Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Hashing with Reconstructive Cross-modal Attention

On many popular social websites, images are usually associated with some meta-data such as textual tags, which involve semantic information relevant to the image and can be used to supervise the representation learning for image retrieval. However, these user-provided tags are usually polluted by noise, therefore the main challenge lies in mining the potential useful information from those noisy tags. Many previous works simply treat different tags equally to generate supervision, which will inevitably distract the network learning. To this end, we propose a new framework, termed as Weakly Supervised Hashing with Reconstructive Cross-modal Attention (WSHRCA), to learn compact visual-semantic representation with more reliable supervision for retrieval task. Specifically, for each image-tag pair, the weak supervision from tags is refined by cross-modal attention, which takes image feature as query to aggregate the most content-relevant tags. Therefore, tags with relevant content will be more prominent while noisy tags will be suppressed, which provides more accurate supervisory information. To improve the effectiveness of hash learning, the image embedding in WSHRCA is reconstructed from hash code, which is further optimized by cross-modal constraint and explicitly improves hash learning. The experiments on two widely-used datasets demonstrate the effectiveness of our proposed method for weakly-supervised image retrieval. The code is available at https://github.com/duyc168/weakly-supervised-hashing .
Across Online Social Network User Identification Based on Usernames

Cross social network user identification aims to identify the same entity on various online social networks to enhance the completeness and accuracy of the persona. There are three broad categories of cross-social network user identification methods: user identification on account of basic user information, user identification on the basis of network topology graphs, and user identification based on the user's origin. This paper analyzes users’ display names from different social networks to determine whether they are the same person. The process consists of three steps: first, we obtain information about users and bring their display names from social networking sites. Secondly, we analyze the user's name, get a series of values from the user's name through similarity calculation methods, and match the similarity. We perform similarity matching on the real dataset by using some classification models. Our model performs well, with F1 values reaching 97.07%, 94.65%, and 92.05% for the three datasets, respectively. This paper can provide a high-quality dataset for downstream NLP tasks of high research significance and value.
A Data-Driven Algorithm for Large-Scale Multi-camera Calibration

Multi-camera calibration, which is the establishment of a mapping relationship between the 2D coordinates of individual cameras and the 3D world coordinates, has been a major challenge in computer vision technology. The model-driven multi-camera calibration, which starts from the imaging model of the camera, is computationally complex and difficult to consider the imaging distortion comprehensively, while the data-driven multi-camera calibration is often difficult to meet the needs of large-scale scenes in terms of the calibration range. To solve the above two common problems, this paper designs a multi-camera image acquisition system, which collects millions of point cloud coordinate samples in a large-scale space and uses neural networks to infinitely approximate the transformation model of 2D images and 3D world. After the calibration experiments, the scheme is simple and effective, and it can accomplish the requirement of high precision calibration for large-scale space in both spatial positioning and reprojection.
Digital Storage of Minority Image Based on Hadoop Technology

Hadoop technology is a distributed storage system architecture, which has the advantages of high reliability, low cost, and high scalability and can realize efficient and reliable distributed storage of massive data. By means of video inheritance, combined with modern media technology, and video communication through media such as TV and the Internet, it is possible to expand the influence of minority cultures, attract more audiences, and make them better developed. This paper designs a digital image storage system for ethnic minorities based on Hadoop technology. Based on Hadoop architecture, the conceptual structure and logical structure of digital storage are designed. The storage system adopts the Hadoop architecture to meet the storage and computing requirements of massive data information and at the same time realizes the extended database environment through the SQL Server database.
Overview of High School Education Reform in Sweden and China

