Reviewerly/exHarmony huggingface dataset:
Timed Concurrent Language for Argumentation: An Interleaving Approach

Time is a crucial factor in modelling dynamic behaviours of intelligent agents: in a real-world environment, activities have a determined temporal duration and the behaviour of agents is influenced by the actions previously taken. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents both to communicate and to reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps (with maximal parallelism for time elapsing). Following this approach, at each moment only one of the enabled agents is executed.
Security Analysis of a Next Generation TF-QKD for Secure Public Key Distribution with Coherent Detection over Classical Optical Fiber Networks

Twin-field quantum key distribution (TF-QKD) has recently attracted attention for its ability to overcome the fundamental limits of secret key rate for point-to-point connectivity without quantum repeaters in QKD. Coherent-based TF-QKD or CTF-QKD, which utilizes coherent states for both transmissions and detections, has allowed systems to be designed for existing optical fiber communication networks allowing for improved performance compared to conventional QKD. Here, we report a theoretical study of CTF-QKD security from an eavesdropper. Compared to conventional QKD and TF-QKD systems, CTF-QKD system is not limited to using only single photon sources/detectors allowing this system to have comparable performance and range as current optical fiber networks. Using commercially available simulation software, we verify the efficacy by studying three different preventative measures for different modulation formats to prevent an eavesdropper from obtaining any secret key information. Results show that parameters can be limited to prevent an eavesdropper from obtaining any information. The simulation also demonstrates that the photon number-phase uncertainty principle for coherent states starts to play major role of security at 128-QAM modulation. Additional security measures are also described to detect the presence of an eavesdropper and improve the system integrity.
A Modified Deep Q-Learning Algorithm for Control of Two-qubit Systems

Quantum control refers to the manipulation of dynamical quantum systems to force them to complete given tasks such as preparing a desired state and tracking a designed trajectory. We consider the state preparation problem of a two-qubit closed quantum system from an initial to a desired state. The aim is to achieve a high fidelity in a given fixed time with limited control resources. The Deep Q-learning (DQL) for solving the quantum state preparation problem is explored in this paper. We propose a novel semi-Markov DQL algorithm based on a modified action selection procedure and improved replay memory to enhance performance of standard DQL algorithm. The proposed algorithm shows high performance for discovering high-fidelity control protocols and for converging to a good policy, compared with standard DQL. The proposed modifications enhance the exploration-exploitation ability for DQL agent and the robustness for solving quantum control problem with high-fidelity at different numbers of control steps. Numerical results on a two-qubit closed system show effectiveness of the proposed algorithm.
Outsourced Mutual Private Set Intersection Protocol for Edge-Assisted IoT

The development of edge computing and Internet of Things technology has brought convenience to our lives, but the sensitive and private data collected are also more vulnerable to attack. Aiming at the data privacy security problem of edge-assisted Internet of Things, an outsourced mutual Private Set Intersection protocol is proposed. The protocol uses the ElGamal threshold encryption algorithm to rerandomize the encrypted elements to ensure all the set elements are calculated in the form of ciphertext. After that, the protocol maps the set elements to the corresponding hash bin under the execution of two hash functions and calculates the intersection in a bin-to-bin manner, reducing the number of comparisons of the set elements. In addition, the introduction of edge servers reduces the computational burden of participating users and achieves the fairness of the protocol. Finally, the IND-CPA security of the protocol is proved, and the performance of the protocol is compared with other relevant schemes. The evaluation results show that this protocol is superior to other related protocols in terms of lower computational overhead.
Performance analysis of complex pseudo-spectrum based velocity filtering for fluctuating target track-before-detect

Complex pseudo-spectrum (CPS) based track-before-detect (TBD) method is capable of improving the detection performance through the use of phase information in integration process. However, existing CPS based approach is derived for non-fluctuating target detection. Its analysis and design are not suitable for fluctuating target detection. This paper considers multiframe detection of fluctuating targets via CPS based velocity filtering in complex Gaussian noise. The common Swerling 1 and 3 models are used to describe target amplitude fluctuation from scan to scan. The theoretical output envelope of the fluctuating target is derived in detail. The velocity filter bank of CPS based approach for fluctuating targets is investigated and designed according to the derived target envelope. A numerical strategy is provided for filter bank design in the case of target fluctuation. The theoretical receiver operating characteristic (ROC) curves in the case of Swerling 1 and 3 fluctuations are derived in detail, respectively. Simulation experiments are provided to evaluate the validity of CPS based TBD in dealing with Swerling 1 and 3 types of fluctuating targets.
An Explainable Person-Job Fit Model Incorporating Structured Information

