Reviewerly/exHarmony huggingface dataset:
Decisions of Knowledge Payment Product Supply Chain Considering Government Subsidies and Anti-Piracy Efforts: Based on China’s Knowledge Payment Market

Knowledge payment is an emerging business mode that has developed in the era of social media. With the impact of Internet technology, the knowledge payment market is rapidly expanding, especially in China. However, piracy leads to more intense competition and affects the profits of knowledge providers and knowledge payment platforms. Government policies combined with the anti-piracy measures of these platforms have become effective methods to combat piracy. This paper investigates the impact of government subsidies and anti-piracy efforts on a knowledge provider’s and platform’s optimal decisions. We develop a two-stage knowledge payment product supply chain with a knowledge provider and a knowledge payment platform. The platform in the leader position of the supply chain has anti-piracy efforts and the government subsidizes the platform. Stackelberg game models are constructed and compared to examine the equilibrium solution in relation to three modes (no government subsidies and no anti-piracy efforts; only anti-piracy efforts; and both government subsidies and anti-piracy efforts). Our analysis shows that (1) both government subsidies and anti-piracy efforts increase the quality level and unit quality signing bonus of the knowledge payment product. Meanwhile, the product’s retail price increases with anti-piracy efforts but decreases within a certain subsidy range. (2) The knowledge provider’s profit always increases with the anti-piracy effort level, while the platform’s profit is an inverted U-shaped relationship with the anti-piracy effort level. (3) Government subsidy behavior can always increase a knowledge provider’s profit but may not necessarily increase the platform’s profit. Moreover, there exists a certain threshold: when subsidies are lower (higher) than this threshold, it is more beneficial to the knowledge provider (platform). In addition, we also find that the method of high subsidies combined with a low anti-piracy effort level benefits both parties and that the subsidized party will be more sensitive. The results will provide knowledge providers and platforms with new market management insights from the perspective of government subsidies and anti-piracy efforts and guide them to make optimal decisions.
Listening

Abstract This chapter discusses the importance of listening to the life of every musician. By being trained to recognize tones and melody through listening, composers and arrangers become very adept in creating musical colors and performers become sensitized to tone qualities, intonation, balance, and anticipating the sounds they want to make. The reader is asked to consider how they learned to listen, and what they regularly listen for whether consciously or not. The best teachers spend a great deal of time training their students to listen. They know that even the most technically capable students will be severely limited as musicians if their listening skills are underdeveloped. The chapter also explains the different modes of listening—objective, critical, selective, and subjective, and how each is used. Intonation and tuning systems are discussed.
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach

We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minimize an average quadratic cost while keeping their data private. To exploit the similarity of the agents' dynamics, we propose to use federated learning (FL) to allow the agents to periodically communicate with a central server to train policies by leveraging a larger dataset from all the agents. With this setup, we seek to understand the following questions: (i) Is the learned common policy stabilizing for all agents? (ii) How close is the learned common policy to each agent's own optimal policy? (iii) Can each agent learn its own optimal policy faster by leveraging data from all agents? To answer these questions, we propose a federated and model-free algorithm named FedLQR. Our analysis overcomes numerous technical challenges, such as heterogeneity in the agents' dynamics, multiple local updates, and stability concerns. We show that FedLQR produces a common policy that, at each iteration, is stabilizing for all agents. We provide bounds on the distance between the common policy and each agent's local optimal policy. Furthermore, we prove that when learning each agent's optimal policy, FedLQR achieves a sample complexity reduction proportional to the number of agents M in a low-heterogeneity regime, compared to the single-agent setting.
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning

With the increasing size of pre-trained language models (PLMs), fine-tuning all the parameters in the model is not efficient, especially when there are a large number of downstream tasks, which incur significant training and storage costs. Many parameter-efficient fine-tuning (PEFT) approaches have been proposed, among which, Low-Rank Adaptation (LoRA) is a representative approach that injects trainable rank decomposition matrices into every target module. Yet LoRA ignores the importance of parameters in different modules. To address this problem, many works have been proposed to prune the parameters of LoRA. However, under limited training conditions, the upper bound of the rank of the pruned parameter matrix is still affected by the preset values. We, therefore, propose IncreLoRA, an incremental parameter allocation method that adaptively adds trainable parameters during training based on the importance scores of each module. This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead. We conduct extensive experiments on GLUE to demonstrate the effectiveness of IncreLoRA. The results show that our method owns higher parameter efficiency, especially when under the low-resource settings where our method significantly outperforms the baselines. Our code is publicly available.
PEMBUATAN GAME EDUKASI BERBASIS ANDROID GUNA MELATIH DAYA INGAT ANAK (STUDI KASUS: 4-6 TAHUN)

