Reviewerly/exHarmony huggingface dataset:
An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs

Abstract The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs.
Research on heat map generation of passenger flow distribution in subway station based on binocular vision

Monitoring passenger flow distribution in real-time in subway stations plays a key role in large passenger flow control and intelligent passenger flow management. A binocular vision based method for generating heat map of passenger flow distribution in subway stations is proposed in this work, based on the extraction and analysis of the monitoring video images in subway stations. Firstly, the binocular camera is used to collect the depth information of the passenger flow in the subway station. Secondly, a Yolov5-based pedestrian detection algorithm is applied to obtain the passenger flow information and coordinate positions from the image data. Then after coordinate conversion, the actual position information of passengers is obtained. Finally, based on passenger location coordinates, the heat map of passenger flow density can be generated to monitor passenger flow in real-time. The experimental results show that the average accuracy of passenger detection is 95%, which is 2.1% higher than before the improvement. ing the passenger’s coordinates in the camera coordinate system to the actual coordinates, the resulting error at 5m, 10m, and 15m is 0.4m, 0.9m, and 1.3m, respectively. and the presentation of the heatmap is consistent with the actual situation. The research results are applicable to real-time passenger flow monitoring and statistics in subway systems, enabling operators to have a real-time understanding of passenger flow distribution and timely guidance, and have certain application prospects.
Chinese abstractive summarization based on pre-trained module assisted with noise co-attention

The encoder-decoder framework underpins the majority of Chinese abstract generative models. Although the generated summaries are comparable to the source text, they invariably suffer from generating inaccurate or redundant words, which can result in imprecise content or insufficient summaries. To address this issue, we propose a Roberta-based model with noise co-attention. In our way, we propose a novel noise co-attention to correct and remove the learned biased and redundant information from the source text. We first use the pre-trained model ROBERTA to obtain high-quality word vector representations and then build UNILM based on the first step, in particular adding noise co-attention layer to guide the summary generation process. Related experiments are being carried out on the large Chinese abstract dataset LCSTS, using ROUGE as the evaluation metric. According to the results, our invention surpasses numerous cutting-edge models for the summary generation and offers greater improvements over baseline methods.
Quartz-tourmaline metasomatites in gold deposits in Aktyuz-Boоrdinsky ore district (Northern Tien Shan, Kyrgyzstan)

In the Aktyuz-Boordinsky ore district in the Northern Tien Shan, numerous deposits and ore occurrences of gold, rare and rare earth metals and base metals are discovered. Gold deposits are characterized by the presence of a number of metasomatites: beresites, listvenites, K-feldspatites, etc. Many of them are also characterized by quartz-tourmaline gold-bearing metasomatites. A mineralogical and petrographic description of the metasomatic zoning of quartz-tourmaline metasomatites from weakly to highly altered zones is given. Quartz-tourmaline metasomatites form vein-like bodies and metasomatic breccias, where they act as cement, and the fragments can consist of intrusive rock, beresites, K-feldspatites, and listvenites. For the Aktyuz-Boordinsky ore district, quartz-tourmaline metasomatites are a prospecting feature for gold, and for other areas they are a prospecting criterion.
Lattice attacks on pairing-based signatures

