Reviewerly/exHarmony huggingface dataset:
A novel weighted complex evidence combination with its application in multisource information fusion

With the development of complex evidence theory in the application of multisource information fusion, classical Dempster–Shafer evidence theory has been extended to complex plane, called complex evidence theory. However, counterintuitive result may occur in the case when fusing conflicting complex evidences. To address this problem, a new multisource information fusion method is proposed by means of complex evidential distance function. This proposed method can reduce the impact of abnormal complex evidence on the fusion results to better support decision. A numerical example and an application of medical diagnosis verify the feasibility and effectiveness of the proposed fusion method.
One more check for the new tracker and just disappeared tracker

We propose a fast and accurate multi-target tracking method, Twice Check. This method is a multi-target tracking method, which matches the newly appeared track and the just disappeared track and adds the low-frame quadratic verification and linear interpolation to repair the track. Our method, combined with the current SOTA method, can improve the effect of the original method on the open Multi-Target Tracking challenge (MOT) dataset. The experimental results show that our Twice Check can effectively increase the effect of the current tracking model, and our method is simple and does not increase the tracking time.
Fraudulent website identification and classification system based on machine learning

With the rapid development of the Internet, there has been a high incidence of telecom fraud cases. Among the existing methods for identifying and classifying fraudulent websites, most of them use machine learning to classify and identify URLs and static files (HTML text and image resources) of web pages. These methods cannot take into account the text features contained in the pictures in the static files of the web page, and the content to be verified is too complex to recognize and classify the website quickly and accurately. In view of this, this system proposes a method combining SVM algorithm and improved website text features to quickly and efficiently identify and classify websites. Experimental results show that the system has excellent ability to identify and classify websites.
An anchor-free 3D object detection method with camera and liDAR based on H23D R-CNN

As an important part of automatic driving technology, the performance of perception determines the safety and availability of automatic driving. Generally, we need specific three-dimensional target information, which is difficult to gain from the camera. As a result, LiDAR plays a significant role in 3D detection due to its advantages of high resolution, immunity and capability to obtain specific location information. In order to overcome the problem that LiDAR often fails to detect objects with similar structures, we propose a method with Camera and LiDAR model based on H<sup>2</sup>3D RCNN, which can be divided into three sections. Firstly, a Foreground View (FV) map and a Bird Eye’s View (BEV) map are transformed from original point cloud, and the FV features, and image feature will augment the expression ability of features from the BEV maps and point cloud. Then, we introduce a center-based detection head on the BEV maps to generate the heatmaps and box refinement to minimize the redundancy and accelerate the operation of the model. Finally, we present the result of the second stage on the point features to acquire more accurate 3D bounding boxes. To validate our model, we test it on the official KITTI dataset and get a better performance compared to the original method and also get a nearly 5ms speed up.
Cybersecurity and Technical Patient Privacy Protection

Plastic surgery offices are subject to a wide variety of cybersecurity threats, including ransomware attacks that encrypt the plastic surgeon's information and make it unusable, as well as data theft and disclosure attacks that threaten to disclose confidential patient information. Cloud-based office systems increase the attack surface and do not mitigate the effects of breaches that can result in theft of credentials. Although employee education is often recommended to avoid the threats, a single error by a single employee has often led to security breaches, and it is not reasonable to expect that no employee will ever make an error. A recognition of the two most common vectors of these breaches, compromised email attachments and surfing to compromised websites, allows the use of technical networking tools to both prevent email attachments from being received and to prevent employee use of unsanctioned and potentially compromised websites. Further, once compromised code is allowed to run within the office network, that code must necessarily make outbound connections to exploit the breach. Preventing that outbound traffic can mitigate the effects of a breach. However, most small office network consultants design firewalls to only limit incoming network traffic and fail to implement technical measures to stop the unauthorized outbound traffic that is necessary for most network attacks. Detailed techniques are provided which can be used to direct IT consultants to properly limit outbound network traffic as well as incoming email attachments, with more information at https://officenetworksecurity.com.
A Hybrid CNN-LSTM model for temperature forecasting

