Reviewerly/exHarmony huggingface dataset:
Aspect-level Sentiment Analysis Based on Feature Enhancement and GCN

The aspect-level sentiment analysis study ignores the combination of syntactic distance, physical distance and lexicality, and only considers the syntactic structure of the sentence, lacking the mining of the complete syntactic structure and deep sentiment polarity knowledge, we propose the aspect-level sentiment analysis model FE-GCN based on feature enhancement and graph convolutional network. First the hybrid encoding part incorporates lexical features, physical distance features and syntactic distance features and feeds into the output of BiGRU, then the graph convolutional network layer performs aspect weighting enhancement on the tree structure and incorporates knowledge of sentiment polarity to obtain important information, and finally the sentiment category of aspect words is obtained after the classification layer. Experiments were conducted on three public datasets, Lap14, Rest14 and Twitter, with accuracy rates of 84.49%, 87.99% and 78.48%, respectively, indicating that FE-GCN can better integrate semantic, lexical, physical distance, syntactic distance and sentiment polarity knowledge and can store more complete syntactic structures, and has some advantages in the field of aspect-level sentiment analysis.
System Design of a Mobile Dual-Arms Robot with Autonomous Navigation

In this paper, a mobile dual-arms robot with autonomous navigation is designed and built. In order to build the robot, a four levels mobile chassis is firstly designed and set up at the bottom of the robot, which can realize the function of automatic navigation and obstacle avoidance. For the purpose of grasping and transporting objects or interaction with human beings or environment, two humanoid arms with eight degrees-of-freedoms are designed and fixed on the trunk of robot. A depth camera is installed on the robot's head to detect and locate objects in front of it. There is a touch screen on the chest of the robot, which can be used to operate the robot or display its status. With these combined functions, the robot can be used as a guider in the bank, business hall or as a housekeeper at home.
A Multi-feature Fusion Method for Smart Contract Classification

The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Compared with other baselines and variants, experimental results on a real-world dataset show that MFF-SC achieves an accuracy rate of 83.9%.
An Improved Soft Q Imitation Learning based on Normalized Reward

The effect of imitation on expert demonstrations has been one of the main focuses of research in reinforcement learning for a long time, from simple behavioral cloning to inverse reinforcement learning and generative adversarial imitation learning, the robustness of learning has been improved but the learning process has become more and more tedious. SQIL (Soft Q Imitation Learning) could learn more efficiently by combining behavioral cloning and soft Q learning with constant rewards. Moreover, DSAC (Discriminator Soft Actor Critic) uses a reward function based on adversarial inverse reinforcement learning instead of constant rewards, and the experimental effect is greatly improved. To make dynamic rewards easier and less time-consuming to obtain, we propose a new method that uses a normalized reward function to generate reward values, which does not require additional network training. We compare our algorithm with SQIL and DSAC in the same Pybullet experimental settings.
Improved YOLOx-s industrial pointer instrument detection method

Aiming at the problems of slow detection speed and low detection accuracy of existing industrial field pointer instruments, a detection method of industrial pointer instruments based on improved YOLOx-s is proposed. This method is based on YOLOx-s. Firstly, the backbone network of the original model is optimized, and a new feature extraction network is proposed based on the idea of Ghost Module, which improves the detection speed of the network without changing the accuracy. Secondly, introduce attention mechanisms in the backbone network to improve the network's feature extraction capabilities. Finally, the Neck module is improved, streamlined, reduced the complexity of the model and increased the detection rate. Experiments on the self-made industrial field pointer instrument data set show that the improved YOLOx-s algorithm is 1.17% higher than the original algorithm mAP, and 3.2% higher than the two-stage algorithm Faster R-CNN mAP, and it has obvious advantages in detection speed, and 65.45 pictures can be detected per second. The experimental results show that the algorithm has good robustness and universality in industrial pointer instrument detection, and provides guarantee for subsequent accurate acquisition of instrument readings.
Optimization of Object Detection Algorithm in Low Light Scene