As the number of online job postings and users grows dramatically, the accuracy and explainability of personjob fit systems are of increasing concern. An explainable personjob fit system can show reasons when making recommendations to both Human Resources and Job Seekers, building trust between uses and recommendation system while providing accurate recommendation results. However, the existing research on content-based person-job fit mainly focuses on 1) dealing with unstructured statements without effectively using structured information in resumes and jobs, and 2) the explanations of the model stay at the level of giving a few sentences, which leads to a lack of explanations. In this paper, we propose an explainable person-job fit model based on the attention mechanism. We model the resume text through a hierarchical attention mechanism and capture the semantic connections between the resume, structured job text, and unstructured job text through a collaborative attention mechanism to better model the job content and provide both structured and unstructured levels of recommendation explanation. Experiments on a large real dataset show that our model outperforms existing baseline models and provides job recommendation reasons at both levels.
The Forgotten Cryptocurrencies: Beyond Bitcoin

Cryptocurrencies are receiving massive attention from both the professional and academic worlds. Since the cryptocurrency crash in early 2018, scepticism has originated around them. Today, it seems that the market is gaining confidence in cryptocurrencies. But, are all cryptos the same? Is it all about Bitcoin? If not, what makes them different from each other? Supply limit, maximum block size, forks, validation algorithms, mean block time or block rewards are just examples of the differences between some of the more than 1,600 cryptocurrencies that exist today. The grey area that surrounds these new currencies is something that is still to be covered in the academic world. Users and firms willing to make use of blockchain’s currencies should understand the implications, advantages and disadvantages when betting on their cryptocurrency choice. This survey helps answer these questions and others, through an examination of the most important cryptos in blockchain’s biggest networks.
A location privacy protection method in spatial crowdsourcing

Spatial crowdsourcing is widely used in our daily life, via applications such as DiDi, Uber. With the popularity of smart phone, this paradigm will be more and more popular. However, the popularity of crowdsourcing has increased concerns about the user's privacy. Without adequate privacy protection, no one will accept the task of crowdsourcing. To address the problem above, a new location privacy protection method is proposed in this paper. The method proposed in this paper can not only protect the user's location privacy, but also protect the crowdsourcing task's location privacy. Compared with others, the success rate of task allocation is higher and the travel distance of crowdsourcing workers is shorter. First of all, the coordinates of the worker's location are converted to polar coordinates, and the differential privacy transformation is performed on the location record of polar coordinates. Less noise is added to the polar radius, and more noise is added to the polar angle, which can improve the utility of the sanitized dataset. Finally, the crowdsourcing server allocates the tasks to the crowdsourcing workers according to the sanitized dataset. Experiments are conducted on two real-world datasets to verify its performance. The experimental results show that this method has the advantage of less information loss.
HA<sup>2</sup>RS: HTTP Adaptive Augmented Reality Streaming System

This paper presents a novel HTTP adaptive augmented reality (AR) streaming system. The goal of the proposed system is to provide high quality AR streaming services with low latency through a time-varying wireless network. In the proposed system, we employ a progressive mesh technique to enhance the scalability of AR streaming services, and adopt the metafile structure to support various attributes of progressive meshes necessary for rendering, displaying, and scheduling according to wireless network conditions. Based on this metafile, the proposed system requests the next chunk among AR contents to improve human visual perceptual quality considering the displaying scale values of AR contents on the screen. The proposed system is fully implemented by using well-known open sources (e.g., Android, OpenCV, OpenGL, and Nexus) and C/C++. Finally, the system is examined in real wireless network environments.
Towards Privacy-Friendly Smart Products

