Reviewerly/exHarmony huggingface dataset:
VTAG: Virtual Lab for Tree-Adjoining Grammar-Based Research

This paper presents a state-of-the-art virtual research lab (vTAG) for creating, updating, analyzing, and maintaining multilingual large-scale tree-adjoining grammar for natural languages (NL). vTAG has designed to be language independent and tested its performance by constructing grammar of many Indian and European languages with significant reductions in grammar development time by auto rule creation mechanism with the help of a supervised machine learning algorithm. vTAG provides an integrated development environment (IDE) that can generate grammar of any natural language based on tree-adjoining grammar (TAG) formalism by using a specially designed graphical user interface without focusing on the programming aspect. vTAG also contains an advanced age experimental workspace to evaluate and improvise TAG-based parsers, part-of-speech tagger, sematic text analyzer, and associated NLP tools. vTAG can be utilized as an ‘interactive training kit’ for upcoming young talent to empower them for learning and developing advanced tools solving practical problems in NLP field. Furthermore, it can serve as foundation for understanding and building end-to-end machine translation solutions. vTAG uses object oriented model (OOM) for handling the NLP resources so every entity within the environment is in form of an object that can be encrypted which makes linguistic resources convenient for maintenance and secure to exchange and distribution, thus vTAG consists of a workbench, development kit, a graphical user interface, experimental workspace, and language resources on a single platform by combining major concepts from modern computer science, artificial intelligence, and linguistics.
Semantic Matching Research based on Interactive Multi-head Attention Mechanism

Semantic matching plays a crucial technical supporting role in question answering systems. For semantic matching, it is mainly based on neural networks to solve the sentence representation and interaction in semantic matching. The Siamease network structure is a commonly used structure in semantic matching. In view of the problem of information exchange and semantic extraction not saving points in the independent training of the network structure using shared parameters for the input two sequences. Therefore, in this paper, a multi-head attention mechanism is added to the Siamease model based on bidirectional LSTM to solve the problem of insufficient semantic extraction, and on this basis, it is proposed to add fine-grained matching information in the form of an interactive multi-head attention mechanism to solve the interaction problem. Experimental results show that the performance of the model is further improved compared to previous deep learning models.
Deep Attentive Interest Collaborative Filtering for Recommender Systems

Collaborative filtering (CF) is a pivotal building block in commercial recommender systems due to its strength and utility in user interest modeling. Recently, many researchers have turned to deep learning as a way to capture richer collaborative signals from user-item feature interactions. However, most deep-based methods only consider nonlinear, high-order interactions while ignoring the explicit collaborative signals in low-order interactions. They also typically ignore the quality of the user and item profiles. These are cornerstones in item recommendation that, we argue, must be considered for high-quality recommendations. Hence, we propose Deep Attentive Interest Collaborative Filtering (DAICF) to overcome these limitations. DAICF profiles users based on their interactive items, i.e., user neighborhood information. Similarly, item profiles are based on users who had interacted with it, i.e., item neighborhood information. Given a user's profile varies over different items, DAICF accurately models his attentive interests based on the specific target item. Low-order collaborative signals are captured by a shallow component, and high-order collaborative signals are captured by a deep component. These two complementary collaborative signals are then fused to provide rich recommendations that cut through today's information overload. By designing a personalized feature extraction method based on bilateral neighborhood information to solve the data sparsity problem in recommender systems, DAICF can dynamically distinguish the importance of a user's historical interaction items for predicting user preferences for a specific target item. A set of experiments against four real-world datasets validate that DAICF outperforms the most recent state-of-the-art recommendation algorithms and justifies the effectiveness and interpretability of our method.
Research on code entity recognition matching technology

Nowadays, in the process of program development, it is unavoidable to use third-party code repositories, but the safety of third-party code is not guaranteed. Therefore, the research of software composition analysis (SCA) is very important. In this paper, we first proposed the value dependency analysis technology for feature extraction and entity recognition of code. From the level of functions, classes, files, packages, and projects, the normalized extraction of the intermediate representation of open-source code. In view of the insufficient accuracy of the existing models based on text, identification, syntax, and other analysis technologies, the value dependency analysis technology is used for the first time to build a more accurate normalized model of control flow and data flow, effectively improving the accuracy of feature extraction and anti-aliasing.
An improved γ-CLAHE image enhancement algorithm for dot matrix invisible code