Memory is a person's ability to store, process, and retrieve information that has been absorbed by the brain. Neutron elements produced by the brain play a role in storing information. When the brain is stimulated to produce neutrons intensively, memory will become sharper. Based on these problems, the authors are interested in making an educational game to train the memory of children 4-6 years based on Android. There are several software used in making this math educational game such as Adobe Photoshop CC 2018, DB Browser (SQLite), and Android Studio. The result of this scientific writing is in the form of a matchmaking game application for kids. With this application, it is proven to be able to train children's memory and make the learning process fun.&#x0D; Keywords: Android, Educational Game, Memory.
Performance Reliability of Reinforcement Learning Algorithms in Obstacle Avoidance Game with Differing Reward Formulations

When formulating environments for complex application areas, using analogies to games is beneficial as they provide convenient models to test algorithm performance in ways that are transferable to realistic environments. We propose a Frogger like grid based environment containing a simple action space, dynamic obstacles, and discrete game loop for testing Proximal Policy Optimization 2 and Deep Q-Network with comparisons to a heuristic and random agent. The environment contains four different reward function implementations, along with two different environment variations to explore adaptability. Seeing how these different parameters effect not just the average performance of the algorithm, but also the reliability of the performance is of concern as reliability determines the expectations of any single performance of an reinforcement learning agent. Experiments in these environments demonstrate common behaviors of reinforcement learning algorithms showing possible strengths and weakness of the approaches when applied to more complex decision-making scenarios. We explore these behaviors through evaluation techniques meant to measure the agents accumulation of reward in the game. Cross comparing these evaluation techniques elucidates the causation behind agent performance.
HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images

We propose a robust and accurate method for reconstructing 3D hand mesh from monocular images. This is a very challenging problem, as hands are often severely occluded by objects. Previous works often have disregarded 2D hand pose information, which contains hand prior knowledge that is strongly correlated with occluded regions. Thus, in this work, we propose a novel 3D hand mesh reconstruction network HandGCAT, that can fully exploit hand prior as compensation information to enhance occluded region features. Specifically, we designed the Knowledge-Guided Graph Convolution (KGC) module and the Cross-Attention Transformer (CAT) module. KGC extracts hand prior information from 2D hand pose by graph convolution. CAT fuses hand prior into occluded regions by considering their high correlation. Extensive experiments on popular datasets with challenging hand-object occlusions, such as HO3D v2, HO3D v3, and DexYCB demonstrate that our HandGCAT reaches state-of-the-art performance. The code is available at https://github.com/heartStrive/HandGCAT.
Dynamic Dense-Sparse Representations for Real-Time Question Answering

Existing real-time question answering models have shown speed benefits on open-domain tasks. However, they possess limited phrase representations and are susceptible to information loss, which leads to low accuracy. In this paper, we propose modified contextualized sparse and dense encoders to improve the context embedding quality. For sparse encoding, we propose the JM-Sparse, which utilizes joint multi-head attention to focus on crucial information in different context locations and subsequently learn sparse vectors within an n-gram vocabulary space. Moreover, we leverage the similarity-enhanced dense(SE-Dense) vector to obtain rich contextual dense representations. To effectively combine dense and sparse features, we train the weights of dense and sparse vectors dynamically. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method compared with other query-agnostic models.
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model

Random matrices are used to filter the center of gravity (CoG) and the covariance matrix of measurements. However, these quantities do not always correspond directly to the position and the extent of the object, e.g. when a lidar sensor is used.In this paper, we propose a Gaussian processes regression model (GPRM) to predict the position and extension of the object from the filtered CoG and covariance matrix of the measurements. Training data for the GPRM are generated by a sampling method and a virtual measurement model (VMM). The VMM is a function that generates artificial measurements using ray tracing and allows us to obtain the CoG and covariance matrix that any object would cause. This enables the GPRM to be trained without real data but still be applied to real data due to the precise modeling in the VMM. The results show an accurate extension estimation as long as the reality behaves like the modeling and e.g. lidar measurements only occur on the side facing the sensor.
CLPL: A Self-supervised Contrastive Learning Pseudo-Labeling Framework for Tabular Data