Temperature prediction has always been a difficult point in meteorological prediction. It not only affects the global climate and ecosystem, but also has a profound impact on people 's social practice. In recent years, the temperature prediction mostly adopts the method of constructing neural network, because the method of decision tree, statistics and vector regression ignores the time series dependence, but the neural network-based models have limited its accuracy. Many scholars like to use CNN or LSTM model alone, and both of them are still lacking. Therefore, this paper proposes to combine CNN and LSTM models, which can not only give full play to the advantages of CNN model in processing data quickly, but also use the memory function of LSTM model to process temperature data with strong time series. The experimental results show that the CNN-LSTM model is more accurate than the CNN and LSTM models alone in predicting temperature.
Semantic segmentation algorithm of underwater image based on improved DeepLab v3+

Underwater images are easily affected by external factors such as light and water impurities, resulting in low segmentation accuracy of underwater images. Aiming to solve the problem, this paper proposes a semantic segmentation method of underwater images by introducing a new branch to improve the structure of the DeepLab v3+ model. It exploits the low-semantic features of the input image to improve the performance of the network. In the new branch, down-sampling is used to adjust the resolution of the input image, and the dimension of the feature map is adjusted by 1×1 convolution. The attention mechanism is used to focus on the channel of the feature map, and finally, the feature map of the existing branch is merged. In the experiments on the SUIM underwater dataset, the average intersection ratio of the model is 72.88%, and the average pixel accuracy is 84.03%. In the experiments on the PASCAL VOC dataset, the average intersection ratio of the model is 75.85%, and the average pixel accuracy is 84.5%. Compared with existing mainstream algorithms, the proposed algorithm achieves better results.
Research on image encryption algorithm based on one-dimensional logistic and two-dimensional Hénon chaotic system

Aiming at the problems of high leakage risk and serious information loss that may exist in the encryption and decryption of digital images, an image encryption and decryption algorithm based on chaos theory is proposed. The algorithm introduces both one-dimensional logistic mapping and two-dimensional Hénon mapping. In the encryption process, the digital image is dislocation encrypted by logistic mapping and diffusion encrypted by Hénon mapping, and finally the cipher image is obtained. Compared with other similar algorithms, this algorithm has the advantages of low algorithm complexity and high operation efficiency. The simulation experiment is carried out on the MATLAB platform, and the results show that the algorithm can meet the test standard in the field of digital image encryption.
Message Queuing Telemetry Transport Based Data Logger

The phenomenon of logging data occurs daily. When a person sees something or experiences an occurrence, their memory of it is stored in their brains for future use. Similar to that, data logging is the process of gathering, examining, and storing data for eventual use. Applications in science, medicine, and industry, knowledge is required for the course of temperature and relative humidity at a specific moment. For recovery of this data from different sources or sensors, data loggers may be employed. A data logger is an electronic instrument that senses temperature and relative humidity by combining analogue and digital measurements with programming techniques. The data loggers take input from the DHT11 sensor. The database receives this info after that. for storing and analyzing the monitored data. A temperature and humidity data logger's design and simulation are presented in this paper.
NLP and Deep Learning Based POS Tagging and Blank Prediction Assistant

This paper talks about the implementation of various features of a Grammar correction and guidance website using NLP technology. Correct grammar and the right usage of words are really important to ensure good communication. Different NLP models are used across different features implemented. This paper talks about the core deep learning technology on which these algorithms are based. LSTMs and BERT are used to predict words for blanks positioned anywhere in the sentence. The spell checker was implemented using the N-gram model. POS tagging saw the use of the Hidden Markov Model (HMM). Overall, the application built is very user-oriented and will be able to act as a grammar guide for a variety of applications.
Chi-Square Features Selection in Unsupervised Learning Algorithm for Measuring Key Performance Indicators in Riau Province

The process of obtaining population documents which still takes time, causes the population service need to find a pattern of community satisfaction as an improvement. Key Performance Indicator has been regulated by the Ministry of Administrative Reform has 14 service elements that must be considered, with many of these elements making it difficult to find data patterns and can reduce the value of the accuracy and performance of an algorithm. In this study, feature selection was applied using Chi-Square and applying the K-Means, K-Medoid, FCM, SOM, and DBSCAN algorithms to find elemental patterns that must be prioritized in increasing community satisfaction. After processing the algorithm, the best cluster validity was obtained in the DBSCAN algorithm which used the Chi-Square feature selection with a value of 0.47 and obtained 5 features that were very influential in measuring community satisfaction, especially in the implementation of Sistem Informasi Administrasi Kependudukan (SIAK) in Dumai City population agency.
Nowhere to Disguise: Spot Camouflaged Objects via Saliency Attribute Transfer