The development of deep learning technology has given rise to the prosperity of the field of target detection. The existing target detection algorithms show extraordinary performance and robustness in the scene with sufficient light, but they do not perform well in the low light scene. In order to improve the detection performance of the existing target detection algorithm in the low light scene, a low light enhancement module is designed. According to the idea of multitask learning, the low light enhancement is integrated into the back end of the detection algorithm, so that the algorithm can learn the target detection and brightness enhancement tasks at the same time to improve the performance of target detection. The proposed method can effectively improve the detection performance in the low light scene without increasing the reasoning cost of the algorithm.
IMU Based Human Gestures Recognition using Deep Learning for Wearable Devices

In recent years, the emergence of wearable devices, such as smartwatches and smart glasses. Based on this, human gesture recognition (HGR) for wearable devices has been a popular topic in the community of computer vision. Previous human gesture recognition approaches are mainly based on statistical methods, the breakthrough of deep learning enables researchers to make use of different neural network architectures to learn features of the human gestures that are collected from wearable devices. Most of these methods are based on the convolutional neural network, which extracts deep features from local manna. However, this way impedes the models from learning global information. To solve the problem, we propose a novel approach that leverages the attention mechanism to capture global information about human gestures. We conduct experiments on a benchmark, and the experimental results demonstrate that our proposed method is superior to the other baselines.
Application Research of security policy in the Linux operating system

This paper is based on the security of application software of operating system as the breakthrough point, aiming at the security problems of Linux operating system. The mandatory access control, the rules and mechanisms of security policy are emphatically analyzed, including the configuration process of the security policy in detail to ensure the security of the system.
Design of Intelligent Control System and Positioning Algorithm for Deployable Wheeled Mobile Robot

With the increasing difficulty of tasks to be performed by robots and the more complex working environment, wheeled mobile robots must improve their adaptability to achieve real autonomy, which puts forward higher requirements for control theory and positioning technology. In order to solve the shortcomings of the existing intelligent control system design and positioning algorithm research of the deployable wheeled mobile robot, this paper discusses the controller hardware selection and the overall performance index of the deployable wheeled mobile robot based on the discussion of the intelligent control system mode of the deployable wheeled mobile robot and FastSLAM2.0 and Kalman filtering positioning algorithm, And the design of intelligent control system and positioning algorithm are discussed. Finally, through the comparison experiment between the multisensor information fusion technology and Kalman filtering algorithm designed in this paper and FastSLAM algorithm. The experimental results show that the positioning accuracy of multi-sensor information fusion technology and Hermann filtering algorithm is above 98% on average. Therefore, the design of intelligent control system and positioning algorithm of deployable wheeled mobile robot designed in this paper has high practical value.
Intel round 2: starting from square one

This article explains how the General Court’s January 2022 Intel judgment raises the bar for assessments of as-efficient competitor (AEC) tests, with reference to six specific aspects of the European Commission’s (the Commission’s) analysis.
The Digital Ludeme Project: Combining archaeological and computational methods for the study of ancient board games

Archaeologists and computer scientists have both studied board games since the early days of their fields. Early archaeologists had an interest in identifying ways of playing the games of antiquity, and they applied diffusionist models fashionable at the time to trace the development of games from antiquity to the games played in nineteenth century Europe and North America. In time, a huge amount of data on ancient games was collected, and in the last thirty years archaeologists have studied games as they relate to social processes. In parallel to this, artificial intelligence (AI) research has utilized board games, primarily as testbeds for developing AI techniques, but also as an application domain. Archaeological and AI methods are combined in the Digital Ludeme Project, which documents the preserved knowledge of ancient games and uses computational techniques to evaluate research questions that can be addressed through AI playouts of proposed rulesets for games.
Distributed state estimation with state equality constraints in the presence of packet dropping

In this paper, we investigate distributed Kalman consensus filter with state equality constraints in the presence of packet dropping. Firstly, the unconstrained distributed Kalman filter is designed by utilizing the local measurement information of each sensor node, and then the consensus term is added to it to derive the unconstrained distributed Kalman consensus filter. Secondly, we research the equality constrained distributed Kalman consensus filter based on the projection operator to achieve better filter performance, where the unconstrained estimation gain is calculated by solving a modified algebraic Riccati equation and a Lyapunov equation. Additionally, we design the second type of equality constrained distributed Kalman consensus filter by using time-stamping technology and projection operator to eliminate the effect of the Lyapunov equation in the infinite time domain on the convergence analysis, and its unconstrained estimation gain only requires solving a modified algebraic Riccati equation. Finally, the simulation experiments demonstrate that the equality constrained distributed Kalman consensus filter outperforms the unconstrained filter and that the second type of estimator exhibits superior performance compared to the first.
Constructions of linear codes with two or three weights from vectorial dual-bent functions

