Reviewerly/exHarmony huggingface dataset:
A compositional approach to probabilistic knowledge compilation

Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of many real-world use cases, that in principle can be modeled by BNs, suffers however from the computational complexity of inference. Inference methods based on Weighted Model Counting (WMC) reduce the cost of inference by exploiting patterns exhibited by the probabilities associated with BN nodes. However, these methods require a computationally intensive compilation step in search of these patterns, which effectively prohibits the handling of larger BNs. In this paper, we propose a solution to this problem by extending WMC methods with a framework called Compositional Weighted Model Counting (CWMC). CWMC reduces compilation cost by partitioning a BN into a set of subproblems, thereby scaling the application of state-of-the-art innovations in WMC to scenarios where inference cost could previously not be amortized over compilation cost. The framework supports various target representations that are less or equally succinct as decision-DNNF. At the same time, its inference time complexity O(nexp⁡(w)), where n is the number of variables and w is the tree-width, is comparable to mainstream algorithms based on variable elimination, clustering and conditioning.
Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks

Action recognition has been recognized as an activity in which individuals' behaviour can be observed. Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events. A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is proposed. Our proposal is based on the Spatial Temporal-Graph Convolutional Network (ST-GCN) framework. In this study, we proposed an improved set of label mapping methods for the ST-GCN framework. We introduce three split techniques (full distance split, connection split, and index split) as an alternative approach for the convolution operation. The experiments presented in this study have been trained using two benchmark datasets: NTU-RGB + D and Kinetics to evaluate the performance. Our results indicate that our split techniques outperform the previous partition strategies and are more stable during training without using the edge importance weighting additional training parameter. Therefore, our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.
Model-Data Integration Driven Based Power System Frequency Response Model

The power system frequency response models based on model-driven or data-driven methods have contradictions between calculation speed, accuracy, and result interpretability. Considering the strengths and disadvantages of various models in the real-time application, a model-data integration driven method is proposed. The method uses System Frequency Response (SFR) model to predict the frequency response dynamic process and uses Extreme Learning Machine (ELM) Optimized by Particle Swarm Optimization (ELM) model to correct the error of the predicted results. It can greatly improve calculation accuracy while keeping a high calculation speed. Moreover, the model-data integration driven method is less dependent on the sample data to improve the interpretability of the calculation results. The simulation on the WSCC 9-bus system verifies that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance, and has a good generalization ability. It can further provide an emergency assistance decision for the dispatching and control of the power system to prevent the frequency crash.
Design and Analysis of Uplink and Downlink Communications for Federated Learning

In this paper, we study the efficient communication design, including both uplink and downlink communications, for wireless federated learning (FL). We answer the question of what and how to communicate between clients and the parameter server and evaluate the impact of the various quantization and transmission options of the updated model on the learning performance. We provide new convergence analysis of the well-known FEDAVG under non-i.i.d. dataset distributions, partial clients participation, and finite-precision quantization in uplink and downlink communications. These analyses reveal that, in order to achieve an $\mathcal{O}(1/T)$ convergence rate with quantization, transmitting the weight requires increasing the quantization level at a logarithmic rate, while transmitting the weight differential can keep a constant quantization level. Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous – a carefully designed 1-bit quantization (3.1% of the floating-point baseline bandwidth) achieves 99.8% of the floating-point baseline accuracy at almost the same convergence rate on MNIST, representing the best known bandwidth-accuracy tradeoff to the best of the authors’ knowledge.
A Technique for Transitioning Of Plan Driven Software Development Method to Distributed Agile Software Development

In the era of twenty first century where human being is moving forward from semi – automated systems to fully automatic and machine learning based systems, software development (SD) mechanisms have also moved from static and pretended process following mechanisms to fast, ready for advices and adaptability based software development methods. Due to this traditional software development methods following heavy documentation based step by step processes have become obsolete and software development industry is transitioning towards dynamic, incremental, iterative, customer – centered, light – documentation based system development mechanisms referred as Agile Software development (ASD). The transition becomes more attention grabbing in distributed environment referred as distributed Agile software development (DASD). Certain processes like – task allocation, backlog prioritization, test case reduction and prioritization use manual processes rather than semi or fully automated processes. This methodology of transformation needs transitioning changes in existing DASD systems. In this paper we have proposed framework for transitioning of plan driven software development method to distributed Agile software development. The major area of concern is to automate the manual processes followed and convert them to semi – automated and fully – automated processes.
Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease

