Reviewerly/exHarmony huggingface dataset:
Establishment of a whole slide imaging-based frozen section service at a cancer center

In recent years, there has been a surge of interest in clinical digital pathology (DP). Hardware and software platforms have matured and become more affordable, and advances in artificial intelligence promise to transform the practice of pathology. At our institution, we are launching a stepwise process of DP adoption which will eventually encompass our entire workflow. Out of necessity, we began by establishing a whole slide imaging (WSI)-based frozen section service.We proceeded in a systematic manner by first assembling a team of key stakeholders. We carefully evaluated the various options for digitizing frozen sections before deciding that a WSI-based solution made the most sense for us. We used a formalized evaluation system to quantify performance metrics that were relevant to us. After deciding on a WSI-based system, we likewise carefully considered the various whole slide scanners and digital slide management systems available before making decisions.During formal evaluation by pathologists, the WSI-based system outperformed competing platforms. Although implementation was relatively complex, we have been happy with the results and have noticed significant improvements in our frozen section turnaround time. Our users have been happy with the slide management system, which we plan on utilizing in future DP efforts.There are various options for digitizing frozen section slides. Although WSI-based systems are more complex and expensive than some alternatives, they perform well and may make sense for institutions with a pre-existing or planned larger DP infrastructure.
The Subject Construction and Role Mental Model Construction of Erotic Movies Based on Lacan’s Desire Theory

Jacques Lacan is a famous French psychoanalyst, but his influence has long gone beyond the scope of psychoanalysis and has affected almost all fields of “human science.” Film and psychoanalysis coincided almost simultaneously and influenced each other. But with the development of film, researchers found that film studies in the period of classic film and film semiotics have come to an end. It has certain theoretical value to explore the construction of the subject and the mental model of the erotic film based on Lacan’s desire theory. Method. The body in the film and video described in this paper includes both the material body as an aesthetic object and the spiritual body with aesthetic consciousness. The so-called artistic presentation of the body element means that the beauty of the body form displayed by the actor’s body in the film video is perceived by the audience, and the desire to stare is captured by the audience, thereby completing the audience’s entire body composed of eyes, body, and mind. The body is not only an aesthetic object but also an aesthetic subject, and the human cognitive system is not just a closed brain. Because the nervous system, body, and environment are constantly changing and interacting, true cognition is a unified system of all three. The main part of this paper uses Lacan’s psychoanalytic theory to deeply analyze the relationship between the characters shown in the film. From the cognitive impairment of the self to the failure of communication between characters, the language of uncertainty entangled by desire is removed, and the behavior and facial expressions of the movie characters are used as the analysis basis to fully interpret the inner world of the characters. This includes love, hatred, pain, struggle, and many other contradictory emotions. In addition, this paper also analyzes the metaphor of the relationship between the characters from the perspective of semiotics and discusses the description of the relationship between the characters from the lens language from the perspective of cinematography. The special meaning conveyed by the recurring elements in the film is emphasized, and the correlation between the pictures is explained from the less mentioned photographic aspect of the film. Results/Discussion. Looking at Lacan’s theory, the subject, the other, and desire are linked by the subject spirit, which is the core of the connection between the three, and only through the resistance and struggle of the subject spirit can the illusion of the subject be broken. Bewilderment and the suppression of the other and desire finally become the self. And the embodiment of this true self is particularly prominent in film art. Lacan’s purpose is not merely to dissolve the subject but to seek a stand in the midst of destruction, pursuing the ultimate transcendence of man’s desire for the external other and internal desire.
Study of tourism recommendation system

We are living today in the era of technology. Automation in every field is the need in today’s world. Recommender systems help people to select the right item from an available large set of choices. Web services in every field begin to introduce IT for development. If we take our country India, tourism has a major contribution to the economy. The use of new technologies in tourism is expected. A recommender system in tourism helps people to be self-capable. This paper focuses on studying various recommender systems for tourism. In this different recommender systems like TIP, GeoNote, MacauMap, Proximo, etPlanner, mobiDENKu, AccessSights, etc. were studied in detail. This paper will help the new researcher to understand the tourism recommender system.
Application-Oriented Selection of Privacy Enhancing Technologies

