Reviewerly/exHarmony huggingface dataset:
A Self-Adaptive Chimp-Driven Modified Deep Learning Framework for Autonomous Vehicles to Obtain Autonomous Object Classification

The development of the future’s smart cities will increasingly depend on autonomous vehicles. For example, object detection, tracking, path planning, and sentiment or intent recognition, among other things, have all been the subject of several suggestions in recent years in an effort to address specific components of the working pipeline and develop a useful end-to-end system. This article presents a simple benchmark to evaluate the performance of object identification algorithms under deteriorating image quality. In this article, the input image is taken from the car object detection dataset, then, it gets preprocessed. The preprocessing involved Histogram equalization-based contrast enhancement, geometric transformations, and image augmentation. After getting preprocessed the region of interest is segmented with the help of a mask convolutional recurrent neural network (MCRNN). Then, the segmented region is classified using the long short-term memory (LSTM) with the self-improved chimp optimization algorithm. Object detection is performed through the proposed model called MCRNN-SICLSTM. The implementation is performed using MATLAB software. The performance of the proposed model is compared with the existing techniques using the performance metrics accuracy, precision, recall, F-Measure, mean absolute error, mean square error, false positive rate, false negative rate. The accuracy achieved is 99.23%.
hdt-rs: A Rust library for the Header Dictionary Triples binary RDF compression format

We present the Rust library hdt-rs (named "hdt" in the context of Rust libraries, such as on crates.io)for the Header Dictionary Triples (HDT) binary RDF compression format.This allows the writing of high-performance Rust applications that load and query HDT datasets using triple patterns.Existing Rust applications that use the Sophia library (Champin, 2020) can easily and greatly reduce their RAM usage by using the provided Sophia HDT adapter. Preliminaries RDFThe Resource Description Framework (RDF) is a data model that represents information using triples, each of which consists of a subject, a predicate, and an object.A set of triples is called an RDF graph, where the subjects and objects can be visualised as nodes and the predicates as labelled, directed edges.Predicates are always IRIs (Internationalised Resource Identifiers), which are generalisations of a URIs that allow additional characters.Subjects and objects can also be blank nodes and objects can also be literals.There are several text-based RDF serialisation formats with different compromises between verbosity, ease of automatic processing, and human readability.For example, the N-Triples representation of the fact "the mayor of Leipzig is Burkhard Jung" from DBpedia (Lehmann et al., 2015) is:
Revenue Recovery System

Revenue recovery is one of the major tasks of the government, which is used to raise the funds to be used in development process. At district level, to manage the revenue recovery system, there include many authorities from village level to district level. This online web application will ease the working of all the revenue officials from managers of different departments to Revenue Divisional Officer to District Collector. Security with respect to payments, receipts and balance sheet will be provided, since only the official authorities will be able to access them. Instant statistics of the balance sheet will be available to District Collector to examine the financial report, rate of products per quantities, amount utilized by various sectors etc. Managers, accountants as well as Revenue Divisional Officer will do their duty being more responsible since their activities would be transparent to higher authorities. The main objective of the Revenue Recovery System project is providing a completely transparent, responsive and optimized Revenue Recovery System. Another purpose is for the speedy generation of reports to maintain the monthly, quarterly, half-yearly and annual statistics. This Revenue Recovery System project will completely automate the existing system of revenue collection, reducing the burden on District Collector. The revenue collection will be become easier, speedy as well as flawless when it comes to collect revenue from heads like educational cess, stamp duty etc. Existing System: Revenue Recovery System is a tedious task when it comes to villages. All data is maintained manually and there is no transparency. So, the system is more prone to corruption. Moreover, generating any report will take a huge time and manual effort. Even after that, one cannot be sure that the data provided is complete in all respects and not missing. Further adding, it becomes very difficult to monitor at different levels like District level, Mandal level and Division level. The data flow is simply non-systematic, unsecure and highly time consuming. Proposed System: The new fully automated Revenue Recovery System will remove all the inconsistencies imparted by the existing system. All the revenue recovered reports can be generated by a single mouse click by just specifying the time frame (monthly, quarterly, half-yearly or annually). Even the graphical representation of the data over a time frame can also be generated for a better understanding of the scenario. This Revenue Recovery System software will certainly enhance the functionality of the revenue department by providing it the required transparency, responsiveness and security. It will reduce the manual effort wasted in report generation thus reducing the work load of the higher officials. As the data provided will be completely authentic, it will help in better analysis of the revenue recovered till date. With everything going flawless, work can be completed in the given time frame as well as level wise abstraction of data become easier. This software had additional advantage of storing and keeping a backup of the data
Argument by False Analogy: The Mistaken Classification of Bitcoin as Token Money

