Reviewerly/exHarmony huggingface dataset:
Logical qubit implementation for quantum annealing: augmented Lagrangian approach

Abstract Solving optimization problems on quantum annealers (QA) usually requires each variable of the problem to be represented by a connected set of qubits called a logical qubit or a chain. Chain weights, in the form of ferromagnetic coupling between the chain qubits, are applied so that the physical qubits in a chain favor taking the same value in low energy samples. Assigning a good chain-strength value is crucial for the ability of QA to solve hard problems, but there are no general methods for computing such a value and, even if an optimal value is found, it may still not be suitable by being too large for accurate annealing results. In this paper, we propose an optimization-based approach for producing suitable logical qubits representations that results in smaller chain weights and show that the resulting optimization problem can be successfully solved using the augmented Lagrangian method. Experiments on the D-Wave Advantage system and the maximum clique problem on random graphs show that our approach outperforms both the default D-Wave method for chain-strength assignment as well as the quadratic penalty method.
DBE: Dynamic belief entropy for evidence theory with its application in data fusion

Belief entropy is an effective uncertainty measurement in Dempster–Shafer evidence theory. However, the weight ratio between discord and non-specificity in the belief entropy is static and cannot be further modified according to different environments. To overcome this issue, this paper proposes dynamic belief entropy (DBE), which is a generalization of belief entropy by introducing a dynamic parameter. Compared with belief entropy, DBE can be flexibly modified based on the dynamic parameter, so as to improve the performance of measuring uncertainty in different environments. Besides, some properties of DBE are presented and illustrated with examples. Also, we design a dynamic data fusion method based on DBE. Compared with the existing methods, the proposed method utilizes DBE-based dynamic techniques, thereby enhancing the classification performance. Moreover, to illustrate the general applicability, the proposed method is verified on classification problems. The experimental results show that the proposed method outperforms the existing methods with a classification accuracy of 95.93% and an F1 score of 96.08%, demonstrating the effectiveness of our method.
IoT Based E-Voting System to Avoid Fraud Voting

The voting system plays a major role during elections. Traditionally, the election commission in India uses electronic voting machines which need more man- power, time- consuming and also they are less trustworthy. For avoiding misconceptions during elections, there are lot of advanced techniques are being proposed using various methods. But in the field of biometric identification, we can get the better results and it is also trustworthy. we provide the various works which are being proposed based on the voting system which uses biometric identification as a major concept.
Conversational AI Chatbot for Education Institute

Now a days everything was online and becoming easy for humans in order to get any information in the place where they are. On this note most of the websites were creating the human like chatbots in order to give the solutions for the human queries about the particular things. The chatbots are the software which provides a platform for the humans by interacting with them. Because so many peoples are not able to visit a for particular place for clarifying the doubts maybe of different reasons like long distance, no proper transportation etc…. Such kind of people can utilize the option of chatbot in there specific websites. Here we mainly discussed about the chatbot for the college website. Because most of the students will be having the lots of doubts before joining like about college ,staff, placements, transportation etc…Even though the college administration are not able to answer for all the queries which makes them burden .And it is also not practically good because so many students will visit, they cannot answer for each of them’s queries. So that we are focusing to get rid from this problem and make a easy way to know know all information about the college.
Linguistic Contact in Perspective: Lateral Coda in Principense Portuguese

Abstract This paper discusses the lateral /l/ in coda in Principense Portuguese (PP). Based on an elicited corpus recorded in situ , composed of 852 occurrences of 78 lexical items, we found that the lateral /l/ in coda can be vocalized or deleted. For instance, bolso ‘pocket’ can be pronounced as [ˈbow.sʊ] or [ˈbo.sʊ], as reported in some Brazilian Portuguese varieties. In PP, vocalization and deletion are phenomena that arise from a confluence of factors. It gathers characteristic features of the formative grammatical structure of Portuguese. At the same time, it reflects the linguistic contact scenario in which PP is inserted, since the lenition of consonants in semivowels, as well as strategies to avoid codas, are also reported in Lung’Ie (LI), a language in synchronic contact and present in the constitution of the PP (Agostinho, Ana Lívia. 2015. Fonologia e método pedagógico do lung’Ie [Phonology and pedagogical method of Lung’Ie]. São Paulo: University of São Paulo dissertation). Both Portuguese and Lung’Ie support vocalization and deletion in their grammars, fostering a relevant overlap of features to the alternation [ɫ] ∼ [w] ∼ ∅ in PP. We conclude that, in addition to specific features of Portuguese grammatical structure, PP’s linguistic contact situation must be considered as a relevant factor for the deletion and vocalization of laterals in coda in PP.
Improve retrieval of regional titles in streaming services with dense retrieval

