Reviewerly/exHarmony huggingface dataset:
Tail Recursion Modulo Context: An Equational Approach

The tail-recursion modulo cons transformation can rewrite functions that are not quite tail-recursive into a tail-recursive form that can be executed efficiently. In this article we generalize tail recursion modulo cons (TRMc) to modulo contexts (TRMC), and calculate a general TRMC algorithm from its specification. We can instantiate our general algorithm by providing an implementation of application and composition on abstract contexts, and showing that our context laws_ hold. We provide some known instantiations of TRMC, namely modulo evaluation contexts (CPS), and associative operations , and further instantiantions not so commonly associated with TRMC, such as defunctionalized evaluation contexts, monoids , semirings , exponents , and cons products . We study the modulo cons instantiation in particular and prove that an instantiation using Minamide’s hole calculus is sound. We also calculate a second instantiation in terms of the Perceus heap semantics to precisely reason about the soundness of in-place update. While all previous approaches to TRMc fail in the presence of non-linear control (for example induced by call/cc, shift/reset or algebraic effect handlers), we can elegantly extend the heap semantics to a hybrid approach which dynamically adapts to non-linear control flow. We have a full implementation of hybrid TRMc in the Koka language and our benchmark shows the TRMc transformed functions are always as fast or faster than using manual alternatives.
Making a Type Difference: Subtraction on Intersection Types as Generalized Record Operations

In programming languages with records, objects, or traits, it is common to have operators that allow dropping, updating or renaming some components. These operators are useful for programmers to explicitly deal with conflicts and override or update some components. While such operators have been studied for record types, little work has been done to generalize and study their theory for other types. This paper shows that, given subtyping and disjointness relations, we can specify and derive algorithmic implementations for a general type difference operator that works for other types, including function types, record types and intersection types. When defined in this way, the type difference algebra has many desired properties that are expected from a subtraction operator. Together with a generic merge operator, using type difference we can generalize many operations on records formalized in the literature. To illustrate the usefulness of type difference we create an intermediate calculus with a rich set of operators on expressions of arbitrary type, and demonstrate applications of these operators in CP , a prototype language for Compositional Programming . The semantics of the calculus is given by elaborating into a calculus with disjoint intersection types and a merge operator. We have implemented type difference and all the operators in the CP language. Moreover, all the calculi and related proofs are mechanically formalized in the Coq theorem prover.
Avatar-Based Metaverse Interactions: A Taxonomy, Scenarios and Enabling Technologies

In virtual worlds like online games and metaverse platforms, an avatar represents a user in several graphical representations.In this paper, we offer a taxonomy for avatar-based interactions in the metaverse, highlighting scenarios that are relevant for various features of the used avatars.By using avatar types, flexibility, fidelity, and interaction as our chosen criteria, we try to include recent enabling technologies and existing avatar-based interactions in the taxonomy that is being provided.We explore how current metaverse research, platforms, and interaction technologies might be positioned in various circumstances to demonstrate the viability and usefulness of our approach.
Blockchain adoption and security management of large scale industrial renewable-based systems: Knowledge-based approach

Smart networks, e.g., smart grids (SGs) as complex interconnected systems have become more complex and require greater processing and communication capabilities due to the growing use of renewable energy (RE). In addition to being a reliable method for collecting and managing information, wireless networks (WNs) are capable of enhancing the performance of RE management. As a result of wireless data transfer risks and centralized power trades, SGs using WNs are unable to provide the security of the electricity market or ensure a high level of RE utilization. Blockchains (BCs), which are new technologies that enable data sharing, are gaining popularity, and it is thought to be able to address these issues. A BC-based, reliable power-exchange scheme is presented for SGs using WNs in this study. Using the BC for recording power data obtained from the WNs allows the smart contract for making appropriate trades according to the information. Energy trading and RE consumption are more efficient in a dual-chain architecture that consists of local energy trade BCs and RE trade BCs. A BC-based RE incentive framework is designed to have greater stability and scale among RE producers. Overall, the proposed technique not only can provide higher security and reliability to the system but also can mitigate fraud, as well as providing less operation cost for the system. Ultimately, the 56-bus test feeder layout is used to validate the framework.
Neural collapse inspired attraction–repulsion-balanced loss for imbalanced learning

Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model in sub-optimums when facing extreme class imbalance. It seriously harms the classification precision, especially on the minor classes. The essential reason is that the gradients of the classifier weights are imbalanced among the components from different classes. In this paper, we propose Attraction-Repulsion-Balanced Loss (ARB-Loss) to balance the different components of the gradients. We perform experiments on the large-scale classification and segmentation datasets and our ARB-Loss can achieve state-of-the-art performance via only one-stage training instead of 2-stage learning like nowadays SOTA works.
AIC-GNN: Adversarial information completion for graph neural networks

Graph neural networks (GNNs) have attracted extensive attention due to their demonstrated powerful performance in various graph mining tasks. The implicit assumption of GNNs being able to work is that all nodes have adequate information for meaningful aggregation. However, this is not easy to satisfy because the degrees of a real-world graph commonly follow the power-law distribution, where most nodes belong to low-degree nodes with limited neighborhoods. In this paper, we argue that to make GNNs better handle the low-degree node representation learning is the key to solving the above problem and propose a pluggable framework named Adversarial Information Completion Graph Neural Networks (AIC-GNN). It introduces a novel Graph Information Generator to fit adaptively the node missing information distribution. Then, the Graph Embedding Discriminator distinguishes between the node embeddings with the ideal information and the node embeddings after information completion. The representational capacity of the model is enhanced by adversarial training between the Generator and Discriminator. Meanwhile, the dual node embedding alignment mechanisms are employed to guide the high quality of predicted information. Extensive experiments demonstrate that AIC-GNN outperforms state-of-the-art methods on four real-world graphs.
Research on a face recognition algorithm based on 3D face data and 2D face image matching

Under the condition of weak light or no light, the recognition accuracy of the mature 2D face recognition technology decreases sharply. In this paper, a face recognition algorithm based on the matching of 3D face data and 2D face images is proposed. Firstly, 3D face data is reconstructed from the 2D face in the database based on the 3DMM algorithm, and the face depth image is obtained through orthogonal projection. Then, the average curvature map of the face depth image is used to enhance the data of the depth image. Finally, an improved residual neural network based on the depth image and curvature is designed to compare the scanned face with the face in the database. The method proposed in this paper is tested on the 3D face data in three public face datasets (Texas 3DFRD, FRGC v2.0, and Lock3DFace), and the recognition accuracy is 84.25%, 83.39%, and 78.24%, respectively.
Lexical knowledge enhanced text matching via distilled word sense disambiguation

This study proposes a method to improve text matching through integration of lexical knowledge from external resources to model the senses of potentially ambiguous words. Specifically, a sense-aware mechanism is designed wherein a word sense disambiguation (WSD) model is introduced into text matching and both tasks (WSD and matching) are simultaneously optimized via multi-task learning. The proposed WSD is a lightweight model that distills a pre-trained BERT-based model by leveraging the lexical knowledge obtained from WordNet. The sense information obtained from the WSD is integrated into matching explicitly and adaptively through the fusion of the learned sense representations with the word context representations generated from the baseline matching model. The effectiveness of the proposed approach is verified through extensive experiments with three distinct matching-based tasks: natural language inference, paraphrase identification, and answer selection. The results obtained for the respective datasets indicate that the proposed sense knowledge-enhanced matching mechanism outperforms several BERT-based baselines and other recent matching approaches.
Three-dimensional reconstruction for highly reflective diffuse object based on online measurement

