Reviewerly/exHarmony huggingface dataset:
Application of Improved K-means Clustering Algorithm in UAV Reconnaissance Mission Planning

In this paper, an improved K-means clustering analysis algorithm is proposed, which is applied to the situation that there are too many reconnaissance targets in the battlefield environment. The purpose is to reduce the number of target points, so as to reduce the number of reconnaissance tasks of UAV, so that the UAV can achieve the same reconnaissance effect at a shorter distance. After the clustering analysis of target points, the traditional genetic algorithm is used to plan the trajectory for the UAV. According to the results, the reconnaissance effect before and after the improvement is compared. The results show that the improved method reduces the number of reconnaissance points and the length of the UAV’s trajectory, and achieves better optimization effect with less cost.
Discovering skill

This paper shows how identical skills can emerge either from instruction or discovery when both result in an understanding of the causal structure of the task domain. The paper focuses on the discovery process, extending the skill acquisition model of Anderson et al. (2019) to address learning by discovery. The discovery process involves exploring the environment and developing associations between discontinuities in the task and events that precede them. The growth of associative strength in ACT-R serves to identify potential causal connections. The model can derive operators from these discovered causal relations just as does with the instructed causal information. Subjects were given a task of learning to play a video game either with a description of the game's causal structure (Instruction) or not (Discovery). The Instruction subjects learned faster, but successful Discovery subjects caught up. After 20 3-minute games the behavior of the successful subjects in the two groups was largely indistinguishable. The play of these Discovery subjects jumped in the same discrete way as did the behavior of simulated subjects in the model. These results show how implicit processes (associative learning, control tuning) and explicit processes (causal inference, planning) can combine to produce human learning in complex environments.
Lambertian Model-Based Normal Guided Depth Completion for LiDAR-Camera System

Depth completion is an essential task for the dense scene reconstruction on light detection and ranging (LiDAR)-camera system. Learning-based method achieves precise depth completion results on specific data sets. However, for the general outdoor scenes with insufficient labeled data sets, an efficient nonlearning method is still required. In this letter, from the geometrical constraint between depth and normal, a novel nonlearning normal guided depth completion method is proposed. For the objects in the outdoor scene, local brightness normal (LBN) constraint is derived from the Lambertian model. It is used to recover dense normal from RGB image and sparse normal. After that, we present a pipeline for depth completion with the guidance of dense normal. Extensive experiments on the KITTI depth completion data set demonstrate that our method achieves smaller root mean squared error (RMSE) than current nonlearning methods.
Masked Triples

A classic approach to MPC uses preprocessed multiplication triples to evaluate arbitrary Boolean circuits. If the target circuit features conditional branching, e.g. as the result of a IF program statement, then triples are wasted: one triple is consumed per \(\mathtt {AND}\) gate, even if the output of the gate is entirely discarded by the circuit's conditional behavior.
Stereo and Mono Depth Estimation Fusion for an Improved and Fault Tolerant 3D Reconstruction

Depth estimation approaches are crucial for environment perception in applications like autonomous driving or driving assistance systems. Solutions using cameras have always been preferred to other depth estimation methods, due to low sensor prices and their ability to extract rich semantic information from the scene. Monocular depth estimation algorithms using CNNs may fail to reconstruct due to unknown geometric properties of certain objects or scenes, which may not be present during the training stage. Furthermore, stereo reconstruction methods, may also fail to reconstruct some regions for various other reasons, like repetitive surfaces, untextured areas or solar flares to name a few. To mitigate the reconstruction issues that may appear, in this paper we propose two refinement approaches that eliminate regions which are not correctly reconstructed. Moreover, we propose an original architecture for combining the mono and stereo results in order to obtain improved disparity maps. The proposed solution is designed to be fault tolerant such that if an image is not correctly acquired or is corrupted, the system is still able to reconstruct the environment. The proposed approach has been tested on the KITTI dataset in order to illustrate its performance.
Bidder Scalable M+1st-Price Auction with Public Verifiability

