Reviewerly/exHarmony huggingface dataset:
HULTIG-C: NLP Corpus and Services in the Cloud

Abstract Nowadays, the use of language corpora for many purposes has increased significantly. General corpora exist for numerous languages, but research often needs more specialized corpora. The Web’s rapid growth has significantly improved access to thousands of online documents, highly specialized texts and comparable texts on the same subject covering several languages in electronic form. However, research has continued to concentrate on corpus annotation instead of corpus creation tools. Consequently, many researchers create their corpora, independently solve problems, and generate project-specific systems. The corpus construction is used for many NLP applications, including machine translation, information retrieval, and question-answering. This paper presents a new NLP Corpus and Services in the Cloud called HULTIG-C. HULTIG-C is characterized by various languages that include unique annotations such as keywords set, sentences set, named entity recognition set, and multiword set. Moreover, a framework incorporates the main components for license detection, language identification, boilerplate removal and document deduplication to process the HULTIG-C. Furthermore, this paper presents some potential issues related to constructing multilingual corpora from the Web.
Coupled three-mode squeezed vacuum: Gaussian steering and remote generation of Wigner negativity

Multipartite Einstein-Podolsky-Rosen (EPR) steering and multimode quantum squeezing are essential resources for various quantum applications. The paper focuses on studying a coupled three-mode squeezed vacuum (C3MSV), which is a typical multimode squeezed Gaussian state and will exhibit peculiar steering property. Using the technique of integration within ordered products, we give the normal-ordering form for the coupled three-mode squeezing operator and derive the general analytical expressions of the statistical quantities for the C3MSV. Under Gaussian measurements, we analyze all bipartite Gaussian steerings (including no steering, one-way steering, and two-way steering) in detail and study the monogamy relations for the C3MSV. Then, we study the decoherence of all these steerings in noisy channels and find that sudden death will happen in a certain threshold time. Through the steerings shared in the C3MSV, we propose conceptual (and ideal) schemes of remotely generating Wigner negativity by performing appropriate photon subtraction(s) in the local position. Our obtained results may lay a solid theoretical foundation for a future practical study. We also believe that the C3MSV will be one of good candidate resources in future quantum protocols.
Person segmentation and identification across multiple wearable cameras

Recent major developments in the understanding of human social interactions have greatly contributed to the development of computers with social interaction capabilities. Many studies have investigated the understanding of human interaction from cameras. Identification of people across multiple videos is important for exploring human social interactions in group activities. We propose a framework for person segmentation and identification across videos captured by multiple wearable cameras. The proposed method comprises a local tracking module for tracking people in a single video and a global matching module for matching people across multiple videos. The method uses global consistency to identify people across multiple videos as well as ensures spatial-temporal consistency in a single video. We have demonstrated the effectiveness of our proposed method in comparison with a baseline method by using public datasets and our own dataset.
Active pairwise distance learning for efficient labeling of large datasets by human experts

Abstract In many machine learning applications, the labeling of datasets is done by human experts, which is usually time-consuming in cases of large data sets. This raises the need for methods to make optimal use of the human expert by selecting model instances for which the expert opinion is of most added value. This paper introduces the problem of active pairwise distance learning (APDL), where the goal is to actively learn the pairwise distances between all instances. Any distance function can be used, which means that APDL techniques can e.g., be used to determine likeness between faces or similarities between users for recommender systems. Starting with an unlabeled dataset, each round an expert determines the distance between one pair of instances. Thus, there is an important choice to make each round: ‘Which combination of instances is presented to the expert?’ The objective is to accurately predict all pairwise distances, while minimizing the usage of the expert. In this research, we establish upper and lower bound approximations (including an update rule) for the pairwise distances and evaluate many domain-independent query strategies. The observations from the experiments are therefore general, and the selection strategies are ideal candidates to function as baseline in future research. We show that using the criterion max degree consistently ranks amongst the best strategies. By using this criterion, the pairwise distances of a new dataset can be labeled much more efficiently.
EFEKTIVITAS PENGGUNAAN MEDIA PUZZLE TERHADAP HASIL BELAJAR MATEMATIKA KELAS 1 SD

