Reviewerly/exHarmony huggingface dataset:
Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems

Decentralized stochastic gradient descent methods have attracted increasing interest in recent years. Numerous methods have been proposed for the nonconvex finite-sum optimization problem. However, existing methods have a large sample complexity, slowing down the empirical convergence speed. To address this issue, in this paper, we proposed a novel decentralized stochastic gradient descent method for the nonconvex finite-sum optimization problem, which enjoys a better sample and communication complexity than existing methods. To the best of our knowledge, our work is the first one achieving such favorable sample and communication complexities. Finally, we have conducted extensive experiments and the experimental results have confirmed the superior performance of our proposed method.
A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data

Inferring the causal structure of a set of random variables is a crucial problem in many disciplines of science. Over the past two decades, various approaches have been pro- posed for causal discovery from observational data. How- ever, most of the existing methods are designed for either purely discrete or continuous data, which limit their practical usage. In this paper, we target the problem of causal structure learning from observational mixed-type data. Although there are a few methods that are able to handle mixed-type data, they suffer from restrictions, such as linear assumption and poor scalability. To overcome these weaknesses, we formulate the causal mechanisms via mixed structure equation model and prove its identifiability under mild conditions. A novel locally consistent score, named CVMIC, is proposed for causal directed acyclic graph (DAG) structure learning. Moreover, we propose an efficient conditional independence test, named MRCIT, for mixed-type data, which is used in causal skeleton learning and final pruning to further improve the computational efficiency and precision of our model. Experimental results on both synthetic and real-world data demonstrate that our proposed hybrid model outperforms the other state-of-the-art methods. Our source code is available at https://github.com/DAMO-DI-ML/AAAI2022-HCM.
The influence of learning mode and learning sharing behavior on the synchronicity of attention of sharers and learners

Attention is the concentration of mental activities to a certain object, and students' inattentiveness in class directly affects their learning efficiency. As an emerging technology of educational application, augmented reality (AR) technology combines virtual reality and three-dimensional reconstruction to bring multisensory stimulation to students, enhancing immersion and attention in learning. A quantitative study was conducted on third-grade pupils. Study 1 examined whether learning mode and learning sharing behavior affect the synchronization of sharers' and learners' attention. Study 2 examined the impact of learning mode and sharing role on sharer and shared. The results showed that compared with learning alone, when sharing, the attention score of AR group is higher than that of text group. Whether it is the sharer or the shared, the attention score of AR group is higher than that of text group. AR has more advantages than text in terms of learning attention. In future research, it is optional to diversify AR learning materials and further use near-infrared spectroscopy technology to study interactive learning in AR mode.
PlanVerb: Domain-Independent Verbalization and Summary of Task Plans

For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-expert users who may find it easier to read natural language descriptions. In this paper, we propose PlanVerb, a domain and planner-independent method for the verbalization of task plans. It is based on semantic tagging of actions and predicates. Our method can generate natural language descriptions of plans including causal explanations. The verbalized plans can be summarized by compressing the actions that act on the same parameters. We further extend the concept of verbalization space, previously applied to robot navigation, and apply it to planning to generate different kinds of plan descriptions for different user requirements. Our method can deal with PDDL and RDDL domains, provided that they are tagged accordingly. Our user survey evaluation shows that users can read our automatically generated plan descriptions and that the explanations help them answer questions about the plan.
Distributed Randomized Sketching Kernel Learning

We investigate the statistical and computational requirements for distributed kernel ridge regression with randomized sketching (DKRR-RS) and successfully achieve the optimal learning rates with only a fraction of computations. More precisely, the proposed DKRR-RS combines sparse randomized sketching, divide-and-conquer and KRR to scale up kernel methods and successfully derives the same learning rate as the exact KRR with greatly reducing computational costs in expectation, at the basic setting, which outperforms previous state of the art solutions. Then, for the sake of the gap between theory and experiments, we derive the optimal learning rate in probability for DKRR-RS to reflect its generalization performance. Finally, to further improve the learning performance, we construct an efficient communication strategy for DKRR-RS and demonstrate the power of communications via theoretical assessment. An extensive experiment validates the effectiveness of DKRR-RS and the communication strategy on real datasets.
CAN PICTURE BOOKS IN ENGLISH CLASSROOM LEAD TO INCREASE VOCABULARY LEARNING?

