Reviewerly/exHarmony huggingface dataset:
FogSurv: A Fog-Assisted Architecture for Urban Surveillance Using Artificial Intelligence and Data Fusion

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Urban surveillance</i> , of which airborne urban surveillance is a vital constituent, provides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situational awareness</i> (SA) and timely response to emergencies. The significance and scope of urban surveillance has increased manyfold in recent years due to the proliferation of unmanned aerial vehicles (UAVs), Internet of things (IoTs), and multitude of sensors. In this article, we propose FogSurv—a fog-assisted surveillance architecture and framework leveraging artificial intelligence (AI) and information/data fusion for enabling real-time SA and monitoring. We also propose an AI- and data-driven information fusion model for FogSurv to help provide (near) real-time SA, threat assessment, and automated decision-making. We further present a latency model for AI and information fusion processing in FogSurv. We then discuss several use cases of FogSurv that can have a huge impact on multifarious fronts of national significance ranging from safeguarding national security to monitoring of critical infrastructures. We conduct an extensive set of experiments to demonstrate that FogSurv using AI and data fusion help provide near real-time inferences and SA. Experimental results demonstrate that FogSurv provides a latency improvement of 37% on average over cloud architectures for the selected benchmarks. Results further indicate that combining AI with data fusion as in FogSurv can provide a speedup of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.8\times $ </tex-math></inline-formula> over AI without data fusion while also maintaining or improving the inference accuracy. Additionally, results show that AI combined with fusion of different image modalities obtained through UAVs in FogSurv results in improved average precision of target detection for surveillance as compared to AI without data fusion for different target scales and environment complexity.
Channel-Wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks

Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection between feature groups within one block, which increases the possible receptive fields and improves the system's detection generalizability. However, such a residual-like connection is performed by a direct addition between feature groups without channel-wise priority. We argue that the information across channels may not contribute to spoofing cues equally, and the less relevant channels are expected to be suppressed before adding onto the next feature group, so that the system can generalize better to unseen attacks. This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups. This gating mechanism dynamically selects channel-wise features based on the input, to suppress the less relevant channels and enhance the detection generalizability. Three gating mechanisms with different structures are proposed and integrated into Res2Net. Experimental results conducted on ASVspoof 2019 logical access (LA) demonstrate that the proposed CG-Res2Net significantly outperforms Res2Net on both the overall LA evaluation set and individual difficult unseen attacks, which also outperforms other state-of-the-art single systems, depicting the effectiveness of our method.
Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.
TTS-GAN: A Transformer-Based Time-Series Generative Adversarial Network

Signal measurements appearing in the form of time series are one of the most common types of data used in medical machine learning applications. However, such datasets are often small, making the training of deep neural network architectures ineffective. For time-series, the suite of data augmentation tricks we can use to expand the size of the dataset is limited by the need to maintain the basic properties of the signal. Data generated by a Generative Adversarial Network (GAN) can be utilized as another data augmentation tool. RNN-based GANs suffer from the fact that they cannot effectively model long sequences of data points with irregular temporal relations. To tackle these problems, we introduce TTS-GAN, a transformer-based GAN which can successfully generate realistic synthetic time-series data sequences of arbitrary length, similar to the real ones. Both the generator and discriminator networks of the GAN model are built using a pure transformer encoder architecture. We use visualizations and dimensionality reduction techniques to demonstrate the similarity of real and generated time-series data. We also compare the quality of our generated data with the best existing alternative, which is an RNN-based time-series GAN. TTS-GAN source code: github.com/imics-lab/tts-gan
Hamiltonian Engineering with Multicolor Drives for Fast Entangling Gates and Quantum Crosstalk Cancellation

Quantum computers built with superconducting artificial atoms already stretch the limits of their classical counterparts. While the lowest energy states of these artificial atoms serve as the qubit basis, the higher levels are responsible for both a host of attractive gate schemes as well as generating undesired interactions. In particular, when coupling these atoms to generate entanglement, the higher levels cause shifts in the computational levels that lead to unwanted $ZZ$ quantum crosstalk. Here, we present a novel technique to manipulate the energy levels and mitigate this crosstalk with simultaneous off-resonant drives on coupled qubits. This breaks a fundamental deadlock between qubit-qubit coupling and crosstalk. In a fixed-frequency transmon architecture with strong coupling and crosstalk cancellation, additional cross-resonance drives enable a 90 ns CNOT with a gate error of $(0.19\ifmmode\pm\else\textpm\fi{}0.02)%$, while a second set of off-resonant drives enables a novel CZ gate. Furthermore, we show a definitive improvement in circuit performance with crosstalk cancellation over seven qubits, demonstrating the scalability of the technique. This Letter paves the way for superconducting hardware with faster gates and greatly improved multiqubit circuit fidelities.
Conversational question answering: a survey