Semi-supervised and self-supervised learning methods have been recently applied to many domains and applications due to their promising solution in alleviating the need for large labeled datasets. One of the most recent trending semi/self-supervised learning approaches that have achieved significant interest in the research community is pseudo-labeling. Pseudo-labeling algorithms exploit a small set of labeled and an extensive collection of unlabeled data to learn the underlying representation of input data. In unstructured domains like image, text, and audio, pseudo-labeling can significantly benefit from contrastive learning to obtain a generalized model by learning abstract representations in an unsupervised fashion. However, contrastive learning is hardly applicable to the tabular domain as it requires data augmentation by applying a set of predefined transformations, while there are no such transformations that can be safely applied to the tabular data. Thus, we introduce a contrastive learning pseudo-labeling framework (CLPL) that reduces the generalization error by learning the underlying structure of class-specific representations. To the best of our knowledge, contrastive learning has not been used for pseudo-labeling tasks in the tabular domain. Our experiments show the effectiveness of CLPL by comparing it with the known state-of-the-art pseudo-labeling methods proposed for the tabular data.
HCI for Mobile Healthcare During the COVID-19 Pandemic

Human-Computer Interaction (HCI) is a field of study within technology that can encompass multiple disciplines in practice. The general focus being on the design of computer technology and the interaction between humans and that technology. HCI has been a continuously evolving field since the 1980's. The concept of HCI itself has expanded since its inception and now covers many aspects of information technology and design. The ideal designer of an interactive system now requires expertise in a broad range of topics such as psychology and cognitive science. These skills can be used to obtain knowledge of a user's perceptual, cognitive, and problem-solving skills during system development. Through this paper we will be researching the concept of HCI and how it has helped in the development and advancement of mobile healthcare. This is especially relevant in recent years given the current state of health care regarding the COVID-19 pandemic and how HCI has developed since the beginning of its spread worldwide. Topics such as remote healthcare consultation, accommodations for individuals with disabilities, compliance and security concerns, as well as mobile healthcare application development will also be discussed in this paper.
A Smart Trust Based Approach for Sybil Node Detection in Online Social Networks

Forged identities (Sybil) have the potential to damage the trust of the platform in the long run by doing malicious works. Owing to the severity of the threats posed by these Sybil nodes, various graph based mechanisms have been proposed to identify them. However, these approaches are more suitable for the classical scenario which considers that it is difficult to establish the linkage between sybil and honest nodes. Contrary to this assumption, the modern scenario assumes that such linkage can be easily established using simple strategies. The work proposed in this paper makes use of a trust based approach to detect sybil nodes in an online social network. The proposed approach is more suitable for the modern scenarios since the strategies to link sybil and honest nodes are based upon the concept of the trustworthiness of the nodes. The proposed approach calculates aggregate trust value of the nodes which is further compared with a benchmark threshold value for deciding whether the node is Sybil or Benign. The proposed model has been theoretically analyzed and the obtained results clearly exhibit the efficiency of the proposed work over the existing approaches.
Skeleton-based Multi-Feature Sharing Real-Time Action Recognition Network for Human-Robot Interaction

Human-Robot Interaction (HRI) is one of the directions that deserves to be studied, and it is used in various fields (e.g., emergency rescue, telemedicine, and astronaut assistance). Action-based HRI has been proven practical in many application scenarios (e.g., industrial teleoperation and telemedicine). Among these many applications, speed and accuracy are two common optimization goals for researchers. A skeleton-based multi-feature sharing real-time action recognition network (MSR-Net) has been proposed to ensure its high accuracy and speed. Three pairs of feature inputs are proposed to make the network input more informative, which contain: joint distance-joint distance motion (JD-JDM), angle-angle motion (A-AM), slow motion coordinates-fast motion coordinates (SMC-FMC). To ensure that features are fully extracted while reducing the number of model parameters, one-dimensional convolutional neural networks (1DCNN) are used to build multi-feature sharing two-stage feature extraction networks. As a result, MSR-Net outperforms state-of-the-art models in accuracy on the JHMDB (86.8%) and SHREC datasets (96.8% on coarse class and 93.8% on fine class). Application experiments were carried out on the HRI platform to demonstrate the effectiveness of MSR-Net in HRI. The video demonstration of the experimental results of the HRI application can be accessed on https://youtu.be/NzXrngh7BbQ.
Cybersecurity Management System of the Czech Republic