Both salient object detection (SOD) and camouflaged object detection (COD) are typical object segmentation tasks. They are intuitively contradictory, but are intrinsically related. In this paper, we explore the relationship between SOD and COD, and then borrow successful SOD models to detect camouflaged objects to save the design cost of COD models. The core insight is that both SOD and COD leverage two aspects of information: object semantic representations for distinguishing object and background, and context attributes that decide object category. Specifically, we start by decoupling context attributes and object semantic representations from both SOD and COD datasets through designing a novel decoupling framework with triple measure constraints. Then, we transfer saliency context attributes to the camouflaged images through introducing an attribute transfer network. The generated weakly camouflaged images can bridge the context attribute gap between SOD and COD, thereby improving the SOD models' performances on COD datasets. Comprehensive experiments on three widely-used COD datasets verify the ability of the proposed method. Code and model are available at: https://github.com/wdzhao123/SAT.
Neuro or Symbolic? Fine-tuned Transformer with Unsupervised LDA Topic Clustering for Text Sentiment Analysis

For text sentiment analysis, state-of-the-art neural language models have demonstrated promising performance. However, they lack interpretability, require vast volumes of annotated data, and are typically specialized for tasks. In this paper, we explore a connection between fine-tuned Transformer models and unsupervised LDA approach to cope with text sentiment analysis tasks, inspired by the concept of Neuro-symbolic AI. The Transformer and LDA models are combined as a feature extractor to extract the hidden representations of the input text sequences. Subsequently, we employ a feedforward network to forecast various sentiment analysis tasks, such as multi-label emotion prediction, dialogue quality prediction, and nugget detection. Our proposed method obtains the best results in the NTCIR-16 dialogue evaluation (DialEval-2) task, as well as cutting-edge results in emotional intensity prediction using the Ren_CECps corpus. Extensive experiments show that our proposed method is highly explainable, cost-effective in training, and superior in terms of accuracy and robustness.
Research on Rapid 3D Reconstruction for Teleoperation in Manned Lunar Exploration Mission

Teleoperation can greatly improve the whole mission benefits of manned lunar exploration, while it is necessary to provide a real and effective environment for astronauts through rapid 3D reconstruction. The requirements of teleoperation and the characteristics of the operated objects in the future manned lunar exploration mission are analyzed. According to the characteristics of the lunar environment, a 3D rapid reconstruction scheme based on the combination of motion structure recovery and binocular vision is proposed, and the proposed scheme and its accuracy are verified through ground tests.
AN ANALYSIS OF THE COHESION OF THE STUDENTS’ DESCRIPTIVE TEXT WRITING IN SMP NEGERI 01 TAYU IN THE ACADEMIC YEAR 2021/2022

This final project studied the analysis of the cohesion found on the students’ descriptive test writing in SMP N 01 Tayu. The design of the research was descriptive qualitative research. The objectives of the study were (1) To find out the type analysis of cohesion competence by the eight grade SMP 01 Tayu in producing descriptive text, (2) To show the dominant in producing cohesion at their descriptive text at eight grade SMP 01 Tayu, (3) to describe the students’ cohesion competence in producing descriptive text. The object of the research were eight grade SMP N 1 Tayu. The instruments of the study were the data from the students descriptive text. This discussions from prior chapters are to be summarized to achieve the purpose of the study. The first one is the grammatical cohesion device, and the second one is the lexical cohesion devices. The grammatical cohesion device are the reference, substitution, ellipsis, and conjunction. Meanwhile, the lexical cohesion devices are the reiteration and the collocation. Then the are 5 types of the reiteration lexical devices. There were repetition, synonymy, hyponymy. Meronymy, and antonymy. And then, the last part is the collocation.
Analysis of storage methods and visual data models at developing applications for the OS Android

The researches results of possible ways for storing data in applications under the Android OS and a review of a number of scientific papers on this topic are presented. The absence of mathematical and technical description of data storage technology for such applications is revealed. The analysis and comparison of the solutions spectrum in this area are carried out. A typical approach for interacting with various databases is formulated. The reasons for using Room DAO, on the basis of which the necessary descriptions of operations with data are selected, are considered; an algorithm for processing data is proposed, and features of encapsulation when working with data are noted. Methods and algorithms for interaction with the Room Database are proposed. The general requirements for data storage based on Android, which are necessary to create a workable application, are formed. These technologies may be required in the mobile application market. Keywords Room DAO, Room Database, OC Android, internal storage, external storage, migrations
The Role of English in Relation to Foreign Tourists’ Revisit Intention for Tourism Development in Mentawai, Indonesia