Abstract This paper documents inconsistent terminologies and misleading analogies in current discussions of digital money and payments. It offers a more consistent framework for understanding the potential of technological innovation in providing the functions of money and payments: as media of exchange, stores of value, and units of account and the implications of cryptographic technologies underpinning cryptocurrencies for the future of money and payments. These could support efficiency gains in money and payments, but decentralization is not inherent to their application. Radical reform leading to improved economic outcomes is conceivable, but not through disruptive displacement of existing institutional arrangements.
High-precision real-time UAV target recognition based on improved YOLOv4

In recent years, unmanned aerial vehicles (UAVs) have gained widespread use in both military and civilian fields with the advancement of aviation technology and improved communication capabilities. However, the phenomenon of unauthorized UAV flights, or "black flying", poses a serious threat to the safe flight of aircraft in airspace and public safety. To effectively interfere with and attack UAV targets, it is crucial to enhance the detection and identification of "low, slow and small" UAVs. This study focuses on achieving high-precision and lightweight detection and identification of four-rotor, six-rotor, and fixed-wing UAVs in low-altitude complex environments. By combining deep learning target detection with superresolution feature enhancement, a lightweight UAV detection model is designed and field-tested for verification. To address the challenge of detecting small UAV targets with limited information, the feature fusion network is enhanced based on the traditional YOLOv4 algorithm to improve the detection ability of small targets via small target enhancement and candidate box adjustment. The feasibility of the improved network is quantitatively and qualitatively analyzed. Channel pruning and layer pruning are then applied to the network, significantly reducing its depth and width and realizing a lightweight network. Finally, reasoning quantification is conducted on the embedded platform to enable end-side deployment of the target detection algorithm.
Router &amp; Waterjet Cutting Machine Showcase

AAG held an ‘Expo’ event at its Telford base last month where it showcased its portfolio of AXYZ routers and WARDJET waterjet machines. New and existing customers had the chance to see the machines in action with application demonstrations
3D generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements

Bias compensated pseudolinear Kalman filter (BC-PLKF) degrades for bearings-only tracking if measurements are corrupted with time-correlated noises. Generalized bias compensated pseudolinear Kalman filter (GBC-PLKF), on the other hand, outperforms BC-PLKF and many other comparison filters for two-dimensional (2D) cases under colored noise. However, GBC-PLKF is not practicable for three-dimensional (3D) real-life problems and due to the coupling between lateral and longitudinal planes, its 3D extension is not trivial. In this paper, bias analysis of 3D pseudolinear Kalman filter (PLKF) with azimuth and elevation measurements including colored noise is performed. Then, the low-cost recursive filter, 3D-GBC-PLKF is proposed for 3D real-life applications. The performance and effectiveness of the proposed algorithm is demonstrated with an air-to-surface guided missile pursuing a typical target.
DictPrompt: Comprehensive dictionary-integrated prompt tuning for pre-trained language model

The textual semantics contained in the PLM (Pre-trained Language Model) is constrained by the text distribution in the original training corpus. Due to the lack of sufficient contextual training corpus, the low-frequency word representations in the PLM often have difficulty capturing their actual semantics. Previous research has shown that using semantic information from dictionaries can alleviate this problem. Unfortunately, these works neglected the infinite potential of example sentences from different target words with various meanings. To re-explore methods for enhancing PLM using the dictionary, we propose a novel Comprehensive Dictionary-based tuning approach integrating the latest Prompt learning (DictPrompt). We first collect a dataset based on the Oxford Advanced Learner's English Dictionary. Then, we designed a set of comprehensive prompt templates with the corpus combining the word, the definition, and its example sentence. Finally, we insert a word game training task between pre-training and fine-tuning using these templates, allowing the model to inject more semantic information into PLM. We test our Dictprompt tuning method on three commonly used PLMs. The testing results on five fine-grained semantic tasks show that our dictionary-based secondary tuning can bring additional gains to the model's performance. The best accuracy improves 3.09% on average with our tuning on the WiC task and 7.93% on the WSC task. We also plot the sentence embedding scatters of polysemy words. Our method can smooth the decision boundary and help the model output more distinguishable embedding. The code is available at https://github.com/xbdxwyh/Dictprompt.
Stereoscopic image discomfort prediction using dual-stream multi-level interactive network