The learning outcomes of mathematics for first-grade students at SDN Muktiharjo Kidul 03 tend to be low due to teacher-centered instruction, the application of conventional teaching models, and the lack of utilization of supportive learning media. The purpose of this study is to determine the effectiveness of puzzle media on student learning outcomes, particularly in the topic of comparing sizes. This research is a quantitative study using a pre-experimental design with a one-group pretest-posttest approach. The testing steps include: 1) Normality test, and 2) Mean comparison test. The conclusion of this research is that the use of puzzle media is effective for improving student learning outcomes in mathematics for first-grade students.
The Effects of Using Flashcard Learning Media to Improve Reading Comprehension in Children with Dyslexia

Dyslexia is a specific learning disability characterized by difficulties in reading. This study aims to examine the effectiveness of flashcard learning media in improving reading comprehension between ages 8 and 11 in the group of children identified with dyslexia. The current research used one group experimental design with five children as participants. Based on the results of the study, showed that there was an influence of flashcard learning media on improving the reading ability of dyslexic children. The results of data analysis using t-test showed that the post-test score (M=20,8) was significantly higher than the pre-test score (M=10,4). Analyses of the whole sample indicated that levels of reading comprehension had improved. The finding of this study could be discussed that using flashcard learning media had a positive effect on reading comprehension in dyslexia children. The Flashcard media were designed to be colourful and easy to understand could stimulate children’s willingness to learn and read.
Efficient Multi-UAV Path Planning for Collaborative Area Search Operations

Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a “square wave signal” curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the “square wave signal” curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the “square wave signal” curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV.
EMG Signal Controlled Wheelchair

EMG Signal Controlled Wheelchair is one possible way to help amputees, quadriplegics and people with limb impairment in day-to-day locomotion.The design described in this paper aims to enable movement by simple hand twitches.The paper describes the project from conceptualization to implementation.The EMG signals detected through electrodes undergo Signal Acquisition System and provide digital output to the microcontroller which in turn drives the respective motor.The Control Code is designed as when the person twitches its left or right hand the wheelchair turns left or right respectively and when both twitched simultaneously the wheelchair moves forward.
Perancangan Absensi Berbasis Web Dengan Metode Waterfall (Studi Kasus: PT. GlobalRiau Data Solusi)

Employee attendance is an important activity for the company because it can affect the amount of revenue. In implementing the recording procedure, this attendance requires accuracy in order to produce a good attendance report. PT GlobalRiau Data Solution (GDS) is a company or industry that provides Internet connections and other related services. GDS has a communication infrastructure connected to the Internet, where GDS then shares its network connection capacity with customers who need network connection services. Previously, attendance at PT.GDS was carried out manually within the company, but this manual process had many obstacles, including manipulation of attendance even with ineffective data storage. Therefore, the research aims to design a website-based attendance system or attendance recording using the waterfall method so that it can improve the attendance process to be more efficient. Web technology is used because it offers advantages, such as being able to work on any system as long as there is an internet connection and does not require a computer with a high capacity to run a web application while the use of the waterfall method is applied sequentially and systematically. The waterfall method begins design to maintenance and is implemented in stages.
Categorification of Negative Information using Enrichment

In many engineering applications it is useful to reason about "negative information".For example, in planning problems, providing an optimal solution is the same as giving a feasible solution (the "positive" information) together with a proof of the fact that there cannot be feasible solutions better than the one given (the "negative" information).We model negative information by introducing the concept of "norphisms", as opposed to the positive information of morphisms.A "nategory" is a category that has "nom"-sets in addition to hom-sets, and specifies the interaction between norphisms and morphisms.In particular, we have composition rules of the form morphism + norphism → norphism.Norphisms do not compose by themselves; rather, they use morphisms as catalysts.After providing several applied examples, we connect nategories to enriched categtory theory.Specifically, we prove that categories enriched in de Paiva's dialectica categories GC, in the case C = Set and equipped with a modified monoidal product, define nategories which satisfy additional regularity properties.This formalizes negative information categorically in a way that makes negative and positive morphisms equal citizens.
System Design and Platform Implementation for AI-Based Metaverse Music

