Reviewerly/exHarmony huggingface dataset:
Modal Functional (Dialectica) Interpretation

We adapt our light Dialectica interpretation to usual and light modal formulas (with universal quantification on boolean and natural variables) and prove it sound for a non-standard modal arithmetic based on Goedel's T and classical S4. The range of this light modal Dialectica is the usual (non-modal) classical Arithmetic in all finite types (with booleans); the propositional kernel of its domain is Boolean and not S4. The `heavy' modal Dialectica interpretation is a new technique, as it cannot be simulated within our previous light Dialectica. The synthesized functionals are at least as good as before, while the translation process is improved. Through our modal Dialectica, the existence of a realizer for the defining axiom of classical S5 reduces to the Drinking Principle (cf. Smullyan).
POVM Design for Quantum State Discrimination

A qubit pure state can be specified by a point on the Bloch sphere, and similarly certain quantum measurements can be specified by M points on a sphere which we refer to as an Etro sphere. A key objective of this paper is to compare the performance for discriminating between two qubit pure states using POVM designs based on the distribution of M points on the Etro sphere. We specifically address the case where the alignment of the M points relative to the coordinates of the sphere is unknown. Of particular interest is the insensitivity of the POVM designs as measured by the difference between the maximum and minimum probabilities of error over all alignments. We consider distributions of the M points corresponding to Platonic solids as well as optimal distributions with respect to several criteria including, for example, maximum nearest neighbor distance and minimum Riesz s-energy. We provide evidence through simulation of various performance tradeoffs such as the tradeoff between stability and best case performance.
MAJORITY-3SAT (and Related Problems) in Polynomial Time

Majority-SAT (a.k.a. MAJ-SAT) is the problem of determining whether an input n-variable formula in conjunctive normal form (CNF) has at least 2^(n-1) satisfying assignments. Majority-SAT and related problems have been studied extensively in various AI communities interested in the complexity of probabilistic planning and inference. Although Majority-SAT has been known to be PP-complete for over 40 years, the complexity of a natural variant has remained open: Majority-kSAT, where the input CNF formula is restricted to have clause width at most k. We prove that for every k, Majority-kSAT is in P; in fact, the problem can be solved in linear time (whereas the previous best-known algorithm ran in exponential time). More generally, for any positive integer k and constant p in (0,1) with bounded denominator, we give an algorithm that can determine whether a given k-CNF has at least p(2^n) satisfying assignments, in deterministic linear time. We find these results surprising, as many analogous problems which are hard for CNF formulas remain hard when restricted to 3-CNFs. Our algorithms have interesting positive implications for counting complexity and the complexity of inference, significantly reducing the known complexities of related problems such as E-MAJ-kSAT and MAJ-MAJ-kSAT. Our results immediately extend to arbitrary Boolean CSPs with constraints of arity k. At the heart of our approach is an efficient method for solving threshold counting problems by extracting and analyzing various sunflowers found in the corresponding set system of a k-CNF. Exploring the implications of our results, we find that the tractability of Majority-kSAT is somewhat fragile, in intriguing ways. For the closely related GtMajority-SAT problem (where we ask whether a given formula has greater than 2^(n-1) satisfying assignments) which is also known to be PP-complete, we show that GtMajority-kSAT is in P for k at most 3, but becomes NP-complete for k at least 4. We also show that for Majority-SAT on k-CNFs with one additional clause of arbitrary width, the problem is PP-complete for k at least 4, is NP-hard for k=3, and remains in P for k=2. These results are counterintuitive, because the “natural” classifications of these problems would have been PP-completeness, and because there is a stark difference in the complexity of GtMajority-kSAT and Majority-kSAT for all k at least 4.
Differential Privacy Protection and Game Analysis of Intelligent Transportation Data

With the informatization and intelligentization of the Internet of Vehicle (IoV), location-based services have brought great convenience to users, but it has also brought about the security problem of privacy exposure. Vehicle users hope that their private data will be protected and at the same time they will be able to obtain good road service quality. In response to the above problems, this paper proposes a location privacy protection method based on local differential privacy. Firstly, we use the Voronoi diagram to divide and number the road network space; then, we perform K-RR random perturbation on the user's location data to make the result meet the local differential privacy. Thirdly, we set up an incentive mechanism, build a game model, and define the user's payment matrix under the verification strategy and the server's cheating strategy is analyzed, and the user's expected revenue is analyzed. The experimental results show that the user's benefit first decreases and then increases with the increase of the privacy protection parameters. The benefit is linear, and the road condition service quality has a relatively large impact on the benefit of users' shared location.
Bayesian Nonparametric Derivation of Spawning in Multi-Object Tracking: from Association to Tracking