To create privacy-friendly software designs, architects need comprehensive knowledge of privacy-enhancing technologies (PETs) and their properties. Existing works that systemize PETs, however, are outdated or focus on comparison criteria rather than providing guidance for their practical selection. In this short paper we present an enhanced classification of PETs that is more application-oriented than previous proposals. It integrates existing criteria like the privacy protection goal, and also considers practical criteria like the functional context, a technology’s maturity, and its impact on various non-functional requirements.
Designing a didactic fairy tale as a key to advanced Physics and Mathematics training in primary schools

The relevance of the chosen research topic stems from its development outcomes, which aim to resolve contradictions between society's needs for personality-oriented training in Physics and Mathematics basics at comprehensive secondary school and the insufficient basic (necessary and sufficient) conditions to provide this in primary grades. Teachers also lack a structured and academically supported method of immersing pupils in a didactic fairy tale atmosphere. The primary goal of the study is to present the author's conceptual vision of the essence and principles of didactic fairy tale creation in order to transform it into a basic instrument for teaching Physics and Mathematics to 1st–4th-grade students.
Facial Password Data Augmentation

We present a series of data augmentation methods that utilize noise in a positive test case as multiple negative test cases. These data augmentation methods are utilized to increase the accuracy of a facial authentication system that uses facial motion concurrently with conventional facial identification to verify a person’s identity. We propose using non moving frames from a video of a facial motion, as negative cases to increase the significance of the negative cases used during training. We will also use single frames repeated from the motion to supply more negative samples of non-moving faces. These methods are useful in training the network to distinguish a facial motion from a non moving face. The data augmentation will also be used during evaluation of the network to assign each facial motion password a strength value based on how they compare to the augmented data.
Quantum resource changes and distributions during catalytic transformations

One physical system always evolves from a state of low disorder to another of high disorder. This phenomenon can be identified by increased entropy. As we know, quantum resource theories provide a mathematical framework for examining diverse quantum resources in states, with the primary problem being whether quantum states can be transformed into each other by free operations. Unlike natural transformations, free operations in quantum resource theories never increase the resource measure of a primary system. Generally, the standard approach to this central problem is to study approximate transformations and apply catalysts to broaden the set of possible state transformations. By combining approximate transformations and catalytic transformations, we show that the quantum correlation between the primary system and the catalytic system after approximate catalytic transformations determines quantum resource changes in the primary system as well as the distribution of quantum correlations among different primary systems. We first find and establish that the resource measure of the primary system does not decrease under global free operations on the primary system and the corresponding catalytic system using the universal freezing technology and the catalytic transformation methods. A resource measure is monotonic under free operations, additive on tensor products, and continuous. Using these features, we then access relations between variations in the resource measure of the primary system under global free operations and quantum correlations between the primary and catalytic systems. Therefore, quantum correlations exist between the primary and catalytic systems after catalytic transformations, which are essential for nondecreasing resource measures of the primary system. Finally, we establish relationships among different primary systems after repeatedly applying the same catalyst. The correlation is closely associated with the reuse times of the same catalyst.
Collocation2Text: Controllable Text Generation from Guide Phrases in Russian

Large pre-trained language models are capable of generating varied and fluent texts. Starting from the prompt, these models generate a narrative that can develop unpredictably. The existing methods of controllable text generation, which guide the narrative in the text in the user-specified direction, require creating a training corpus and an additional time-consuming training procedure. The paper proposes and investigates Collocation2Text, a plug-andplay method for automatic controllable text generation in Russian, which does not require fine-tuning. The method is based on two interacting models: the autoregressive language ruGPT-3 model and the autoencoding language ruRoBERTa model. The idea of the method is to shift the output distribution of the autoregressive model according to the output distribution of the autoencoding model in order to ensure a coherent transition of the narrative in the text towards the guide phrase, which can contain single words or collocations. The autoencoding model, which is able to take into account the left and right contexts of the token, “tells” the autoregressive model which tokens are the most and least logical at the current generation step, increasing or decreasing the probabilities of the corresponding tokens. The experiments on generating news articles using the proposed method showed its effectiveness for automatically generated fluent texts which contain coherent transitions between user-specified phrases.
Studying the Role of Named Entities for Content Preservation in Text Style Transfer

Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. However, the majority of the existing approaches were tested on such domains as online communications on public platforms, music, or entertainment yet none of them were applied to the domains which are typical for task-oriented production systems, such as personal plans arrangements (e.g. booking of flights or reserving a table in a restaurant). We fill this gap by studying formality transfer in this domain. We noted that the texts in this domain are full of named entities, which are very important for keeping the original sense of the text. Indeed, if for example, someone communicates the destination city of a flight it must not be altered. Thus, we concentrate on the role of named entities in content preservation for formality text style transfer. We collect a new dataset for the evaluation of content similarity measures in text style transfer. It is taken from a corpus of task-oriented dialogues and contains many important entities related to realistic requests that make this dataset particularly useful for testing style transfer models before using them in production. Besides, we perform an error analysis of a pre-trained formality transfer model and introduce a simple technique to use information about named entities to enhance the performance of baseline content similarity measures used in text style transfer.
Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods

The development of large and super-large language models, such as GPT-3, T5, Switch Transformer, ERNIE, etc., has significantly improved the performance of text generation. One of the important research directions in this area is the generation of texts with arguments. The solution of this problem can be used in business meetings, political debates, dialogue systems, for preparation of student essays. One of the main domains for these applications is the economic sphere. The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora. In this paper, we use translated versions of the Argumentative Microtext, Persuasive Essays and UKP Sentential corpora to fine-tune RuBERT model. Further, this model is used to annotate the corpus of economic news by argumentation. Then the annotated corpus is employed to fine-tune the ruGPT-3 model, which generates argument texts. The results show that this approach improves the accuracy of the argument generation by more than 20 percentage points (63.2% vs. 42.5%) compared to the original ruGPT-3 model.
Çevrimiçi Sosyal Ağlarda Makine Öğrenmesi Tabanlı Aldatma Tespit Sistemi

The rapid dissemination of Internet technologies makes it easier for people to live in terms of access to information. However, in addition to these positive aspects of the internet, negative effects cannot be ignored. The most important of these is to deceive people who have access to information whose reliability is controversial through social media. Deception, in general, aims to direct the thoughts of the people on a particular subject and create a social perception for a specific purpose. The detection of this phenomenon is becoming more and more important due to the enormous increase in the number of people using social networks. Although some researchers have recently proposed techniques for solving the problem of deception detection, there is a need to design and use high-performance systems in terms of different evaluation metrics. In this study, the problem of deception detection in online social networks is modeled as a classification problem and a methodology that detects misleading contents in social networks using text mining and machine learning algorithms is proposed. In this method, since the content is text-based, text mining processes are performed and unstructured data sets are converted to structured data sets. Then supervised machine learning algorithms are adapted and applied to the structured data sets. In this paper, real public data sets are used and Support Vector Machine, k-Nearest Neighbor (k-NN), Naive Bayes, Random Forest, Decision Trees, Gradient Boosted Trees, and Logistic Regression algorithms are compared in terms of many different metrics.
Hierarchical Context Tagging for Utterance Rewriting

Utterance rewriting aims to recover coreferences and omitted information from the latest turn of a multi-turn dialogue. Recently, methods that tag rather than linearly generate sequences have proven stronger in both in- and out-of-domain rewriting settings. This is due to a tagger's smaller search space as it can only copy tokens from the dialogue context. However, these methods may suffer from low coverage when phrases that must be added to a source utterance cannot be covered by a single context span. This can occur in languages like English that introduce tokens such as prepositions into the rewrite for grammaticality. We propose a hierarchical context tagger (HCT) that mitigates this issue by predicting slotted rules (e.g., "besides_") whose slots are later filled with context spans. HCT (i) tags the source string with token-level edit actions and slotted rules and (ii) fills in the resulting rule slots with spans from the dialogue context. This rule tagging allows HCT to add out-of-context tokens and multiple spans at once; we further cluster the rules to truncate the long tail of the rule distribution. Experiments on several benchmarks show that HCT can outperform state-of-the-art rewriting systems by ~2 BLEU points.
Exact bounds on the energy gap of transverse-field Ising chains by mapping to random walks

Based on a relationship with continuous-time random walks discovered by Igl\'oi, Turban, and Rieger [Phys. Rev. E {\bf 59}, 1465 (1999)], we derive exact lower and upper bounds on the lowest energy gap of open transverse-field Ising chains, which are explicit in the parameters and are generally valid for arbitrary sets of possibly random couplings and fields. In the homogeneous chain and in the random chain with uncorrelated parameters, both the lower and upper bounds are found to show the same finite-size scaling in the ferromagnetic phase and at the critical point, demonstrating the ability of these bounds to infer the correct finite-size scaling of the critical gap. Applying the bounds to random transverse-field Ising chains with coupling-field correlations, a model which is relevant for adiabatic quantum computing, the finite-size scaling of the gap is shown to be related to that of sums of independent random variables. We determine the critical dynamical exponent of the model and reveal the existence of logarithmic corrections at special points.
Implementation of Asynchronous Microservices Architecture on Smart Village Application