<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M+ 1\text{st}$</tex> -price auction, also called Vickrey auction, is a type of sealed-bid auction to sell <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> identical goods. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> bidders secretly choose a price from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> bidding points as their bid. The top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> bidders can buy the goods at the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M+ 1\text{st}$</tex> bidding price. A trusted manager is commonly used to compare these sealed-bids. In our research, trusted manager and trusted mix servers used by mix and match are removed. Instead of cooperating all managers or bidders to find out the winning bidders, winning bidders prove that they are a winner by themself. By further adopt a greedy strategy on searching the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M+ 1\text{st}$</tex> -price, the time complexity of each bidder can be reduced to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(P)$</tex> , which is the same as most previous researches. Thus, we construct a scheme that removed the manager without increasing bidders' time complexity. The implementation shows that the gas usage reduced 87% from a manager architecture in a 3 bidder and 6 bidding price setting. The cost to participate in this auction is 12,000, 000P gas or 600P US dollars at this moment, which is enough practical.
Non-Gaussian Continuous-Variable Graph States

Mode-tunable photon subtraction is a viable method to introduce non-Gaussian features in continuous-variable graph states. Non-Gaussian properties are shown to spread up to next-to-nearest neighbours of the graphs vertex in which the photon was subtracted.
FunDL

Feature-based description logics replace the notion of roles, interpreted as binary relations, with features, interpreted as unary functions. Another notable feature of these logics is their use of path functional dependencies that allow for complex identification constraints to be formulated. The use of features and path functional dependencies makes the logics particularly well suited for capturing and integrating data sources conforming to an underlying object-relational schema that include a variety of common integrity constraints. We first survey expressive variants of feature logics, including the boundaries of decidability. We then survey a restricted tractable family of feature logics suited to query answering, and study the limits of tractability of reasoning.
Affective Tendency of Movie Reviews based on BERT and TCN

For the large amount of movie review information on the web, this paper proposes a BERT (Bidirectional Encoder Representation from Transformers, BERT) and TCN (Temporal convolutional network, TCN) based sentiment propensity analysis network for movie reviews. The network uses a dynamic pre-training model of BERT to obtain the word vector representation of text, and uses TCN to efficiently obtain the high and low dimensional features of the vectors. Finally, a softmax classifier is used for classification prediction to obtain the sentiment tendency of text information. In this paper, the network is experimentally validated on two movie review datasets, and the results show that the proposed network for text has a good prediction effect.
Design of Campus Health Information System using Face Recognition and Body Temperature Detection

Currently under the epidemic crisis of COVID-19, campus epidemic prevention has become a hot topic, and temperature detecting equipment has become a necessity in public spaces. However, temperature detection systems that are widely sold on the market are relatively simple and cannot recognize personal identity. Besides, they cannot record individual temperature changes, and they are still inadequate in terms of managing personal health information. In this study, we proposed a system that can meet the needs of campus epidemic prevention called CHIS (Campus Health Information System). In CHIS, an infrared sensor is used for temperature detection and combined with face recognition. The body temperature is recorded while face recognition is performed, and the face ID and the collected real-time body temperature are transmitted to the cloud for viewing and managing by the school. The data will be managed centrally in the cloud and will be cleaned during daily processing. In the end, the student's health data history will be stored only in their personal Pod (Personal online datastore), a decentralized personal cloud data model that prevents the risk of large-scale data leakage due to centralized management. The combination of body temperature detection and face recognition avoids substituting the presence of a real person with photos or pictures, which further enhances security. It also reduces the risk of infection prompted by human detection, which increases safety.
Multiwavelength Neuromorphic Photonics

Neuromorphic photonics promises orders of magnitude improvements in both speed and energy efficiency over digital electronics. We will give an overview of neuromorphic photonic systems and their application to optimization and machine learning problems.
Fourth international workshop on health recommender systems (HealthRecSys 2019)

HealthRecSys 2019 was the 4th International Workshop on Health Recommender Systems held in conjunction with the 2019 ACM Conference on Recommender Systems in Copenhagen, Denmark. This workshop followed on from of the previous workshop in 2018 [4] and focused on the application and potentials of recommender systems on health promotion, health care and health-related topics. By engaging the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure.
Data Preparation

In the data understanding phase we have explored all available data and carefully checked if they satisfy our assumptions and correspond to our expectations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see the following chapters), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.
Definiteness projection

