Reviewerly/exHarmony huggingface dataset:
Label-Efficient Video Object Segmentation With Motion Clues

Video object segmentation (VOS) plays an important role in video analysis and understanding, which in turn facilitates a number of diverse applications, including video editing, video rendering, and augmented reality / virtual reality. However, existing deep learning-based approaches rely heavily on a large number of pixel-wise annotated video frames to achieve promising results, which is notoriously laborious and costly. To address this, in this paper, we formulate unsupervised video object detection by exploring simulated dense labels and explicit motion clues. Specifically, we first propose an effective video label generator network based on the sparsely annotated frames and the flow motion between them. It can largely alleviate our dependence and limitation on the sparse labels. Furthermore, we propose a transformer-based architecture to model the appearance and motion clues simultaneously with the cross-attention module, in order to maximally overcome non-linear motion with potential occlusions. Extensive experiments show that the proposed method outperforms recent VOS methods on four popular benchmarks (i.e., DAVIS-16, FBMS, Youtube-VOS and SegTrack-v2). Moreover, the proposed method can be further applied to a wide range of wild scenes such as wild forests and animals. Because of its effectiveness and generalization, we believe that our method could serve as a useful basis for alleviating the dependence on dense annotation in video data.
Indexing Translators and Traces

Is a metadata record “almost” expressed in the same language you used for your filter criteria?
Malicious Account Classification Using CNN for Ethereum Blockchain’s Accounts

The use of cryptocurrencies for transactions has grown over the past few years. Today, cryptocurrency is the most widely used and rapidly expanding currency in the global financial market. This increase can be attributed to blockchain networks, which offer transparent and secure transactions and record cryptocurrency transactions. However, as the volume of transactions increases, fraud also surfaces, resulting in significant losses for the Ethereum account holders involved. Machine learning has been used to address this issue in a previous study; however, the study only provided a limited set of performance metrics. In this study, a CNN-based algorithm is proposed to identify fraudulent accounts in the Ethereum network. The CNN model is applied to a dataset that includes legitimate and fraudulent transactions over the Ethereum network. The results reveal that the CNN-based model successfully identified fraudulent accounts with an accuracy of 98.67%.
Few-Shots Novel Space-Time View Synthesis from Consecutive Photos

Despite the remarkable result of Neural Scene Flow Fields [10] in novel space-time view synthesis of dynamic scenes, the model has limited ability when a few input views are provided. To enable the few-shots novel space-time view synthesis of dynamic scenes, we propose a new approach that extends the model architecture to use shared priors learned across scenes to predict appearance and geometry at static background regions. Throughout the optimization, our network is trained to rely on the image features extracted from a few input views or from the learned knowledge for reconstructing unseen regions based on the camera view direction. We conduct multiple experiments on NVIDIA Dynamic Scenes Dataset [23] that demonstrate our approach results in a better rendering quality compared to the prior work when a few input views are available.
A Case Study of Online Writing Class about Life Culture for Female Immigrants through Marriage

Objectives The purpose of this study is to perform a case study of real-time online writing class for female immigrants through marriage in order to investigate the class patterns from the female immigrants perspective and to examine their cultural understanding and response by exploring the characteristics of online writing class on the topics of life and culture.&#x0D; Methods As research participants, 11 female immigrants through marriage who had been taking the basic Korean language class at ** Multicultural Family Support Center in Gyeonggi-do were recruited. After 14 sessions of class, in-depth interviews were conducted based on a semi-structured questionnaire, and the data were analyzed with an analytic induction method.&#x0D; Results First, the advantages of the real-time online writing class for female immigrants through marriage included location flexibility, time saving, and learning and taking care of children at the same time. On the other hand, difficulty in asking questions and keeping focused was answered as a disadvantage. Second, as for the characteristics of the real-time online class with the topics of life and culture, it provided grammatical knowledge using familiar topics of life and culture. Learning about life and culture in neighboring countries and gaining knowledge on a range of cultures piqued the students’ intellectual curiosity. Third, for the understanding of culture in the research subjects, they took a flexible attitude toward culture in the process of actively researching and sharing data from outside textbooks by thinking outside the standard ideas of other cultures. They showed a difference in the level of language proficiency and understanding of culture. Due to the limitations of the virtual platform, however, the direct experience was not available for the students.&#x0D; Conclusions In the real-time online writing class, life and culture topics were familiar to the female immigrants. Therefore, a range of grammatical and linguistic applications was provided, and at the same time, the students were able to broaden their understanding of multicultural culture.
Rancangan Sistem Prediksi Harga Saham dengan Menggunakan Metode LSTM dan ARMA klasik

