Reviewerly/exHarmony huggingface dataset:
Learning Small Decision Trees with Large Domain

One favors decision trees (DTs) of the smallest size or depth to facilitate explainability and interpretability. However, learning such an optimal DT from data is well-known to be NP-hard. To overcome this complexity barrier, Ordyniak and Szeider (AAAI 21) initiated the study of optimal DT learning under the parameterized complexity perspective. They showed that solution size (i.e., number of nodes or depth of the DT) is insufficient to obtain fixed-parameter tractability (FPT). Therefore, they proposed an FPT algorithm that utilizes two auxiliary parameters: the maximum difference (as a structural property of the data set) and maximum domain size. They left it as an open question of whether bounding the maximum domain size is necessary. The main result of this paper answers this question. We present FPT algorithms for learning a smallest or lowest-depth DT from data, with the only parameters solution size and maximum difference. Thus, our algorithm is significantly more potent than the one by Szeider and Ordyniak as it can handle problem inputs with features that range over unbounded domains. We also close several gaps concerning the quality of approximation one obtains by only considering DTs based on minimum support sets.
The motivation of university students in speaking English on extracurricular activity: Extrinsic or intrinsic?

Research on motivation of students in speaking English on extracurricular activity has been really conducted but students are still afraid of speaking English.. This manuscript is a research result dealing with motivation of English Education students in Department of Language and Art Education at Faculty of Teacher Training and Education, Tadulako University in speaking English on extracurricular activity. English speaking skill should be developed on extracurricular activity to make ideal atmosphere, but that activity has not been effective yet on all study programs except English Education at that faculty, so that the students have not been able to speak English yet. This research aims at identifying what motivates the university students and how their intention is. Its target is they taste different academic athmosphere to have English skill to interact with English speakers. Respondents of the research were 25 English education students. Its data were collected through Identification (as a procedure) by using Questionnaire (as an instrument) then analysed by using Descriptive Method. The university students join it to be able to communicate in English and get certificate. Those are extrinsic moivation and intrinsic one. Most of them were selfmotivated, so their intrinsic motivations were greater than their extrinsic one. Motivation by themselves or selfmotivator is the greatest. This implies to support decision making in developing English speaking skill of university students. They hopefully carry out and join extracurricular activities to practice their English orally for having English speaking skill proficiently.
Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey

A large field-of-view fisheye camera allows for capturing a large area with minimal numbers of cameras when they are mounted on a high position facing downwards. This top-view omnidirectional setup greatly reduces the work and cost for deployment compared to traditional solutions with multiple perspective cameras. In recent years, deep learning has been widely employed for vision related tasks, including for such omnidirectional settings. In this survey, we look at the application of deep learning in combination with omnidirectional top-view cameras, including the available datasets, human and object detection, human pose estimation, activity recognition and other miscellaneous applications.
BMRN: Boundary Matching and Refinement Network for Temporal Moment Localization with Natural Language

Temporal moment localization (TML) aims to retrieve the best moment in a video that matches a given sentence query. This task is challenging as it requires understanding the relationship between a video and a sentence, as well as the semantic meaning of both. TML methods using 2D temporal maps, which represent proposal features or scores on all moment proposals with the boundary of start and end times on the m and n axes, have shown performance improvements by modeling moment proposals in relation to each other. The methods, however, are limited by the coarsely pre-defined fixed boundaries of target moments, which depend on the length of training videos and the amount of memory available. To overcome this limitation, we propose a boundary matching and refinement network (BMRN) that generates 2D boundary matching and refinement maps along with a proposal feature map to obtain the final proposal score map. Our BMRN adjusts the fixed boundaries of moment proposals with predicted center and length offsets from boundary refinement maps. In addition, we introduce a length-aware proposal feature map that combines a cross-modal feature map and a similarity map between the predicted duration of the target moment and moment proposals. Our approach leads to improved TML performance on Charades-STA and ActivityNet Captions datasets, outperforming state-of-the-art methods by a large margin.
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection

The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms, e.g., Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git.
Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments

Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in known mathematical definitions of shape. This paper presents a deep-learning model inspired by geometric moments, a classically well understood approach to measure shape-related properties. The proposed method consists of a trainable network for generating coordinate bases and affine parameters for making the features geometrically invariant yet in a task-specific manner. The proposed model improves the final feature’s interpretation. We demonstrate the effectiveness of our method on standard image classification datasets. The proposed model achieves higher classification performance compared to the baseline and standard ResNet models while substantially improving interpretability.
Implicit Neural Mapping for a Data Closed-Loop Unmanned Aerial Vehicle Pose-Estimation Algorithm in a Vision-Only Landing System