English plays an important role in tourism. In tourism activity in Mentawai cultural tourism, foreign tourists involve in cultural activities in which they are served in English by Mentawaian people. Previous studies on Mentawai tourism show that before covid-19 pandemic, foreign tourists from different countries visiting Mentawai to experience Mentawai indigenous tradition were served in English. Unfortunately it is not examined yet whether or not the service in English has effects on tourists intention to revisit cultural tourism villages in Mentawai. Their revisit intention is certainly related to the service with which they have a tourism experience. This study examines the role of the English service in relation to foreign tourists’ revisit intention with the aim of finding out the effect of English on the revisit intention. The study is important for the developing of Mentawai tourism in this era of new normal and post-covid 19 in the future. The study conducted quantitatively resulted in that service in English had a positive effect on foreign tourists ‘ revisit intention, meaning that English supported tourists’ cultural contact with Mentawai tradition in tourism activity. Tourism service in English influenced tourists’ intention in the future time to visit cultural tourism villages in Mentawai. English plays an important role in improving tourists’ revisit intention to cultural tourism villages in Mentawai.
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios

Audio-visual question answering (AVQA) is a challenging task that requires multistep spatio-temporal reasoning over multimodal contexts. Recent works rely on elaborate target-agnostic parsing of audio-visual scenes for spatial grounding while mistreating audio and video as separate entities for temporal grounding. This paper proposes a new target-aware joint spatio-temporal grounding network for AVQA. It consists of two key components: the target-aware spatial grounding module (TSG) and the single-stream joint audio-visual temporal grounding module (JTG). The TSG can focus on audio-visual cues relevant to the query subject by utilizing explicit semantics from the question. Unlike previous two-stream temporal grounding modules that required an additional audio-visual fusion module, JTG incorporates audio-visual fusion and question-aware temporal grounding into one module with a simpler single-stream architecture. The temporal synchronization between audio and video in the JTG is facilitated by our proposed cross-modal synchrony loss (CSL). Extensive experiments verified the effectiveness of our proposed method over existing state-of-the-art methods.
Excursus on modulation spaces via metaplectic operators and related time-frequency representations

Modulation spaces were originally introduced by Feichtinger in 1983. Since the 2000s there have been thousands of contributions using them as correct framework; they range from PDEs, pseudodifferential operators, quantum mechanics, signal analysis. This justifies a deep study of such spaces and the related Wiener ones. Recently, metaplectic Wigner distributions, which contain as special examples the $\tau$-Wigner distributions, the ambiguity function and the Short-time Fourier transform, have proved to characterize modulation spaces, under suitable assumptions. We investigate the metaplectic action which is hidden in their construction and guarantees equivalent (quasi-)norms for such spaces. We add a new result on this topic and conclude with an exhaustive vision of these characterizations. Similar results hold for the Wiener amalgam ones.
The Logic of Filtering: How Noise Shapes the Sound of Recorded Music. By Melle Jan Kromhout. Oxford: Oxford University Press, 2021. 167 pp. ISBN 978-0-190-07014-4

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The Classification of V-A Resultative Construction and the Analysis of its Grammatical Blending

In this paper, through the investigation and analysis of the structure and semantic features of V-A Resultative Constructions(VA動結式) in Modern Chinese(such as "打碎" "洗乾淨" etc). The structure is re-divided into two types of which is the prototype ‘NP1+V+R(A)+NP2’ and for the variable-type is ‘NP+V+R(A)’. Then use the grammatical blending theory to analyze the formation mechanism of V-A Resultative Construction. As a result, the following conclusions are drawn: First, the essence of the V-A Resultative Construction formula is a causative meaning. Both events are causative, which the construction is composed of causing events and resulting events. Second, self-variable meaning(自變義) is a variant of agent causative meaning(施事類致使義). When the causer of the causing events and the receiver of the resultant event are unified in the imaginary space, and the verb is intransitive, a self-variable semantic V-A Resultative Construction is obtained, and vice versa, the action behavior is verb-object Structure, it will be defined as the type of agent causative meaning(施事類致使義). Third, deviating meaning(The result is different from what humans expect) and non-deviating meaning(The results are independent of human expectations) are two different categories. Although they have no ‘Object’ in terms of syntactic structure, in fact the internal semantic generation mechanism is different. And to draw the following conclusion, Grammatical blending provides a new perspective of innovative thinking for the research on the construction of V-A Resultative Construction.