Customers search for movie and series titles released across the world on streaming services like primevideo.com (PV), netflix.com (Netflix). In non-English speaking countries like India, Nepal and many others, the regional titles are transliterated from native language to English and are being searched in English. Given that there can be multiple transliterations possible for almost all the titles, searching for a regional title can be a very frustrating customer experience if these nuances are not handled correctly by the search system. Typing errors make the problem even more challenging. Streaming services uses spell correction and auto-suggestions/auto-complete features to address this issue up to certain extent. Auto-suggest fails when user searches keywords not in scope of the auto-suggest. Spell correction is effective at correcting common typing errors but as these titles doesn’t follow strict grammar rules and new titles constantly added to the catalog, spell correction have limited success.
Personalized style recommendation via reinforcement learning

Pinterest fashion and home decor searchers often have different style tastes. Some existing work adopts users’ past engagement to infer style preference. These methods cannot help users discover new styles. Other work requires users to provide text or visual signals to describe their style preference, but users often are not familiar with style terms and do not have the right image to start with. In this paper, we propose a reinforcement learning (RL) method to help users explore and exploit style space without requiring extra user input. Experimental results show that our method improves the success rate of Pinterest fashion and home decor searches by 34.8%.
Adding visual attention into encoder-decoder model for multi-modal machine translation

Multi-modal neural machine translation (MNMT) aims to integrate visual and textual information to translate source sentences into target and attracts a lot of attentions. Existing methods contribute a lot for capturing interactions between visual and textual features to improve the performance of neural machine translation (NMT). However, most of them don't consider the multi-modal consistency for MNMT. In fact, the image provides the semantic global consistency between different languages. We believe that adding bilingual-visual agreement into the encoder and decoder simultaneously can obtain bilingual representations and is useful for NMT. In this paper, we propose to simultaneously integrate visual information in encoder and decoder to learn the interactions between visual and textual features, in this paper called the model VMNMT. As the visual information provide global context, the encoder and decoder can learn the bilingual representations. Besides, we introduce a new bilingual-visual agreement decoder to learn to better representations of corresponding image-sentence pairs. In the experiment, the improvement was 2.02 BLEU on the English-German16 dataset and 1.9 BLEU on the English-German17 dataset. The results show that our method can outperform baselines on several widely-used datasets in terms of various metrics.
Specific features of educational programs for training digital economy staff

The article considers the problem of training the staff for digital economy. It has been stated that large-scale work with digital data requires highly qualified specialists, especially in the field of computer science. It is shown that university graduates whose future professional activity is related to processing large arrays of poorly structured data should be able to apply end-to-end digital technologies. A list of such technologies with examples of relevant sub-technologies is given. The need for such specialists requires the development of new programs of higher education. The programs should contain educational modules aimed at developing the digital competencies that are in demand in the corresponding priority sector of the economy. The task of developing the programs of higher education containing such educational modules is investigated. The competence models of graduates developed by the universities, which include competencies necessary for the digital economy, are analyzed. The results of the analysis of the basic professional educational programs aimed at forming competencies for the use of end-to-end digital technologies are discussed. Assessment of the readiness of universities to develop such programs is given, as well as typical comments on the competence models of graduates and on updated basic professional educational programs are given.
CyberBully Detection Using Neural Network