Starting with the background of Swedish high school education reform, this paper analyzes the Swedish education reform system and illustrates the basic situation of Swedish education reform.At the same time, compared with the background of high school education reform in China, as well as the policies and systems of the new college entrance examination and the new course, the situation after the reform of our school is compared with the Swedish schools, causing deep thinking. High school education reform background in SwedenIn the whole process of education, high school education plays a role of connecting the preceding and the following.It is not only related to the personal development of each student and the development of each family, but also related to the development of a country's education level and the overall quality of the people.Realizing this importance, Sweden has carried out a series of educational reforms according to its national conditions, with fruitful results.According to the Swedish Education Law, high school education is the continuous development of compulsory education.Based on compulsory education, high school education attaches great importance to individual development, helps them find the correct life orientation, and lays a good foundation for entering the society to become useful talents to the country and society in the future.Prior to the 1970s, high school education in Sweden was divided into senior secondary schools and vocational schools.Different types of people have clear boundaries between schools, and students and courses are relatively closed and disconnected.Different types of schools limit students' future development and direction of employment, so that students can limit their future from high school.It also stipulates that only regular high school graduates can enter universities for higher education.The final result of this restriction is a school with the main purpose of admission.If students do not want to continue their study after graduation, they cannot find employed, while for the purpose of employment, even if students have the desire to learn, they have no desire to study ability.This overly clear education system caused the dissatisfaction of many parents and students at that time, but it also did not meet the needs of social, economic and scientific and technological development.Accordingly, Sweden has undergone a series of reforms.From 1960 to 1971, Sweden has been carrying out the reform of high school education, after continuous exploration and improvement, Sweden established a new type of comprehensive high school, making Sweden's high school education reform in the forefront of the world [1].
Investigating Students’ Problems In Writing Recount Text To The Eight Grade Students

Abstract. This study aimed to describes students’ writing recount problem. This research used descriptive method and was conducted at SMP Negeri 20 Pontianak. It revealed the most serious problem in writing recount text was in the area of grammar. It found when the students wrote recount text which applied past tense with regular or irregular verb. The second problem came from the vocabulary aspect. It happened when the students demonstrated to state the main idea and include choosing the word that supporting in writing recount text. The third problems found in the area of organization aspects. It happened when the students composed the text paragraph coherently. The last problems found in the part of mechanics. It came when the students wrote down the recount text paragraph in the zone of spelling and punctuation
Improving Students’ Writing To The Eighth Grade Students By Using Clustering Technique

Abstract. This study aimed to optimize the students writing by implementating the clustering technique that technique help students to generate ideas. The method used the classroom action research as an appropriate method for this research. The subject in this research was done at SMP Negeri 02 Suhaid, Kapuas Hulu. This is a Junior High School which is located on Lintas Mensusai Street No.1 Mensusai, Kapuas Hulu Regency. The researcher conducted the research for 24 students’ of VIII class. In qualitative data finding based on the observation checklist and field note, the students very enthusiastic during teaching learning process and their showed positive attitude to the activities that were conducted by the researcher in clustering technique. Besides, there were some students who active and often asked the questions when they found some difficulties in writing. Moreover, in quantitative data finding had conclusion was that clustering technique could be used to improve the students’ writing skill. The students’ writing ability increased.
Implementation of Project Management Knowledge Areas by Project Managers

Academic and professional literature records the primary areas of interest within a discipline and how they change across time.However, it does not always keep up to date with the practical needs of various industries.This article discusses the Project Management Body of Knowledge (PMBOK) areas and their implementation among active project managers (PMs).The PMBOK guide defines terms, guidelines, and knowledge areas within project management.A survey was conducted among 117 active PMs who were asked to rate the degree to which they implement the PMBOK areas.PMs reported integration, cost, and procurement as being implemented by them to the greatest extent, while their implementation of quality, scope, and stakeholders was not reported.This points out the need for reinforcement among PMs.No correlation was found between the degree to which additional knowledge was implemented by PMs and the degree to which the knowledge areas were covered in the last generation of textbooks.Textbooks do not align with the PMs' implementation in their working fields.The study identified gaps between the degree to which the knowledge areas of project management were implemented and the importance that the authors of the textbooks attach to the above areas of knowledge.The research findings may contribute greatly to reducing these gaps.They can also contribute to training programs targeted at project managers.
Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic Aggregate Approximation

With the development of automatic electrical devices in smart grids, the data generated by time and transmitted are vast and thus impossible to control consumption by humans. The problem of abnormal detection in power consumption is crucial in monitoring and controlling smart grids. This article proposes the detection of electrical meter anomalies by detecting abnormal patterns and learning unlabeled data. Furthermore, a framework for big data and machine learning-based anomaly detection framework are introduced. The experimental results show that the time series anomaly detection for electric meters has better results in accuracy and time than the expert alternatives.
Blockchain-Based Supply Chain Financial Ciphertext Retrieval System