The chapter explores the implications of deep learning in medical sciences, focusing on deep learning concerning natural language processing, computer vision, reinforcement learning, big data, and blockchain influence on some areas of medicine and construction of end-to-end systems with the help of these computational techniques. The deliberation of computer vision in the study is mainly concerned with medical imaging and further usage of natural language processing to spheres such as electronic wellbeing record data. Application of deep learning in genetic mapping and DNA sequencing termed as genomics and implications of reinforcement learning about surgeries assisted by robots are also overviewed.
Visualization Analysis of Research on Unmanned-Platform Based Battlefield Situation Awareness

Unmanned platforms have been extensively used in the process of battlefield situation awareness (BSA). We make a visualization analysis of the research on unmanned-platform based BSA, with the CNKI (China National Knowledge Infrastructure) database mainly considered. Research on unmanned-platform based BSA has entered a rapid growth stage since 2017, and shows great development potential now. The mode of BSA is evolving to the Space-Air-Ground integrated cooperative situation awareness, and corresponding theoretical research has gradually shifted to group and autonomous perception based on intelligent unmanned platforms. Research hotspots are concentrated on the model construction, conformance assessment, battle conception and key technology discussion of unmanned-platform based collaborative BSA. Future research on unmanned-platform based BSA should focus on combat concept innovation, key technology breakthrough and practical application, so as to transform the theoretical research outcomes into combat effectiveness and further improve the existing BSA level.
SEPS: Efficient public-key based secure search over outsourced data

With the recent advancement of cloud storage services, many cloud customers (data owners) are migrating their data from local storage to cloud data storage to reduce maintenance cost. Once data is outsourced, the data owners loose control over their data, evolving various security and privacy issues. To resolve this issue, data encryption is an effective solution. Meanwhile, to increase the utilization of the encrypted outsourced data, searchable encryption would be an efficient tool capable enough to search over encrypted data. In this work, we propose a novel public-key based searchable encryption scheme (SEPS) using an inverted index data structure. We show that SEPS achieves better performance than previous efforts (both theoretically and experimentally). We prove the security of the proposed scheme using Bilinear Diffie–Hellman assumption. SEPS withstands keyword guessing attack and achieves trapdoor privacy. On the top, the index generation process of the proposed scheme is secure against chosen keyword attack. AVISPA tool is also used for the formal security verification of SEPS and found to be attack resistant.
Node Classification through Graph Embedding Techniques

The purpose of this paper is to collate advanced embedding techniques for the classification of nodes with the help of state-of-art technology named <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</sup> Deep Learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</sup> , The main agenda of this classification is to predict the most likely labels of nodes in the network. It is considered to be efficient only if the network dimension is reduced before predicting the labels. Hence, the graph embedding technique is used to reduce it to a low-dimensional network. It also helps to capture and preserve the structure of a network. Comparison is done using various available graph embedding techniques to observe the accuracy.
Enhanced DSSM (deep semantic structure modelling) technique for job recommendation