This paper discusses the implementation of microservices architecture in smart village applications.The smart village application is a village-based online marketplace that facilitates various business actors' buying and selling process in a village.This application manages five types of products: lodging reservations, tourist attraction tickets, culinary purchases, and purchasing knickknacks show tickets.The complexity of processes, data, and high potential users requires that the system architecture is designed to produce a scalable, fault-tolerant system and easy to develop.Microservices architecture is one of the recommended architectures for building a scalable, fault-tolerant, and maintainable application.This architecture has several variations, ranging from variations in communication between services to the technology used.The suitability of applications with architectural variations and the complexity is a challenge in implementing this architecture.This paper describes how to implement the microservices architecture in smart village applications.Design and implementation of the microservices architecture in the smart village application was followed the WSIM or Web Services Implementation Methodology stage.The implementation results show that the application is easier to manage because it is divided into independent microservices.Implementing asynchronous communication and a choreographic approach to each service makes the client application response faster; besides, it did not affect other services if there is a problematic service.
Forensics in Medical Imaging: Techniques and Tools

In recent times, finding and identification of tampering in digital imagery is an evolving and essential module in the handling of imagery data. Digital image contributes a dynamic part in providing proof and evidence for any strange and criminal occurrence. Nevertheless, the image forgery could hide indication of alteration and inhibits the recognition of such unlawful circumstances due to progression in image treating and accessibility of cultured software damage of a picture that can be effortlessly accomplished. With the discovery of innovative communiqué knowledge and expertise, new properties and services are delivered in an intelligent healthcare context. The features and facilities target to provide a continuous, easy-to-use, precise, and real-time medical service to the regular consumer. As well-being and fitness are delicate concerns, it is better to be taken care of with precaution and carefulness of supreme safety and cautiousness. In this chapter, complete assessment and analysis of the current trend algorithms are discussed based on numerous image falsifications and criminological knowledge. Several practices like distinguishing and identifying fake imagery in the recent exploration include techniques like imagery watermarking, digitally signed images, copy-move, re-sampling, and splicing that have been reviewed in recent proposed works. Also, the available free forensics tools are presented in this chapter. The exploration prepared in this chapter may help the researchers, scholars, and students to apprehend the benefit and handling of the existing knowledge to generate better competent processes of image forgery discovery. Furthermore, the relative investigation explores the prevailing forgery detection algorithms that take account of the deep learning model with its merits and demerits.
A Data-Driven Paradigm for Precomputed Radiance Transfer

In this work, we explore a change of paradigm to build Precomputed Radiance Transfer (PRT) methods in a data-driven way. This paradigm shift allows us to alleviate the difficulties of building traditional PRT methods such as defining a reconstruction basis, coding a dedicated path tracer to compute a transfer function, etc. Our objective is to pave the way for Machine Learned methods by providing a simple baseline algorithm. More specifically, we demonstrate real-time rendering of indirect illumination in hair and surfaces from a few measurements of direct lighting. We build our baseline from pairs of direct and indirect illumination renderings using only standard tools such as Singular Value Decomposition (SVD) to extract both the reconstruction basis and transfer function.
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot

The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast adaptation to dynamically changing environments. In this work, we focus on the instance segmentation task and provide a comprehensive study of different techniques that allow adapting an object segmentation model in presence of novel objects or different domains. We propose a pipeline for fast instance segmentation learning designed for robotic applications where data come in stream. It is based on an hybrid method leveraging on a pre-trained CNN for feature extraction and fast-to-train Kernel-based classifiers. We also propose a training protocol that allows to shorten the training time by performing feature extraction during the data acquisition. We benchmark the proposed pipeline on two robotics datasets and we deploy it on a real robot, i.e. the iCub humanoid. To this aim, we adapt our method to an incremental setting in which novel objects are learned on-line by the robot. The code to reproduce the experiments is publicly available on GitHub.