The three-dimensional (3D) reconstruction for highly reflective diffuse object has always been challenged in fringe projection profilometry. So a 3D reconstruction method for highly reflective diffuse object based on online measurement is proposed. Only a fixed grating needs to be projected onto the highly reflective diffuse object. While this object is moving online across the projection area, partial highly reflective area (PHRA) may be changed in accordance. As long as PHRAs in the two online captured deformed patterns do not overlap, the profiles without PHRAs of the object at these two positions can be well reconstructed respectively by single-shot computer-generated moiré profilometry. Then a grayscale histogram threshold determination method is proposed to automatically and accurately locate these PHRAs, and the 3D profiles in these PHRAs can be made up by using feature match and effective point cloud non-rigid registration and fusion. So the complete profile of the object can be successfully reconstructed. Experimental results show the feasibility and effectiveness of the proposed method. Compared with the traditional 3D measuring methods for highly reflective diffuse object, the proposed method has simpler structural light control and higher accuracy.
Hasse sensitivity level: A sensitivity-aware trajectory privacy-enhanced framework with Reinforcement Learning

LBS services generate massive amounts of trajectory data over time, which will be shared with others for further intelligent services. Due to the ubiquity and openness of LBS, the user's trajectory may be collected and interfered by attackers. Existing solutions cannot take into account the privacy and availability of trajectory data at the same time. In order to achieve the balance between privacy and availability, the Hasse Diagram Sensitivity Differential Privacy with Reinforcement Learning (HDS-DPRL) is designed in this paper. The first module uses the ameliorated K-means clustering to reduce redundant position coordinate points. The second one includes an algorithm for calculating sensitive positions based on Hasse Diagram, which is used to store trajectories and construct a partial order relationship based on the access frequency of position point visits to calculate sensitive locations. The third module employs Reinforcement Learning to compute the optimal Laplace boundary and adds bounded Laplace noise to the Hasse Diagram that stores sensitive locations implement differential privacy. Extensive experiments on synthetic datasets and real-world datasets demonstrate the superior performance of our HDS-DPRL compared to the existing solutions while providing availability and privacy. Thus, HDS-DPRL can be applied to privacy-enhanced applications of trajectories.
Blind image deblurring using GLCM and negans obtuse mono proximate distance

Image deblurring is a challenging problem in computer vision, which aims to recover the sharp image from a blurred observation. This paper proposes the image deblurring algorithm based on Gray-Level Co-occurrence Matrix (GLCM) and Negans obtuse mono proximate distance to extract informative regions for better deblurring process without user guidance. The high-frequency layer is extracted from blurred image using 2D Haar wavelet, sparse approximation to estimate the sharper and more detailed high-frequency layer. From the high-frequency layer, rich edge region is extracted using GLCM along sliding window concepts after the canny edge detection. Finally, the extracted regions are applied for negans obtuse mono proximate distance. The proposed method avoids over-fitting data and reduces blurring. The proposed deblurring algorithm deblurs the blurred image by restoring image textures and details. The experiments performed are compared with existing deblurring algorithms to demonstrate the effectiveness of the proposed deblurring algorithm.
Trustful Blockchain-Based Framework for Privacy Enabling Voting in a University

In this study, we explore the challenges and potential solutions to blockchain-based voting. As a first step, we present a comparison of the relevant platforms for implementing smart contracts in decentralized applications (dApps). We analyze the top platforms, highlighting their advantages and disadvantages, their architecture, and which are more reliable for developing smart contracts. The goal is to find a technology that offers various facilities to the developer and multiple functionalities and performance in the development of smart contracts in a field that has seen an incredible pace of innovation. Based on the findings from our research, we propose a framework based on blockchain technology and smart contracts for university-level voting based on blockchains.
Research on the Application of Data Mining Technology in Physical Training

Data extraction methods have been widely used in different departments and achieved good results. The most typical example is the combination of data extraction technology and economics, which plays an important role in product classification, customer classification and market segmentation. This paper discusses the application of data mining technology in physical training. First, collect a large amount of data from sports training, and then use the data extraction method to extract useful information. On the one hand, in order to improve the performance of athletes, on the other hand, we can organize scientific training on the physical status indicators of athletes to prevent injuries. This paper discusses several common motion data extraction algorithms.
AGIM-net based subject-sensitive hashing algorithm for integrity authentication of HRRS images