Existing stereoscopic image discomfort prediction methods may fail to work well because they are difficult to extract discomfort features from stereoscopic image’s statistical information since the mechanism of human binocular vision is very complex. In this work, we propose a dual-stream multi-level interactive network that is completely end-to-end trainable for stereoscopic image discomfort prediction. This method first extracts multi-level fusion and difference features from stereoscopic images through a multi-level interaction network. Then, the low-, medium- and high-level feature maps are concatenated to simulate the complicated visual interaction mechanism of the human visual system (HVS). Finally, two fully connected layers are used as a non-linear regression function that maps the feature vectors to stereoscopic image discomfort scores. Extensive experiments demonstrate that our approach performs favorably against the existing prediction models on the IEEE-SA dataset and NBU-S3D dataset.
Contrastive learning enhanced deep neural network with serial regularization for high-dimensional tabular data

As the scale of data shows rapid growth in various fields, big data’s vast amount of information can facilitate scientific discovery or decision-making. Deep neural network prevails in modeling big data such as images and text in computer vision and natural language processing. However, there is currently no widespread deep neural network for high-dimensional tabular data (HTD), as HTD could increase the model’s complexity and make estimating the parameters more difficult. Therefore, this paper proposes CLDNSR, a contrastive learning-enhanced deep neural network with serial regularization. This method combines relaxed Bernoulli distribution-based L0 regularization and adaptive L2 regularization for important feature selection and adaptive redundancy control to effectively handle high-dimensional input features. In addition, a tabular contrastive pre-training method is proposed to stabilize the supervised training process through better parameter initialization. Experiments on eleven real-world high-dimensional tabular datasets demonstrate that CLDNSR outperforms the baseline models designed for high-dimensional data.
Cluster analysis for the selection of potential discriminatory variables and the identification of subgroups in archaeometry

In this article, three variable selection methods based on Gaussian mixture models were compared to find a subset of variables that provided the “best” clustering. The use of an appropriate transformation for compositional data, whose geometric space is the Simplex, is emphasized. The comparison revealed the ability of the models to cluster data in multiple phases, showing to be more convenient to select the relevant variables than to perform an analysis based on 2D plots or by simultaneously including all the available variables in a multivariate analysis. Once the informative variables for the clustering were obtained, we used a method called Databionic Swarm (DBS). This method uses unsupervised machine learning, taking advantage of emergence and swarm intelligence applied to find natural chemical groups in the input data space. DBS can visualize high-dimensional distances in the projection through a 3D topographic map with hypsometric tints. The results were compared in terms of accuracy, both in the selection of the variables and in the classification, using a supervised accuracy index for clustering and two unsupervised indexes (the Heatmap and the Silhouette plot). The concepts and methods were illustrated by applying them to two published archaeological glass data sets. The first set consisted of 245 Romano-British glass vessels and the second set of 180 glass vessels from the 15th-17th century in Antwerp. In these applications, it was found that the methods for the selection of variables increased the accuracy of the classification compared to traditional methods.
Book review

Korean demonstratives developed into a number of DMs, including ilen ‘this kind of’ and celen ‘that kind of’ via ellipsis. From the perspective of stance-taking, these contrasting DMs exhibit intriguing differences amid seemingly similar functions. The two DMs carry the subjective function of marking surprise (mirativity), a function closely related to ellipsis, i.e., the speaker’s inability to complete the utterance due to surprise. This further developed into the intersubjective function of marking feigned surprise, a strategy of dramatizing the narrative or inviting the interlocutor into the evaluative common ground. The two DMs further mark the speaker’s stance of engagement, commitment, discontent, reproach, or sarcasm, etc. all in emotional contexts. A notable difference is that the proximal vs. distal distinction spills over to their DM functions, i.e., the distal-based celen is used in contexts where the speaker is distancing them self from what is being referred to, typically in condemning others, whereas the proximal based ilen is used in contexts where the referred object/proposition is mentally close to the speaker, thus signaling engagement or commitment. These contrastive DMs show how the physical distance encoded in demonstratives is iconically reflected in stance marking functions of the DMs derived from them.
Orchestrating model to improve utilization of IaaS environment for sustainable revenue