ABSTRACTPractical implementations of cryptosystems often suffer from critical information leakage through side-channels (such as their power consumption or their electromagnetic emanations). For public-key cryptography on embedded systems, the core operation is usually group exponentiation – or scalar multiplication on elliptic curves – which is a sequence of group operations derived from the private-key that may reveal secret bits to an attacker (on an unprotected implementation). We present lattice-based polynomial-time (heuristic) algorithms that recover the signer’s secret in popular pairing-based signatures when used to sign several messages under the assumption that blocks of consecutive bits of the corresponding exponents are known by the attacker. Our techniques rely upon Coppersmith's method and apply to many signatures in the so-called exponent-inversion framework in the standard security model (i.e. Boneh-Boyen, Gentry and Pontcheval-Sanders signatures) as well as in the random oracle model (i.e. Sakai-Kasahara signatures).KEYWORDS: Coppersmith’s methodCryptanalysisLattice attacksMSC 68P25, 94A60Pairing-based signaturesSide-channel attacks Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. For the ease of exposition, we consider so-called Type-1 bilinear maps (Galbraith et al. Citation2008), but our results apply to all possible instantiations of the considered signature schemes (i.e. using Type-1, Type-2, or Type-3 bilinear maps).2. It is well known that the computational complexity of Gröbner basis algorithm may be exponential or even doubly exponential. In our setting, the number of variables and the total total degree of the input polynomials are fixed and the theoretical complexity is polynomial in the field size (and thus in the security parameter).3. We recall that a combinatorial class is a finite or countable set on which a size function is defined, satisfying the following conditions: (i) the size of an element is a non-negative integer and (ii) the number of elements of any given size is finite.4. In order to reach this asymptotic bound, the constructed matrix is of huge dimension and the resulting polynomial system has a very large number of variables and the computation which is theoretically polynomial-time becomes in practice prohibitive.5. Pointcheval-Sanders signature scheme can be instantiated with Type-3 bilinear maps but for consistency and the ease of exposition, we present it using Type-1 bilinear maps.
BibRank: Automatic Keyphrase Extraction Platform Using Metadata

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing.
Clustering of Feature Vectors and Recognition of Bodo Phoneme Using MLP Technique

The process through which a computer can identify spoken words is termed as speech recognition. After analysis and finding of features of the speech sound, one can go towards the recognition of the speech. The extraction of feature vector is known as the feature extraction process or the front-end process. This front-end process is considered as the 1st stage of speech recognition. Pattern matching process is the 2nd stage or final stage of speech recognition where actual search is carried out to decode the spoken utterances by matching the sequence of feature vectors against the acoustic and language models stored in the recognizer. To reduce this problem, clustering technique is used. Clustering makes it possible to look at properties of whole clusters instead of individual objects - a simplification that might be useful when handling large volume of data. Clustering is nothing but the assignment of a set of observations into subsets so that the observations in the same cluster are similar in some sense.
Online Student Clearance Management System

This Online Student Clearance Management System addressed the challenges in the manual process of signing student clearances in one of the academic institutions in Pagadian City, Zamboanga del Sur, Philippines, as a prerequisite before the release of a final permit which is a requirement to take the final period examinations. The current process of signing a student clearance involves a piece of paper indicating the office names, and office heads or representatives must affix their signatures. The students will carry this paper and submit it to various offices for signing. This system has eliminated the challenges present in the current process through a web-based approach where clearance signing will now be processed online. This system integrates into the existing online portal of the institution, where students may view their clearance status. The office heads or representatives will approve or disapprove student clearances through the existing portal as a new mechanism for signing the clearance. This system also features QR code technology as a mechanism for the finalization of clearance signing, whereby students will only have to present the code instead of the signed piece of paper as evidence of the accomplishment of the clearance. These new approaches transformed the current processes into a complete paperless transaction
A Combined Semantic Dependency and Lexical Embedding RoBERTa Model for Grid Field Relational Extraction

Relationship extraction is a crucial step in the construction of a knowledge graph. In this research, the grid field entity relationship extraction was performed via a labeling approach that used span representation. The subject entity and object entity were used as training instances to bolster the linkage between them. The embedding layer of the RoBERTa pre-training model included word embedding, position embedding, and paragraph embedding information. In addition, semantic dependency was introduced to establish an effective linkage between different entities. To facilitate the effective linkage, an additional lexically labeled embedment was introduced to empower the model to acquire more profound semantic insights. After obtaining the embedding layer, the RoBERTa model was used for multi-task learning of entities and relations. The multi-task information was then fused using the parameter hard sharing mechanism. Finally, after the layer was fully connected, the predicted entity relations were obtained. The approach was tested on a grid field dataset created for this study. The obtained results demonstrated that the proposed model has high performance.
Unsupervised one-point attack based on differential evolution