Although English Language is taught in the Malaysian national and national-type primary schools for six years, the competence of the language among primary school students is still low. One of the areas where students are observed to be weak in is vocabulary where the teaching of vocabulary has always been neglected as language teachers often felt that the acquisition could be acquired through the teaching of the 4 language skills. There are many ways to teach vocabulary, but picture books in teaching vocabulary is not widely used in Malaysian context. This qualitative research attempted to explore the use of picture books in English language classes in Sekolah Rendah Jenis Kebangsaan (C). The respondents of this research were nine Chinese primary school pupils aged from seven to eleven in Kubang Pasu District, Kedah. Focus group discussion and semi – structured interviews were the instruments used to collect the data and they were analysed thematically. The use of picture books in SJK (C) classrooms was believed to be helpful to the pupils. Pictures, short sentences and careful choice of words in the picture books help pupils to understand new and difficult vocabulary better. Furthermore, the use of picture books also enables the visual learners to understand the story plot well too. Different displays of the picture books are one of the merits to attract pupils and maintain the interest of reading.
Towards Bridging Sample Complexity and Model Capacity

In this paper, we give a new definition for sample complexity, and further develop a theoretical analysis to bridge the gap between sample complexity and model capacity. In contrast to previous works which study on some toy samples, we conduct our analysis on more general data space, and build a qualitative relationship from sample complexity to model capacity required to achieve comparable performance. Besides, we introduce a simple indicator to evaluate the sample complexity based on continuous mapping. Moreover, we further analysis the relationship between sample complexity and data distribution, which paves the way to understand the present representation learning. Extensive experiments on several datasets well demonstrate the effectiveness of our evaluation method.
MENINGKATKAN MOTIVASI DAN HASIL BELAJAR SISWA KELAS VIII PADA MATERI GELOMBANG DAN ALAT OPTIK DENGAN MODEL DISCOVERY LEARNING

This study aims to describe the increase in learning motivation and cognitive learning outcomes of students. This research is a classroom action research. The research subjects were all eighth grade students of SMP N 1 Payakumbuh which gathered 34 students. The results of this study indicate that the initial motivation obtained an average score of 61.59 which is in the low category. While the average final motivation after the application of the discovery learning model obtained an average score of 70.68 in the high category. Cognitive learning outcomes of students in the first cycle obtained an average value of student learning outcomes in the knowledge aspect of 77.12 with a classical mastery percentage of 76% for wave material. In the second cycle, the average score of students increased to 81.97, the percentage of completeness was 85% for the optical instrument material. Based on the results of the study said that the application of Discovery Learning learning model can increase learning motivation students and student cognitive learning outcomes.
Construct Effective Geometry Aware Feature Pyramid Network for Multi-Scale Object Detection

Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introduce geometric prior into FPN to obtain more discriminative features. In this paper, we propose Geometry-aware Feature Pyramid Network (GaFPN), which mainly consists of the novel Geometry-aware Mapping Module and Geometry-aware Predictor Head.The Geometry-aware Mapping Module is proposed to make full use of all pyramid features to obtain better proposal features by the weight-generation subnetwork. The weights generation subnetwork generates fusion weight for each layer proposal features by using the geometric information of the proposal. The Geometry-aware Predictor Head introduces geometric prior into predictor head by the embedding generation network to strengthen feature representation for classification and regression. Our GaFPN can be easily extended to other two-stage object detectors with feature pyramid and applied to instance segmentation task. The proposed GaFPN significantly improves detection performance compared to baseline detectors with ResNet-50-FPN: +1.9, +2.0, +1.7, +1.3, +0.8 points Average Precision (AP) on Faster-RCNN, Cascade R-CNN, Dynamic R-CNN, SABL, and AugFPN respectively on MS COCO dataset.
Orthogonality catastrophe and the speed of quantum evolution in a qubit-spin-bath system

Abstract The orthogonality catastrophe (OC) of quantum many-body systems is an important phenomenon in condensed matter physics. Recently, an interesting relationship between the OC and the quantum speed limit (QSL) was shown (Fogarty 2020 Phys. Rev. Lett. 124 110601). Inspired by the remarkable feature, we provide a quantitative version of the quantum average speed as another different method to investigate the measure of how it is close to the OC dynamics. We analyze the properties of an impurity qubit embedded into an isotropic Lipkin-Meshkov-Glick spin model, and show that the OC dynamics can also be characterized by the average speed of the evolution state. Furthermore, a similar behavior of the actual speed of quantum evolution and the theoretical maximal rate is shown which can provide an alternative speed-up protocol allowing us to understand some universal properties characterized by the QSL.
PEMBELAJARAN BAHASA JEPANG PERHOTELAN BIDANG TATA HIDANGAN BERBASIS ROLE PLAYING PADA PROGRAM STUDI DIPLOMA IV PERHOTELAN UNIVERSITAS TRIATMA MULYA