At the same time, the IaaS cloud is tapped with a double-edged sword of over and underutilization. Where overutilization causes higher costs, poor performance, and low prices (i.e., leads to fines), underutilization wastes the underlying resources. All these factors extremely annoy the customers as well as providers. The industry’s automation and predictions of 75 billion devices and 75 ZB data; increase this risk even more. For such massive growth, the IaaS cloud needs autonomous techniques that optimally manage both sides and utilize the resources optimally. Lots of studies have been carried on in this regard, however, improvement is still required. To address the under and overutilization challenges, the proposed model employs insourcing and outsourcing migration techniques. As a federated cloud, however, providers are not obligated to hire from a particular alliance. If the service provider is overloaded, it can engage external resources from anywhere that is convenient and cost-effective. Algorithms are used to optimize the different parameters such as resource utilization, customer satisfaction, SLA violation and prices for the provider’s profit maximization.
Educational resource of new social media: problem statement

The article is devoted to the problem of the implementation of the educational resource of new social media in the educational process of educational organizations. The author conducts a systematic review of modern scientific and methodological literature, identifies the problem that has developed in the modern theory and practice of using new social media in pedagogical activity, and determines its current state in modern Russia. The article presents the results of a study of the attitude of high school students and their teachers to the use of social media for educational purposes, their ideas about the effectiveness of such a means of teaching and upbringing, about its main strengths and possible threats. The main conclusions of the conducted research are the results of the conducted survey, which suggest that the majority of both high school students and young teachers have a positive attitude to the integration of new social media into the educational process of the school.There are also a lot of questions related to the lack of scientific research of educational opportunities of the developing institute of social media, questions about how to use them in an educational environment, what rules and regulations should be followed, what is the content of a new educational resource, what are its risks and threats, what is the algorithm of its actualization and how to combine it with traditional teaching and upbringing technologies.
Design and Implementation of Automatic Quick Report Function for WeChat on Earthquake Information

This paper designs the Liaoning earthquake quick report pushing system which based on WeChat public platform. After applying for registration of unit WeChat public platform account and verification, this system builds the earthquake information WeChat public platform by using the developer center, crawls the earthquake quick report information data through the WeChat public platform interface, calls the Access_token verification, uses the GET method to access, and sends the Json The data packet is sent to the public platform interface address via POST to drive to the WeChat public platform. By automatically pushing the earthquake quick report information to the public at the first time, the public can understand the three elements of the earthquake: time of earthquake, location of earthquake, and magnitude of earthquake as early as possible, providing the public with fast and accurate information, and improving the efficiency of rapid risk avoidance and earthquake emergency rescue.
Application of Association Rule Mining Algorithm in Civil Engineering Optimization Design

Engineering optimization design is a dynamic process of repeatedly analyzing and evaluating structure, searching for optimization, modifying design and analyzing and evaluating structure. Each intermediate result contains a complex multivariate data set; For example, vectorial values of design variables can represent the material composition, topological structure and shape size of the design scheme. The vector value of constraint function can represent its cost and efficiency. The derivative information of objective function and constraint function represents the local variation trend of various corresponding variables. This paper studies the application of association rule mining (ARM) algorithm in civil engineering optimization design, which promotes the optimization of civil engineering optimization design application. Data test shows that the application of ARM algorithm in civil engineering optimization design has high performance in civil engineering optimization design.
Cross-Modal Retrieval Based on Deep Hashing in the Context of Data Space

The advent of the Big Data era has led to the heterogeneity of data from multiple sources, and traditional database management techniques are overstretched in the face of the increasing complexity and variability of data. As a result, the concept of data spaces has been developed. The multiple and heterogeneous nature of data in the current context makes it necessary to provide a variety of query methods in the data space. As heterogeneous data contains various types of data structures, and traditional information retrieval mainly targets text documents to establish indexing relationships, it cannot provide queries to meet the needs of multiple sources of heterogeneous data. Therefore, this paper compares the current advanced algorithms used in cross-modal retrieval based on the data space to further understand cross-modal retrieval.
Food Safety Big Data Classification Technology Based on BP Neural Network

Food safety is directly related to people's life safety and is the material basis of people's life. People have low tolerance and high attention to food safety events directly related to their own life safety. This paper discusses the structure of BP neural network and big data food supervision technology, analyzes the limitations of BP neural network and the problems existing in the application of food safety data, summarizes the network data classification method, and tests the recommended BP neural network algorithm model. The results show that algorithm B improves the problem that the score prediction accuracy of the traditional algorithm is not ideal under the sparse score matrix. Compared with algorithm a, algorithm B has higher score prediction accuracy.