There are various types of enterprises involved in supply chain finance. The content and standard of internal information data disclosure of each enterprise are different, and the information quality of enterprises with different qualifications is uneven, so it is impossible to provide comparable credit data for financial entities. We propose a blockchain-based supply chain financial ciphertext retrieval system, which is a multi-keyword fuzzy search based on blockchain. In this paper, blockchain technology is used to ensure the security of index, and intelligent contract is used to realize permission control, which ensures the privacy protection of users. Bloom filter is used to generate fixed-length index vectors corresponding to keyword sets, thus reducing the storage complexity of keyword indexes. The elements in the index vector are randomized by permutation, and this permutation is shared by attribute encryption technology. Only legitimate users can obtain this permutation and construct the trap of query keywords. Finally, the security analysis shows that this algorithm can not only resist the selective plaintext attack and selective ciphertext attack, but also has good practicality.
Research on Key Technologies of Chinese Spelling Check Based on Machine Learning

Chinese spelling error correction technology is an important technology that teachers of Chinese as a foreign language should master. The research on Chinese spelling error correction technology can not only be applied to Pinyin input method, document editing tools, search engines, chat robots, voice assistants, etc., but also help Chinese learners at home and abroad to learn Chinese, improve learning efficiency and reduce learning pressure. However, the existing Chinese spelling error correction technology has some problems, such as insufficient context information of word vector, and the generated candidate words are limited to the confusion set. This is an urgent need for new Chinese spelling error correction technology to automatically detect and correct data to improve the error correction performance of text., In this paper, we propose a Spell model based on machine learning. After obtaining the final candidate words, we extract the feature information of the candidate words and their sentences, and use XGBoost classifier to filter out the correct words. The experiment shows that the performance of Spell model is significantly improved compared with the traditional model.
Query performance optimization method for blockchain carbon footprint data

Block chain can be used as a new kind of distributed database system, can be applied in areas such as carbon footprint. However, its practical application generally faces problems such as simple query function and low query performance due to the limitation of data storage mode. Therefore, for the application scenario of blockchain in carbon footprint, query performance optimization is a research topic that cannot be ignored. In this paper, we first introduce the current popular blockchain query schemes. Then we adopt a query optimization method based on MySQL, an external database, to support more complex and efficient query requirements. Finally, we verify the effectiveness and feasibility of the proposed scheme through experiments.
Method for Extracting Intentional Motion Using Force Sensor in Hand-held Robotic Forceps and Its Effect on Performance

To enhance the performance of multi-degree-of-freedom hand-held forceps that support minimally invasive surgery, it is important to correctly sense the operator's intended operation input.In our hand-held robotic forceps that use a force sensor as an input device, it was necessary to solve the problem of unintended operation input due to gripper operation to move the joint of the forceps.We describe a method for eliminating this unintended input to the interface of the hand-held robotic forceps and present the results of an evaluation of its performance.We used a virtual grasp-and-reach task with cursor manipulation of the forceps' input device in our experiments and evaluated the throughput on the basis of Fitts' law, which is used to evaluate human interfaces; the evaluation was based on the results of manipulations by two participants.The results showed that the mean throughput was 1.5 to 2 times higher when the proposed processing was used.This suggests that the proposed processing of the operational input of the interface contributes to the improvement of performance with the hand-held robotic forceps.
Deep-Gamma: deep low-excitation fluorescence imaging global enhancement

Lowering the excitation to reduce phototoxicity and photobleaching while numerically enhancing the fluorescence signal is a useful way to support long-term observation in fluorescence microscopy. However, invalid features, such as near-zero gradient dark backgrounds in fluorescence images, negatively affect the neural networks due to the network training locality. This problem makes it difficult for mature deep learning-based image enhancement methods to be directly extended to fluorescence imaging enhancement. To reduce the negative optimization effect, we previously designed Kindred-Nets in conjunction with a mixed fine-tuning scheme, but the mapping learned from the fine-tuning dataset may not fully apply to fluorescence images. In this work, we proposed a new, to the best of our knowledge, deep low-excitation fluorescence imaging global enhancement framework, named Deep-Gamma, that is completely different from our previously designed scheme. It contains GammaAtt, a self-attention module that calculates the attention weights from global features, thus avoiding negative optimization. Besides, in contrast to the classical self-attention module outputting multidimensional attention matrices, our proposed GammaAtt output, as multiple parameters, significantly reduces the optimization difficulty and thus supports easy convergence based on a small-scale fluorescence microscopy dataset. As proven by both simulations and experiments, Deep-Gamma can provide higher-quality fluorescence-enhanced images compared to other state-of-the-art methods. Deep-Gamma is envisioned as a future deep low-excitation fluorescence imaging enhancement modality with significant potential in medical imaging applications. This work is open source and available at https://github.com/ZhiboXiao/Deep-Gamma.
A Hypothesis Testing-based Framework for Software Cross-modal Retrieval in Heterogeneous Semantic Spaces