Dot matrix invisible code is widely used in anti-counterfeiting and traceability of goods, dot matrix invisible code is a kind of technology that "disappears" in the package decoration. This kind of code will neither destroy the overall effect of packaging decoration nor affect the function of barcode, it provide technical support for anti-counterfeit traceability means, which is difficult to be seen by the naked eye and needs to be read under special lighting conditions, merchants and consumers can access product information by identifying the code, it often has the problem of poor contrast due to light intensity, shooting angle and other reasons. The image enhancement technology is used to improve the image quality and lay the foundation for the subsequent work. This paper proposes an improved γ-CLAHE image enhancement algorithm for dot matrix invisible code, which converts the image into the color space with color and brightness separation, performs histogram equalization enhancement processing on the brightness components, combines with Gamma correction, so that the enhanced image quality is significantly improved. In this study, the CLAHE algorithm and the improved algorithms on LAB, HSV and YCrCb color spaces are compared separately, the experimental results show that the improved algorithm is much more effective than the CLAHE algorithm, and the improved algorithm in YCrCb color space is more suitable for image enhancement of dot matrix invisible codes than other color and bright separation color spaces, It has obvious superiority in indicators such as information entropy, mean gradient, standard deviation, etc., and can effectively improve the contrast of low-quality dot matrix invisible codes, at the same time, this study provide ideas for similar invisible code image enhancement.
A Framework for Privacy Preserving Localized Graph Pattern Query Processing

This paper studies privacy preserving graph pattern query services in a cloud computing paradigm. In such a paradigm, data owner stores the large data graph to a powerful cloud hosted by a service provider (SP) and users send their queries to SP for query processing. However, as SP may not always be trusted, the sensitive information of users' queries, importantly, the query structures, should be protected. In this paper, we study how to outsource the localized graph pattern queries (LGPQs) on the SP side with privacy preservation. LGPQs include a rich set of semantics, such as subgraph homomorphism, subgraph isomorphism, and strong simulation, for which each matched graph pattern is located in a subgraph called ball that have a restriction on its size. To provide privacy preserving query service for LGPQs, this paper proposes the first framework, called Prilo, that enables users to privately obtain the query results. To further optimize Prilo, we propose Prilo* that comprises the first bloom filter for trees in the trust execution environment (TEE) on SP, a query-oblivious twiglet-based technique for pruning non-answers, and a secure retrieval scheme of balls that enables user to obtain query results early. We conduct detailed experiments on real world datasets to show that Prilo* is on average 4x faster than the baseline, and meanwhile, preserves query privacy.
UTILITY OF FNAC IN THE EVALUATION OF BREAST LESIONS WITH HISTOPATHOLOGICAL CORRELATION

Introduction: Most of the women with breast pathology present as lump in their breast. Malignancy of Breast is the most common problem with increasing incidence in females. Early diagnosis and appropriate treatment of a breast lump is lifesaving. Fine-Needle Aspiration Cytology (FNAC) is a simple, safe, inexpensive method with wide acceptance and used as a rst-line diagnostic procedure for diagnosis of breast lesions. The main aim of the study w Aim: as to diagnose any palpable lump of the breast in females by FNAC technique and to correlate with histopathology reports. This was a retrospe Setting and Design: ctive study done out in the Department of Pathology, Kurnool Medical College, Kurnool. Study period was for 2 years (January 2021 to De Materials and Methods: cember 2022). For all breast lumps FNAC was done, processed; Haematoxylin and Eosin stain and Papanicolaou stain was done wherever necessary. For same patients who ever undergone Tru-cut biopsies, excisional biopsies, and surgical specimens were received and histopathological correlation was done. Data was collected and entered in MS excel sheet. The sensitivi Statistical Analysis: ty, specicity, positive predictive value, negative predictive value, and accuracy were calculated. Among 531 cases, 426(80.22%) were benign Results: and 105(19.77%) were malignant on cytology. Out of this, 215(40.48%) cases underwent histopathological examination, in which 118(54.88%) were benign and 97(45.11%) were malignant cases. The sensitivity, specicity, positive predictive value, negative predictive value, and accuracy of the present study were 84.82%, 98.05%, 97.93%, 85.59%, and 91.96% respectively. The FNAC is the common and most important prelimi Conclusion: nary diagnostic test in palpable breast lumps and there was high degree of correlation with a nal histopathological report
Inventory System Design Using Weight Average Cost (WAC) and Graphic Methods