Abstract We argue that definite noun phrases give rise to uniqueness inferences characterized by a pattern we call definiteness projection . Definiteness projection says that the uniqueness inference of a definite projects out unless there is an indefinite antecedent in a position that filters presuppositions. We argue that definiteness projection poses a serious puzzle for e-type theories of (in)definites; on such theories, indefinites should filter existence presuppositions but not uniqueness presuppositions. We argue that definiteness projection also poses challenges for dynamic approaches, which have trouble generating uniqueness inferences and predicting some filtering behavior, though unlike the challenge for e-type theories, these challenges have mostly been noted in the literature, albeit in a piecemeal way. Our central aim, however, is not to argue for or against a particular view, but rather to formulate and motivate a generalization about definiteness which any adequate theory must account for.
Distributed Utterances

I propose an apparatus for handling intrasentential change in context. The standard approach has problems with sentences with multiple occurrences of the same demonstrative or indexical. My proposal involves the idea that contexts can be complex. Complex contexts are built out of ("simple") Kaplanian contexts by ordered n-tupling. With these we can revise the clauses of Kaplan's Logic of Demonstratives so that each part of a sentence is taken in a different component of a complex context. I consider other applications of the framework: to agentially distributed utterances (ones made partly by one speaker and partly by another); to an account of scare-quoting; and to an account of a binding-like phenomenon that avoids what Kit Fine calls "the antinomy of the variable."
A hand-eye calibration algorithm of binocular stereo vision based on multi-pixel 3D geometric centroid relocalization

The vision-guided robotic machining accuracy highly depends on the hand-eye calibration accuracy between robot and vision equipment. In order to address the problem of less parameter constraints in existing hand-eye calibration methods, in this paper a hand-eye calibration algorithm of binocular stereo vision is proposed based on multi-pixel 3D geometric centroid relocalization. The algorithm mainly includes three steps:1) the checkerboard relocalization images of multiple sets of fixed-point pose transformations are captured by the binocular stereo vision; 2) the robot tool center point (TCP) coordinates in the binocular coordinate system are obtained by an iterative reweighted least squares algorithm based on sub-pixel corner extraction, and 3) the hand-eye transformation matrix between the binocular system and the robot is obtained by the singular value decomposition (SVD). The experimental results show that both the average error and the mean square error of the proposed hand-eye calibration algorithm can reach 0.45mm and 0.21mm, respectively, which are much smaller than the existing algorithms and can meet the accuracy requirements of robotic positioning and machining.
Multinode State Transfer and Nonlocal State Preparation via a Unidirectional Quantum Network

Quantum networks with the ability to coherently transfer quantum states between different nodes open up possibilities for upscaling quantum technologies. In this work, we propose a theoretical protocol to implement the multinode state transfer and nonlocal state preparation in a one-dimensional network consisting of many quantum nodes coupled to a common unidirectional channel. By time-dependently modulating the coupling strength between the nodes and the channel, a quantum state stored in one node can be distributed to the other nodes through the channel, while a series of dark-state conditions ensure a desired final state. This protocol can also be used to prepare nonlocal entanglement states of these nodes, such as the W -type state. Importantly, it can be realized in various quantum platforms with currently available technologies.
Manipulating the symmetry of transverse momentum entangled biphoton states

Bell states are a fundamental resource in photonic quantum information processing. These states have been generated successfully in many photonic degrees of freedom. Their manipulation, however, in the momentum space remains challenging. Here, we present a scheme for engineering the symmetry of two-photon states entangled in the transverse momentum degree of freedom through the use of a spatially variable phase object. We demonstrate how a Hong-Ou-Mandel interferometer must be constructed to verify the symmetry in momentum entanglement via photon "bunching/anti-bunching" observation. We also show how this approach allows generating states that acquire an arbitrary phase under the exchange operation.
Exploring possibilities for solar irradiance prediction from solar photosphere images using recurrent neural networks