Smart products, such as toy robots, must comply with multiple legal requirements of the countries they are sold and used in. Currently, compliance with the legal environment requires manually customizing products for different markets. In this paper, we explore a design approach for smart products that enforces compliance with aspects of the European Union’s data protection principles within a product’s firmware through a toy robot case study. To this end, we present an exchange between computer scientists and legal scholars that identified the relevant data flows, their processing needs, and the implementation decisions that could allow a device to operate while complying with the EU data protection law. By designing a data-minimizing toy robot, we show that the variety, amount, and quality of data that is exposed, processed, and stored outside a user’s premises can be considerably reduced while preserving the device’s functionality. In comparison with a robot designed using a traditional approach, in which 90% of the collected types of information are stored by the data controller or a remote service, our proposed design leads to the mandatory exposure of only 7 out of 15 collected types of information, all of which are legally required by the data controller to demonstrate consent. Moreover, our design is aligned with the Data Privacy Vocabulary, which enables the toy robot to cross geographic borders and seamlessly adjust its data processing activities to the local regulations.
Cascaded Cross-Layer Fusion Network for Pedestrian Detection

The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.
A detection method of individual fare evasion behaviours on metros based on skeleton sequence and time series

Fare evasion behaviours in metro stations happen all the time with the continuous improvement of the metro network, leading to likely mass panic and unhealthy effect on the metro operation. This paper proposes an approach of detecting individual fare evasion behaviours. The method first estimates human pose by the algorithm of OpenPose to obtain human skeleton information and conducts multi-target tracking by the algorithm of Pose Flow to obtain human skeleton sequence. It then extracts features from the human pose, followed by the adoption of the relative distance, the angle and Random Forests to establish a fare evasion behaviour detection model. Since missing problems and false detections occur in detections in a single frame, time series are used to increase the accuracy. The output of the time series curve is processed by One-Hot Coding, Kalman Filtering and optimum threshold to obtain the final prediction of human status. Extensive experiments are conducted, and results show that our method can effectively recognize the human status and detect individual fare evasion behaviours, including jumps and squats.
Exact Time Evolution of Genuine Multipartite Correlations for N-Qubit Systems in a Common Thermal Reservoir

We investigate the dynamical evolution of genuine multipartite correlations for N-qubits in a common reservoir considering a non-dissipative qubits-reservoir model. We derive an exact expression for the time-evolved density matrix by modeling the reservoir as a set of infinite harmonic oscillators with a bilinear form of interaction Hamiltonian. Interestingly, we find that the choice of two-level systems corresponding to an initially correlated multipartite state plays a significant role in potential robustness against environmental decoherence. In particular, the generalized W-class Werner state shows robustness against the decoherence for an equivalent set of qubits, whereas a certain generalized GHZ-class Werner state shows robustness for inequivalent sets of qubits. It is shown that the genuine multipartite concurrence (GMC), a measure of multipartite entanglement of an initially correlated multipartite state, experiences an irreversible decay of correlations in the presence of a thermal reservoir. For the GHZ-class Werner state, the region of mixing parameters for which there exists GMC, shrinks with time and with increase in the temperature of the thermal reservoir. Furthermore, we study the dynamical evolution of the relative entropy of coherence and von-Neumann entropy for the W-class Werner state.
Scientific Landscape of Publications in Natural Language Processing in the ASEAN Region on COVID-19: A Bibliometric Approach

This study presents the quantitative and qualitative analyses of NLP scientific literature during the COVID-19 pandemic in the ASEAN region that used machine and/or deep learning approaches. From the 55 Scopus-indexed documents, 34 were manually identified and included in the study. The analyses reveal that 61.8 percent of these are journal articles and 38.2 percent are conference papers. Research productivity leaders in the ASEAN are Singapore for citation density; Indonesia for volume of work; and Malaysia for network strength. Thailand must fortify its link strength, but it poses as a research powerhouse with its robust number of citations. There is a trend in collaborative work with non-ASEAN countries, specifically with China, Saudi Arabia, Japan and the United States, and both local and international research funding sponsorships help increase research outputs. Prevailing themes are COVID-19 pandemic, social media, and sentiments on social media pertaining to the pandemic. More research funding sponsorships will close the research productivity gap among ASEAN countries. Tapping other sources of data, verifying social media perceptions using other sources of sentiments, and using Application Interface (API) will further enrich research on the use of machine learning (ML) and deep learning (DL) methodologies in the NLP research in the ASEAN during the COVID-19 pandemic.
Towards activity recognition in smart homes using multimodal data