Linear codes with few weights are an important class of codes in coding theory and have attracted a lot of attention. In this paper, we present several constructions of q-ary linear codes with two or three weights from vectorial dual-bent functions, where q is a power of an odd prime p. The weight distributions of the constructed q-ary linear codes are completely determined. We illustrate that some known constructions in the literature can be obtained by our constructions. In some special cases, our constructed linear codes can meet the Griesmer bound. Furthermore, based on the constructed q-ary linear codes, we obtain secret sharing schemes with interesting access structures.
Forcibly bipartite and acyclic (uni-)graphic sequences

The paper concerns the question of which properties of a graph are already determined by its degree sequence. The classic degree realization problem asks to characterize graphic sequences, i.e., sequences of positive integers, which are the degree sequence of some simple graph. Erdős and Gallai [11] solved this problem. Havel and Hakimi [14], [17] provide a different characterization implying an algorithm to generate a realizing graph (if one exists). It is known that a graphic sequence can have several non-isomorphic realizations. We characterize graphic sequences where every realization has some given graph property P. Such sequences are called forcibly P-graphic. In particular, we present complete results characterizing forcibly (connected) bipartite, forcibly acyclic, and forcibly tree-graphic sequences. In those four models, we also characterize the sequences with a unique realizing graph, called unigraphic sequences. Finally, we address the problem of counting the number of sequences in each model.
Commitment Capacity of Classical-Quantum Channels

We study commitment scheme for classical-quantum channels. To accomplish this we define various notions of commitment capacity for these channels and prove matching upper and lower bound on it in terms of the conditional entropy. Our achievability (lower bound) proof is quantum generalisation of the work of one of the authors (arXiv:2103.11548) which studied the problem of secure list decoding and its application to bit-string commitment. The techniques we use in the proof of converse (upper bound) is similar in spirit to the techniques introduced by Winter, Nascimento and Imai (Cryptography and Coding 2003) to prove upper bound on the commitment capacity of classical channels. However, generalisation of this technique to the quantum case is not so straightforward and requires some new constructions, which can be of independent interest.
Financial Big Data Intelligent Service System Based on Cloud Computing of Internet of Things

The financial big data intelligent service system belongs to the technical field of financial data management. It is an innovation of the client and server of the financial service system, which promotes the electronic office of the financial system. This paper aimed to analyze the cloud computing means of the Internet of things (IoT), select a more suitable specific algorithm, and conduct an in-depth study of the financial big data intelligent service system so that it can better serve the current financial situation. This paper gave a general introduction to the cloud computing of the Internet of things, researched and analyzed the financial big data intelligent service system machine, and applied the cloud computing of the Internet of things to the research of the financial big data intelligent service system. Based on the experiments in this paper, it can be seen that among the students in the three colleges and universities in place A, 567 people thought that they can adapt to the intelligent financial system better than the already employed salesmen, and 245 people held a negative attitude. It showed that the intelligent development of financial systems is a trend, but at the same time, it is also a development trend to strengthen the business training capabilities of professionals. The experimental results of this paper showed that the process of studying the financial big data intelligent service system based on the cloud computing of the IoT is more scientific and effective than using other means to analyze the experimental data, and it has greater reference significance for the intelligent development of the financial system.
English for tourism and AR-assisted context-aware ubiquitous learning: a preliminary design-based research study

With the rise of augmented reality (AR) and context-aware ubiquitous learning (CAUL), pedagogical designers in computer assisted language learning are increasingly developing authentic English for Specific Purposes (ESP) learning environments. However, there has been little research regarding the development of evidence-based principles for English for tourism purposes (ETP) through AR-based CAUL. The purpose of this study was to provide a preliminary analysis of the design principles and develop a learning model based on a rigorous four-phase Design-Based Research procedure. The researchers first developed a location-based AR application for ETP and formulated the first set of design principles. This application was further tested and refined in an iterative process, with data collected from three different sources. The data collection methods included an online questionnaire, onsite observations, and semi-structured expert interviews. All the data was triangulated and analyzed, and a team of experts evaluated the design principles. Results showed that the technical affordances of mobile AR devices should form the basis for the design. The application needs to seamlessly provide multimodal scenery-based AR learning supports to fulfill individual needs in blended learning contexts. Furthermore, a user-friendly interface with personalized functions and a portfolio recording the learning progress needs to be included. Multimodal authentic ETP dialogs and terminology are also necessary in order to build students’ ETP speaking competence. Moreover, the learning material should be compatible with the main theories which drive language learning in CAUL. In light of the findings, a learning model was established to guide AR-based ETP learning and serve as template for future studies in other ESP fields.
Speed Control of a Mobile Robot Using Confidence of an Image Recognition Model

