Reviewerly/exHarmony huggingface dataset:
Variational quantum process tomography of unitaries

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to $8$ qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least $2$ orders of magnitude less than required by the standard quantum process tomography.
Growing Echo State Network With an Inverse-Free Weight Update Strategy

An echo state network (ESN) draws widespread attention and is applied in many scenarios. As the most typical approach for solving the ESN, the matrix inverse operation of high computational complexity is involved. However, in the modern big data era, addressing the heavy computational burden problem is necessary. In order to reduce the computational load, an inverse-free ESN (IFESN) is proposed for the first time in this article. Besides, an incremental IFESN is constructed to attain the network topology with theoretical proof on the training error's monotone decline property. Simulations and experiments are conducted on several numerical and real-world time-series benchmarks, and corresponding results indicate that the proposed model is superior to some existing models and possesses excellent practical application potential. The source code is publicly available at https://github.com/LongJin-lab/the-supplementary-file-for-CYB-E-2021-04-0944.
Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment.
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining

High-quality phrase representations are essential to finding topics and related terms in documents (a.k.a.topic mining).Existing phrase representation learning methods either simply combine unigram representations in a contextfree manner or rely on extensive annotations to learn context-aware knowledge.In this paper, we propose UCTOPIC, a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining.UCTOPIC is pretrained in a large scale to distinguish if the contexts of two phrase mentions have the same semantics.The key to pretraining is positive pair construction from our phrase-oriented assumptions.However, we find traditional in-batch negatives cause performance decay when finetuning on a dataset with small topic numbers.Hence, we propose cluster-assisted contrastive learning (CCL) which largely reduces noisy negatives by selecting negatives from clusters and further improves phrase representations for topics accordingly.UCTOPIC outperforms the state-of-the-art phrase representation model by 38.2% NMI in average on four entity clustering tasks.Comprehensive evaluation on topic mining shows that UCTOPIC can extract coherent and diverse topical phrases.
A Comparative Study of Faithfulness Metrics for Model Interpretability Methods

Interpretation methods to reveal the internal reasoning processes behind machine learning models have attracted increasing attention in recent years.To quantify the extent to which the identified interpretations truly reflect the intrinsic decision-making mechanisms, various faithfulness evaluation metrics have been proposed.However, we find that different faithfulness metrics show conflicting preferences when comparing different interpretations.Motivated by this observation, we aim to conduct a comprehensive and comparative study of the widely adopted faithfulness metrics.In particular, we introduce two assessment dimensions, namely diagnosticity and time complexity.Diagnosticity refers to the degree to which the faithfulness metric favours relatively faithful interpretations over randomly generated ones, and time complexity is measured by the average number of model forward passes.According to the experimental results, we find that sufficiency and comprehensiveness metrics have higher diagnosticity and lower time complexity than the other faithfulness metrics.
The Application of Improved Harmony Search Algorithm to Multi-UAV Task Assignment

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints.
Transfer learning for estimating occupancy and recognizing activities in smart buildings

Activities Recognition (AR) and Occupancy Estimation (OE) are vital to many smart systems that work on providing good services in smart buildings. Many applications, such as energy management need information like activities and occupancy to provide good assistance. Most of the previous research about AR and OE focused on applying supervised machine learning methods. Researchers train a model and evaluate it using data collected from the same environment (Domain). A model trained in a specific domain will not generalize well in other domains. Creating a trained model to every environment is not feasible due to the lack of data. Collecting sufficient data can be time consuming and infeasible in some cases. Computational power can be a challenge for researchers by increasing the training time due to the lack of the required computing resources. Using traditional machine learning methods, the obtained performance may be unsatisfactory, and can not lead to optimal solutions. For all these reasons, we need a solution that helps us overcome the stated problem and obtain models with acceptable results. In this work, we present and discuss different transfer learning methods that help us transfer knowledge from a source domain to a target domain. The goal is to reuse as much as possible information from the source domain to enhance the performance of the model at the target domain. This type of approaches will solve the problems mentioned before such as the lack of data and will provide us with good results due to the use of knowledge from multiple source domains. We tested five Transfer learning (TL) approaches: a principal component analysis (PCA)-like method that creates a transformation like the PCA transformation and apply it to the data to create new common domain, a PCA based method that creates common domain using PCA, a PCA-SMOTE method that balances the data and creates common domain, a basic method based on a simple matching between similar features from source and target domain, and a sparse coding-based method that creates a common domain where the data representation will be as sparse as possible. The impressive results that we obtained in both tasks prove that the presented methods can be applied to transfer knowledge across different domains.
Towards an energy-efficient Data Center Network based on deep reinforcement learning