Now a day’s recommendation system take care of the issue of the massive amount of information overload problem and it provides the services to the candidates to concentrate on relevant information on job domain only. The job recommender system plays an important role in the recruitment process of fresher as well as experienced today. Existing job recommender system mainly focuses on content-based filtering to extricate profile content and on collaborative filtering to capture the behaviour of the user in the form of rating. Dynamic nature of job market leads cold start and scalability issues. This problem can be addressed by item-based collaborative filtering with a machine learning technique, it learns job embedding vector and finds similar jobs content-wise. Existing model in job recommender domain uses the confining model to address the cold start and scalability issue and provide better recommendation, but they fail to accept the complex relationships between job description and candidate profile. In this paper, we are proposing a Deep Semantic Structure Algorithm that overcome the issue of the existing system. Deep semantic structure modelling (DSSM) system uses the semantic representation of sparse data and it represent the job description and skill entities in character trigram format which increases the efficacy of the system. We are comparing the results to three variation of DSSM model with two different dataset (Naukari.com and CareerBuilder. com) and it gives satisfactory results. Experimental results shows that the DSSM Embedding model and its other variants are provides promising results in solving cold start problem in comparison with several variants of embedding model. We used Xavier initializer to initialise the model parameter and Adam optimizer to optimize the system performance.
Potent Real-Time Recommendations Using Multimodel Contextual Reinforcement Learning

Widespread digitalization has led to almost all utilities and services thrive on an online medium. A real-time, personalized, and trend grasping recommendation system is necessary to enhance user experience and boost business on E-commerce platforms. We propose the Multimodel Contextual Reinforcement Learning (MMCR) constituting three novel features for real-time and customized recommendations. The first feature is user-item interactive state embedding which uses not only item information but also assigns weightage to this information according to its usage history. It gives higher importance to the newly clicked items by the users than the older ones. Second, we devised Contextual Cluster Exploration (CCE) strategy. This strategy enhances the item-choice recommendations by consistently reducing the randomness during exploration. The third novelty is an item-based multi-agent framework that can tackle the case of sparsely chosen items. Generally, such items are disregarded in a single agent model as the more popular items take supremacy. Our technique ensures that the user-item history per item is learned separately; thus, no item is neglected. MMCR has shown an average of 5% increase in CTR rate. Moreover, CCE exploration gives a considerably higher score than state-of-the-art exploration strategies. Thorough experimentation demonstrates that our proposed strategy has shown significantly improved results over various state-of-the-art strategies.
Classifying Wi-Fi from Raw Power Measurements using a Neural Network Adapted from WaveNet

Research into identifying coexisting wireless devices and technologies has been growing in importance as the number of wireless devices increase every year. While many various methods have been proposed over the years, most of them usually rely on using features from the frequency domain. These can usually be distinguishable among different wireless standards. The feature space in the frequency domain is usually limited but less complex than those in the time domain. However, when trying to identify wireless devices that use different specifications of the same Wi-Fi standard, utilizing the frequency domain feature is difficult as the features are too similar between specifications.In this paper, we investigate utilizing a neural network to identify the Wi-Fi standard using raw power measurements. More specifically, we adapt the WaveNet model to take advantage of its capability to handle time series data with thousands of timesteps, an advantage that both recurrent neural networks (RNN) and long short-term memory (LSTM) networks do not share. Wi-Fi signals are collected across versions 802.11n, 802.11ac, and 802.11ax both individually and with multiple standards coexisting across a range of different throughputs. The data is then pre-processed and used to train a neural network adapted from the WaveNet model. Results indicate that high accuracy detection can be achieved by utilizing this method.
Ontology and Reinforcement Learning Based Intelligent Agent Automatic Penetration Test

Penetration testing (PT) is the best method for vulnerabilities assessment and evaluating the security of the system under test, by planning and generating possible attack exploits, consist of a series of complex and time consuming trial-and-error stages. Many automated Pentest tools have made. Current Reinforce Learning (RL) based PT tools can do systematic and regular tests to save human resources. Without the aid of prior knowledge, RL-based penetration is somehow more like brute-force test. In this paper, we propose a novel ontology based BDI-agent RL automatic PT framework. By combining SWRL penetration testing knowledge base and RL in a BDI (belief-desire-intention) agent, the proposed model can make use of the ontology based knowledge base (prior knowledge) to optimize the planning problem in the uncertain and dynamic environment. Finally, the simulation on ASL simulation platform Jason proved the new BDI-agent auto-PT model can improve the accuracy and speed performance.
Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.
Relation-Based Deep Attention Network with Hybrid Memory for One-Shot Person Re-Identification