In tracking multiple objects, it is often assumed that each observation (measurement) is originated from one and only one object. However, we may encounter a situation that each measurement may or may not be associated with multiple objects at each time step –spawning. Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death. In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. Number of objects at each time step, itself, is also assumed to be unknown. We, then, show through experiments the advantage of nonparametric modeling to scenarios with spawning events. Our experiment results also demonstrate the advantages of our framework over the existing methods.
Energy-aware Preprocessing for Distributed Training in D2D Edge Network with Non-iid Data

Inherent non-iid characteristic of heterogeneous devices' local dataset slows down the model training process and decreases the training accuracy. To tackle this problem, we propose a dataset reconstruction scheme to transform training device's non-iid dataset into iid dataset via data exchange among trusted devices. Two data exchange modes are proposed to serve the scheme. Considering the transmission overhead, we formulate an optimization problem named NORS to minimize the total energy consumption of devices. We then design an approximation algorithm named X-GTR to obtain a near-optimal auxiliary devices set for dataset reconstruction, while meeting the variance constraint of NORS. For comparison, we propose a baseline algorithm that randomly selects auxiliary devices for dataset reconstruction. Numerical results show that, with the total number of devices increasing, our proposed algorithm outperforms baseline algorithm on reducing energy consumption.
Cryptanalysis and Improvement of an Access Control Protocol for Wireless Body Area Networks

Since the emerge of wireless body area networks (WBANs) as a new technology in telemedicine, the challenges of secure communications in these networks have been noticed, extensively. Recently, Gao et al. have designed an efficient access control protocol for WBANs and claimed that their proposal can authenticate the physician to the patient and satisfy the confidentiality of the request message sent from the physician to the patient concurrently, in a certificateless setting. Moreover, at the end of the protocol the physician and the patient establish a session key for their next secure communications. They first designed a certificateless signcryption (CL-SC) scheme and then implied it to propose their access control protocol. In this paper, we design a key replacement attack against the Gao et al.'s CL-SC scheme, in which the adversary can obtain the confidential request message sent from the physician to the patient. Moreover, based on our designed attack, the adversary can obtain the session key established by the physician for the next communications to the patient. Afterwards, we fix the scheme to be secure against our proposed attacks.
Towards Corruption-Agnostic Robust Domain Adaptation

Great progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domains are independent and identically distributed with training target domains. However, due to unpredictable corruptions (e.g., noise and blur) in real data, such as web images and real-world object detection, domain adaptation methods are increasingly required to be corruption robust on target domains. We investigate a new task, corruption-agnostic robust domain adaptation (CRDA), to be accurate on original data and robust against unavailable-for-training corruptions on target domains. This task is non-trivial due to the large domain discrepancy and unsupervised target domains. We observe that simple combinations of popular methods of domain adaptation and corruption robustness have suboptimal CRDA results. We propose a new approach based on two technical insights into CRDA, as follows: (1) an easy-to-plug module called domain discrepancy generator (DDG) that generates samples that enlarge domain discrepancy to mimic unpredictable corruptions; (2) a simple but effective teacher-student scheme with contrastive loss to enhance the constraints on target domains. Experiments verify that DDG maintains or even improves its performance on original data and achieves better corruption robustness than baselines. Our code is available at: https://github.com/YifanXu74/CRDA .
On Improving Perceptual Image Hashing Using Reference Image Construction

This paper proposes an improved framework for perceptual image hashing, which is able to improve existing image hashing schemes. In our framework, hash sequence contains two parts. The first one is generated by an existing perceptual image hashing scheme on an input image. Then, a reference image is constructed by executing Wiener filtering on the input image. Subsequently, the same hashing scheme is executed on the reference image to obtain the second part. Finally, the two parts are combined together to form the improved hash. With the improved hash, the properties of main content of image can be fully extracted. Experimental results show that the performances of existing perceptual image hashing schemes, e.g., perceptual robustness, discrimination capability, can be improved using our framework.
GTT-Net: Learned Generalized Trajectory Triangulation