Japanese language used in the hotel industry requires students to use Japanese Language according to department in the hotel. Students in the Diploma IV Hospitality study program are prepared to become hoteliers who can provide services to guests using Japanese Language based on hotel service standards, which is in food and beverage.This study aims to determine the role playing-based teaching Japanese Hospitality at Food and Beverage Department in the Diploma IV Hospitality study program at Triatma Mulya University and to find out the obstacles found in the application of the role playing. The number of students who participated in the study was 26 people. This research uses the observation method to observe the learning process of Japanese Hospitality at Food and Beverage Department. Based on the results of the study, hospitality Japanese learning was applied using conversation scenarios between staff and guests who communicated about how to welcome guests at the restaurant, conversations in situations where guests were taken to their seats, provided menus, explained recommended menus, and asked for impressions and messages from guests. The obstacles in learning are inappropriate gestures and intonations during role play in Japanese Language.
Learning Mixture of Domain-Specific Experts via Disentangled Factors for Autonomous Driving

Since human drivers only consider the driving-related factors that affect vehicle control depending on the situation, they can drive safely even in diverse driving environments. To mimic this behavior, we propose an autonomous driving framework based on the two-stage representation learning that initially splits the latent features as domain-specific features and domain-general features. Subsequently, the dynamic-object features, which contain information of dynamic objects, are disentangled from latent features using mutual information estimator. In this study, the problem in behavior cloning is divided into several domain-specific subspaces, with experts becoming specialized on each domain-specific policy. The proposed mixture of domain-specific experts (MoDE) model predicts the final control values through the cooperation of experts using a gating function. The domain-specific features are used to calculate the importance weight of the domain-specific experts, and the disentangled domain-general and dynamic-object features are applied in estimating the control values. To validate the proposed MoDE model, we conducted several experiments and achieved a higher success rate on the CARLA benchmarks under several conditions and tasks than state-of-the-art approaches.
End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

Label-specific features serve as an effective strategy to learn from multi-label data with tailored features accounting for the distinct discriminative properties of each class label. Existing prototype-based label-specific feature transformation approaches work in a three-stage framework, where prototype acquisition, label-specific feature generation and classification model induction are performed independently. Intuitively, this separate framework is suboptimal due to its decoupling nature. In this paper, we make a first attempt towards a unified framework for prototype-based label-specific feature transformation, where the prototypes and the label-specific features are directly optimized for classification. To instantiate it, we propose modelling the prototypes probabilistically by the normalizing flows, which possess adaptive prototypical complexity to fully capture the underlying properties of each class label and allow for scalable stochastic optimization. Then, a label correlation regularized probabilistic latent metric space is constructed via jointly learning the prototypes and the metric-based label-specific features for classification. Comprehensive experiments on 14 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.
Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph Classification

Graph neural networks are a promising architecture for learning and inference with graph-structured data. Yet, how to generate informative, fixed dimensional features for graphs with varying size and topology can still be challenging. Typically, this is achieved through graph-pooling, which summarizes a graph by compressing all its nodes into a single vector. Is such a “collapsing-style” graph-pooling the only choice for graph classification? From complex system’s point of view, properties of a complex system arise largely from the interaction among its components. Therefore, we speculate that preserving the interacting relation between parts, instead of pooling them together, could benefit system level prediction. To verify this, we propose SLIM, a graph neural network model for Structural Landmarking and Interaction Modelling. The main idea is to compute a set of end-to-end optimizable sub-structure landmarks, so that any input graph can be projected onto these (spatially) local structural representatives for a faithful, global characterization. By doing so, explicit interaction between component parts of a graph can be leveraged directly in generating discriminative graph representation. Encouraging results are observed on benchmark datasets for graph classification, demonstrating the value of interaction modelling in the design of graph neural networks.
Comparison and extension of autoencoder models for uni- and multivariate signal compression in IIoT