Stocks are one of the types of assets that are currently popular with the wider community, just like gold and all other types of assets, the value of stocks tends to move up and down over time, therefore stock investors invest in stocks to achieve the desired profit (capital gain), Due to the movement of stocks that go up and down over time, it is difficult for investors to determine when to buy or sell stocks, therefore this study was conducted to compare the multivariate Long-Short Term Memory (LSTM) method, and the classic ARMA, then see which is suitable in forecasting stock prices, the comparison is seen from the results of the error evaluation metrics of the two methods.
FAIR-Enabling Services Redux

I have sought to identify and enumerate core FAIR-enabling services. I attempted a five-week experiment to expand on my tentative list, but I did not complete it. The list wasn’t compelling for me. I have been brewing an updated list of core FAIR-enabling services, which I hope to be less bombastic about.
Cross feature enhanced graph convolutional network for aspect-based sentiment analysis

Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model’s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications.
Theory: Parameter Estimation

Theoretical chapter devoted to the domain of parameter estimation by quantum measurements. It first details the implications of the Heisenberg inequality and gives the expression of the quantum Cramér–Rao bound, a limit that is optimized over all data processing strategies and measurements on a given parameter-dependent quantum state. Measurement optimization over different quantum states and different optical modes in which the quantum state is defined is also discussed in various situations. The chapter then focusses on the measurement-induced perturbation bydiscussing the Heisenberg microscope and the Ozawa inequality, then on different implementations of quantum nondemolition (QND) measurements using the crossed-Kerr effect in quantum optics and opto-mechanics.
Algebraic Structures in Statistical Methodology

Algebraic structures arise naturally in a broad variety of statistical problems, and numerous fruitful connections have been made between algebra and discrete mathematics and research on statistical methodology. The workshop took up this theme with a particular focus on algebraic approaches to graphical models, causality, axiomatic systems for independence and non-parametric models.
On some distributional peculiarities of the high unrounded vowels in Russian

The article aims at the explanation of some distributional peculiarities of two high unrounded vowels [i] and [È] in Russian.More generally, it looks at some phonotactic constraints of Russian vowels which are directly related to a broader topic of palatalization and vowel reduction in this language.Although the discussion in this paper concerns only a tiny section of Russian phonology, which is the distribution of high unrounded vowels, it is necessary to introduce several facts from Russian phonology, such as palatalization, velarization, stress and vowel reduction.They, at first sight, may look pretty much irrelevant to the main topic of the paper but, as it will become evident, are closely related and actually indispensable to the understanding of vowel distribution including the two high unrounded vowels in Russian.
Research Review Based on Improved Ant Colony Algorithm

The ant colony algorithm is a probabilistic algorithm for finding optimal paths. It is mainly applied to the global path planning problem of available maps. Ant colony algorithm has the advantages of fast convergence speed, good robustness, and strong environmental adaptability. However, it also has many shortcomings. For early blind search easy to cause local paths to cross; it is possible to fall into particular obstacles such as U-shaped; it is prone to slow convergence and easy to fall into local optimum, and scholars in China and abroad have proposed improvements. This paper summarizes the improvement of the ant colony algorithm, analyzes the advantages and disadvantages of the improved ant colony algorithm, and puts forward further improvement based on the improved ant colony algorithm.
Research on the Application of Blockchain in Electricity Market

In recent years, the rapid development and expansion of electricity trading market has also faced many problems. The development of power trading is restricted by problems such as transaction settlement risk, transaction fraud risk, transaction intention risk and contract traceability risk. The characteristics of blockchain, such as decentralization, non tampering and automatic execution of smart contracts, can solve the problems faced by the power market. Aiming at the problems involved in the power market, analyzes the feasibility of applying blockchain technology to the power market, and puts forward a solution based on blockchain technology.
Adaptive XGBoost Algorithm Based on Matthews Correlation Coefficient