Software cross-modal retrieval is a popular yet challenging direction, such as bug localization and code search. Previous studies generally map natural language texts and codes into a homogeneous semantic space for similarity measurement. However, it is not easy to accurately capture their similar semantics in a homogeneous semantic space due to the semantic gap. Therefore, we propose to map the multi-modal data into heterogeneous semantic spaces to capture their unique semantics. Specifically, we propose a novel software cross-modal retrieval framework named Deep Hypothesis Testing (DeepHT). In DeepHT, to capture the unique semantics of the code’s control flow structure, all control flow paths (CFPs) in the control flow graph are mapped to a CFP sample set in the sample space. Meanwhile, the text is mapped to a CFP correlation distribution in the distribution space to model its correlation with different CFPs. The matching score is calculated according to how well the sample set obeys the distribution using hypothesis testing. The experimental results on two text-to-code retrieval tasks (i.e., bug localization and code search) and two code-to-text retrieval tasks (i.e., vulnerability knowledge retrieval and historical patch retrieval) show that DeepHT outperforms the baseline methods.
Experimental Investigation on the Security Vulnerability of Electro-optics Self-Feedback Phase Encryption Loop for Secure Optical Communication

We propose an experimental setup for cracking the TDS of electro-optic self-feedback phase loop, and demonstrate the feasibility of the cracking scheme for modulating NRZ signals at different rates by combining statistical analysis techniques with hardware devices.
Evaluation of Computer Application using DEMATEL Method

Computer Application. Software, also known as a set of instructions or code created in a program to carry out a task or function on a computer, is referred to as an "application." Applications are end-user programs that assist users in performing several tasks on a computer, hence they play a significant part in a computer. The knowledge required to use application software on a computer is provided by a computer applications program. Word processing, spreadsheets, databases, desktop publishing, the Internet, and Windows operating systems are examples of applications. A computer is a handy instrument that can be used for decision-making, processing student data, creating manuals and references, and teaching and learning. Online learning also makes use of computers. occupations. Computers are used for budgeting, process control, and industrial research. Any computer's operating system is the most crucial piece of software. An operating system governs the operation of every computer system. Applications for computers facilitate communication, workflow, and efficiency. It makes sense that companies would wish to exploit them. However, all programs require intelligent users, as we well know. Companies seek qualified personnel who can reach their full potential. A utility system is a business-oriented system, such as a public ledger. DEMATEL (Decision-Making Trial and Evaluation Laboratory). They are divided into analyses using the Computer Application of the Reliability, Security, Data Storage, Data location, Mobility Evaluation Parameters Reliability, Security, Data Storage, Data location, Mobility in the value. Reliability, Security, Data Storage, Data location, Mobility. Reliability, Security, Data Storage, Data location, Mobility. Data Storage has the highest rank whereas Data location has the lowest rank.
NLP (Natural Language Processing) and NLP (Natural Language Programming) Using Decision Making Test and Evaluation Laboratory (DEMATEL) Method

In order to drive CDS, convey clinical knowledge and CDS treatments in standardised formats, and improve clinical narratives, "natural language processing (NLP)" is essential.Following the early, ground-breaking Faced by the researchers in the medical narrative, regular research was undertaken at significant medical centres, and there was a significant shift in interest toward biomedical NLP.This review primarily focuses on fundamental NLP techniques and the recent resurgence of interest in the creation of "NLP systems" for CDS.There includes discussion of current solutions to problems brought on by distinct testing also helps to identify, consumer target groups, and support goals.In this study, a combined approach based on coarse set theory and sophisticated DEMATEL ISM is designed to address the difficulties.The suggested approach combines the benefits of the advanced de "DEMATEL Method" approach for investigating cause-and-effect relationships by taking the strength of constraints into account, the benefit of the ISM method for constructing a hierarchical system of constituents, and the merit of a challenging parametric study for handling ambivalence without the use of data sources or prior suppositions.The alternatives and evaluation parameter are Matching, Translation, structured prediction and sequential decision process.The final result the sequential decision process is got first rank and Matching is got the lowest rank.
Oxygen storage modeling of a three-way catalyst based on a NARX network

Using the Nonlinear Auto-Regression with eXogenous input (NARX) dynamic neural network to establish a Three-Way Catalyst (TWC) model for vehicles, achieving high-precision prediction of oxygen storage capacity and shortening calculation times.