Companies or business units are currently utilizing the role of the internet for company activities. Applications to support company activities that have been made have started to be web-based, both related to administration, finance, marketing and management of goods or inventory so that information can be obtained more quickly and easily accessed. wherever and whenever. However, until now there is no system that is able to manage data about inventory stock, for this reason the company wants to have a system that can be used to find out inventory, so that it can easily find out the amount and place to store inventory, that the inventory inventory system is not yet effective because doing data processing is still done in a way that is still manual. The results of the analysis are applied in an application capable of processing inventory system data using a web-based programming language. With the design of this new system, it is hoped that it will make it easier for companies/business units to process existing inventory system data, and can increase work effectiveness and efficiency.
IMAGE CAPTIONING: NOW EASILY DONE BY USING DEEP LEARNING MODELS

Abstract—Image captioning helps the reader to understand the context that is to be conveyed. It is highly used in the applications like finding the missing or hidden patterns, in guiding the self driving cars, in robots which are automated using Artificial Intelligence and Machine Learning. It is also used to scrutinize large amount of images. This paper includes different image captioning models by using the concept of deep learning. Here we discuss about the frameworks like CNN-CNN, CNN-RNN and some more with their implementation overview with their advantages and disadvantages
Sprachdatenbasierte Modellierung von Wissensnetzen in der mittelalterlichen Romania (ALMA): Projektskizze

Abstract We present ALMA, a new research project aimed at investigating the interaction between language and knowledge practices from AD 1100 to 1500. Our primary question is how Medieval Italian, French, Occitan, Catalan, and Spanish developed into languages of knowledge and scholarship (German Wissen(schafts)sprachen ) in permanent opposition to and exchange with the predominant Latin (but also with Arab, Greek, and Hebrew). Focusing on two domains, medicine and law, the project combines linguistics, text philology, and the history of science with the Digital Humanities and ontology engineering. ALMA will create two multi-lingual, domain-specific text corpora by integrating text editions of hitherto unedited manuscripts and incunabula, and digitized printed editions. Our corpus-linguistic exploration of the ALMA corpora will provide the basis for lexical-semantic studies that analyze emerging knowledge networks and the depth of their linguistic representations. We hypothesize that language evolution and the development of more complex linguistic structures will allow for measuring the impact of knowledge practices on medieval vernacular languages. We will trace the dissemination of lexical material across languages, language varieties, cultural spaces, and periods. This will enable us to follow specific vernacular communication channels. We will use cutting-edge technologies to compile, publish, and share our findings, and to model them in the form of historicized ontologies and Linked Data. Our onomasiological, ontology-driven approach will result in the creation of domain models that can be re-used within the Semantic Web. This has great potential to be relevant for researchers from different disciplines.
Towards a Diversification of Research on Burials: Application of Network Visualization and the Concept of ‘Assemblage’

Research on burials in archaeology has mainly involved the identification of tomb type and the classification of the material. Although such an arrangement of the data is necessary prior to analysis, the method of ‘grouping based on similarities or differences’, which has been commonly used to organize archaeological data, is not without its problems. This is because the existing method of classification cannot handle ‘outlier’ tombs that deviate from the general trend; it is also difficult to highlight the differences that exist between tombs grouped together based on similarity, or, conversely, the similarities between the tombs divided into different groups. In this paper, ‘network visualization’ was presented a san alternative method of organizing data to compensate for such problems. First of all, a theoretical review of ‘categorization’ and ‘classification’ was conducted in order to suggest how ‘network visualization’ could provide an alternative method of categorization. This possibility was demonstrated using the case study of the burials of the Sangwoon-ri site, in Wanju. In addition, Deleuze and Guattari’s concept of Assemblage was introduced, as a means of supporting a break away from essentialism in categorization. As a result, a new perspective on archaeological style, form, and type was proposed and it was argued, using the example of the Nangnang (Lelang) tombs, that the ‘sub-types’ of the tombs could be viewed as ‘events’ that appear in the ‘creation process of types’, rather than as fixed entities. Finally, visualization methods of multivariate statistical analysis results were also examined to confirm whether the methodological and epistemological possibilities of network visualization presented in this paper are unique to it. By presenting the archaeological usefulness of the concept of network visualization and Assemblage, the paper aims to stimulate further critical thought on the possibility of alternative methods of categorization that can consider similarities and differences together, or indeed the fluidity of styles, forms, and types.
INTERDISCIPLINARY APPROACH OF LINEAR ALGEBRA IN QUANTUM COMPUTING

In this paper we discuss the interdisciplinary approach of Linear Algebra in quantum computing. Everything in quantum computing, from the representation of Qubits and gates to circuit's functionality can be described using various forms of Linear Algebra. The most advanced topics Inner product spaces, Vectors, Spectral theorem, Hilbert Spaces, exponential matrices, tensor product etc., of linear algebra are required to predict what a quantum computer does in response to a sequence of instructions
A Study on the Technological Competitiveness of Korea and China in the EV Industry using Topic Modeling

