Reviewerly/exHarmony huggingface dataset:
Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.
Unsupervised Alignment of Distributional Word Embeddings

Cross-domain alignment plays a key role in tasks ranging from image-text retrieval to machine translation. The main objective is to associate related entities across different domains. Recently, purely unsupervised methods operating on monolingual embeddings have successfully been used to infer a bilingual lexicon without relying on supervision. However, current state-of-the art methods only focus on point vectors although distributional embeddings have proven to embed richer semantic information when representing words. This paper investigates a novel stochastic optimization approach for aligning word distributional embeddings. Our method builds upon techniques in optimal transport to resolve the cross-domain matching problem in a principled manner. We evaluate our method on the problem of unsupervised word translation, by aligning word embeddings trained on monolingual data. We present empirical evidence to demonstrate the validity of our approach to the bilingual lexicon induction task across several language pairs.
CT-SAT: Contextual Transformer for Sequential Audio Tagging

Sequential audio event tagging can provide not only the type information of audio events, but also the order information between events and the number of events that occur in an audio clip.Most previous works on audio event sequence analysis rely on connectionist temporal classification (CTC).However, CTC's conditional independence assumption prevents it from effectively learning correlations between diverse audio events.This paper first introduces the Transformer into sequential audio tagging, since Transformers perform well in sequence-related tasks.To better utilize contextual information of audio event sequences, we draw on the idea of bidirectional recurrent neural networks, and propose a contextual Transformer (cTransformer) with a bidirectional decoder that could exploit the forward and backward information of event sequences.Experiments on the real-life polyphonic audio dataset show that, compared to CTC-based methods, the cTransformer can effectively combine the fine-grained acoustic representations from the encoder and coarse-grained audio event cues to exploit contextual information to successfully recognize and predict the audio event sequence in polyphonic audio clips.
Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain

This paper presents the solution designed by the team “Boston Team Party” for the ACM RecSys Challenge 2022. The competition was organized by Dressipi and was framed under the session-based fashion recommendations domain. Particularly, the task was to predict the purchased item at the end of each anonymous session. Our proposed two-stage solution is effective, lightweight, and scalable. First, it leverages the expertise of several strong recommendation models to produce a pool of candidate items. Then, a Gradient-Boosting Decision Tree model aggregates these candidates alongside several hand-crafted features to produce the final ranking. Our model achieved a score of 0.18800 in the public leaderboard. To aid in the reproducibility of our findings, we open-source our materials.
The Implementation of E-Learning Supported by Social Reality Videos in Mobile Applications: Its Impact on Student’s Learning Outcomes

This study aims to implement e-learning supported by social reality videos in a mobile application and explore its impact on improving student learning outcomes. This study used an experimental design (pretest-posttest control group design), two sample groups were used, they were the experimental and control groups. The experimental group was treated with e-learning supported by social reality videos in a mobile application, while the control group was given face-to-face learning and relied on the expository method. The number of research samples in each of the two groups was 28 students, they were students who took basic natural science courses at the Mandalika Education University. Pre-test and post-test were given with the same task in both groups. This study focuses on measuring student cognitive learning outcomes which are considered as the impact of induced learning in both treatment groups. Each learning outcome data was analyzed descriptively (analysis of average learning outcomes and n-gain), and statistical analysis (testing the hypothesis of differences in student learning outcomes between treatment groups), statistical analysis at a significance level of 0.05. In general, the results of the study shows that e-learning supported by social reality videos in a mobile application has a significant impact on improving student learning outcomes, and is found to be better than the expository method. We recommend its simultaneous use in regular lectures in the classroom.
Supplemental Material: Intense metamorphism-generated radiogenic Sr regulated Cenozoic water Sr isotope evolution on the NE Tibetan Plateau: A perspective on Qilian orogen denudation and Asian eolian transport

&lt;p&gt;Tables S1–S16.&lt;/p&gt;
End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation

For interventional procedures, a real-time mapping between treatment guidance images and planning data is challenging yet essential for successful therapy implementation. Because of time and machine constraints, it involves imaging of different modalities, resolutions and dimensions, along with severe out-of-plane deformations to handle. In this paper, we introduce MSV-RegSyn-Net, a novel, scalable, deep learning-based framework for concurrent slice-to-volume registration and high-resolution modality transfer synthesis. It consists of an end-to-end pipeline made up of (i) a cycle generative adversarial network for multimodal image translation combined with (ii) a multi-slice-to-volume deformable registration network. The concurrent nature of our approach creates mutual benefit for both tasks: image translation is naturally eased by explicit handling of out-of-plane deformations while registration benefits from bringing multimodal signals into the same domain. Our model is fully unsupervised and does not require any ground-truth deformation or segmentation mask. It obtains superior qualitative and quantitative performance for multi-slice MR to 3D CT pelvic imaging compared to state-of-the-art traditional and learning-based methods on both tasks.
Hybrid Ant Colony Optimization Algorithms—Behaviour Investigation Based on Intuitionistic Fuzzy Logic

