Reviewerly/exHarmony huggingface dataset:
An Adaptive 3D Artificial Potential Field for Fail-safe UAV Navigation

This article presents an obstacle avoidance framework for unmanned aerial vehicles (UAVs), with a focus on providing safe and stable local navigation in critical scenarios. The framework is based on enhanced artificial potential field (APF) concepts, and is paired with a nonlinear model predictive controller (NMPC) for complete local reactive navigation. This paper will consider a series of additions to the classical artificial potential field that addresses UAV-specific challenges, allows for smooth navigation in tightly constrained environments, and ensures safe human-robot interactions. The APF formulation is fundamentally based on using raw LiDAR pointcloud data as input to decouple the safe robot navigation problem from the reliance on any map or obstacle detection software, resulting in a very resilient and fail-safe framework that can be used as an additional safety layer for any 3D-LiDAR equipped UAV in any environment or mission scenario. We evaluate the scheme in both laboratory experiments and field trials, and also place a large emphasis on realistic scenarios for safe human-robot interactions.
Quantum medical images processing foundations and applications

Medical imaging is considered one of the most important areas within scientific imaging due to the rapid and ongoing development in computer-aided medical image visualisation, advances in analysis approaches, and computer-aided diagnosis. Here, the principles of quantum computation and information to develop the field of medical image processing will be reviewed. The advancement of quantum computation in image processing has proved its outstanding properties for processing and storage capacity compared to the classical methods. This review provides a comprehensive summary of the common advanced approaches, methodologies and advanced applications in medical images based on quantum computation.
SSSB: An Approach to Insurance for Cross-Border Exchange by Using Smart Contracts

Traditional commercial systems face many problems in terms of transportation, information latency, and reliability, thus affecting the whole process. Blockchain technology and smart contracts have been seen as a panacea for these problems. Specifically, all information about packages, orders, and ties between relevant parties (i.e., seller and buyer) is stored in a distributed ledger. Thereby, the seller and the buyer can access the necessary information about their package. However, the most significant difference between the international trading system and the rest is the need for an arbitrator/third party (i.e., a middleman) to judge when there is a conflict between the seller and the buyer (e.g., the bank provides the letter of credit). Setting up the rules/policies is essential during the order/package creation. It helps the middleman issue penalty fees if the stakeholders violate the constraints in the contract. Due to the above reasons, this paper introduces a system based on blockchain and smart contracts named Safe Seller Safe Buyer (SSSB) system as an approach to insurance for cross-border exchange. This article also implements SSSB on the three common platforms currently supporting Ethereum’s ERC20, including Ethereum, BNB Smart Chain, and Fantom. By evaluating the actual implementation of the Gas, SSSB has the cheapest fee when installed on the Fantom platform. We provide the implementation/deployment of smart contracts on the three platforms to encourage further research on this topic.
Solving traveling salesman problems via a parallel fully connected ising machine

Annealing-based Ising machines have shown promising results in solving combinatorial optimization problems. As a typical class of these problems, however, traveling salesman problems (TSPs) are very challenging to solve due to the constraints imposed on the solution. This article proposes a parallel annealing algorithm for a fully connected Ising machine that significantly improves the accuracy and performance in solving constrained combinatorial optimization problems such as the TSP. Unlike previous parallel annealing algorithms, this improved parallel annealing (IPA) algorithm efficiently solves TSPs using an exponential temperature function with a dynamic offset. Compared with digital annealing (DA) and momentum annealing (MA), the IPA reduces the run time by 44.4 times and 19.9 times for a 14-city TSP, respectively. Large scale TSPs can be more efficiently solved by taking a k-medoids clustering approach that decreases the average travel distance of a 22-city TSP by 51.8% compared with DA and by 42.0% compared with MA. This approach groups neighboring cities into clusters to form a reduced TSP, which is then solved in a hierarchical manner by using the IPA algorithm.
STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal

The integration of agricultural Internet of Things (IoT) and blockchain has become the key technology of precision agriculture.How to protect data privacy and security from data source is one of the difficult issues in agricultural IoT research.This work integrates cryptography, blockchain and Interplanetary File System(IPFS) technologies, and proposes a general IoT blockchain terminal system architecture, which strongly supports the integration of the IoT and blockchain technology.This research innovatively designed a fine-grained and flexible terminal data access control scheme based on the ciphertext-policy attribute-based encryption(CP-ABE) algorithm.Based on CP-ABE and DES algorithms, a hybrid data encryption scheme is designed to realize 1-to-N encrypted data sharing.A "horizontal + vertical" IoT data segmentation scheme under blockchain technology is proposed to realize the classified release of different types of data on the blockchain.The experimental results show that the design scheme can ensure data access control security, privacy data confidentiality, and data high-availability security.This solution significantly reduces the complexity of key management, can realize efficient sharing of encrypted data, flexibly set access control strategies, and has the ability to store large data files in the agricultural IoT.
FogDedupe: A Fog-Centric Deduplication Approach Using Multi-Key Homomorphic Encryption Technique

The advancements in communication technologies and a rapid increase in the usage of IoT devices have resulted in an increased data generation rate. Storing, managing, and processing large quantities of unstructured data generated by IoT devices remain a huge challenge to cloud service providers (CSP). To reduce the storage overhead, CSPs implement deduplication algorithms on the cloud storage servers. It identifies and eliminates the redundant data blocks. However, implementing post-progress deduplication schemes does not address the bandwidth issues. Also, existing convergent key-based deduplication schemes are highly vulnerable to confirmation of file attacks (CFA) and can leak confidential information. To overcome these issues, FogDedupe, a fog-centric deduplication framework, is proposed. It performs source-level deduplication on the fog nodes to reduce the bandwidth usage and post-progress deduplication to improve the cloud storage efficiency. To perform source-level deduplication, a distributed index table is created and maintained in the fog nodes, and post-progress deduplication is performed using a multi-key homomorphic encryption technique. To evaluate the proposed FogDedupe framework, a testbed environment is created using the open-source Eucalyptus v.4.2.0 software and fog project v1.5.9 package. The proposed scheme tightens the security against CFA attacks and improves the storage overhead by 27% and reduces the deduplication latency by 12%.
Projective Linear Codes From Some Almost Difference Sets

Projective linear codes are a special class of linear codes whose duals have minimum distance at least 3. The columns of the generator matrix of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> projective code over finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{q}$ </tex-math></inline-formula> can be viewed as points in the projective space <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {PG}(k-1, {\mathbb {F}}_{q})$ </tex-math></inline-formula> . Projective codes are of interest not only because their duals have good error correcting capability but also because they may be related to interesting combinatorial structures. The objective of this paper is to construct projective linear codes with five families of almost difference sets. To this end, the augmentation and extension techniques for linear codes are used. The parameters and weight distributions of the projective codes are explicitly determined. Several infinite families of optimal or almost optimal codes including MDS codes, near MDS codes, almost MDS odes and Griesmer codes are obtained. Besides, we also give some applications of these codes.
A Minimum Adjustment Cost Consensus Framework Considering Harmony Degrees and Trust Propagation for Social Network Group Decision Making

In consensus-based social network group decision-making (SN-GDM) problems, the low degree of harmony caused by opinion discrepancies and weak trust relationships may lead to high consensus costs and conflict among experts. To address these problems, we design a minimum adjustment cost consensus framework considering harmony degrees in SN-GDM with incomplete 2-tuple linguistic trust. First, considering the decay of trust through its propagation process, a discount-factor-based 2-tuple linguistic trust propagation operator is proposed to estimate the unknown trust relationships among experts. Then, the harmony degrees are measured based on opinion discrepancies and 2-tuple linguistic trust values, and experts’ weights could be obtained. To improve the consensus levels of the inconsistent experts, a minimum adjustment cost consensus model considering harmony degrees is explored to generate interval recommendations. The existence of optimal solutions and the convergence of the proposed consensus model are also proved. Finally, the validity of the proposed consensus framework is verified by the case of base station selection. With the higher overall harmony degree and individual consensus level, we extend the classical minimum cost consensus model and incorporate the harmony degrees among experts into the consensus reaching process.
An Aerial Parallel Manipulator With Shared Compliance