Due to their low cost, interference resistance, and concealment of vision sensors, vision-based landing systems have received a lot of research attention. However, vision sensors are only used as auxiliary components in visual landing systems because of their limited accuracy. To solve the problem of the inaccurate position estimation of vision-only sensors during landing, a novel data closed-loop pose-estimation algorithm with an implicit neural map is proposed. First, we propose a method with which to estimate the UAV pose based on the runway’s line features, using a flexible coarse-to-fine runway-line-detection method. Then, we propose a mapping and localization method based on the neural radiance field (NeRF), which provides continuous representation and can correct the initial estimated pose well. Finally, we develop a closed-loop data annotation system based on a high-fidelity implicit map, which can significantly improve annotation efficiency. The experimental results show that our proposed algorithm performs well in various scenarios and achieves state-of-the-art accuracy in pose estimation.
Executable Map Paper (EMaP) for Archaeological LiDAR

Archaeological LiDAR has evolved into an indispensable component of archaeological prospection and landscape archaeology. However, it is frequently employed as a black-box digital method, which confines it to the realm of a specialized field. Making scientific publications more accessible, transparent, and reproducible is one of the steps required to turn LiDAR into a background method for all archaeologists. This is possible, according to our proposal, through the Executable Map Paper concept. This concept can be understood as a type of executable paper that pursues Open Science&rsquo;s goals. The proposed technical solution consists of a PDF frontend, a persistence layer, and a hyperlinked interactive map. Executable Map Paper is applicable to all map-dependent sciences, including geography, geology, and any geoscience. In this paper, we outline the theoretical context, propose technical solutions, and provide a practical illustration.
User Interaction-Aware Knowledge Graphs for Recommender Systems

The performance of recommender systems can be improved effectively by using knowledge graphs as auxiliary information. However, most of the knowledge graph-based recommendations focus on learning item representations in knowledge graphs, capture the collaborative signals between user interactions inadequately. The user-item bipartite graph contains explicit preference information of users, and the collaborative signals of user-item interactions help to enhance representations of users. A user interaction-aware knowledge graph recommendation model (UIKR) is proposed, which enhances user representation and introduces the higher-order collaborative signals in user interactions into the representation learning of items in knowledge graphs. Specifically, the high-order collaborative signals hidden in the user-item bipartite graph are captured to strengthen user representations. Then, the enhanced user representation is applied to the representation learning of items in knowledge graphs. A hybrid attention function is proposed to aggregate neighbor representation of items, which augments the propagation of user preferences in knowledge graphs and helps to learn personalized item representations. Finally, the user interaction-aware item representations and the enhanced user representations are used for recommendations. Extensive experiments are conducted on two standard datasets and the results show that proposed UIKR model outperforms current state-of-the-art baselines.
Depression Emotion Multi-Label Classification Using Everytime Platform With DSM-5 Diagnostic Criteria

The prevalence of depressive disorder in South Korea is the highest among countries in the Organization for Economic Cooperation and Development.The rate of depressive disorders among young people in their 20s is significantly high.Because depressive disorders lead to a decrease in daily function, it is important to identify them quickly and treat them appropriately.In many countries, including Korea, mental disorders are diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) classification criteria for mental disorders.However, research on the prediction of depressive disorders using DSM-5 classification criteria is sparse.In this study, we designed a method of predicting depressive disorders by identifying the characteristics of people in their twenties who are experiencing depression.We employed data from the Everytime platform and created a multi-label classification methodology that meets the DSM-5 diagnostic criteria for depressive disorders.Moreover, a gated recurrent unit was used to calculate the probability for each label.
A clustering algorithm for overlapping Gaussian mixtures

Gaussian mixture models (GMM) are widely used as a probabilistic model for density estimation for multivariate data and as an unsupervised clustering algorithm to provide a soft clustering to the available data. The GMM relies on the expectation-maximization algorithm for maximizing the likelihood. A new approach is proposed in the present work, which depends on Approximate Bayesian Computation and aims not only to estimate the population parameters but also to assign each observation to a specific subpopulation. The performance, in terms of mean estimates for the parameters of each subpopulation, number of observations assigned to each cluster, accuracy, and other performance metrics, of the new approach is compared with the expectation-maximization algorithm for the GMM and the K-means algorithm under several challenging simulation scenarios and is applied to a real data set.
Cyclic Hypersequent System for Transitive Closure Logic

Abstract We propose a cut-free cyclic system for transitive closure logic (TCL) based on a form of hypersequents , suitable for automated reasoning via proof search. We show that previously proposed sequent systems are cut-free incomplete for basic validities from Kleene Algebra (KA) and propositional dynamic logic ( $$\text {PDL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>PDL</mml:mtext> </mml:math> ), over standard translations. On the other hand, our system faithfully simulates known cyclic systems for KA and $$\text {PDL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>PDL</mml:mtext> </mml:math> , thereby inheriting their completeness results. A peculiarity of our system is its richer correctness criterion, exhibiting ‘alternating traces’ and necessitating a more intricate soundness argument than for traditional cyclic proofs.
Singular value decomposition for longitudinal, transverse and mixed ray transforms of 2D tensor fields

Abstract The operators of longitudinal, transverse and mixed ray transforms acting on two-dimensional symmetric tensor fields of arbitrary degree m in an unit disk are considered in the article. The singular value decompositions of the operators for a parallel scheme of data acquisition are constructed. Orthogonal bases in original spaces and image spaces are constructed using harmonic, Jacobi and Gegenbauer polynomials. Based on the obtained decompositions the polynomial expressions for the (pseudo)inverse and adjoint operators are obtained.
A Regulatable Mechanism for Transacting Data Assets