The advancements in technology, as well as the digitization of relationships, had a significant impact on the centennials' decision to maintain a social media account. Despite the entertainment provided by social media, cyberbullying has been identified as a real issue all over the world, with many centennials becoming victims. However, a few studies have been reported in detecting cyberbullying attempts on social media. As a result, a solution that employs appropriate data science techniques to detect cyberbullying attempts on social media would be ideal. The suspicious tweets dataset from Kaggle was used in this study to build three supervised learning predictive models, namely Naive Bayes, which were tuned using Random Grid Search and Keras tuner to indicate a suitable solution.
Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures

Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11% to 17% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.
Name Entity Recognition Tasks: Technologies and Tools

The task of named entity recognition (NER) is to identify and classify words and phrases denoting named entities, such as people, organizations, geographical names, dates, events, terms from subject areas. While searching for the best solution, researchers conduct a wide range of experiments with different technologies and input data. Comparison of the results of these experiments shows a significant discrepancy in the quality of NER and poses the problem of determining the conditions and limitations for the application of the used technologies, as well as finding new solutions. An important part in answering these questions is the systematization and analysis of current research and the publication of relevant reviews. In the field of named entity recognition, the authors of analytical articles primarily consider mathematical methods of identification and classification and do not pay attention to the specifics of the problem itself. In this survey, the field of named entity recognition is considered from the point of view of individual task categories. The authors identified five categories: the classical task of NER, NER subtasks, NER in social media, NER in domain, NER in natural language processing (NLP) tasks. For each category the authors discuss the quality of the solution, features of the methods, problems, and limitations. Information about current scientific works of each category is given in the form of a table for clarity. The review allows us to draw a number of conclusions. Deep learning methods are leading among state-of-the-art technologies. The main problems are the lack of datasets in open access, high requirements for computing resources, the lack of error analysis. A promising area of research in NER is the development of methods based on unsupervised techniques or rule-base learning. Intensively developing language models in existing NLP tools can serve as a possible basis for text preprocessing for NER methods. The article ends with a description and results of experiments with NER tools for Russian-language texts.
Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach

This paper presents a Deep Learning (DL) and Image-Processing (IP) pipeline that addresses exposure recovery in challenging lighting conditions for enhancing First Responders’ (FRs) Situational Awareness (SA) during rescue operations. The method aims to improve the quality of images captured by FRs, particularly in overexposed and underexposed environments while providing a response time suitable for rescue scenarios. The paper describes the technical details of the pipeline, including exposure correction, segmentation, and fusion techniques. Our results demonstrate that the pipeline effectively recovers details in challenging lighting conditions, improves object detection, and is efficient in high-stress, fast-paced rescue situations.
A Peer-To-Peer based System with Blockchain for Secured Voting Scheme (E-Voting)

In today’s world, when information safety is essential, blockchain is supreme for distributing that information because it gives instantly, provides, and completely unambiguous information stored on an immutable (unchangeable) ledger that can be accessed only by authorized network members. Blockchain increases trust, safety, clarity (Transparency), and the traceability of information transferred across a network. Today voting systems tolerate different safety threats and similarly distributed denial of service attacks (DDoS). Polling booth capturing, vote modification, and unfairly, gaining unlawful(unofficial) access to a computer system. The main objective of a similar fashion would be to deliver a decentralized(single organization cannot handle it) design to run and boost a voting system that is openly and separately(Independently) confirmable. The benefit that we get using of E-voting scheme would be to lessen election expenditures involving material, employee salary, and logistics records. If a voter is not physically present(out of the station) then he can vote remotely. That is it enhances a great degree of attendance. E-voting can be beneficial because anybody can effortlessly access the election. The security of E-voting in terms of verification(Authentication), clone(duplicate) votes, and non-repudiation of votes, is very less. E-voting is being studied hugely, and most of the execution is verified and even used for a while. That’s why very few executions (Implementation) are reliable sufficiently and are still in use
RDF Playground: An Online Tool for Learning about the Semantic Web