The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed. This paper proposes a new subject-sensitive hashing algorithm for the integrity authentication of HRRS images. This algorithm takes AGIM-net (Attention Gate-based improved M-net) proposed in this paper to extract the subject-sensitive features of the HRRS images, and uses Principal Component Analysis (PCA) based method to compress and encode the extracted features. AGIM-net is an improved U-net based on attention mechanism, adding multi-scale input in the encoder stage to extract rich image features; adding multi-scale output in the decoder stage, and suppressing the features irrelevant to the subject through Attention Gate to improve the robustness of the algorithm. Experiments show that the proposed algorithm has improved robustness compared with existing algorithms, and the tamper sensitivity and security are basically equivalent to the existing algorithms.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation

Sequential recommendation (SR) learns users’ preferences by capturing the sequential patterns from users’ behaviors evolution. As discussed in many works, user–item interactions of SR generally present the intrinsic power-law distribution, which can be ascended to hierarchy-like structures. Previous methods usually handle such hierarchical information by making user–item sectionalization empirically under Euclidean space, which may cause distortion of user–item representation in real online scenarios. In this article, we propose a Poincaré-based heterogeneous graph neural network named Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation (PHGR) to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously. Specifically, for the purpose of explicitly capturing the hierarchical information, we first construct a weighted user–item heterogeneous graph by aliening all the user–item interactions to improve the perception domain of each user from a global view. Then the output of the global representation would be used to complement the local directed item–item homogeneous graph convolution. By defining a novel hyperbolic inner product operator, the global and local graph representation learning are directly conducted in Poincaré ball instead of commonly used projection operation between Poincaré ball and Euclidean space, which could alleviate the cumulative error issue of general bidirectional translation process. Moreover, for the purpose of explicitly capturing the sequential dependency information, we design two types of temporal attention operations under Poincaré ball space. Empirical evaluations on datasets from the public and financial industry show that PHGR outperforms several comparison methods.
Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.
Supervisory Control for Dynamic Feature Configuration in Product Lines

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems.
Real-Time Interaction for 3D Pixel Human in Virtual Environment

Conducting realistic interactions while communicating efficiently in online conferences is highly desired but challenging. In this work, we propose a novel pixel-style virtual avatar for interacting with virtual objects in virtual conferences that can be generated in real-time. It consists of a 2D segmented head video stream for real-time facial expressions and a 3D point cloud body for realistic interactions, both of which are generated from RGB video input of a monocular webcam. We obtain a human-only video stream with a human matting method and generate the 3D avatar’s arms with a 3D pose estimation method, which improves the stereoscopic realism and sense of interaction of conference participants while interacting with virtual objects. Our approach fills the gap between 2D video conferences and 3D virtual avatars and combines the advantages of both. We evaluated our pixel-style avatar by conducting a user study; the result proved that the efficiency of our method is superior to other various existing avatar types.
Solving Optimization Problems Using an Extended Gradient-Based Optimizer

This paper proposes an improved method for solving diverse optimization problems called EGBO. The EGBO stands for the extended gradient-based optimizer, which improves the local search of the standard version of the gradient-based optimizer (GBO) using expanded and narrowed exploration behaviors. This improvement aims to increase the ability of the GBO to explore a wide area in the search domain for the giving problems. In this regard, the local escaping operator of the GBO is modified to apply the expanded and narrowed exploration behaviors. The effectiveness of the EGBO is evaluated using global optimization functions, namely CEC2019 and twelve benchmark feature selection datasets. The results are analyzed and compared to a set of well-known optimization methods using six performance measures, such as the fitness function’s average, minimum, maximum, and standard deviations, and the computation time. The EGBO shows promising results in terms of performance measures, solving global optimization problems, recording highlight accuracies when selecting significant features, and outperforming the compared methods and the standard version of the GBO.