to exercise a feature to generate the required execution trace. Second, the developer formulates a text query to describe the feature of interest in a natural language, to retrieve and rank the relevant methods from the generated execution trace. Distance software metrics are proposed to measure the distance between the raked methods. The solution is evaluated by using the ArgoUML Java case study to show the effectiveness of the proposed solution. The results of the evaluation show the accuracy of allocating methods in the source code to the related feature is increased by using the proposed methodology.
NLP-based system for automatic processing of quality demands in italian public procedure: a system engineering formalization

Natural Language Processing (NLP) is widely used to solve several tasks in different construction fields.However, there are no applications related to the predesign phase.Analysing the Italian public design call for tender procedure, possible information criticalities can be identified.The study proposes a formalization, from a System Engineering (SE) perspective, of the implementation of an NLP-based system in the pre-design phase of an Italian public procedure.SE focuses on the formalization of main stakeholders, system values and functions via IDEF0, Analytical Hierarchy Process (AHP), and Quality Function Deployment (QFD).
Implementation of the K-Means Algorithm on Learning Outcomes and Self-Regulated Learning

This study aims to apply data mining to Student Self-Regulated Learning and learning outcomes in online learning in higher education using the k-means algorithm. The cluster analysis method in this research is the k-means method. The research was conducted by taking data from students who took the Statistical course Informatics Engineering, the faculty of Computer Science at Duta Bangsa University Surakarta. The number of student data was 22 data. The results show that the number of clusters according to the k-means algorithm is 3.