With the continuous improvement of artificial intelligence technology and the rapid development of big data applications, deep learning technology has been widely used in various fields. Although deep learning has achieved great success in many fields, it still faces some potential problems. For example, the insecurity of neural networks has raised concerns as this may lead to security vulnerabilities in critical areas. This article proposes an adversarial attack method based on unsupervised algorithms to search for sensitive points in time series dataset and use differential evolution algorithm to perform one-point attack on sensitive points. The experiment was conducted under various experimental parameters. All experimental results are presented in the form of a heatmap, and the article provides a detailed analysis of the experimental results.
3D Single Object Tracking with Multi-View Unsupervised Center Uncertainty Learning

Center point localization is a major factor affecting the performance of 3D single object tracking. Point clouds themselves are a set of discrete points on the local surface of an object, and there is also a lot of noise in the labeling. Therefore, directly regressing the center coordinates is not very reasonable. Existing methods usually use volumetric-based, point-based, and view-based methods, with a relatively single modality. In addition, the sampling strategies commonly used usually result in the loss of object information, and holistic and detailed information is beneficial for object localization. To address these challenges, we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker (MUT). MUT models the potential uncertainty of center coordinates localization using an unsupervised manner, allowing the model to learn the true distribution. By projecting point clouds, MUT can obtain multi-view depth map features, realize efficient knowledge transfer from 2D to 3D, and provide another modality information for the tracker. We also propose a former attraction probability sampling strategy that preserves object information. By using both holistic and detailed descriptors of point clouds, the tracker can have a more comprehensive understanding of the tracking environment. Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8% and 0.6% in precision and success rate, respectively, and on the NuScenes dataset by 1.4%, and 6.1% in precision and success rate, respectively. The code is made available at https://github.com/abchears/MUT.git.
A type-directed, dictionary-passing translation of method overloading and structural subtyping in Featherweight Generic Go

Abstract Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping, and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver. This article shows a different approach by defining a type-directed translation from ${\textrm{FGG}^{-}}$ to an untyped lambda-calculus. ${\textrm{FGG}^{-}}$ includes all features of FGG but type assertions. The translation of an ${\textrm{FGG}^{-}}$ program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary. Every program in the image of the translation has the same dynamic semantics as its source ${\textrm{FGG}^{-}}$ program. The proof of this result is based on a syntactic, step-indexed logical relation. The step index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods. Although being non-deterministic, the translation is coherent.
CREATION THE MATHEMATICAL SKILLS AMONG TECHNICAL UNIVERSITY STUDENTS FOR SUCCESSFUL DEVELOPMENT OF A GENERAL PHYSICS COURSE

The paper is devoted to the issue of mathematical knowledge of students who are beginning to study physics at a technical university. It is noted that students do not understand that the mathematical skills created at school could be applied for solving physical problems, and than be used in describing physical processes in technical and technological systems. The ways of solving this problem are proposed by transferring the beginning to study the general physics at second semester, intensive training of applicants in elective courses at school and propaedeutic courses at universities. The methods of problem-based and personality-oriented teaching at propaedeutic physics courses, which authors used at practice, as well as methods of activating the students mental activity, are analyzed.
Hands-on Activities for Digital Forensics Education

As teaching professors we have noticed that hands-on activities for a digital forensics course fall into the following three categories: 1) activities that assist students in learning how to use common digital forensics tools; 2) activities that help students gain in-depth understanding of the basic concepts and fundamental knowledge that are presented in class lectures; 3) activities that promote students the development of mindsets and data analytical skills that are needed for a digital forensic investigator. Various formats are employed to develop these hands-on exercises in different categories. The educational objectives and student learning outcomes map well to the CAE-CD (Centers of Academic Excellence - Cyber Defense) outcomes by completing their forensic knowledge units.
Bus Ticket Reservation And Seat Allocation System Using Blockchain Technology