Because the size of the data stream, the arrival time and the appearance order of the data in the data stream cannot be determined, the data stream has the phenomenon of concept drift and class imbalance, which makes the classification task difficult to carry out smoothly. Aiming at the phenomenon of concept drift and class imbalance, the Matthews Adaptive XGBoost (MAXGB) algorithm is proposed, which uses the Matthews correlation coefficient as the evaluation index of the base classifier, and can select the base classifier that adapts to the current data stream. MAXGB uses random feature subspace, resampling and sliding window methods to train base classifiers to ensure the diversity among base classifiers. The experimental analysis of MAXGB is carried out on both synthetic and real dataset. The results show that the average performance of MAXGB in the two classification performance index of Kappa and G-mean is increased by 4.5% and 9.3%, respectively. At the same time, the average training speed of MAXGB is also increased by 19%.
Design of Intelligent Visual Navigation Robot System Based on Baidu Paddle

An intelligent visual navigation robot system based on Baidu paddle was developed in this research. The hardware of the system is based on the embedded AI computing card of EdgeBoard as the core controller, Woboart of the ATmega2560 core as the lower machine, and six steering gear and four degrees of freedom robotic arms to grasp and classify targets. The system software adopts paddle-lite as a deployment module for deep learning, and AI studio as a platform for model training. The car runs the program in the EdgeBoard controller, and the navigation robot will choose the strategy according to the steering curvature obtained by the model and the detected side markers, and then send the control instructions to the ATmega2560 microcontroller, so as to control the navigation robot to track or control the robot arm to make corresponding actions, which can complete the specific tasks well.
Future Knowledge That Can Correct Errors In Chat GPT and Wolfram Alpha

In order to have high-quality future education, future Education need to integrate with technology. But if a knowledge has flaws and not corrected , if we program the uncorrected flaws knowledge to technology such as Chat GPT and Wolfram Alpha, it will cause the Chat GPT and Wolfram Alpha giving some wrong answer. This will not giving a high quality of education. Google loses £100BILLION as AI chatbot Bard answers question wrong, shows how important it is for AI chatbots to give the right answer.There is evidence that Chat GPT and Wolfram Alpha also face the problem of giving wrong answers. Therefore, in order to prevent users from being misled by some wrong answers, we must program corrected flawed knowledge into Chat GPT and Wolfram Alpha.Programming the Future Knowledge, Peter Chew Correction for Sine Rule into the Education 4.0 Calculator enables the calculator to give the correct answer. This proves that, with future knowledge, Peter Chew Correction for Sine Rule can correct errors in Chat GPT and Wolfram Alpha.
Application of Geographic Information System in Optimal Allocation of Land Use Spatial Structure

In view of the poor effect of the current optimal allocation of land use spatial structure, this paper puts forward the application method of geographic information system in the optimal allocation of land use spatial structure. Firstly, the geographic information system is used to collect land use spatial information, and the evaluation algorithm of land use spatial structure is constructed. Finally, it is confirmed by experiments. The application method of GIS in the optimal allocation of land use spatial structure has high practicability and fully meets the research requirements.
Application of Power Big Data in Hollow Village Monitoring

For “hollow door” and “hollow village” increasingly prominent problems, this paper puts forward a kind of auxiliary government solutions, real-time monitoring of the hollow village power big data as the core, take full advantage of power use data in combination with the government statistical population data, hollow door recognition model is constructed and the rural hollowing out analysis model, realize the real-time computation of hollow rate at the same time, The hollow village monitoring platform based on power big data has been built to provide auxiliary support for agricultural and rural decision-making in Heilongjiang Province. The results show that the research scheme is effective, which can accurately analyze the hollow rate of rural areas, identify “hollow households” and “hollow villages”, and provide data support for the rural revitalization of Heilongjiang Province.
Implementation of the Saw Method to Discover the Optimum Internet Service Recommendations for Online Gaming

Currently, the development and use of the Internet have a more complex function so that it can change the paradigm of people's lives, including in aspects of entertainment, especially games. With the rise of numerous ISPs in Indonesia, different internet service packages are now available, particularly for gamers, such as Indihome, Biznet, First Media, and My Republic. The variety of services makes it difficult for users to choose an internet package that suits their needs. Therefore, this research aims to build a decision support system that can facilitate users in choosing the ideal internet service for gamers based on five criteria: quota, network speed, connection, cost, and the number of users using the SAW method. The data collection methods used are observation, questionnaires, and interviews. The research results obtained from data processing using the SAW method through Microsoft Excel are then implemented into a website-based program. With this program, it is hoped that it can be a tool for users in determining the service package to be purchased.
Ethereum Phishing Detection Based on Graph Structure and Transaction Sequence Features Fusion