Accessing and interacting with difficult to reach surfaces at various orientations is of interest within a variety of industrial contexts. Thus far, the predominant robotic solution to such a problem has been to leverage the maneuverability of a fully actuated, omnidirectional aerial manipulator. Such an approach, however, requires a specialised system with a high relative degree of complexity, thus reducing platform endurance and real-world applicability. The work here presents a new aerial system composed of a parallel manipulator and conventional, underactuated multirotor flying base to demonstrate interaction with vertical and non-vertical surfaces. Our solution enables compliance to external disturbance on both subsystems, the manipulator and flying base, independently with the goal of improved overall system performance when interacting with surfaces. To achieve this behaviour, an admittance control strategy is implemented on various layers of the flying base's dynamics together with torque limits imposed on the manipulator actuators. Experimental evaluations show that the proposed system is compliant to external perturbations while allowing for differing interaction behaviours as compliance parameters of each subsystem are altered. Such capabilities enable an adjustable form of dexterity in completing sensor installation, inspection and aerial physical interaction tasks.
Protecting World Leader Using Facial Speaking Pattern Against Deepfakes

Face forgery instances involving celebrities are on the rise, owing to the ease with which their large quantity of videos may be accessible on the Internet, world leaders particularly. While current face manipulation detectors have achieved impressive results on several open datasets, which incorporate persons with various identities, they show performance degradation on these high-quality ones targeting at celebrities. What is more, these online videos usually undergo compression processing, marking the detection task harder. Besides, more face manipulation techniques arise for celebrities other than face-swap, such as lip-synchronize and image-animation, with which most works have not been concerned. This paper proposes a dual stream learning facial and speaking patterns method to protect celebrities against deepfakes. We design an action unit module based on facial action coding system along with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Action Unit Transformer</i> (AUT) to exploit facial expressions embeddings. Besides, our method's dual stream architecture utilizes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Temporal Convolutional Network</i> (TCN) to extract lip motion pattern and learns the relatedness between facial and speaking patterns. Our method could protect the person of interest (POI) against deepfakes in an end-to-end manner. Extensive experiments show that our method achieves better performance and has a higher resistance to video compression than state-of-the-art detection models.
Variational quantum extreme learning machine

Extreme learning machine (ELM), with fast training speed and high generalization performance, has been widely used in many fields. However, it becomes inefficient or even impossible to process data with extremely large feature spaces, which is expected to be solved by quantum computing with an exponentially large quantum state space. Here, we propose a novel variational quantum extreme learning machine (VQELM). In detail, we design a special feature mapping method to achieve nonlinear transformation of the input data, replacing the hard-to-construct activation function on quantum devices. Considering that the Harrow-Hassidim-Lloyd algorithm is difficult to solve the ELM parameters on near-term quantum devices, we adopt a variational framework to facilitate implementation on the near-term noisy intermediate scale quantum computer. On both classification and regression tasks, our proposed method outperforms classical ELM in classical simulations. Moreover, the classification tasks achieved on IBM quantum simulator also show comparable classification accuracy. The final analysis shows that our proposed algorithm has an exponential improvement over classical ones for high-dimensional data processing, and is a powerful application of quantum machine learning on near-term quantum devices.
Long-working-distance 3D measurement with a bionic curved compound-eye camera

The bionic curved compound-eye camera is a bionic-inspired multi-aperture camera, which can be designed to have an overlap on the field of view (FOV) in between adjacent ommatidia so that 3D measurement is possible. In this work, we demonstrate the 3D measurement with a working distance of up to 3.2 m by a curved compound-eye camera. In that there are hundreds of ommatidia in the compound-eye camera, traditional calibration boards with a fixed-pitch pattern arrays are not applicable. A batch calibration method based on the CALTag calibration board for the compound-eye camera was designed. Next, the 3D measurement principle was described and a 3D measurement algorithm for the compound-eye camera was developed. Finally, the 3D measurement experiment on objects placed at different distances and directions from the compound-eye camera was performed. The experimental results show that the working range for 3D measurement can cover the whole FOV of 98° and the working distance can be as long as 3.2 m. Moreover, a complete depth map was reconstructed from a raw image captured by the compound-eye camera and demonstrated as well. The 3D measurement capability of the compound-eye camera at long working distance in a large FOV demonstrated in this work has great potential applications in areas such as unmanned aerial vehicle (UAV) obstacle avoidance and robot navigation.
Experimental symmetric private information retrieval with measurement-device-independent quantum network

Secure information retrieval is an essential task in today's highly digitised society. In some applications, it may be necessary that user query's privacy and database content's security are enforced. For these settings, symmetric private information retrieval (SPIR) could be employed, but its implementation is known to be demanding, requiring a private key-exchange network as the base layer. Here, we report for the first time a realisation of provably-secure SPIR supported by a quantum-secure key-exchange network. The SPIR scheme looks at biometric security, offering secure retrieval of 582-byte fingerprint files from a database with 800 entries. Our experimental results clearly demonstrate the feasibility of SPIR with quantum secure communications, thereby opening up new possibilities in secure distributed data storage and cloud computing over the future Quantum Internet.
Understanding the visual constraints on lexical processing: New empirical and simulation results.