We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry. We build on a graph-theoretic formulation of the generalized trajectory triangulation problem, where non-concurrent multi-view imaging geometry is known but global image sequencing is not provided. GTT-Net learns pairwise affinities modeling the spatio-temporal relationships among our input observations and leverages them to determine 3D geometry estimates. Experiments reconstructing 3D motion-capture sequences show GTT-Net outperforms the state of the art in terms of accuracy and robustness. Within the context of articulated motion reconstruction, our proposed architecture is 1) able to learn and enforce semantic 3D motion priors for shared training and test domains, while being 2) able to generalize its performance across different training and test domains. Moreover, GTT-Net provides a computationally streamlined framework for trajectory triangulation with applications to multi-instance reconstruction and event segmentation.
End-to-End Supervised Zero-Shot Learning with Meta-Learning Strategy

Zero-shot learning (ZSL) is a challenging but practical task in the computer vision field. ZSL tries to recognize new unknown categories by provided with training data from other known categories. Recently, the ZSL problem can be solved in a supervised learning way by using deep generative models to synthesize data as the training data for unknown categories. In this work, we design an end-to-end supervised ZSL method in which the data generation network and the object classification network are trained jointly. To enhance the generalization performance of the proposed supervised ZSL method, meta-learning strategy is introduced to mitigate the domain shift problem between the synthesized data and the real data of unknown categories. Experimental results on ZSL standard datasets demonstrate the significant superiority of the end-to-end strategy and the meta-learning strategy for the proposed model in ZSL tasks.
An Adaptive Lattice of Full-Domain Generalization for Sequential Data Publishing

Sequential data publishing is a scenario of multiple releases in which the attributes are changed for each release. To protect the privacy of individuals in a sequential dataset, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l$</tex> - diversity can be applied, and full-domain generalization is a generalization technique that can transform a given dataset to satisfy the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l$</tex> -diversity requirement. In full-domain generalization, an answer of domain generalization can be searched from a lattice of domain generalization. However, a lattice of domain generalization consists of all possible domain generalizations, i.e., nodes, that is not efficient for searching. Therefore, this paper presents an adaptive lattice of domain generalization for a sequential dataset that can adapt the number of nodes in the lattice based on our proposed common root node. Our experimental results show that the adaptive lattice of domain generalization can considerably reduce the number of nodes in the lattice of domain generalization compared with the traditional lattice.
Deep neural network method to study computer game

Computer game is the main research direction in the field of artificial intelligence, the research object of this computer gameselection for card games of incomplete information game dou landlord, aiming at the problem of traditional modeling methods need to manually extract the features of, through the analysis of the principle of rival modeling algorithm and characteristics, this paper proposes a neural network and decision tree based on depth assessment modeling method of rivals, The tedious manual feature extraction process is avoided. The experimental results show that the overall winning chip is higher than that of the agents using static evaluation method, which improves the level of the system game to a certain extent.
Optical Flow Fusion Synthesis Based on Adversarial Learning from Videos for Facial Action Unit Detection

Human expression often happens simultaneously with head posture in real-time video, facial Action Units (AUs) is the key factor in facial expression detection. Optical flow can effectively capture weak motion displacements, that is, it can capture facial AUs caused by facial expressions. But the optical flow would produce a lot of noise, which would adverse the detection performance. To achieve a better facial AU detection performance, we propose a novel Optical Flow Synthesis Generative Adversarial Network (OFS-GAN). Firstly, we calculate the optical flow vector of the source frame and the target frame pair that randomly selected from video clips of facial expressions to promote the robustness of OFS-GAN. Secondly, in the generator of OFS-GAN, representation input into the encoder is yield by the optical flow vector concatenated with the source frame. The feature map output from the encoder is fed into the decoder to synthesize a target frame named generated target frame. In the end, through the comparison of the generated target frame and the target frame randomly selected from the target stage, OFS-GAN learns discriminative and robust facial action feature for facial AU detection following the principle of adversarial learning. Our novel OFS-GAN has been tested on DISFA+ and CK+ dataset with LOSO evaluation method. Qualitative results of our experiment demonstrate that OFS-GAN approaches or exceeds existing optical flow or deep learning algorithms.
Descriptive Statistics based on Frequency Distribution

To work with distributional data, the definitions of univariate and bivariate descriptive statistics are fundamental concepts. The first descriptive concepts for histogram-valued variables have to take into account the variability within data and were derived from the empirical density functions defined for this kind of variables. Concepts such as sample mean, variance and covariance were derived following a similar reasoning as for the classical concepts of mean and variance of a real-valued variable. Because important properties are not always verified, for some of the symbolic descriptive measures, more than one definition has been proposed for the same concept as variance and covariance.
Cognitive Pairwise Comparisons for Human Centric Design in Artificial Intelligence and Data Science

