Reviewerly/exHarmony huggingface dataset:
EyeBox: A Toolbox based on Python3 for Eye Movement Analysis

Eye tracking technology can reflect human attention and cognition, widely used as a research tool. To analyze eye movement data, users need to determine a specific area known as areas of interests (AOIs). Although existing tools offer dynamic AOIs functions to process visual behavior on moving stimuli, they may ask users to use markers to specify contours of moving stimuli in the physical environment or define AOIs manually on screen. This paper proposes a toolbox named Eyebox to 1) recognize dynamic AOIs automatically based on SIFT and extract eye movement indicators, as well as 2) draw fixations. We also design a user-friendly interface for this toolbox. Eyebox currently supports processing data recorded from the Pupil Core device. We compared results processed by manual with by Eyebox in a custom eye-tracking dataset to evaluate this toolbox. The accuracy of 3/4 data for AOI1 is above 90%, and the accuracy of 4/5 data for AOI2 is higher than 90%. Finally, we conducted a user study to test the usability and user experience of EyeBox.
Processing and analysis of phosphoric acid production data using machine learning methods

The attack and filtration unit is the main section of the wet process phosphoric acid production. It must ensure maximum production of phosphoric acid while reducing P2O5 losses. The main purpose of this work is to search for a machine learning model able to predict the production of phosphoric acid. Industrial data collected during the operation of the process was used to find the appropriate machine learning model for the prediction of phosphoric acid production. The collected data are first prepared and cleaned, then we elaborated the correlation matrix for all the variables Xj and the output of filter A and filter B and look for the relations between the inputs and outputs. Then we identified the variation ranges of the parameters of each filter for a best production of phosphoric acid at the two filters. Finally, three models were analyzed and tested. These are the "K Neighbors", "Random Forest", and "Decision Tree" models. We conclude that the last two models present the best results with an MSE of 4%.
Exception Handling in SARL as a Responsibility Distribution

Exception handling has been successfully proposed in software engineering practice as a simple, but effective, technology to address abnormal situations possibly occurring at runtime. Such mechanisms support the robust composition of heterogeneous software components, promoting code modularity, decoupling, and separation of concerns. Multi-agent systems bring these features to an extreme, but often lack systematic mechanisms for treating exceptions as part of their design. In this paper, we show how exception handling mechanism can be introduced in SARL, leveraging the abstractions that characterize its programming model. We introduce a new kind of space, supporting the responsibility distribution among agents concerning the handling of exceptional situations.
Application of Biomechanics Based on Intelligent Technology and Big Data in Physical Fitness Training of Athletes

Physical training has a high degree of participation all over the world. With the opening of the era of national fitness, physical training has become more popular from the original specialization, and the complex training methods and contents have gradually become simplified. The development and change of physical training has also brought many problems to the professional training of athletes, such as high training intensity but poor effect, insufficient training posture, and long-term physical injury. In order to help athletes achieve better results in physical training and reduce the probability of injury, taking sprint training as an example, this article adopted the sports and body data of elite athletes through intelligent technology and big data analysis, established a human motion model from the perspective of biomechanics, and then conducted a corresponding test run experiment for athletes. The experimental results suggested that drag resistance running could improve the specific strength quality of sprinting. At the same time, when using resistance load for training, the maximum speed should not exceed 90% of the maximum speed without resistance. The average horizontal maximum velocity decreased by approximately 9% when training under a resistance load, and the best training results were obtained by training athletes within this range.
Depth Completion for Close-Range Specular Objects

Many objects in the real world exhibit specular reflections. Due to the limitations of the basic RGB-D cameras, it is particularly challenging to accurately capture their 3D shapes. In this work, we present an approach to correct the depth of close-range specular objects using convolutional neural networks. We first generate a synthetic dataset containing such close-range objects. We then train a deep convolutional network to estimate normal and boundary maps from a single image.With these results, we propose an algorithm to detect the incorrect area of the raw depth map. After removing the erroneous zone, we complete the depth channel.
Imbalanced Problem in Initial Coin Offering Fraud Detection