Recognizing the activities of residents of a Smart Home is an essential prerequisite to derive their intentions and typical behaviors and, subsequently, to adapt the functions of the Smart Home to these. In this paper, we describe an experimental setup for Human Activity Recognition using a special multisensor platform. In the course of the experiment, sensor data and the associated activity labels are acquired and aggregated into a comprehensive dataset. This data will be used to train and validate different machine learning models that analyze the data in real time. These models will be used to perform realtime streaming predictions of activities on an edge device that is located in the home of the user. The goal is to provide activity and context recognition as a basic service for subsequent action routines. Possible use cases can be found in the areas of comfort management and assisted living for, e.g., elderly people.
Chatbot for Virtual Travel Assistant with Random Forest and Rapid Automatic Keyword Extraction

The purpose of this research is to create a web- based Chatbot application by implementing the Random Forest (RF) algorithm and Rapid Automatic Keyword Extraction (RAKE). The chatbot application will be used to become a virtual tour guide regarding tourist objects in Bandung, Indonesia. This is because the need for information is very important for most people to help and accelerate the work to be done. The data used in this research is a set of data in the form of questions as many as 192 data. The dataset is divided into training and testing data of 150 and 42 data. RF algorithm will work as an intent classifier, and RAKE will work as an entity recognizer to determine the answer to the user. The classification model is evaluated using training and testing data scenarios after the pre-processing process, such as case folding, removing regular expression, word tokenizing, and stop-words removal. The evaluation using the confusion matrix shows that an average accuracy value of the chatbot application using RF and RAKE is 98%, the precision value is 96%, and a recall value is 96%.
Learning Internet of Things by using Augmented Reality

This research is to find an Augmented Reality (AR) apps that suitable use for education purpose. Today, in school, students and teachers have been introduce to Internet of Thing (IoT) in technology design subject. It has considered using technology application can motivate and increase performance and student achievement in their learning units.
Convolutional Variational Autoencoders for Image Clustering

The problem of data clustering is one of the most fundamental and well studied problems of unsupervised learning. Image clustering, refers to one of the most challenging specifications of clustering, concerning image data. Thankfully, the emerging Deep Neural Networks, and in particular Deep Autoencoders lead to the automation of image clustering, which until recently, was time consuming and labor intensive. However, the effect of the consideration of local structure during feature extraction from a Variational Autoencoder on clustering, is still an unstudied subject in the literature, while simultaneously constitute a baseline approach for supervised learning (Convolutional Neural Networks). For this reason, the methodology proposed in this paper, is composed from a Variational Autoencoder (VAE) surrounded by a convolutional network in a symmetric way. The resulting embedded image data are fed to various established clustering algorithms to examine clustering performance. In addition, we propose a modification of this approach, able to reduce complexity while achieving similar or even better clustering performance. Finally, we investigate the combination of VAE&#x2019;s produced embedding and manifold learning for image clustering. The extensive experimental analysis, verified the importance of the proposed methodology, exposing the potential for further developments.
Dynamic Convolution With Global-Local Information for Session-Invariant Speaker Representation Learning

Various mismatchedconditions result in performance degradation of the speaker verification (SV) systems. To address this issue, we extract robust speaker representations by devising a global-local information-based dynamic convolution neural network. In the proposed method, both global and local information of the input features are exploited to dynamically modify the convolution kernel values. This increases the model capability of capturing speaker characteristics by compensating both the inter- and intra-session variabilities. Extensive experiments on four publicly available SV datasets show significant and consistent improvements over the conventional approaches. The effectiveness of the proposed method is further investigated using ablation studies and visualizations.
Classical and Deep Learning Data Processing Techniques for Speech and Speaker Recognitions

Both information of context and speaker-specific information are involved in the speech utterance in the form of its features; hence, speech is noticed as a potential human behavioral trait to cope with various smart systems. Influence of speech-driven devices, whether it is speech recognition based or speaker recognition based, can be marked at various places in today's life. This chapter discusses the private and public sources of speech data that contributes in the research and development of these systems. Speech and speaker recognition systems make use of various feature extraction techniques and machine learning (ML) techniques at front-end and back-end stages, respectively. This chapter presents the analysis and processing steps for speech signal that provides the deep knowledge of context of speech and information of speaker. Proper sequence of these steps makes a feature extraction technique. Analysis of this chapter indicates that classical feature extraction techniques of cepstral domain like Mel Frequency Cepstral Coefficients (MFCC) are the most popular and better in performance for speech and speaker recognition tasks. This chapter provides the implementation details of these feature extraction techniques. Along with these classical feature extraction techniques, this work highlights that deep learning (DL) is adding new insights by passing end-to-end row waveform to the model for information extraction at the front end.