Reviewerly/exHarmony huggingface dataset:
Towards practical anonymous quantum communication: A measurement-device-independent approach

The multipartite Greenberger-Horne-Zeilinger (GHZ) entangled state is an important resource for anonymous quantum communication tasks. But its low intensity and fragility limit the practical applications of these anonymous communication tasks under current conditions. Here, we present a feasible anonymous communication protocol in the measurement-device-independent framework. We analyze its performance in a quantum network under certain types of noise. Then we compare the performance of our protocol with the GHZ-state-based anonymous protocol. We demonstrate that our protocol has a unique advantage, i.e., it can tolerate an arbitrary number of nonresponsive nodes. We prove that the security of our protocol in an active adversary scenario in which active malicious agents and an untrusted ancillary node are taken into account. Utilizing the measurement-device-independent technology, our proposal suggests an important avenue for practical anonymous quantum communication.
Cooperative Path Optimization for Multiple UAVs Surveillance in Uncertain Environment

Research on multiple unmanned aerial vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications, such as smart cities, precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This article is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a multi-UAV cooperative path optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle-avoidance selection strategy with two mechanisms in mind: 1) when some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance and 2) when all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas–Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3-D simulation experiment environment based on the UE4 platform and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs but also has satisfactory formation flexibility and surveillance accuracy.
Sign Language Translation with Hierarchical Spatio-Temporal Graph Neural Network

Sign language translation (SLT), which generates text in a spoken language from visual content in a sign language, is important to assist the hard-of-hearing community for their communications. Inspired by neural machine translation (NMT), most existing SLT studies adopted a general sequence to sequence learning strategy. However, SLT is significantly different from general NMT tasks since sign languages convey messages through multiple visual-manual aspects. Therefore, in this paper, these unique characteristics of sign languages are formulated as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions. Particularly, high-level graphs represent the patterns in the regions such as hands and face, and fine-level graphs consider the joints of hands and landmarks of facial regions. To learn these graph patterns, a novel deep learning architecture, namely hierarchical spatio-temporal graph neural network (HST-GNN), is proposed. Graph convolutions and graph self-attentions with neighborhood context are proposed to characterize both the local and the global graph properties. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed method.
Improving Autonomous Nano-Drones Performance via Automated End-to-End Optimization and Deployment of DNNs

The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10cm robotic platforms are envisioned as next-generation ubiquitous smart-sensors and unobtrusive robotic-helpers. However, the limited computational/memory resources available aboard nano-UAVs introduce the challenge of minimizing and optimizing vision-based CNNs – which to date require error-prone, labor-intensive iterative development flows. This work explores methodologies and software tools to streamline and automate all the deployment of vision-based CNN navigation on a ULP multicore system-on-chip acting as a mission computer on a Crazyflie 2.1 nano-UAV. We focus on the deployment of PULP-Dronet (Palossi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019), a state-of-the-art CNN for autonomous navigation of nano-UAVs, from the initial training to the final closed-loop evaluation. Compared to the original hand-crafted CNN, our results show a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> reduction of memory footprint and a speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> in inference time while guaranteeing the same prediction accuracy and significantly improving the behavior in the field, achieving: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) obstacle avoidance with a peak braking-speed of 1.65m/s and improving the speed/braking-space ratio of the baseline, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) free flight in a familiar environment up to 1.96m/s (0.5m/s for the baseline), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) lane following on a path featuring a 90deg turn – all while using for computation less than 1.6% of the drone’s power budget. To foster new applications and future research, we open-source all the software design in a ready-to-run project compatible with the Crazyflie 2.1.
Learning the structure of Bayesian networks with ancestral and/or heuristic partition

Developing efficient strategies for searching larger Bayesian networks in exact structure learning is an open challenge. In this study, ancestral and heuristic partition constraints are proposed to develop a series of exact learning algorithms, in which an ancestral partition is used to prune the order graph of a Bayesian network, and a heuristic partition is utilized to improve the tightness of the heuristic function. Algorithms for calculating these two types of constraints are established through thorough theoretical proof. Comparative experiments have been undertaken with state-of-the-art algorithms. It has been demonstrated that an algorithm improved with the proposed ancestral partition or combined ancestral and heuristic partition outperforms the algorithm in its original form, and it can have lower running time, fewer expanded states, and higher accuracy, as well as the ability to search larger networks within 100 nodes.
A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets

The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard &amp; Poor’s 500 Index (S&amp;P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&amp;P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability.
Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect ~ 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.
Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multimodule circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.
Unsupervised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic Generation

Unsupervised domain adaptation for person re-identification (Re-ID) suffers severe domain discrepancies between source and target domains. To reduce the domain shift caused by the changes of context, camera style, or viewpoint, existing methods in this field fine-tune and adapt the Re-ID model with augmented samples, either translating source samples to the target style or assigning pseudo labels to the target. The former methods may lose identity details but keep redundant source background during translation. In contrast, the latter techniques may give noisy labels when the model meets the unseen background and person pose. We mitigate the domain shift in the former translation direction by cyclically decoupling environment and identity-related features. We propose a novel individual-preserving and environmental-switching cyclic generation network (IPES-GAN). Our network has the following distinct features: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decoupled features instead of fused features:</i> we encode the images into an individual part and an environmental part, which are proved beneficial to generation and adaptation; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyclic generation instead of one-step adaptive generation</i> . We swap source and target environment features to generate cross-domain images with preserved identity-related features conditioned with source (target) background features and then changed again to generate back the input image so that cyclic generation runs in a self-supervised way. Experiments carried out on two significant benchmarks: Market-1501 and DukeMTMC-Reid, reveal state-of-the-art performance.
Certificateless anti-quantum ring signcryption for network coding