This study analyzed the technological competitiveness of Korea and China in the electric vehicle industry through natural language big data processing techniques. In order to establish a standard for comparative analysis, patent abstract data from four countries, including Korea, the United States, Japan, and China, was analyzed with the LDA model to classify electric vehicle technology into 30 technology-specific topics. Next, Korean and Chinese patents were analyzed again using the topic model, divided into 27 and 16 topics, respectively, and then each topic was linked through keywords analysis.&#x0D; As a result, it was found that both Korea and China had advantages in machinery parts and in management of cells, modules and packs. However, the two countries showed overlapping technology fields both competitive technology areas and non-competitive technology areas. When evaluating the quality of patents of both countries, Korea’s level was somewhat higher than that of China’s. However, compared to that of the United States and Japan, countries did not have a high level of technology.&#x0D; Considering the technological characteristics of Korea and China, the electric vehicle industry in Korea needs to introduce necessary technologies from the US or Japan or attempt strategic alliances with companies in these countries rather than a cooperative relationship with China.
Fiber Optic Incidents Detection and Classification with Yolo Method

in this paper we propose an automatic and real time management system of incidents in fiber optic telecommunications networks. The first step is to collect images of incidents in the fiber optic transmission networks of telecommunications operators in Senegal. Then, from these collected images, we develop a convolutional neural network architecture YOLO (You Only Look Once) to automatically detect in real time incidents in the fiber optic telecommunications networks as well as the impacts on the different services of the operator. This proposed system is an extension of two works on incident management systems in fiber optic telecommunications networks with deep learning algorithms based on convolutional neural networks. Indeed, the first work deals with the use of convolutional neural networks (CNN) and the second with the use of regional mask neural networks (Mask-RCNN). The reality is that in the current networks (Internet) in Senegal as everywhere in the world the infrastructure based on optical fiber represents the most important part of the transmission media used for the transport of information. And this infrastructure is growing more and more in parallel with the needs of users of telecommunications networks. So finding ways to detect incidents faster than Mask-RCNN and more efficiently than CNN is essential for better incident handling. Our system ensures better handling through faster, more efficient detection and analysis of downed or cut optical link incidents through the use of the YOLO algorithm. YOLO is designed to detect objects on a digital image, i.e. the status of a link impacted by an incident from the image for our system.
How to simplify the familiarization with the code

Getting involved in an ongoing or completed project is time-consuming. Creating good documentation (README and developer documentation) is not easy, time-consuming and usually questions remain unanswered and implementation details remain hidden. Ideally, members of the development team are available and can be asked. However, it is not uncommon that there is no access to the team anymore and even if this is given, it is not guaranteed that the developers can answer ad-hoc questions. In this...
Reverse Logistics Practices Cases Analysis using Text Analytics

This paper enquiry into Reverse logistics process and some of the best practices identified and corroborated.This study also made an attempt to incorporate the best practices using some successful factors from the existing literature.Some of the cases identified are: Amazon, DHL, Dell, Apple Inc.The observations from the cases are the process of reverse logistics and the strategic component each company has adopted to have a significant impact.Some of the key factors to be considered for best practices in reverse logistics practices are: Collecting, Screening, Refurbish or resale.Using text analytics-Bigram : generic configuration, late postponement, life cycle asset recovery modular design, turnaround time, cosmetic repair, individual repair system reuse, reverse logistics, centralized return, free return logistics hub as weighting scheme factors for four cases considered in the study are identified.
Morfawk.ja: A Japanese Token Level Pattern Matching and Processing Language with Dependency Analysis

The quality of development documents written in natural language is basically ensured by developer's review with quite a few man-hours. Natural language processing (NLP) tools can partially automate the time-consuming review. The authors have developed morpheme level pattern matching and processing tools morfgrep and morfawk for Japanese texts for this purpose; moreover, in this paper, extends them to perform pattern matching on dependency among phrases. Example applications of the tools, proofreading of development documents and domain-specific term extraction, are also presented.
Scutoids

Recent studies in the field of medical science revealed a new type of cell which has more or less a geometric shape. Biologists discovered these cells in the epithelial tissues. This new shape closely resembles Prismatoids with some differences and is called Scutoid. In this paper, we will be looking at various properties of a Scutoid, benefits offered by its unique shape, and the gateways it may open to for further research.