Abstract Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational question answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user’s information needs. While the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.
Robust Supervised Learning

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Benchmarking of Gaussian boson sampling using two-point correlators

Gaussian boson sampling is a promising scheme for demonstrating a quantum computational advantage using photonic states that are accessible in a laboratory and, thus, offer scalable sources of quantum light. In this contribution, we study two-point photon-number correlation functions to gain insight into the interference of Gaussian states in optical networks. We investigate the characteristic features of statistical signatures which enable us to distinguish classical from quantum interference. In contrast to the typical implementation of boson sampling, we find additional contributions to the correlators under study which stem from the phase dependence of Gaussian states and which are not observable when Fock states interfere. Using the first three moments, we formulate the tools required to experimentally observe signatures of quantum interference of Gaussian states using two outputs only. By considering the current architectural limitations in realistic experiments, we further show that a statistically significant discrimination between quantum and classical interference is possible even in the presence of loss, noise, and a finite photon-number resolution. Therefore, we formulate and apply a theoretical framework to benchmark the quantum features of Gaussian boson sampling under realistic conditions.
Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception

Security challenges accompany the efficiency. The pervasive integration of information and communications technologies (ICTs) makes cyber-physical systems vulnerable to targeted attacks that are deceptive, persistent, adaptive, and strategic. Attack instances such as Stuxnet, Dyn, and WannaCry ransomware have shown the insufficiency of off-the-shelf defensive methods including the firewall and intrusion detection systems. Hence, it is essential to design up-to-date security mechanisms that can mitigate the risks despite the successful infiltration and the strategic response of sophisticated attackers. In this chapter, we use game theory to model competitive interactions between defenders and attackers. First, we use the static Bayesian game to capture the stealthy and deceptive characteristics of the attacker. A random variable called the type characterizes users’ essences and objectives, e.g., a legitimate user or an attacker. The realization of the user’s type is private information due to the cyber deception. Then, we extend the one-shot simultaneous interaction into the one-shot interaction with asymmetric information structure, i.e., the signaling game. Finally, we investigate the multi-stage transition under a case study of Advanced Persistent Threats (APTs) and Tennessee Eastman (TE) process. Two-sided incomplete information is introduced because the defender can adopt defensive deception techniques such as honeyfiles and honeypots to create sufficient amount of uncertainties for the attacker. Throughout this chapter, the analysis of the Nash equilibrium (NE), Bayesian Nash equilibrium (BNE), and perfect Bayesian Nash equilibrium (PBNE) enables the policy prediction of the adversary and the design of proactive and strategic defenses to deter attackers and mitigate losses.
An Efficient Density-Based Local Outlier Detection Approach for Scattered Data

After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.
ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition

RGB image classification has achieved significant performance improvement with the resurge of deep convolutional neural networks. However, mono-modal deep models for RGB image still have several limitations when applied to RGB-D scene recognition. 1) Images for scene classification usually contain more than one typical object with flexible spatial distribution, so the object-level local features should also be considered in addition to global scene representation. 2) Multi-modal features in RGB-D scene classification are still under-utilized. Simply combining these modal-specific features suffers from the semantic gaps between different modalities. 3) Most existing methods neglect the complex relationships among multiple modality features. Considering these limitations, this paper proposes an adaptive crossmodal (ACM) feature learning framework based on graph convolutional neural networks for RGB-D scene recognition. In order to make better use of the modal-specific cues, this approach mines the intra-modality relationships among the selected local features from one modality. To leverage the multi-modal knowledge more effectively, the proposed approach models the inter-modality relationships between two modalities through the cross-modal graph (CMG). We evaluate the proposed method on two public RGB-D scene classification datasets: SUN-RGBD and NYUD V2, and the proposed method achieves state-of-the-art performance.
An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet

We address in this paper the problem of abnormal event detection in video-surveillance. In this context, we use only normal events as training samples. We propose to use a modified version of pretrained 3D residual convolutional network to extract spatio-temporal features, and we develop a robust classifier based on the selection of vectors of interest. It is able to learn the normal behavior model and detect potentially dangerous abnormal events. This unsupervised method prevents the marginalization of normal events that occur rarely during the training phase since it minimizes redundancy information, and adapt to the appearance of new normal events that occur during the testing phase. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.
Design and Evaluation of Visualization Techniques of Off-Screen and Occluded Targets in Virtual Reality Environments

This research explores the design and evaluation of visualization techniques of targets that reside outside of users' view and/or are occluded by other elements within a virtual reality environment (VE). We first compare four techniques (3DWedge, 3DArrow, 3DMinimap, and Radar) that use different types of visual elements to provide direction and distance information of targets. To give structure to their evaluation, we also develop a framework of four tasks (one for direction and three for distance) and their assessment criteria. The results of the first study show that 3DWedge is the best-performing and most usable technique. However, all techniques, including 3DWedge, have poor performance in dense scenarios with a large number of targets. To improve support in dense scenarios, a fifth technique, 3DWedge+, is developed by using 3DWedge as its foundation and including additional visual elements that are derived from the other three techniques which are shown to be useful. A second study is conducted to evaluate the performance of 3DWedge+ in relation to the other techniques. The results show that both 3DWedge and 3DWedge+ are significantly better in distinguishing user-to-target distance and that 3DWedge+ is particularly suitable for dense scenarios. Based on these results, we provide a set of recommendations for the design of visualization techniques of off-screen and occluded targets in 3D VE.
Improving Neural Entity Disambiguation with Graph Embeddings

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base.Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities.In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations.Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-ofthe-art neural ED system.
Effect of Process Writing Approach Combined with Video-Based Mobile Learning on Indonesian EFL Learners’ Writing Skill across Creativity Levels

This study aims at investigating the effect of process writing approach combined with video-based mobile learning on the writing skill of Indonesian learners of English as a foreign language (EFL) across their creativity levels.A quasiexperimental study was employed by involving 61 learners of Vocational High School learners in Indonesia.They were assigned into two groups: experimental and control.The treatment was conducted at 5 sessions, including the pre-test and post-test.The scores of pre-test and post-test in writing were used as a base of quantitative data analysis whereas the learners' responses to creativity questionnaire were used to categorize their creativity level.The result revealed that the process writing approach combined with video-based mobile learning was effective to enhance the learners' writing skill.Additionally, it was implied that to apply the combination of process writing approach and video-based mobile learning, teachers should not worry about the difference levels of the learners' creativity.
Pose estimates from online videos show that side-by-side walkers synchronize movement under naturalistic conditions

Marker-less video-based pose estimation promises to allow us to do movement science on existing video databases. We revisited the old question of how people synchronize their walking using real world data. We thus applied pose estimation to 348 video segments extracted from YouTube videos of people walking in cities. As in previous, more constrained, research, we find a tendency for pairs of people to walk in phase or in anti-phase with each other. Large video databases, along with pose-estimation algorithms, promise answers to many movement questions without experimentally acquiring new data.
Tangible BioNets

Biological networks analysis has become a systematic and large-scale phenomenon. Most biological systems are often difficult to interpret due to the complexity of relationships and structural features. Moreover, existing primarily web-based interfaces for biological networks analysis often have limitations in usability as well as in supporting high-level reasoning and collaboration. Interactive surfaces coupled with tangible interactions offer opportunities to improve the comparison and analysis of large biological networks, which can aid researchers in making hypotheses and forming insights. We present Tangible BioNets, an active tangible and multi-surface system that allows users with diverse expertise to explore and understand the structural and functional aspects of biological organisms individually or collaboratively. The system was designed through an iterative co-design process and facilitates the exploration of biological network topology, catalyzing the generation of new insights. We describe a first informal evaluation with expert users and discuss considerations for designing tangible and multi-surface systems for large biological datasets.
Should I Stale or Should I Close? An Analysis of a Bot That Closes Abandoned Issues and Pull Requests

On GitHub, projects use bots to automate predefined and repetitive tasks related to issues and pull requests. Our research investigates the adoption of the stale bot, which helps maintainers triaging abandoned issues and pull requests. We analyzed the bots' configuration settings and their modifications over time. These settings define the time for tagging issues and pull request as stale and closing them. We collected data from 765 OSS projects hosted on GitHub. Our results indicate that most of the studied projects made no more than three modifications in the configurations file, issues tagged as bug reports are exempt from being considered stale, while the same occurs with pull requests that need some input to be processed.