Abstract Today, our lives, work and relationships are interconnected and completely dependent on information technologies and communication networks. Modern society has created cyberspace as an extension of our lives, which has brought us many positive things, but also many new risks that need to be managed. Every organization, whether in the public or private sector, must have a cybersecurity management system in place to survive and prosper in this environment. However, how is the cybersecurity management system established at the state level? Is it possible to consider a state as a certain type of large and complex organization and implement a cybersecurity management system similar to those used by companies and smaller institutions? This article attempts to answer these questions by analyzing the cybersecurity management system at the level of the Czech Republic and by identifying and discussing the specifics of cybersecurity management at the state level.
PMDA: Domain Alignment with Prototype Matching for Cross-Domain Adaptive Segmentation

Cross-domain adaptive segmentation is a practical solution for the scenario that lacks expensive annotations or is inaccessible to ground truth. Prior works have tried to improve cross-domain adaptive segmentation with domain alignment, but most of them ignore the problem of training target deviation of distance-regularizing based domain alignment method. To address this, we propose a novel domain alignment mechanism that unifies the two optimization objectives, domain alignment, and segmentation performance, into one. In addition, existing methods are hard to apply under the source-free setting. We introduce a novel domain adaptive segmentation framework suitable for vanilla Unsupervised Domain Adaptation (UDA) and source-free UDA settings. Experiments show the proposed method outperforms competitive works with much more complicated mechanisms and achieves the state-of-the-art performance on both GTA→Cityscapes and Synthia→Cityscapes benchmarks. Our work can be easily added to existing methods and boost their performance.
USING ESL PODCAST TO IMPROVE STUDENTS LISTENING COMPREHENSION: A CASE STUDY OF THE ELEVENTH GRADE

ABSTRACT&#x0D; Listening comprehension is one of the understandings that requires high understanding and repetition to understand the topic being heard. However, it is rarely reviewed in schools because, in its implementation, schools must have adequate facilities. The purpose of this study is to determine students' listening comprehension before and after treatment using ESL podcasts by Jeff McQuillan to determine whether or not the use of ESL podcasts effectively improves listening comprehension. This quantitative research method used one group of pre-test and post-test samples (34 students) in eleventh-grade SMA Kesatrian 1 Semarang. To test the effectiveness or ineffectiveness of podcasts, researchers used the t-test. As secondary data, there was an open questionnaire regarding supporting and inhibiting factors in listening learning. The results showed that ESL podcasts were effective in improving listening comprehension. This was obtained from the pre-test (M = 42.3529) and post-test (M = 75.0000) with Sig. (2-tailed) ,000 &lt;0.05 based on the t-test decision-making shows significance if &lt; 0.05.Moreover, the results of the questionnaire, 27 students felt that their listening comprehension had improved, and seven students felt an increase in listening comprehension. The highest factor supporting listening learning is focus, and the highest inhibiting factor is the lack of vocabulary.&#x0D; Keywords: ESL Podcast, Listening Comprehension, English Learning
Black-box Attack against Self-supervised Video Object Segmentation Models with Contrastive Loss

Deep learning models have been proven to be susceptible to malicious adversarial attacks, which manipulate input images to deceive the model into making erroneous decisions. Consequently, the threat posed to these models serves as a poignant reminder of the necessity to focus on the model security of object segmentation algorithms based on deep learning. However, the current landscape of research on adversarial attacks primarily centers around static images, resulting in a dearth of studies on adversarial attacks targeting Video Object Segmentation (VOS) models. Given that a majority of self-supervised VOS models rely on affinity matrices to learn feature representations of video sequences and achieve robust pixel correspondence, our investigation has delved into the impact of adversarial attacks on self-supervised VOS models. In response, we propose an innovative black-box attack method incorporating contrastive loss. This method induces segmentation errors in the model through perturbations in the feature space and the application of a pixel-level loss function. Diverging from conventional gradient-based attack techniques, we adopt an iterative black-box attack strategy that incorporates contrastive loss across the current frame, any two consecutive frames, and multiple frames. Through extensive experimentation conducted on the DAVIS 2016 and DAVIS 2017 datasets using three self-supervised VOS models and one unsupervised VOS model, we unequivocally demonstrate the potent attack efficiency of the black-box approach. Remarkably, the J&amp;F metric value experiences a significant decline of up to 50.08% post-attack.
Are Italian and French Public University Websites Sustainable?

The COVID-19 pandemic has led universities to provide several tools for distance learning and made their websites evolve from mere information containers to points of access to ecosystems of complex interactive systems. In this paper, we present a preliminary study on Italian and French public university websites to investigate their impact on the environment regarding energy consumption and carbon dioxide