One-shot person Re-identification, which owns one labeled sample among numerous unlabeled data for each identity, is proposed to tackle the problem of the shortage of labeled data. Considering the scenarios without sufficient labeled data, it is very challenging to keep abreast of the performance of the supervised task in which sufficient labeled samples are available. In this paper, we propose a relation-based attention network with hybrid memory, which can make full use of the global information to pay attention to the identity features for model training with the relation-based attention network. Importantly, our specially designed network architecture effectively reduces the interference of environmental noise. Moreover, we propose a hybrid memory to train the one-shot data and unlabeled data in a unified framework, which notably contributes to the performance of person Re-identification. In particular, our designed one-shot feature update mode effectively alleviates the problem of overfitting, which is caused by the lack of supervised information during the training process. Compared with state-of-the-art unsupervised and one-shot algorithms for person Re-identification, our method achieves considerable improvements of 6.7%, 4.6%, and 11.5% on Market-1501, DukeMTMC-reID, and MSMT17 datasets, respectively, and becomes the new state-of-the-art method for one-shot person Re-identification.
Chinese Relation Extraction Using Extend Softword

In recent years, many scholars have chosen to use word lexicons to incorporate word information into a model based on character input to improve the performance of Chinese relation extraction (RE). For example, Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed the MG-Lattice model in 2019 and achieved state-of-the-art (SOTA) results. However, MG-Lattice still has the problem of information loss due to its model structure, which affects the performance of Chinese RE. This paper proposes an adaptive method to include word information at the embedding layer using a word lexicon to merge all words that match each character into a character input-based model to solve the information loss problem of MG-Lattice. The method can be combined with other general neural system networks and has transferability. Experimental studies on two benchmark Chinese RE datasets show that our method achieves an inference speed up to 12.9 times faster than the SOTA model, along with a better performance. The experimental results also show that this method combined with the BERT pretrained model can effectively supplement the information obtained from the pretrained model, further improving the performance of Chinese RE.
Multi-Stream Adaptive Graph Convolutional Network Using Inter- and Intra-Body Graphs for Two-Person Interaction Recognition

Two-person interaction recognition has become an area of growing interest in human action recognition. The graph convolutional network (GCN) using human skeleton data has been shown to be highly effective for action recognition. Most GCN-based methods focus on recognizing an individual person's actions on the basis of an intra-body graph. However, many of these methods do not represent the relation between two bodies, making it difficult to accurately recognize human interaction. In this work, we propose multi-stream adaptive GCN using inter- and intra-body graphs (MAGCN-IIG) as a new method of human interaction recognition. To ensure highly accurate human interaction recognition, our method cooperatively utilizes two types of graphs: an inter-body graph and an intra-body graph. The inter-body graph, which is newly introduced in this paper, connects the inter-body joints between two people as well as intra-body connections. The adaptive GCN using the inter-body graph captures the relation of joints between two people, even different types of joints located far away from each other. Further, by implementing a multi-stream architecture, our method simultaneously captures both inter-body and intra-body relations in each of two units that represent the position and motion of people. Experiments on interaction recognition using two large-scale human action datasets, NTU RGB+D and NTU RGB+D 120, showed that our method recognized human interactions more accurately than state-of-the-art methods.
MuzLink: Connected beeswarm timelines for visual analysis of musical adaptations and artist relationships

The rise of open data in the cultural domain is democratizing access to complex datasets usually presented as large multivariate and multilayered graphs. However, the exploration of such datasets is challenging for laypersons. The objective of this work is to develop and evaluate a new method for exploring and understanding a specific type of multilayered graph that combines a large bipartite graph with a set of tree structures. This paper proposes MuzLink, an interactive visualization tool that allows the user to navigate, search, locate, and compare collaborative and influential relationships between musical artists through the exploration of musical adaptations. The proposed tool is based on a set of connected timelines visualizing how an artist’s collaborations, inspirations, and influences evolved over time. This design study is conducted in close collaboration with BAnQ, the national library and archives agency of the Quebec government. A controlled user study, done with a group of BAnQ users, and two case studies, show how the proposed approach is capable of performing a considerable set of analytical and exploratory tasks.