Convolutional neural networks (CNNs) are often used for image recognition in automatic driving applications. Object recognition by a CNN generally uses a confidence score, which expresses the degree of recognition certainty. In most cases, the focus is on binary information, such as the size of the confidence score compared with a threshold value. However, in some cases the same recognition result has different scores. Even at intermediate confidence scores, the degree of recognition can be reflected in the control using the confidence score itself. Motivated by this idea, the aim of this study is to develop a method to control a mobile robot on the basis of the continuous confidence score itself, not on a binary judgment result from the score. Specifically, we designed a reference shaper that adjusts the reference speed according to the confidence score in which an obstacle exists. In the proposed controller, a higher score results in a smaller reference speed, which slows the robot. In a control experiment, we confirmed that the robot decelerated according to the confidence score, and demonstrated the effectiveness of the proposed controller.
Folklore-based art performances in digital art space as a form of revitalizing local wisdom in the metaverse era

Indonesia is an archipelagic country with ethnic and cultural diversity, where there is a lot of uniqueness, especially in traditional art in the form of folklore.Folklore is a potential asset in an art performance because it can present interesting displays of representations from the traditional culture of the community.Information and communication technology that develops in this digital era demands innovation in various fields including art.This article discusses the study of folklore-based performing arts using digital art spaces, as an effort to revitalize art into digital performances.This study is qualitative research with an ethnographic approach and experimentation at the final stage of the research.The results of the study show that the innovation of folklore art performances into the digital art space is a powerful way to maintain its existence and be more widely known by future generations in the metaverse era.
The Impact of Arabic Diacritization on Word Embeddings

Word embedding is used to represent words for text analysis. It plays an essential role in many Natural Language Processing (NLP) studies and has hugely contributed to the extraordinary developments in the field in the last few years. In Arabic, diacritic marks are a vital feature for the readability and understandability of the language. Current Arabic word embeddings are non-diacritized. In this article, we aim to develop and compare word embedding models based on diacritized and non-diacritized corpora to study the impact of Arabic diacritization on word embeddings. We propose evaluating the models in four different ways: clustering of the nearest words; morphological semantic analysis; part-of-speech tagging; and semantic analysis. For a better evaluation, we took the challenge to create three new datasets from scratch for the three downstream tasks. We conducted the downstream tasks with eight machine learning algorithms and two deep learning algorithms. Experimental results show that the diacritized model exhibits a better ability to capture syntactic and semantic relations and in clustering words of similar categories. Overall, the diacritized model outperforms the non-diacritized model. We obtained some more interesting findings. For example, from the morphological semantics analysis, we found that with the increase in the number of target words, the advantages of the diacritized model are also more obvious, and the diacritic marks have more significance in POS tagging than in other tasks.
Privacy-Preserving Image Classification Using ConvMixer with Adaptative Permutation Matrix and Block-Wise Scrambled Image Encryption

In this paper, we propose a privacy-preserving image classification method using block-wise scrambled images and a modified ConvMixer. Conventional block-wise scrambled encryption methods usually need the combined use of an adaptation network and a classifier to reduce the influence of image encryption. However, we point out that it is problematic to utilize large-size images with conventional methods using an adaptation network because of the significant increment in computation cost. Thus, we propose a novel privacy-preserving method that allows us not only to apply block-wise scrambled images to ConvMixer for both training and testing without an adaptation network, but also to provide a high classification accuracy and strong robustness against attack methods. Furthermore, we also evaluate the computation cost of state-of-the-art privacy-preserving DNNs to confirm that our proposed method requires fewer computational resources. In an experiment, we evaluated the classification performance of the proposed method on CIFAR-10 and ImageNet compared with other methods and the robustness against various ciphertext-only-attacks.