ICOs, the initial coin offerings, are a common way to raise funds for blockchain projects. Fraudulent ICO projects not only cause financial losses to investors but also cause a loss of confidence in the blockchain capital market. Whitepapers are usually the most important information source, so it is feasible to identify fraudulent ICO programs by analyzing whitepapers. However, the fraud samples are difficult to collect, and the classes are imbalanced. In this study, we attempt to solve this problem by extracting linguistic features from the ICO whitepaper and using a variety of cutting-edge machine learning and deep learning algorithms to train the prediction model and attempt to resample, modify the weight and modify the loss function for imbalanced samples. Our optimal method achieves an AUC of 0.94 and an accuracy of 82%, which is better than other traditional standard methods, and the results provide important implications for ICO fraud detection.
Focusing on the Importance of Features for CTR Prediction

Traditional CTR recommendation models have concentrated on how to learn low-order and high-order characteristics. The majority of them make many efforts at combining low-order and high-order functions. However, they ignore the importance of the attention mechanism for learning input features. The ECABiNet model is proposed in this article to enhance the performance of CTR. On the one hand, the ECABiNet model can learn the importance of features dynamically via the LayerNorm and ECANET layers. On the other hand, through the use of a bi-interaction layer and a DNN layer, it is capable of effectively learning the feature interactions. According to the experimental results on two public datasets, the ECABiNet model is more effective than the previous CTR model.
A Survey of Detection Methods for Software Use-After-Free Vulnerability

Due to the absence of validity detection on pointers and automatic memory rubbish reclaim mechanisms in programming languages such as the C/C++ language, software developed in these languages may have many memory safety vulnerabilities, such as Use-After-Free (UAF) vulnerability. An UAF vulnerability occurs when a memory object has been freed, but it can still be accessed through a dangling pointer that points to the object before it is reclaimed. Since UAF vulnerabilities are frequently exploited by malware which may lead to memory data leakage or corruption, much research work has been carried out to detect UAF vulnerabilities. This paper investigates existing UAF detection methods. After comparing and categorizing these methods, an outlook on the future development of UAF detection methods is provided. This has an important reference value for subsequent research on UAF detection.
HIRE: Distilling high-order relational knowledge from heterogeneous graph neural networks

Researchers have recently proposed plenty of heterogeneous graph neural networks (HGNNs) due to the ubiquity of heterogeneous graphs in both academic and industrial areas. Instead of pursuing a more powerful HGNN model, in this paper, we are interested in devising a versatile plug-and-play module, which accounts for distilling relational knowledge from pre-trained HGNNs. To the best of our knowledge, we are the first to propose a HI gh-order RE lational ( HIRE ) knowledge distillation framework on heterogeneous graphs, which can significantly boost the prediction performance regardless of model architectures of HGNNs. Concretely, our HIRE framework initially performs first-order node-level knowledge distillation, which encodes the semantics of the teacher HGNN with its prediction logits. Meanwhile, the second-order relation-level knowledge distillation imitates the relational correlation between node embeddings of different types generated by the teacher HGNN. Extensive experiments on various popular HGNNs models and three real-world heterogeneous graphs demonstrate that our method obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.
Deep Reinforcement Learning with Fuse Adaptive Weighted Demonstration Data

Traditional multi-agent deep reinforcement learning has difficulty obtaining rewards, slow convergence, and effective cooperation among agents in the pretraining period due to the large joint state space and sparse rewards for action. Therefore, this paper discusses the role of demonstration data in multiagent systems and proposes a multi-agent deep reinforcement learning algorithm from fuse adaptive weight fusion demonstration data. The algorithm sets the weights according to the performance and uses the importance sampling method to bridge the deviation in the mixed sampled data to combine the expert data obtained in the simulation environment with the distributed multi-agent reinforcement learning algorithm to solve the difficult problem. The problem of global exploration improves the convergence speed of the algorithm. The results in the RoboCup2D soccer simulation environment show that the algorithm improves the ability of the agent to hold and shoot the ball, enabling the agent to achieve a higher goal scoring rate and convergence speed relative to demonstration policies and mainstream multi-agent reinforcement learning algorithms.
TU-Net: U-shaped Structure Based on Transformers for Medical Image Segmentation

Recently, the development of deep learning technology in medical image segmentation has become increasingly mature, and the symmetric U-Net has made breakthrough progress. However, because of the inherent limitations of convolution operations, U-Net has some shortcomings in the interaction of global context information. For this reason, this paper proposes TU-Net based on transformers. TU-Net can strengthen the modeling of global context information, enhance the extraction of detailed information and reduce the computational complexity of the algorithm. In patch embedding, successive convolutional layers with small convolutional kernels are proposed to extract features. Cross Attention-Skip is proposed to complete the fusion of shallow and deep features during the skip connection process. TU-Net is performed on the Synapse dataset to segment eight abdominal organs. The experimental results show that TU-Net is superior to ViT, V-Net, U-Net and Swin-Unet.
Replication as a means of assessing corpus representativeness and the generalizability of specialized word lists