Artificial Intelligence ( AI ) refers to the ability to simulate human intelligence through computer programs and equipment.This intelligence can process information such as language, images and sounds, and can make decisions and actions based on this information.Based on artificial intelligence-based intelligent arrangement, intelligent recommendation algorithm, intelligent mixing, virtual reality, blockchain and other technologies, Metaverse Music can carry out the design and development of intelligent arrangement system; intelligent recommendation algorithm design based on deep learning; intelligent mixing technology The innovative design; the virtual performer system design based on virtual reality technology; the copyright management system design based on blockchain technology, and the realization of the metaverse music platform construction.
Dimension Expansion Virtual Reality – Head Mounted Display

The development of virtual reality (VR) devices including novel functions has social significance. When the police station will perform on-site verification of any traffic accidents, in this work, a spatial grasp of the site and communication among staff are necessary. To help with their tasks, we fabricated a VR goggle which possesses a dimension expansion function. Concretely, we create 3D- VR images from one photo by using depth estimation software and the depth fused 3D (DFD) technique. In this way, obtained 3D-VR image can be displayed on the user’s gaze on a goggle.
Analysis of Factors Influencing the Effectiveness of Online Video Communication in the Era of Big Data -- an empirical study based on bilibili

With the continuous development of Internet technology and the online video industry has risen rapidly, and watching online videos has gradually become a part of people's daily life.To improve the viewing experience of users, it is necessary to understand which factors can attract users to watch online videos and affect the dissemination effect of videos.Taking the representative video website bilibili as an example, this paper studies the influence factors of online video communication effect from the three dimensions of video content creator, video characteristics and user characteristics, and constructs a model of video communication effect by using multiple linear regression.According to 3804 pieces of data of bilibili platform, the empirical analysis method is used to verify the results.The results show that the video duration has a negative impact on the online video communication effect, while the number of video creator fans, user satisfaction and user participation have a significant positive influence on the video communication effect.The amount of attention of video creators will play a significant negative role in the impact of user satisfaction and the number of fans on the effect of online video communication and play a positive role in the impact of user participation on the effect of online video communication; and different types, different release time of online video communication effects are significantly different.The results expand the re-search in the field of video communication's effect and pro-vide suggestions for individuals and video platforms to improve the effect of video communication.
Bibliometric Knowledge Mapping of Fishery Biology on Big Data

This paper reviews the research literature of fishery biology, summarizes the previous description of the connotation and characteristics of fishery biology, discusses the research of fishery biology in the development mode, evaluation system and evaluates the existing research results on this basis.The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research by knowledge mapping.Using CiteSpace literature methodology, this study analyzed the data of the science network database, and found that: One is the development of the research field has experienced three stages, and some representative key scholars and key documents have been recognized; the other is the co-occurrence of citations and keywords in the public knowledge map of literature illustrates the hot issues in this aspect.The challenges posed by visualizing big data today are different.At the same time, the theoretical model, structure and structural dimension are still debated.This is a direction that researchers need to continue their studies in the future.
Enhancing Beer Recommendations through Clustering: A Comparison of Hierarchical and K-means Clustering Methods on Normalized Data

The process of normalization confers notable advantages in optimizing gradient descent algorithms for machine learning applications and simplifying data processing.Through normalization, the scale of numeric types in the dataset can be adjusted, thereby facilitating the identification of patterns and trends that would otherwise remain obscured by variations in the magnitude of the features.This technique also reduces the risk of certain features having a disproportionate influence on the model's decision-making process.The scale function is one such means of normalization that effectively mitigates such issues.In the context of clustering, normalization plays a crucial role in enhancing the accuracy and efficiency of the process.It is imperative to note that in the case of an unclassified dataset, where the distribution of data is unknown, unsupervised learning models must be employed.Unsupervised learning models enable the identification of patterns and structures within the dataset without the need for prior classification.Clustering is one such approach that can extract meaningful insights from unstructured datasets.Through clustering data, the present research offer practical insights for businesses seeking to leverage data-driven approaches to enhance their operations and improve customer satisfaction.
Research on Notary Public Node Vote Workings of Blockchain Cross-chain Based on Enhance Collaborative Seive Algorithm