A convolutional variational autoencoder (AE) CBN-<tex>$VAE^{1}$</tex>, a recurrent LSTM-<tex>$AE^{2}$</tex>, and a discrete wavelet transform (DWT) are compared w.r.t compression performance and resource consumption. The existing models for streaming data are slightly adapted to handle both univariate (UTS) and multivariate time-series (MTS). The experiments on two publicly available data sets<sup>3</sup> confirm that the machine learning models compress time-series in a more generalized and robust manner than DWT. This is shown by a higher quality score <tex>$QS= \frac{\text{compression ratio}(CR)}{\text{reconstruction error}(RE)}$</tex> for both AE models compared to the DWT. The following aspects are observed w.r.t. the AE models:
A Sliding Windows Singular Decomposition Model of Monitoring Data for Operational Tunnels

In order to extract the valuable information from massive and usually unstructured datasets, increasingly, a novel nonparametric approach is proposed for detecting early signs of structural deterioration in civil infrastructure systems from vast field-monitoring datasets. The process adopted six-sample sliding window overtime at one-hour time increments to overcome the fact that the sampling times were not precisely consistent at all monitoring points. After data processing by this method, the eigenvalues and eigenvectors were obtained for each moving window, and then an evaluation index was constructed. Monitored tunnel data were analyzed using the proposed method. The required information extracted from an individual moving window is represented by a set of principal components, which become the new orthogonal variables. The resulting evaluation indicator was strongly correlated with measured and calculated values up to 0.89, even for tiny monitoring datasets. Experiments have verified the rationality and effectiveness of the algorithm, which provides a reference for the application of the method in the monitoring data processing.
Instance Selection: A Bayesian Decision Theory Perspective

In this paper, we consider the problem of lacking theoretical foundation and low execution efficiency of the instance selection methods based on the k-nearest neighbour rule when processing large-scale data. We point out that the core idea of these methods can be explained from the perspective of Bayesian decision theory, that is, to find which instances are reducible, irreducible, and deleterious. Then, based on the percolation theory, we establish the relationship between these three types of instances and local homogeneous cluster (i.e., a set of instances with the same labels). Finally, we propose a method based on an accelerated k-means algorithm to construct local homogeneous clusters and remove the superfluous instances. The performance of our method is studied on extensive synthetic and benchmark data sets. Our proposed method can handle large-scale data more effectively than the state-of-the-art instance selection methods. All code and data results are available at https://github.com/CQQXY161120/Instance-Selection.
Finite Entailment of Local Queries in the Z Family of Description Logics

In the last few years the field of logic-based knowledge representation took a lot of inspiration from database theory. A vital example is that the finite model semantics in description logics (DLs) is reconsidered as a desirable alternative to the classical one and that query entailment has replaced knowledge-base satisfiability (KBSat) checking as the key inference problem. However, despite the considerable effort, the overall picture concerning finite query answering in DLs is still incomplete. In this work we study the complexity of finite entailment of local queries (conjunctive queries and positive boolean combinations thereof) in the Z family of DLs, one of the most powerful KR formalisms, lying on the verge of decidability. Our main result is that the DLs ZOQ and ZOI are finitely controllable, i.e. that their finite and unrestricted entailment problems for local queries coincide. This allows us to reuse recently established upper bounds on querying these logics under the classical semantics. While we will not solve finite query entailment for the third main logic in the Z family, ZIQ, we provide a generic reduction from the finite entail- ment problem to the finite KBSat problem, working for ZIQ and some of its sublogics. Our proofs unify and solidify previously established results on finite satisfiability and finite query entailment for many known DLs.
Web application security

This study aims to show how security flaws of web applications can threat information security. Web Application Security is a branch of Information Security which focuses on web application level security flaws and their solutions. Evolution of Web continues with a big momentum. Amount of information shared over Web increases every day, various business domains continue to integrate their operations to digital world. This brings its own risks and makes Information Security of Web Applications more important than ever. Most common and serious Web vulnerabilities have been analyzed along with their solutions. This study focuses on how web developers can already prevent security problems during the development life cycle. What are the best practices to follow before/during the development and post-development phases? Which security tools can be used to support developers? Building totally secure web applications is not an easy job. Following security standards and development cycles with security concerns can already prevent most of the potential problems. A security checklist for web developers came out at the end of the study. Evolving web technologies and new security threats force us to keep this checklist up to date. We are working on a mechanism which will keep this checklist up to date.