Studies of the Sun and the Earth’s atmosphere and climate consider solar variability as an important driver, and its constant monitoring is essential for climate models. Solar total and spectral irradiance are among the main relevant parameters. Physical semi-empirical and empirical models have been developed and made available, and they are crucial for the reconstruction of irradiance during periods of data failure or their absence. However, ionospheric and climate models would also benefit from solar irradiance prediction through prior knowledge of irradiance values hours or days ahead. This paper presents a neural network-based approach, which uses images of the solar photosphere to extract sunspot and active region information and thus generate inputs for recurrent neural networks to perform the irradiance prediction. Experiments were performed with two recurrent neural network architectures for short- and long-term predictions of total and spectral solar irradiance at three wavelengths. The results show good quality of prediction for total solar irradiance (TSI) and motivate further effort in improving the prediction of each type of irradiance considered in this work. The results obtained for spectral solar irradiance (SSI) point out that photosphere images do not have the same influence on the prediction of all wavelengths tested but encourage the bet on new spectral lines prediction.
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many labeling errors for most datasets. We proposes a local label point correction (LLPC) method to improve annotation quality for edge detection and image segmentation tasks. Our algorithm contains three steps: gradient-guided point correction, point interpolation and local point smoothing. We correct the labels of object contours by moving the annotated points to the pixel gradient peaks. This can improve the edge localization accuracy, but it also causes unsmooth contours due to the interference of image noise. Therefore, we design a point smoothing method based on local linear fitting to smooth the corrected edge. To verify the effectiveness of our LLPC, we construct a largest overlapping cervical cell edge detection dataset (CCEDD) with higher precision label corrected by our label correction method. Our LLPC only needs to set three parameters, but yields 30-40$\%$ average precision improvement on multiple networks. The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection. We hope that our study will give a strong boost to the development of the label correction for edge detection and image segmentation. We will release the dataset and code at https://github.com/nachifur/LLPC.
Energy requirement for implementing unitary gates on energy-unbounded systems

Abstract The processing of quantum information always has a cost in terms of physical resources such as energy or time. Determining the resource requirements is not only an indispensable step in the design of practical devices—the resources need to be actually provided—but may also yield fundamental constraints on the class of processes that are physically possible. Here we study how much energy is required to implement a desired unitary gate on a quantum system with a non-trivial energy spectrum. We derive a general lower bound on the energy requirement, extending the main result of Chiribella et al (2021 Phys. Rev. X 11 021014) from finite dimensional systems to systems with unbounded Hamiltonians. Such an extension has immediate applications in quantum information processing with optical systems, and allows us to provide bounds on the energy requirement of continuous variable quantum gates, such as displacement and squeezing gates.
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition

Human Activity Recognition is a field of research where input data can take many forms. Each of the possible input modalities describes human behaviour in a different way, and each has its own strengths and weaknesses. We explore the hypothesis that leveraging multiple modalities can lead to better recognition. Since manual annotation of input data is expensive and time-consuming, the emphasis is made on self-supervised methods which can learn useful feature representations without any ground truth labels. We extend a number of recent contrastive self-supervised approaches for the task of Human Activity Recognition, leveraging inertial and skeleton data. Furthermore, we propose a flexible, general-purpose framework for performing multimodal self-supervised learning, named Contrastive Multiview Coding with Cross-Modal Knowledge Mining (CMC-CMKM). This framework exploits modality-specific knowledge in order to mitigate the limitations of typical self-supervised frameworks. The extensive experiments on two widely-used datasets demonstrate that the suggested framework significantly outperforms contrastive unimodal and multimodal baselines on different scenarios, including fully-supervised fine-tuning, activity retrieval and semi-supervised learning. Furthermore, it shows performance competitive even compared to supervised methods.
Qurzon: A Prototype for a Divide and Conquer-Based Quantum Compiler for Distributed Quantum Systems

When working with algorithms on quantum devices, quantum memory becomes a crucial bottleneck due to low qubit count in NISQ-era devices. In this context, the concept of `divide and compute', wherein a quantum circuit is broken into several subcircuits and executed separately, while stitching the results of the circuits via classical post-processing, becomes a viable option, especially in NISQ-era devices. This paper introduces \textbf{Qurzon}, a proposed novel quantum compiler that incorporates the marriage of techniques of divide and compute with the state-of-the-art algorithms of optimal qubit placement for executing on real quantum devices. A scheduling algorithm is also introduced within the compiler that can explore the power of distributed quantum computing while paving the way for quantum parallelism for large algorithms. Several benchmark circuits have been executed using the compiler, thereby demonstrating the power of the divide and compute when working with real NISQ-era quantum devices.