Data Center Network (DCN) plays a crucial role in orchestrating the physical or virtual resources in data centers to meet the requirements of Internet of Things and Cloud Computing. The energy efficiency should be seriously considered for DCNs with large-scale switch devices which support numerous realtime network flow demands, especially for enormous flow demands from IoT devices. Typically, the network energy conservation can be achieved by optimizing routing and flow scheduling with energy awareness, targeting at powering off as many idle and low-loaded network devices as possible. For energy efficiency objective, in this paper we address a combinatorial optimization problem, named Multi-Commodity Flow (MCF) problem which optimizes the bandwidth allocation and routing to reduce the energy consumption. We propose a framework which has the lookahead ability of predicting flow demands in DCNs to dynamically feed the MCF problem as inputs. A Long Short-Term Memory (LSTM) network is exploited for flow demand prediction in DCNs and a Deep Reinforcement Learning (DRL) algorithm is tailored for solving the MCF problem. In experiments, we evaluate the predicted flow demands which simulate real flow demands and conduct a comparison between our DRL scheme with the baseline and optimizer to show the advantage of the DRL solution in optimality and efficiency.
A case study of using the hybrid model of scrum and six sigma in software development

&lt;span&gt;The world of software engineering is constantly discovering new ways that lead to an increase in team performance in the production of software products and, at the same time, brings the customer's further satisfaction. With the advent of agile methodologies in software development, these objectives have been considered more seriously by software teams and companies. Due to their very nature, agile methodologies have the potential to be integrated with other methodologies or specific managerial approaches defined in line with agility objectives. One of the cases is Six Sigma, which is used in organizations by focusing on organizational change and process improvement. In the present study, attempts were made to present the hybrid software development approach, including Scrum, as the most common agile and Six Sigma methodology. This approach was practically used in a case study, and the obtained results were analyzed. The results of this evaluation showed that this hybrid method could lead to the increased team performance and customer satisfaction. However, besides these two achievements, an increase in the number of re-works, number of defects discovered, and the duration of the project implementation were also observed. These cases are in line with the main objectives of Scrum and Six Sigma and are justifiable and acceptable due to achieving those objectives.&lt;/span&gt;
Blockchain Technology and its Types—A Short Review

Blockchain Technology is the emerging component of IT and Computing and is a kind of encrypted record of data works on distributed database.This is furthermore dealt with the data related to the transaction, contract, independent record, etc.Digital ledger is important and also applicable in various platforms, not bounded in a particular place; and required in financial activities and services and hence it is worthy in healthy digital currency and transactions.Financial transaction is not associated with the third party and it keeps the data encryption and here participants no need to share personal data and thus Blockchain Technology is associated with the data breach.Insignificantly Blockchain not only reduces the data breach but also supports the multiple numbers of shared copies and it works on the same database.It helps in wage a data breach attack.Earlier it was known as Blockchain but gradually it is treated as a Technology and considered as important in Information Technology and impacting the financial sector rapidly.Blockchain Technology is required in various tangible and intangible asset management and required in tracking including recorded effectively within a network and ledger.Blockchain is therefore is a tool, a technique, as well as a procedure for sophisticated financial management.This paper is based on existing literature emphasizing Blockchain Technology with features, characteristics with a focus on its types.
The impact of artificial intelligence, big data analytics and business intelligence on transforming capability and digital transformation in Jordanian telecommunication firms

The major purpose of this research is studying the impact of artificial intelligence, big data analytics and business intelligence on transforming capability and digital transformation in Jordanian telecommunication firms. 303 samples were gathered after the sample checking process was completed. The PLS program was utilized in the research study to process all the collected data. The findings of the research demonstrate a set of relationships and linkages that can enhance transforming capability and digital transformation. Finally, this study found that artificial intelligence, big data analytics and business intelligence have good effects on developing and enhancing the transforming capability and digital transformation.
Exploring Neural Models for Query-Focused Summarization

Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.While recently released datasets, such as QMSum or AQua-MuSe, facilitate research efforts in QFS, the field lacks a comprehensive study of the broad space of applicable modeling methods.In this paper we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models.Within those categories, we investigate existing models and explore strategies for transfer learning.We also present two modeling extensions that achieve state-of-the-art performance on the QMSum dataset, up to a margin of 3.38 ROUGE-1, 3.72 ROUGE-2, and 3.28 ROUGE-L when combined with transfer learning strategies.Results from human evaluation suggest that the best models produce more comprehensive and factuallyconsistent summaries compared to a baseline model.Code and checkpoints are made publicly available: https://github.com/salesforce/query-focused-sum.
Skeleton-Based ST-GCN for Human Action Recognition With Extended Skeleton Graph and Partitioning Strategy