Considerable energy has gone into designing lists of words that are salient in discourse domains of varying breadth. Over the past two decades, most efforts in designing and validating corpus-based frequency lists have focused on three areas: corpus compilation, item selection criteria, and coverage-based demonstrations of list robustness. As a result, modern corpora are now often much larger and better balanced; the application of additional dispersion statistics allows for better targeting of items with desired distributions; and contemporary lexical frequency lists are proving increasingly efficient, providing ever higher coverage of target texts or achieving such coverage with fewer words. However, despite these important advances, relatively minimal attention has been paid to word list reliability—the extent to which lists can be generalized to the wider discourse domain that has been represented by the corpora upon which they are based. This study begins to address this gap, demonstrating via two word list development case studies (one for Environmental Science and one for Applied Linguistics) that adding iterative reliability analysis—via methodological replication with corpora of increasing size and comparison of items on resulting lists—can be used to: 1) inform corpus design beyond what Biber (1991) terms “situational” parameters, allowing us to see whether corpora are adequately representative of lexical distributions in target discourse domains; and 2) provide valuable insight into the degree of generalizability of word lists we have developed.
ANALYSIS OF THE CORRELATION BETWEEN CRYPTO CURRENCIES, S&amp;P500 AND US 10-YEAR TREASURY BOND INDEX WITH GRANGER CAUSALITY TEST

Blockchain-based cryptocurrencies have gained popularity in television and digital media channels with the highest value records of all time broke in a row, both in academic studies and in recent times. In the framework of the study conducted to provide data to those who want to assess their investments in blockchain-based cryptocurrencies. In the research it is aimed to examine correlation between Bitcoin as an independent variable and S&amp;P500 Index, US 10-year Treasury and altcoins like Ethereum, Cardano, Chainlink with Granger causality test. Findings shows that Chainlink as an investment tool has the highest return with 6.22% and it is followed by Cardano with 5.74%, Ethereum with 5.20% and ultimately Bitcoin. The US 10-year Treasury offers not only the lowest rate of return with 10% loss but also riskier tool than Bitcoin. S&amp;P500 Index offers lower rate of return and riskier in comparison with FED interest rate. According to the covariance values, it has been determined that Bitcoin has an increasing linear relationship with Ethereum, Cardano and Chainlink, and a decreasing linear relationship with the FED interest rates and US 10-year Treasury, while it is unrelated to the S&amp;P500 Index.
Lightweight Few-Shot Learning Based on Triple Information for Internet-of-Things Applications

Under the stringent requirements of latency, reliability and privacy in IoT scenarios, IoT intelligence has gradually sunk to endpoint devices. However, in this process of deploying the deep learning models on endpoint devices, the challenges of constrained data resources and computing resources are encountered. In order to address these challenges, we propose in this paper a lightweight few-shot learning algorithm by the use of limited data and small-scale network structure, called TI-FSL (triple information based few-shot learning), which not only has strong generalization ability but also can be deployed on the resource-constrained endpoint devices for IoT applications. Our algorithm proceeds in 3 steps. First, we construct triples for few-shot classification, in which the anchor is from the query set and the positive and the negative belong to the support set. Second, based on the above triples, the triple information is measured by the maximum triplet loss of the anchor, positive class and different negative classes. Third, the lightweight classifier can be learned by optimizing the triple information to update the network structure. We evaluate our algorithm on the most popular few-shot dataset <i>mini</i>ImageNet and an IoT activity recognition dataset <i>SDA</i>; and the results demonstrate the superiority of our algorithm against other state-of-the-art methods.
When Behavior Analysis Meets Social Network Alignment

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users&#x0027; behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users&#x0027; multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users&#x0027; behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.
Congestion-aware Critical Gradient Scheduling for Distributed Machine Learning in Data Center Networks