As a kind of cryptocurrency fraud, Ethereum phishing scam poses a serious security threat to the trading environment, hence the urgency of detecting Ethereum phishing scam. Existing research achieved good results by using network embedding methods to automatically extract features, but most of them only use point-in-time transaction information, while ignoring the information of transaction sequences. Based on this limitation, a Graph Structure and Transaction Sequence Features Fusion (GSTSF) method is proposed. Specifically, considering the continuity of Ethereum account transactions, the temporal transaction sequence features contain richer transaction information. The transaction sequence features can be obtained by using the LSTM algorithm and applying a weighted aggregation method. The graph structure features extracted using the Node2vec algorithm are fused with the transaction sequence features using a linear combination. Then the final features fed into the machine learning algorithm for phishing account classification. The experimental results show that the AUC of the proposed method are better than those of the previous methods of Ethereum phishing detection.
Research on Application of Optimized Hybrid Mobile Edge Computing Algorithm in Workflow Task Scheduling

In the Mobile Edge Computing environment, the calculation of data relies on the heterogeneous computing system in the distributed environment, which can effectively execute parallel and distributed computing. The DAG task model is a classic task model in a computing system. In this mode, the execution of some tasks has data dependencies and strict execution order requirements. This paper studies the DAG task model and proposes a task scheduling algorithm based on genetic algorithm and improved particle swarm optimization algorithm. The data transmission time and execution time that affect the scheduling are considered evaluate the quality of the scheme. The result calculated by genetic algorithm is used as the initial value of the particle swarm, and the inertia weight and learning factor of the particle swarm are adaptively adjusted to improve the quality of the final scheme.
Exploring and Exploiting High-Order Spatial-Temporal Dynamics for Long-Term Frame Prediction

Long-term spatial-temporal frame prediction focuses on predicting future image frames precisely, which has numerous applications in real-world scenarios. Existing deep learning prediction models mainly rely on advanced neural network architectures to model complicated spatial-temporal features, which make few efforts to explore high-order correlations to better capture long-term dynamics. Their prediction on long-term frames suffers from inaccurate visual and motion detail issue. In this article, we propose a high-order prediction model for long-term frame prediction, which improves the appearance and motion details by designing special high-order correlation modules in two aspects. First, to enhance the appearance details of predicted frames, we propose a high-order appearance encoder module, where high-order appearance features can be effectively captured with a carefully designed Non-local ConvLSTM. Second, to guarantee the motion accuracy of predicted sequences, we carefully design a high-order motion encoder module, which can accurately capture and preserve the high-order motion patterns with adaptive motion extractors and progressive memory banks, respectively. Comprehensive experiments are conducted on six challenging datasets from real-world scenarios, which demonstrate the effectiveness and superiority of our proposed method over state-of-the-art methods.
Proactive Perception of Preferences Evolution Based on Graph Neural Networks

User preferences play an important role in intelligent services, smart recommendations, and so on. Unfortunately, user preferences are dynamic and always evolving. How to proactively perceive users’ preferences evolution has become a critical issue. Recently, some wonderful research results about preference learning or preference prediction has been achieved. However, existing works can only perceive the change of preferences implicitly, cannot present users’ preferences change explicitly. To tackle this issue, we propose an approach for proactive perception of preferences evolution based on graph neural networks (PPGNN). Firstly, PPGNN constructs a directed sequence graph based on the user’s historical item interaction sequence, and learns the embedded vector representation of nodes in the directed sequence graph with the gated graph neural network (GGNN). Secondly, PPGNN learns the complex correlation between users and items in the interaction sequence using attention networks to obtain users’ long-term preferences. Meanwhile, PPGNN uses the location-aware network to preserve the location information in the sequence, and extracts user’s short-term preferences from the sequence with the attention network. Thirdly, PPGNN combines the user’s long-term and short-term preferences through a gated-fusion network and recommends an item that the user is most interested in. Finally, PPGNN perceives the evolution of user preferences by comparing the recommended items with the items that the user was previously interested in. Extensive experiments have been conducted to validate the proposed method and demonstrate its effectiveness.