Network coding is a kind of information interaction technology which combines the routing with coding, and it can improve the system robustness and save the wireless network resources. However, the traditional ring signcryption is not suitable for network coding and quantum computing settings. In view of this, we propose a certificateless anti-quantum ring signcryption for network coding (NC-CLAQRSC), which is devised based on the small integer solution and learning with errors problems. NC-CLAQRSC satisfies the confidentiality and existential unforgeability. NC-CLAQRSC has high communication efficiency and low computation cost.
Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.
An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily depends upon their architecture, which is handcrafted by domain expertise and type of investigated problem. On the other hand, for researchers who do not have proficiency in using CNNs, it has been very difficult to explore this topic in their problem statements. In this paper, we have come up with a rank and gradient descent-based optimized genetic algorithm to automatically find the architecture design of CNNs that is vigorously competent in exploring the best CNN architecture for maneuvering the tasks of image classification. In the proposed algorithm, there is no requirement for handcrafted pre- and post-processing, which implies that the algorithm is fully mechanized. The validation of the proposed algorithm on conventional benchmarked datasets has been done by comparing the run time of a graphics processing unit (GPU) throughout the training process and assessing the accuracy of various measures. The experimental results show that the proposed algorithm accomplishes better and more persistent ‘classification accuracy’ than the original genetic algorithm on the CIFAR datasets by using fifty percent less intensive computing resources for training the individual CNN and the entire population.
High-Stability Cryogenic System for Quantum Computing With Compact Packaged Ion Traps

Cryogenic environments benefit ion trapping experiments by offering lower motional heating rates, collision energies, and an ultrahigh vacuum (UHV) environment for maintaining long ion chains for extended periods of time.Mechanical vibrations caused by compressors in closed-cycle cryostats can introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions.Here, we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used in a custom monolithic enclosure.We measure mechanical vibrations of the sample stage using an optical interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location.We packaged a surface ion trap in a cryopackage assembly that enables easy handling while creating a UHV environment for the ions.The trap cryopackage contains activated carbon getter material for enhanced sorption pumping near the trapping location, and source material for ablation loading.Using 171 Yb + as our ion, we estimate the operating pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a probe.We measured the radial mode heating rate of a single ion to be 13 quanta/s on average.The Ramsey coherence measurements yield 330-ms coherence time for counter-propagating Raman carrier transitions using a 355-nm mode-locked pulse laser, demonstrating the high optical stability.
CORNet: Context-Based Ordinal Regression Network for Monocular Depth Estimation

Monocular depth estimation, as one of the fundamental tasks of computer vision, plays a crucial role in three-dimensional (3D) scene understanding and perception. Usually, deep learning methods recover monocular depth maps using continuous regression manners by minimizing the errors between the ground-truth depth and the predicted depth. However, fine depth features may not be fully captured through layer-by-layer coding, which is prone to low spatial resolution depth maps and insufficient details. Furthermore, it usually converges slowly and suffers from unsatisfactory results. To tackle these issues, we propose a novel model, named context-based ordinal regression network (CORNet), to reconstruct monocular depth maps in the ordinal regression manner with context information in this paper. Firstly, we put forward a novel context-based encoder with a feature transformation (FT) module to learn context information and details from inputs, and output multi-scale feature maps. Then, we design a boundary enhancement module (BEM) with a spatial attention mechanism following each operation of feature fusion, which captures boundary features in the scene to enhance the border depth. Finally, a feature optimization module (FOM) is designed to fuse and optimize the multi-scale features and boundary features to strengthen depth learning. What’s more, we introduce an ordinal weighted inference to predict depth maps from probabilities and discretization values. Experiments and results on two challenging datasets, KITTI and NYU Depth V2, demonstrate that our proposed CORNet can estimate monocular depth maps effectively and obtain superior performance in capturing geometric features over existing methods.
Catching Transparent Phish: Analyzing and Detecting MITM Phishing Toolkits

For over a decade, phishing toolkits have been helping attackers automate and streamline their phishing campaigns. Man-in-the- Middle (MITM) phishing toolkits are the latest evolution in this space, where toolkits act as malicious reverse proxy servers of online services, mirroring live content to users while extracting cre- dentials and session cookies in transit. These tools further reduce the work required by attackers, automate the harvesting of 2FA- authenticated sessions, and substantially increase the believability of phishing web pages.
Quality-Aware Deep Reinforcement Learning for Streaming in Infrastructure-Assisted Connected Vehicles

This paper proposes a deep reinforcement learning-based video streaming scheme for mobility-aware vehicular networks, e.g., vehicles on the highway. We consider infrastructure-assisted and mmWave-based scenarios in which the macro base station (MBS) cannot directly provide the streaming service to vehicles due to the short range of mmWave beams so that small mmWave base stations (mBSs) along the road deliver the desired videos to users. Motivated by the fact that a video stream consists of sequential chunks and smooth streaming has to be provided while users pass through multiple mBSs at high speed, the MBS proactively pushes some video chunks of the desired contents to mBSs. This is done to support vehicles that are currently covered and/or will be by each mBS. We formulate the dynamic video delivery scheme that adaptively determines 1) which content, 2) what quality and 3) how many chunks to be proactively delivered from the MBS to mBSs using Markov decision process (MDP). Since it is difficult for the MBS to track all the channel conditions and the network states have extensive dimensions, we adopt the deep deterministic policy gradient (DDPG) algorithm for the DRL-based video delivery scheme. This paper finally shows that the DRL agentlearns a streaming policy that pursues high average quality while limiting packet drops, avoiding playback stalls, reducing quality fluctuations and saving backhaul usage.