Blockchain technology is increasingly utilized for data asset transactions, particularly for private data that should not be shared with unauthorized parties. To ensure auditability and traceability of transaction records and data, we propose a triple-receiver public key encryption scheme based on bilinear mapping. This scheme serves as a regulated mechanism for data asset transactions and is integrated into a blockchain-based platform with a digital asset exchange protocol. In our approach, the sender encrypts a message using its private key and the public keys of three designated receivers. Each receiver can then independently decrypt the message using their private key. We demonstrate that our scheme is secure against chosen-plaintext and adaptive chosen-ciphertext attacks, provided that the gap bilinear Diffie-Hellman problem and the computational Diffie-Hellman problem are computationally infeasible. In comparison to Diameter’s dual-receiver encryption scheme, our method offers similar decryption speeds. Additionally, we incorporate the significant feature that allows both the sender and all three receivers to decrypt independently. Performance evaluations indicate that our scheme is well-suited for data asset transactions in the Internet of Things (IoT), offering decentralized and trust management capabilities.
A randomized network approach to multifractal texture descriptors

Texture recognition is one of the most important tasks in computer vision, with numerous applications in several areas. Despite the recent success of end-to-end deep learning models in image recognition in general, when it comes to texture images, the classical approach based on the combination of a local predefined descriptor with a pooling encoder is still competitive and may be advantageous, especially when we do not have access to a large amount of data for training or when understanding what each step in the algorithm pipeline performs is an important matter. In this context, here we propose a method for texture recognition based on a hybrid approach that combines a randomized neural network with multifractal transform for a local complete and robust representation. The first stage of the method employs a randomized neural network similar to that used in extreme learning machines. We use pixel intensities within each particular neighborhood as inputs and pixels strategically placed around the neighborhood center as our (multiple) targets. The learnable weights (connecting hidden and output layers) are computed over the entire image. In the second stage, we compute a multifractal measure over each pixel and repeat the procedure with the randomized model. Finally, we combine the learnable weights of both representations to compose our final descriptors. The effectiveness of our approach is verified on texture classification, over benchmark databases, and on a practical task of plant species identification. In both cases, our method achieves promising results, being competitive with several approaches in the state-of-the-art of the area.
Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works addressed the issue with the intuition of decomposing complex sentences and linking simple ones, such as rhetorical-structure-theory (RST)-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple sentence decomposition (SSD), etc. However, these works are not applicable for semantic parsing such as abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments with semantic relations and unavailabilities to preserve the original semantics. Following the same intuition and avoiding the deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), for capturing clausal structures of complex sentences, based on the linguistic research of clause hierarchy. With the HCA framework, we annotated a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more-silver annotations. In evaluating the proposed models on our manually annotated HCA dataset, the performances of clause segmentation and parsing resulted in 91.3% F1-scores and 88.5% Parseval scores, respectively. Due to the same model architectures employed, the performance differences of the clause/discourse segmentation and parsing subtasks was reflected in our HCA corpus and compared discourse corpora, where our sentences contained more segment units and fewer interrelations than those in the compared corpora.
Video reconstruction from a single motion blurred image using learned dynamic phase coding

Video reconstruction from a single motion-blurred image is a challenging problem, which can enhance the capabilities of existing cameras. Recently, several works addressed this task using conventional imaging and deep learning. Yet, such purely digital methods are inherently limited, due to direction ambiguity and noise sensitivity. Some works attempt to address these limitations with non-conventional image sensors, however, such sensors are extremely rare and expensive. To circumvent these limitations by simpler means, we propose a hybrid optical-digital method for video reconstruction that requires only simple modifications to existing optical systems. We use learned dynamic phase-coding in the lens aperture during image acquisition to encode motion trajectories, which serve as prior information for the video reconstruction process. The proposed computational camera generates a sharp frame burst of the scene at various frame rates from a single coded motion-blurred image, using an image-to-video convolutional neural network. We present advantages and improved performance compared to existing methods, with both simulations and a real-world camera prototype. We extend our optical coding to video frame interpolation and present robust and improved results for noisy videos.
Return connectedness and volatility dynamics of the cryptocurrency network

The study examines the connectedness of return and volatility spillovers among cryptocurrencies using the Volume Adjusted Return Network Index (VARNI) and the Volume Adjusted Volatility Network Index (VAVNI) developed through the Student-t Copula function and the DCC-MGARCH. The constructed indices, VARNI and VAVNI, identify return contagion and volatility spillover contagion in cryptocurrencies around Coronavirus Disease 2019 (COVID-19) and the recent Russo-Ukrainian War. The results show that VARNI and VAVNI dispersed before the COVID-19 crisis, concentrated during the COVID-19 crisis, and highly concentrated during the Russo-Ukrainian War. Moreover, the Uncertainty Index affects the volatility spillover from one cryptocurrency to another but does not affect the return connectedness.