With the development of blockchain technology, cross-chain technology has become the trend of blockchain development, and notary vote is an important issue in cross-chain technology.Based on the factuall deal facts of blockchain, this newspaper proposes an vote scheme of cross-chain notary nodule in blockchain based on enhance collaborative seive algorithm.Through the analysis of testal data, it is proved that this workings can ensure the safety of cross-chain.
Research on Database Storage Technology based on Consensus Mechanism

With the continuous expanding amount of data and network information, the databases not only require to store the large-scale amount of data, but also demands for protecting the users' privacy information and promise to effectively verify the precise correctness content of the stored data.However, existing databases only guarantee the security of the privacy information and data-sets and have developed numerous encryption techniques to achieve privacy data preserving.There still leaks the verification mechanism for storage technology or the process will cost unacceptable system costs.Therefore, we utilize consensus mechanism to develop a novel database storage technology in the verification procedure in this work.Additionally, the utilization of consensus mechanism can greatly reduce the consensus procedure costs in the databases through our simulation and comparisons with existing storage algorithms.From our extensive experimental results, we can conclude that our model can effectively realize the database storage procedures with reasonable system costs.
Factors Affecting the Success of an Organization: The Case of Zambian Breweries

The main purpose of this study was to assess the factors that affect the success of an organisation. A questionnaire was the main method used to collect data. Respondents answered to a survey that was distributed using the Google Forms platform. The survey found that Zambian Breweries’ organizational structures enabled the management systems to efficiently run the company and boost production in order to help it reach its goals and objectives. Other systems that support the structure of the Zambian Breweries, make it possible to hire the right people for the right jobs and place them in the suitable required positions. The human resource policies and processes in place, the company was able to effectively carry out its strategic planning by having competent personnel and aligning their aptitudes, expertise, and talents to the needs of the Zambian Breweries. It was also found that the management’s successful upkeep of internal communication, the staff were able to understand the short- and long-term goals as well as the direction the company was pursuing. Additionally, effective communication enabled personnel to receive timely feedback, boosting morale and enabling high performance even under tough conditions. Keywords: Organizational Structure, Communication, Strategy, Strategic Management, Leadership.
An Event Coreference Resolution Method Based on Multimodal

It is crucial for many applications of Natural Language Processing (NLP) to recognize correfering events and entities that describe the same events in the real world.Despite this being an important task, the current research has mainly focused on text with very little attention towards other information sources, such as images.Consequently, we propose a neural architecture based on multimodal for Event Coreference Resolution (ECR), combining text features, image features, and text event graph features to identify coreference events.The experimental results demonstrate an average F1 score of 61.2%, which is an improvement on the previous event coreference model using a single text modality.
Research on the Correlation between Digital Cryptocurrencies Based on Spss Statistical Method

In this paper, SPSS software was used to study the correlation of digital cryptocurrencies in the context of the epidemic, and the conclusion that "there is significant correlation between various digital cryptocurrencies" was reached.Thus, it provides reference value for the analysis of the correlation between the price collapse of digital cryptocurrency under the epidemic and the price recovery of digital cryptocurrency after the epidemic.
Research on Traffic Safety Risk Identification of Highway Tunnels Based on Apriori

The traffic accident data onto highway tunnels are statistically analyzed.The improved Apriori algorithm is applied to extract the frequent item set and set the support degree threshold.And the association rule sets of different types of traffic accident characteristic attribute are obtained, the main risk sources and correlation relationships affecting the traffic safety of highway tunnels are found out.The technical support and rationalized prevention advice are provided to improve the safety management of highway tunnel operation, strengthen the identification of accident risks in tunnels, and reduce the losses caused by tunnel accidents to traffic efficiency and personal and property safety.
A Blockchain-Based Housing Lease Transaction Mechanism

In traditional house leasing transactions, real estate intermediaries are used as information transfer and trust guarantee, which virtually increases the cost and risk of both parties to the transaction.At the same time, the transaction process often requires cumbersome communication, negotiation and signing processes.Therefore, this chapter designs a blockchain-based housing lease transaction mechanism, and in order to improve the time for both parties to reach agreement on a transaction, an optimization model for housing lease transaction matching is proposed to improve the efficiency and transaction rate of lease transactions.