Reviewerly/exHarmony huggingface dataset:
Concentrated Coverage Path Planning Algorithm of UAV Formation for Aerial Photography

In the field of aerial photography, UAVs can be used as an ideal carrier of sensors. It is meaningful to plan its coverage path combined with the sensor model. Previous works mainly used decentralized coverage to realize UAV formation cooperative operation, but for the aerial photography scene of a single task, the decentralized coverage algorithm is not effective. In this paper, a centralized coverage algorithm of UAV formation for aerial photography is proposed, which is divided into two sub-problems: coverage path planning and formation control. In the coverage path planning, due to high repetition rate and excessive number of turns of the traditional PRM algorithm, the constraint of the number of turns is introduced, and the back-and-forth mode is used to improve it. In formation control, in order to more accurately evaluate the performance of the coverage path, it refers to the consensus theory and combines the artificial potential field method to guide the reverse-V UAV formation to follow the coverage path. Multi-group simulations show that the proposed algorithm is feasible to implement the concentrated coverage for aerial photography under the comprehensive consideration of path length, number of turns, calculation time, coverage rate and repeating rate.
Error Mitigation and Quantum-Assisted Simulation in the Error Corrected Regime

A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations to a universal set by the addition of `magic' quantum states. In this context, we develop a general framework to discuss the value of the available, non-ideal magic resources, relative to those ideally required. We single out a quantity, the Quantum-assisted Robustness of Magic (QRoM), which measures the overhead of simulating the ideal resource with the non-ideal ones through quasiprobability-based methods. This extends error mitigation techniques, originally developed for Noisy Intermediate Scale Quantum (NISQ) devices, to the case where qubits are logically encoded. The QRoM shows how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit and enables the construction of explicit protocols, interpolating between classical simulation and an ideal quantum computer.
Design of mobile robot navigation controller using neuro-fuzzy logic system

To be successful, autonomous navigation must overcome challenges associated with the functions such as detecting perception, current location, planning, and regulating movements. It is possible to have a purposeful and reactive navigational approach. Robots and humans working in the same area must comply with safety laws regarding navigation. As a result, mobile robots in organized environments now have a navigation controller that can operate in reactive and deliberative approaches. Therefore, a neuro-fuzzy system is introduced to explore the benefits of both deliberative and reactive navigation control. It combines neural networks with a fuzzy logic controller and is used to investigate various strategies and give the robot corrective decisional commands. An amigo Bot equipped with a Kinect sensor is used to test the proposed approach in conjunction with the Virtual Robot Experimentation Platform (V-REP) and ROS Groovy Galapagos.
Unintended consequences of spatial presence on learning in virtual reality

Research on virtual reality (VR) in education and training has found that spatial presence, the perception that the body is inside a mediated environment, increases engagement. However, experiencing spatial presence requires the allocation of limited processing resources, potentially inhibiting the processing of other information. Guided by the frameworks of Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), and the cognitive theory of multimedia learning, two experiments examined the effects of different modalities on spatial presence to test the prediction that spatial presence negatively impacts recall. Study 1 (N = 100) found that VR elicited higher spatial presence than video, but that high spatial presence reduced recall. Individual differences (technology apprehension) moderated spatial presence. Study 2 (N = 260) found that pre-existing interest in the learning content and aversive responses elicited by the learning content increased spatial presence. However, segmenting the VR content to reduce processing load for participants had little effect on spatial presence or information recall. In sum, modality features and individual differences drove user experiences of spatial presence, which negatively impacted recall, but segmentation of VR content had no effect on learning outcomes.
A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.
Prompt Distribution Learning

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts.
Basic petrology

Petrology is the study of rocks, meteorites, and minerals as well as their occurrences, composition, origin, and evolution of the solar system. The processes involved are as follows: tectonic movements of masses, volcanic injections and eruptions, crystallization and solidification, melting and recrystallization, sedimentation, metamorphism, weathering, and megascopic and microscopic identification of minerals and rocks. The interior structure of the Earth from inner and outer core, mantle, lithosphere, oceanic and continental crust, hydrosphere, atmosphere to biosphere is illustrated along with the Nebular theory of origin and age. Rocks have been classified into three major genetic classes: igneous, sedimentary, and metamorphic. The theory of plate tectonics for the current configuration of the Earth lithosphere and component continents with a zone of subduction and plate boundaries such as transform, convergent, and divergent was discussed. The orogenic movements through collision and noncollision were explained. The study of Petrology turns out to be a wider application with the advent of Space Missions during the last five decades. Petrology is playing an immense role to understand the entire Solar System and a peep to the Universe as an overview. The resources in the Solar System appear to be vast.
Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation

Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos. For supervised model training, a temporal annotation of both the start and end time index of each video segment for a sentence (a video moment) must be given. This is not only very expensive but also sensitive to ambiguity and subjective annotation bias, a much harder task than image labelling. In this work, we develop a more accurate weakly-supervised solution by introducing Cross-Sentence Relations Mining (CRM) in video moment proposal generation and matching when only a paragraph description of activities without per-sentence temporal annotation is available. Specifically, we explore two cross-sentence relational constraints: (1) Temporal ordering and (2) semantic consistency among sentences in a paragraph description of video activities. Existing weakly-supervised techniques only consider within-sentence video segment correlations in training without considering cross-sentence paragraph context. This can mislead due to ambiguous expressions of individual sentences with visually indiscriminate video moment proposals in isolation. Experiments on two publicly available activity localisation datasets show the advantages of our approach over the state-of-the-art weakly supervised methods, especially so when the video activity descriptions become more complex.
Action recognition for the robotics and manufacturing automation using 3-D binary micro-block difference

Vision-based control systems play an important role in modern robotics systems. An important task in implementing such a system is developing an effective algorithm for recognizing human actions and the working environment and the design of intuitive gesture commands. This paper proposes an action recognition algorithm for robotics and manufacturing automation. The key contributions are (1) fusion of multimodal information obtained by depth sensors and cameras of the visible range, (2) modified Gabor-based and 3-D binary-based descriptor using micro-block difference, (3) efficient skeleton-based descriptor, and (4) recognition algorithm using the combined descriptor. The proposed binary micro-block difference representation of 3-D patches from video with a complex background in several scales and orientations leads to an informative description of the scene action. The experimental results showed the effectiveness of the proposed algorithm on datasets.
Chatbots

Abstract In this series, we explore technology-related themes and topics. This series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers.
Ethical Guidelines for SARS-CoV-2 Digital Tracking and Tracing Systems

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Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily

Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data. Typical GCN and its variants work under a homophily assumption (i.e., nodes with same class are prone to connect to each other), while ignoring the heterophily which exists in many real-world networks (i.e., nodes with different classes tend to form edges). Existing methods deal with heterophily by mainly aggregating higher-order neighborhoods or combing the immediate representations, which leads to noise and irrelevant information in the result. But these methods did not change the propagation mechanism which works under homophily assumption (that is a fundamental part of GCNs). This makes it difficult to distinguish the representation of nodes from different classes. To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs. To adaptively learn the propagation process, we introduce two measurements of homophily degree between node pairs, which is learned based on topological and attribute information, respectively. Then we incorporate the learnable homophily degree into the graph convolution framework, which is trained in an end-to-end schema, enabling it to go beyond the assumption of homophily. More importantly, we theoretically prove that our model can constrain the similarity of representations between nodes according to their homophily degree. Experiments on seven real-world datasets demonstrate that this new approach outperforms the state-of-the-art methods under heterophily or low homophily, and gains competitive performance under homophily.
Argumentation approaches for explanaible AI in medical informatics

Artificial Intelligence algorithms are powerful in performing accurate predictions, but they are often considered black boxes as they do not provide any explanation about how outputs are derived from inputs or why a decision is taken. Therefore, urgent is the need of a completely transparent and eXplainable Artificial Intelligence (XAI) as also recognized by the explicit inclusion of the right to explanation in the General Data Protection Regulation (GDPR). There has been much study on diagnosis, decision support, and interpretability, and there is significant interest in the development of Explainable AI in the realm of medicine. Interpretability in the medical field is not just an intectual curiosity, but a key factor. Medical choices impact the life of patients, and include risk and responsibility for the clinicians. This proposal investigates the benefit of using logic approaches for eXplainable AI by evidencing how their natural characteristics of explainability and expressiveness help in the design of ethical, explainable and justified intelligent systems. More specifically, the paper focuses on a detailed topic related to the use of argumentation theory in Medical Informatics by overviewing existing approaches in the literature. The overview categorizes approaches on the basis of the specific purpose the argumentation is used for, into the following categories: Argumentation for Medical Decision Making, Argumentation for Medical Explanations and Argumentation for Medical Dialogues.
Generation of Maximally Entangled Long-Lived States with Giant Atoms in a Waveguide

In this Letter, we show how to efficiently generate entanglement between two artificial giant atoms with photon-mediated interactions in a waveguide. Taking advantage of the adjustable decay processes of giant atoms into the waveguide and of the interference processes, spontaneous sudden birth of entanglement can be strongly enhanced with giant atoms. Highly entangled states can also be generated in the steady-state regime when the system is driven by a resonant classical field. We show that the statistics of the light emitted by the system can be used as a witness of the presence of entanglement in the system, since giant photon bunching is observed close to the regime of maximal entanglement. Given the degree of quantum correlations incoherently generated in this system, our results open a broad avenue for the generation of quantum correlations and manipulation of photon statistics in systems of giant atoms.
A comparative study of pretrained language models for long clinical text

Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (eg, Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts.Inspired by the success of long-sequence transformer models and the fact that clinical notes are mostly long, we introduce 2 domain-enriched language models, Clinical-Longformer and Clinical-BigBird, which are pretrained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks.The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results.Our pretrained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pretrained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer.This study demonstrates that clinical knowledge-enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers.
SHAMC: A Secure and highly available database system in multi-cloud environment

Data owners outsource their databases into the cloud to enjoy the quality services provided by the cloud service providers. However, using cloud database makes the private data vulnerable and exposed to the attackers including malicious insiders. Many researchers try to find the way to encrypt the cloud database and execute queries securely on the ciphertext. In this paper, we propose a secure and highly available cloud database system in the multi-cloud named SHAMC. Specifically, we use the idea of secure multiparty computation and homomorphic encryption to store data and execute queries direct on the ciphertext. Besides, the entire database is stored in multiple clouds to avoid service interruption as well as solve the problems of permanent failure and vendor lock-in. We implement the prototype of SHAMC which supports all queries in TPC Benchmark™ H (TPC-H) on the top of the commercial cloud. SHAMC is proved to be highly available and cost-efficient. The evaluation shows it has an acceptable query overhead which is superior to other encrypted cloud databases.
Video Person Re-Identification for Wide Area Tracking Based on Recurrent Neural Networks

In this paper, we propose a video-based person re-identification system for wide area tracking based on a recurrent neural network architecture. Given short video sequences of a person, generated by a tracking algorithm, our video re-identification algorithm links these tracklets in full trajectories across a network of non-overlapping cameras in an open-world scenario. In our system, features are first extracted from each frame using a convolutional neural network. Then, a recurrent layer combines information across time-steps. The features from all time-steps are finally combined using temporal pooling to give an overall appearance feature for the complete sequence. Our system is trained to perform re-identification using a Siamese network architecture. Experiments are conducted on the iLIDS-VID and PRID-2011 video re-identification data sets as well as in the DukeMTMC multi-camera tracking data set.
Efficient Software Implementation of Ring-LWE Encryption on IoT Processors

Embedded processors have been widely used for building up Internet of Things (IoT) platforms, in which the security issue is becoming critical. This paper studies efficient techniques of lattice-based cryptography on these processors and presents the first implementation of ring-LWE encryption on ARM NEON and MSP430 architectures. For ARM NEON architecture, we propose a vectorized version of Iterative Number Theoretic Transform (NTT) for high-speed computation of polynomial multiplication on ARM NEON platforms and a 32-bit variant of SAMS2 technique for fast reduction. For MSP430 architecture, we propose an optimized SWAMS2 reduction technique, which consists of five different basic operations, including Shifting, Swapping, Addition, and two Multiplication-Subtractions. Regarding of the sampling from the discrete Gaussian distribution, we adopt Knuth-Yao sampler, accompanied with optimized methods such as Look-Up Table (LUT) and byte-scanning. Subsequently, a full-fledged implementation of Ring-LWE is presented by both taking advantage of our proposed method and previous optimization techniques re-designed for desired platforms. Our ring-LWE implementation of encryption/decryption at a classical security level of 128 bits requires only 149:4k=32:8k clock cycles on ARM NEON, and 2126:3k=244:5k clock cycles on MSP430. These results are roughly 7 times faster than the fastest ECC implementation on desired platforms with same security level.
Challenge-Response Authentication Using In-Air Handwriting Style Verification

Challenge-response (CR) is an effective way to authenticate users even if the communication channel is insecure. Traditionally CR authentication relies on one-way hashes and shared secrets to verify the identities of users. Such a method cannot cope with an insider attack, where a user can obtained the secret (i.e., the response) from a legitimate user. To cope with it, we design a biometric-based CR authentication scheme (hereafter MoCRA), which is derived from the motions as a user operates emerging depth-sensorbased input devices, such as a Leap Motion controller. We envision that to authenticate a user, MoCRA randomly chooses a string (e.g., a few words), and the user has to write the string in the air. Using Leap Motion, MoCRA captures the user's writing movements and then extracts his / her handwriting style. After verifying that what the user writes matches what is asked for, MoCRA leverages a Support Vecter Machine (SVM) with co-occurrence matrices to model the handwriting styles and can reliably authenticate users, even if what they write is completely different every time. Evaluated on data from 24 subjects over 7 months, MoCRA managed to verify a user with an average of 1.18% (Equal Error Rate) EER and to reject impostors with 2.45% EER.