Word identification is slower and less accurate outside central vision, but the precise relationship between retinal eccentricity and lexical processing is not well specified by models of either word identification or reading. In a seminal eye-movement study, Rayner and Morrison (1981) found that participants made remarkably accurate naming and lexical-decision responses to words displayed more than 3 degrees from the center of vision-even under conditions requiring fixed gaze. However, the validity of these findings is challenged by a range of methodological limitations. We report a series of gaze-contingent lexical-decision and naming experiments that replicate and extend Rayner and Morrison's study to provide a more accurate estimate of how visual constraints delimit lexical processing. Simulations were conducted using the E-Z Reader model (Reichle et al., 2012) to assess the implications for understanding eye-movement control during reading. Augmenting the model's assumptions about the impact of both eccentricity and visual crowding on the rate of lexical processing provided good fits to the observed data without impairing the model's ability to simulate benchmark eye-movement effects. The findings are discussed with a view toward the development of a complete model of reading. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Effects of Emotional Expressiveness on Voice Chatbot Interactions

Speech-based dialog systems primarily interact with users through their spoken responses. Understanding users’ perception of, and subconscious behaviors toward, the system’s speech are crucial for improving their design. In the current study, a voice chatbot designed for having a conversation with users in the domain of music is used to test the impact of emotional expressiveness in its text-to-speech (TTS) output. We parametrically manipulated the degree of emotional expressiveness via prosody and lexical choice across conditions. We used a two-pronged approach to test these effects on users: a user interaction study (Experiment 1 – between-subjects design) and an independent perception study (Experiment 2 – within-subjects design). Both studies provide converging evidence that increasing emotional prosody yields more positive perceptions of chatbot interactions, in increasing perception of emotional expressiveness (Experiments 1 and 2) as well as overall engagement, human-likeness, and likability of the bot (Experiment 2). We discuss these findings in terms of theories of human-computer interaction, as well as their implications for conversational design.
Data-centric machine learning in quantum information science

We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a "toy model" demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.
Counting people inside a region-of-interest in CCTV footage with deep learning

In recent years, the performance of people-counting models has been dramatically increased that they can be implemented in practical cases. However, the current models can only count all of the people captured in the inputted closed circuit television (CCTV) footage. Oftentimes, we only want to count people in a specific Region-of-Interest (RoI) in the footage. Unfortunately, simple approaches such as covering the area outside of the RoI are not applicable without degrading the performance of the models. Therefore, we developed a novel learning strategy that enables a deep-learning-based people counting model to count people only in a certain RoI. In the proposed method, the people counting model has two heads that are attached on top of a crowd counting backbone network. These two heads respectively learn to count people inside the RoI and negate the people count outside the RoI. We named this proposed method Gap Regularizer and tested it on ResNet-50, ResNet-101, CSRNet, and SFCN. The experiment results showed that Gap Regularizer can reduce the mean absolute error (MAE), root mean square error (RMSE), and grid average mean error (GAME) of ResNet-50, which is the smallest CNN model, with the highest reduction of 45.2%, 41.25%, and 46.43%, respectively. On shallow models such as the CSRNet, the regularizer can also drastically increase the SSIM by up to 248.65% in addition to reducing the MAE, RMSE, and GAME. The Gap Regularizer can also improve the performance of SFCN which is a deep CNN model with back-end features by up to 17.22% and 10.54% compared to its standard version. Moreover, the impacts of the Gap Regularizer on these two models are also generally statistically significant (P-value < 0.05) on the MOT17-09, MOT20-02, and RHC datasets. However, it has a limitation in which it is unable to make significant impacts on deep models without back-end features such as the ResNet-101.