Distributed Machine Learning (DML) is proposed not only to accelerate the training of machine learning, but also to solve the inadequate ability for handling a large amount of training data. It adopts multiple computing nodes in data center to collaboratively work in parallel at the cost of high communication overhead. Gradient Compression (GC) is introduced to reduce the communication overhead by reducing the number of synchronized gradients among computing nodes. However, existing GC solutions suffer from varying network congestion. To be specific, when some computing nodes experience high network congestion, their gradient transmission process could be significantly delayed, slowing down the entire training process. To solve the problem, we propose FLASH, a congestion-aware GC solution for DML. FLASH accelerates the training process by jointly considering the iterative approximation of machine learning and dynamic network congestion scenarios. It can maintain good training performance by adaptively adjust and schedule the number of synchronized gradients among computing nodes. We evaluate the effectiveness of FLASH using AlexNet and Resnet18 under different network congestion scenarios. Simulation results show that under the same number of training epochs, FLASH reduces training time 22-71&#x0025;, maintains good accuracy, and low loss, compared with the existing memory top-K GC solution.
Challenges of testing self-adaptive systems

Self-adaptive systems can change their behavior due to internal or external issues detected during operation. Such systems should be able to change their internal structure or functionality to cope with broken motors or changes in the infrastructure. Assuring that the adaptations taken during operation do not impact the desired behavior or functionality of the system is of uttermost interest. In this paper, we contribute to the corresponding quality assurance challenge. In particular, we focus on a specific class of self-adaptive systems utilizing health states for computing repair actions. We discuss the requirements of testing methodologies for such systems and raise relevant research questions.
Knowledge-based configuration of videos using feature models

User-centricity and variability play an increasingly important role in various application domains. A trend in the context of creating and sharing videos is to personalize contents and related navigation features. Such a personalization is needed to be able to take into account the preferences of users in terms of preferred contents, existing knowledge levels (e.g., in the context of teaching videos), and available time for watching a video. In this paper, we present an approach to define variability properties of videos. In this context, we show how the different modeling concepts of feature models can be used to represent variability properties of videos and also discuss related open research challenges.
Variability-aware data migration tool

Relational databases are widely present in the development of software applications. A typical implementation can be seen in content management systems found on most websites. However, the migration of database structure and content between different management systems is not trivial, and the manual creation of scripts makes it difficult to reuse them in other scenarios. This paper presents a tool for database migration by modeling what we call a migration product line. This tool allows to obtain different configurations resulting in final products in a semi-automatic way, i.e., products according to software requirements, considering the variability between any two relational databases. To study the feasibility of our proposal, we have implemented a proof of concept that performs the migration between two databases.
Evaluation Technology of Students’ Learning Status in Chinese Classroom Based on Deep Learning

The evaluation of students’ learning status has a strong guiding role. What is taken as the evaluation standard will determine what education teaches and what students learn. As a basic subject and a lifelong art, Chinese in senior high school plays an important role in the college entrance examination; its importance can be imagined; and it has also attracted more and more attention. The assessment of students’ learning status can greatly promote students’ learning quality and values. However, the current evaluation of students’ learning status still has the score-oriented problem, and lacks enough attention to students’ interests, skills, potential, thinking, values, and other aspects, which is worrying. The function of Chinese evaluation has been narrowed to evaluate students’ knowledge and skills, and it has been euphemistically called the focus of classroom teaching. In Chinese class, the role of students’ learning status assessment has received a lot of attention, and it should play more roles in promoting students’ deep learning, so as to improve the thinking quality of learning and find appropriate learning methods. Deep learning requires students to have strong autonomous learning ability and problem-solving ability, etc. Students will benefit from developing these good habits in their future work and life.
Channel-based coherence of quantum states

Quantum coherence is one of the most fundamental and striking features in quantum physics. Considered the standard coherence (SC), the partial coherence (PC) and the block coherence (BC) as variance of quantum states under some quantum channels (QCs) [Formula: see text], we propose the concept of channel-based coherence of quantum states, called [Formula: see text]-coherence for short, which contains the SC, PC and BC, but does not contain the positive operator-valued measure (POVM)-based coherence. By our definition, a state [Formula: see text] is said to be [Formula: see text]-incoherent if it is a fixed point of a QC [Formula: see text], otherwise, it is said to be [Formula: see text]-coherent. First, we find the set [Formula: see text] of all [Formula: see text]-incoherent states for some given channels [Formula: see text] and prove that the set [Formula: see text] forms a nonempty compact convex set for any channel [Formula: see text]. Second, we define [Formula: see text]-incoherent operations ([Formula: see text]-IOs) and prove that the set of all [Formula: see text]-IOs is a nonempty convex set. We also establish some characterizations of a [Formula: see text]-IO in terms of its Kraus operators. Lastly, we discuss the problem of quantifying [Formula: see text]-coherence and prove some related properties.