Human centric artificial intelligence and data science (HCAD) systems involve human interventions in the loop to offset the limitations and drawbacks of machine algorithms to continuously improve the performance results. Data transformations, features selection, features weighting and learning models selection, which usually require laborious involvement in iterations of AI&DS pipelines, are the challenging and time consuming tasks for the AI&DS development. Cognitive pairwise comparisons (CPC) approach is a human-centric approach to elicit human preferences and priorities in structural format. To endow essential facets of human-centricity, this research proposes the initiatives how CPC for user preference elicitation can be quantified in systematic ways used in these challenging tasks in AI&DS. To show the usability and validity of these facets, this study offers several applications of movie recommender systems, exemplify the ways to show how CPC can be used for the interactions between human judgements and the data in supervised learning, dimension reduction and unsupervised learning algorithms.
Research on Geostress Inversion Method of Computer Big Data Technology in Support Vector Regression Algorithm

The in-situ stress field is the basic data for the excavation and support design of deep my roadways. The article uses support vector machine (SVM) theory based on statistical learning theory to establish a calculation model. At the same time, the data of the horizontal maximum ground stress and pore pressure field measured at the well site were used to perform a fitting and the influence of the kernel function in the fitting calculation was compared and analyzed. Based on the principle of support vector machine, the mechanical parameters and initial stress field of the calculation area are inverted. The calculated results are basically consistent with the measured in-situ stress values and meet the accuracy requirements. This shows that the inversion result is consistent with the actual project, and the inversion method used is reasonable.
A Survey of Adopting Smart Home Healthcare via IoT Technology

With recent advances in internet technologies, Internet of Things (IoT) considered as one of the new emerging technologies, as well as it is a good opportunity to investee, where it expected to reach in 2021 more than three thousand billion dollars. Most of the literature focused on the technical aspects of the IoT services, as well as the benefits sides, while few studies interested on the behavioral studies that illustrate the patients’ perspective about the adoption IoT and use it for home healthcare purposes. The purpose of this paper is to investigate the factors that might have influence on the adoption of IoT at homes for healthcare purposes. Consequently, it has been developed, and tested a conceptual framework of factors determining patients’ acceptance of IoT technology at homes, to demonstrate whether there is a relationship between some factors, and the propensity of patients towards the adoption of IoT. The framework of this paper has been built based on the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Readiness Theory (TRT). supplemented by a critical review of related articles. The outcome of this paper is a conceptual framework that links the performance, and effort expectancy in UTAUT with other factors such as security and privacy in TRT for the IoT services.
A method to visualize the physical assembly sequence of ship intermediate products with augmented reality (AR)

In the manufacturing process of ship, the 3D model carries a large amount of information. These data cannot be used directly, and must usually be converted into a specific format before they can be used. According to the standard manufacturing process for ship hulls, the manufacturing information in the 3D model must be converted to 2D production drawings, then the drawings need to be read by experienced workers. Besides being time-consuming and laborious, this process also requires a lot of experience from the workers. We propose and implement a method for visualization of assembly process data based on Augmented Reality technology so that assembly process information of manufactured parts can be rendered in 3D animation. The form is projected onto the hull parts to assist its assembly and reduce the number of errors caused by data conversion. The method’s effectiveness is demonstrated through one synthesis hull parts.
Multisensor-multitarget tracking based on belief propagation against false data injection attacks and denial of service attacks

This paper is concerned with the multisensor multitarget tracking where the sensor network can potentially be compromised by adversarial attacks, including false data injection (FDI) attacks and denial of service (DoS) attacks. We propose a multisensor multitarget tracking algorithm against FDI and DoS attacks based on belief propagation (BP) message passing method. With the factorization of joint posterior density, the statistical structure of the tracking problem is described by a factor graph. A BP-based algorithm is derived based on the factor graph for an efficient evaluation of the marginal posterior densities of the target states. The marginal posterior densities are then utilized for the detection and estimation of the multitarget states. Then, we develop an efficient Gaussian mixture implementation of the proposed BP-based algorithm for the linear Gaussian measurement and state evolution model. Simulation results illustrate that the proposed multisensor multitarget tracking algorithm can provide reliable tracking performance against FDI and DoS attacks.