Skeleton-based Graph Convolutional Networks (GCN) for human action and interaction recognition have received considerable attention of researchers due to its compact and view-invariant nature of skeleton data.However, the static skeleton graph topology in conventional GCNs does not reflect the implicit relationships of non-adjacent joints, which contain vital latent information for a skeleton pose in an action sequence.Moreover, traditional tri-categorical node partitioning strategy discards much of the motion dependencies along temporal dimension for non-physically connected edges.We propose an extended skeleton graph topology along with extended partitioning strategy to extract much of the nonadjacent joint relational information in the model for robust discriminative features.Extended skeleton graph represents joints as vertices and weighted edges represent intrinsic and extrinsic relationships between physically connected and non-physically connected joints respectively.Furthermore, extended partitioning strategy divides the input graph for GCN as five-categorical fixed-length tensor to encompass maximal motion dependencies.Finally, the extended skeleton graph and partitioning strategy are realized by adopting Spatio-Temporal Graph Convolutional Network (ST-GCN).The experiments carried out over three large scale datasets NTU-RGB+D, NTU-RGB+D 120 and Kinetics-Skeleton show improved performance over conventional state-of-the-art ST-GCNs.
Technological forecasting and social change introduction to the VSI technological innovations to ensure confidence in the digital world

The digital world is increasing humanity's ability to acquire, produce, distribute, and consume information at unprecedented levels, raising questions about the origin, reliability, and use. This introduction is related to the special issue on Technological Innovations to Ensure Confidence in the Digital World, which will bring together relevant papers about technological innovations to ensure confidence in the digital world. The papers included in the special issue focus on trust issues in the adoption of digital innovations, such as the Internet of Things, Big Data, cloud computing, and digital platforms, as well as on information distribution processing in the digital age and how it affects financial market prices.
A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing.At all levels, load balancing should also be implemented in an efficient manner, to increase system performance.In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment.The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state.From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach.In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities.Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing.Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier

Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling problem which aims at separating the foreground object from the background in the video without using the ground truth (GT) mask of the foreground object. Most of the previous UVOS models use the first frame or the entire video as a reference frame to specify the mask of the foreground object. Our question is why the first frame should be selected as a reference frame or why the entire video should be used to specify the mask. We believe that we can select a better reference frame to achieve the better UVOS performance than using only the first frame or the entire video as a reference frame. In our paper, we propose Easy Frame Selector (EFS). The EFS enables us to select an "easy" reference frame that makes the subsequent VOS become easy, thereby improving the VOS performance. Furthermore, we propose a new framework named as Iterative Mask Prediction (IMP). In the framework, we repeat applying EFS to the given video and selecting an "easier" reference frame from the video than the previous iteration, increasing the VOS performance incrementally. The IMP consists of EFS, Bi-directional Mask Prediction (BMP), and Temporal Information Updating (TIU). From the proposed framework, we achieve state-of-the-art performance in three UVOS benchmark sets: DAVIS16, FBMS, and SegTrack-V2.
Gesture-Controlled User Interfaces, What Have We Done and Whats Next?

Abstract: To meet the challenges of present computing, researchers working towards break away from traditional types of interaction. This paper identifies new trends in technology, it’s uses and advantage. We concluded that Gesture controlled user interface gives realistic opportunities especially for those users who are uncomfortable with commonly used devices. It’s appropriate for older and disabled people. Gesture controlled user interface has some potential to improve quality of life especially for older and disabled people along with general users. Keyword: Gesture control, user interfaces, elderly, appearing and ambient devices
Convolutional neural network architecture based automatic face mask detection

The pandemic of COVID – 19 has affected the whole world very badly. It has rapidly affected the way of living as wearing a protective or surgical face mask is new normal. It is necessary to wear a mask before entering a shop or any public place to avail of their services. Therefore, there is a need for face mask detection to help society. In this paper, we are presenting a simplified technique that detects whether a person is wearing a mask or not automatically with percentage accuracy of the fitment of the mask over the face. This technology can be used to stop the entry or warn the person to wear a mask properly before or while entering a shop or any public place. This purpose is achieved using OpenCV, Keras packages and convolutional neural network architecture (MobileNetV2). The accuracy of the face mask detection system is 96.07%.
Fuzzy Logic Based Client Selection for Federated Learning in Vehicular Networks

Federated learning is a promising paradigm for achieving distributed intelligence by protecting user privacy in vehicular networks.Considering limited computing and communication resources, it is important to select appropriate clients from a huge number of users to participate in the training process.In vehicular networks, the problem of choosing proper clients is particularly complex due to the heterogeneity of network users, including the differences in the data, computation capability, available throughput, and samples freshness.We design a fuzzy logic based client selection scheme to address this issue.The proposed scheme considers the number of local samples, samples freshness, computation capability, and available network throughput based on a fuzzy logic approach.Extensive simulation results show that the proposed scheme outperforms other baselines.