We present RDF Playground: a web-based tool to assist those who wish to learn or teach about the Semantic Web. The tool integrates functionalities relating to the key features of RDF, allowing users to specify an RDF graph in Turtle syntax, visualise it as an interactive graph, query it using SPARQL, reason over it using OWL 2 RL, and to validate it using SHACL or ShEx. The tool further provides the ability to import and explore data from the Web through a graph-based Linked Data browser. We discuss the design and functionality of the tool, its implementation, and the results of a usability study considering students from a Web of Data course that used it for lab assignments. We conclude with a discussion of these results, as well as future directions that we envisage for improving the tool.
PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching

The accurate and reliable extraction and matching of distinctive features (keypoints) in multi-view and multi-modal datasets is still an open research topic in the photogrammetric and computer vision communities. However, one of the main milestones is selecting which method is a suitable choice for specific applications. This encourages us to develop an educational tool that encloses different hand-crafted and learning-based feature-extraction methods. This article presents PhotoMatch, a didactical, open-source tool for multi-view and multi-modal feature-based image matching. The software includes a wide range of state-of-the-art methodologies for preprocessing, feature extraction and matching, including deep learning detectors and descriptors. It also provides tools for a detailed assessment and comparison of the different approaches, allowing the user to select the best combination of methods for each specific multi-view and multi-modal dataset. The first version of the tool was awarded by the ISPRS (ISPRS Scientific Initiatives, 2019). A set of thirteen case studies, including six multi-view and six multi-modal image datasets, is processed by following different methodologies, and the results provided by the software are analysed to show the capabilities of the tool. The PhotoMatch Installer and the source code are freely available.
Analysis of the Performance Indicators of the State Scientific Centers of the Russian Federation

The article presents a statistical analysis of the effectiveness of the Russian Federation state scientific centers (SSCs) in 2021, scientific and scientific-technical activities in terms of their publication activity, the creation and use of the intellectual activity results by them. The obtained results allow drawing a conclusion about the nature of the dynamics of changes in the considered indicators.
Regularization techniques for 3D surface reconstruction from four quadrant backscattered electron detector images

In scanning electron microscopy (SEM), a four quadrants backscattered electron detector (FQBSD) provides signals that can be combined to obtain a tridimensional reconstruction of the surface. The main challenge of the reconstruction operation consists of integrating the gradient field obtained as the normalized signal difference from each pair of opposite quadrants. Owing to the presence of electronic noise that eventually turns into image noise, a least square integration approach has been widely adopted for surface reconstruction. In the present work, we demonstrate the possibility of adopting regularization techniques (Tichonov's and Dirichlet's) to the surface reconstruction from FQBSD images to reduce the distortions due to sensitivity variations amongst the detector quadrants or an imprecise alignment of the FQBSD with the gun axis. This allows for a substantial improvement in the 3D surface reconstruction quality in terms of resolution and reduction of artifacts. These procedures have been experimentally validated on AISI 316L stainless steel polished surfaces with hardness indentation and on laser-patterned aluminum and silicon samples showing promising results.
Robust speech recognition based on deep learning for sports game review

Abstract To verify the feasibility of robust speech recognition based on deep learning in sports game review. In this paper, a robust speech recognition model is built based on the generative adversarial network GAN algorithm according to the deep learning model. And the loss function, optimization function and noise reduction front-end are introduced in the model to achieve the optimization of speech extraction features through denoising process to ensure that accurate speech review data can be derived even in the game scene under noisy environment. Finally, the experiments are conducted to verify the four directions of the model algorithm by comparing the speech features MFCC, FBANK and WAVE. The experimental results show that the speech recognition model trained by the GSDNet model algorithm can reach 89% accuracy, 56.24% reduction of auxiliary speech recognition word error rate, 92.61% accuracy of speech feature extraction, about 62.19% reduction of training sample data volume, and 94.75% improvement of speech recognition performance in the speech recognition task under noisy environment. It shows that the robust speech recognition based on deep learning can be applied to sports game reviews, and also can provide accurate voice review information from the noisy sports game scene, and also broaden the application area for deep learning models.