The Bus Ticket Reservation System is an online platform that provides a user-friendly interface for booking and managing bus tickets. The system offers various features such as seat reservation, ticket booking, cancellations, and route inquiries. When all bus tickets are reserved, passengers receive a prompt to make a booking. When a admin accepts the passenger's request, the system automatically sends a notification through email and mobile number, informing the passenger that their request has been accepted. The passenger is then prompted to make the payment process to confirm the seat. Once the payment is completed, the ticket is confirmed, and the passenger is ready to embark on their journey. During boarding, passengers can choose luggage retrieval options: Ticket number input and image upload. For lost luggage, use the complaint section with a boarding day image. You can visualize historical passenger travel data using a bar chart to compare online and offline ticket bookings, while also showing the total number of passengers, total seats, and unbooked tickets.
Case Study: Elo Merchant Category Recommendation

This chapter will take the Kaggle platform's 2019 Elo Merchant Category Recommendation competition (as shown in Fig. 8.1) as an example to explain the real-world practice related to user profiles and illustrate the complete process and precautions of real cases end to end. This chapter is mainly divided into the following parts: question understanding, data exploration, feature engineering, model selection, model integration, efficient scoring, and summarizing competition questions, the common organizational structures of all chapters involving case studies in this book, as well as important components of a competition process. I believe that under the guidance of this book, readers can quickly become familiar with the competition process and apply it in practice.
Case Study: Quora Question Pairs

This chapter will take the Quora Question Pairs competition on the Kaggle competition platform (as shown in Fig. 16.1) as an example to explain the text matching case of natural language processing. Quora is a question-and-answer SNS website, similar to Zhihu (a Chinese question-and-answer website), which provides social question-and-answer services. There are many questions created by users on the Quora platform, and these questions often contain a lot of repetitive content; there will also be related questions, or the same type of questions being asked many times. In the scenario of real business, the above phenomenon often disperses the flow of high-quality answers, so it is necessary to match the same problems in business, so as to normalize repeated problems.
Identity-Based Matchmaking Encryption Secure Against Key Generation Center

Identity-based matchmaking encryption (IB-ME) is a generalization of identity-based encryption (IBE), in which both the sender and receiver can specify the target identity. The key escrow problem, in which the authority is concentrated in the key generation center, has been pointed out as one of the most important issues, when implementing IBE in the real world. Recently, an approach to solve the key escrow problem in IBE has been proposed by introducing a new entity called an Identity Certificate Authority (ICA), which decentralizes the authority. In this paper, we extend the above approach to solve the key escrow problem for IBE to the IB-ME setting. Specifically, we first propose a new formalization of IB-ME solving the key escrow problem (by introducing ICA in its formulation). This new type of IB-ME is called blind IB-ME with certified identities. Then, we provide a concrete construction of blind IB-ME with certified identities based on the standard assumptions over bilinear groups. Finally, we implement our proposed scheme and demonstrate that it is practically efficient compared with existing schemes.
Feature Engineering

In this chapter, we will introduce to you the key part in the algorithm competition that has the heaviest workload and that determines whether the contestants can get a good ranking—feature engineering. Andrew Ng once mentioned in his CS229 machine learning course at Stanford University that machine learning was mostly feature engineering; features determined the upper limit of the prediction effect of machine learning, and the algorithm just kept approaching this upper limit. This shows the significance of feature engineering. Basically, participants will spend 80% of their time and energy on building feature engineering. During the application of machine learning, feature engineering is what between data and algorithms. Feature engineering is to transform the original data source into features, which in turn enable us to characterize samples from a variety of new dimensions. Features can better describe potential problems to the prediction model, thereby improving the accuracy of the model's predictive analysis of unseen data. High-quality features help to improve the generalization performance of the model as a whole, and features are largely associated with basic problems. Feature engineering is both a science and a fun art, which is why data scientists spend a lot of their time on data preparation before modeling while enjoying it.