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. The theory of intuitionistic fuzzy logic, which is the basis of InterCriteria analysis (ICrA), is used to study the proposed hybrid algorithms for ant colony optimization (ACO). Different algorithms for ICrA implementation are applied on the results obtained by hybrid ACO algorithms for Multiple Knapsack Problem. The hybrid algorithms behavior is compared to the traditional ACO algorithm. Based on the obtained numerical results from the algorithms performance and from the ICrA the efficiency and effectiveness of the proposed hybrid ACO algorithms, combined with appropriate local search procedure, are confirmed.
Electronic Textbooks as a Means of Modern Education

The article discusses the effectiveness and role of e-books in the learning process. The creation of electronic textbooks is one of the most urgent problems in the educational process today. The expansion of the use of modern information technologies has necessitated the development of electronic learning tools that increase the efficiency of independent learning of students and the quality of education. The use of electronic learning tools in the educational process is effective for both the teacher and the student. The use of electronic learning tools in the educational process does not contradict the traditional teaching method, but rather complements the use of its achievements. It also considers such comparison parameters as: page and spread; movement of the reader through the book; plane and depth; book block; individual appearance of the book; text; illustration; tactile sensations; reliability of information storage and available updating of information; advantages and disadvantages of both forms of the book. He advantages of an e-book in relation to a printed one are almost exclusively related to its greater information capacity and ease of use accompanied by sound effects. With all the convenience of the e-book format, it is possible that the reader does not want to download it and prefers to look directly on the site or like in a bookstore, wants to first scroll through the publication, and then decide whether to download it or not. The main shortcomings of the e-book come down to the loss of its individual artistic appearance. Currently, there are many tools for creating e-learning tools. For example, SunRav BookEditor, SunRav BookReader, TurboSite, Flipsnack, EdApp, Canva, etc. The possibilities of the program as SunRav BookEditor, TurboSite as well as the free online software Flipsnack are considered. SunRav BookEditor, TurboSite is a convenient and easy to use tool for creating eLearning tools. Using these programs, you can create e-books of any complexity with graphic capabilities.
Analysis of Sentiment Tendency Based on Major Public Health Events

The outbreak of the COVID-19 has seriously affected the lives of the public. On the network platform, there has been a lot of controversy about this issue, which caused panic among the people. Accurate Sentiment propensity analysis of user statements on various platforms can better guide public opinion and avoid unnecessary panic. This paper classifies data based on the Naive Bayesian algorithm because the traditional Naive Bayesian algorithm does not consider that the feature weights of the same feature word in different classes are different when classifying. Under the strong hypothesis of independence between features, the same feature word has the same importance, which will reduce the accuracy of the classifier. Therefore, this paper uses an improved TF-IDF algorithm to weight it and performs classification experiments. The experimental results show that this model can achieve better performance in text classification.
Session-Dependent Token-Based Payload Enciphering Scheme for Integrity Enhancements in Wireless Networks

Wireless networks have continued to evolve to offer connectivity between users and smart devices such as drones and wireless sensor nodes. In this environment, insecure public channels are deployed to link the users to their remote smart devices. Some of the application areas of these smart devices include military surveillance and healthcare monitoring. Since the data collected and transmitted to the users are highly sensitive and private, any leakages can have adverse effects. As such, strong entity authentication should be implemented before any access is granted in these wireless networks. Although numerous protocols have been developed for this purpose, the simultaneous attainment of robust security and privacy at low latencies, execution time and bandwidth remains a mirage. In this paper, a session-dependent token-based payload enciphering scheme for integrity enhancements in wireless networks is presented. This protocol amalgamates fuzzy extraction with extended Chebyshev chaotic maps to boost the integrity of the exchanged payload. The security analysis shows that this scheme offers entity anonymity and backward and forward key secrecy. In addition, it is demonstrated to be robust against secret ephemeral leakage, side-channeling, man-in-the-middle and impersonation attacks, among other security threats. From the performance perspective, the proposed scheme requires the least communication overheads and a relatively low execution time during the authentication process.
Practices of Protest in the Conditions of the Developing Academic Capitalism

The article focuses on the problem of professional motivation of University teaching staff in the context of developing “academic capitalism” and presents the results of the empirical study carried out by the authors at the North-Western Institute of management of RANEPA in March–June 2020. Based on the data obtained, it is revealed that teachers, experiencing deep deprivation of their social needs – the needs for respect, recognition and honor, are trying to find their place in the modern system, get out of the grip of double pressure, on the one hand, from the administration, which purposefully imposes rating systems and effective contracts, increasing competition between teachers, and on the other hand – students, who are now positioned as clients of universities. In the context of developing academic capitalism, teachers choose one of two adaptation strategies: 1) conformism as an opportunity to integrate into a constantly changing situation; 2) the practice of quiet protest as an opportunity to demonstrate the inefficiency of the entrepreneurial model of higher education, at least in its current version, and 3) neutral position to protect the classical values of the academic community.
Security Analysis of Gaussian and Discrete Modulations in FSO/CV-QKD Systems Employing LLO Under Phase and Amplitude Attacks

Continuous variable quantum key distribution (CV-QKD) using a local-local oscillator (LLO) is recently proposed to overcome security issues in conventional CV-QKD systems. However, Eve can manipulate the phase or amplitude of the phase reference pulse (PRP) transmitted over the insecure quantum channel, which has opened the door to new security issues. Maintaining optimal performance and preventing Eve’s activities on the quantum channel depends on such a choice of modulation technique. In this paper, the performance of CV-QKD employing LLO over the free space optical (FSO) channel under weak turbulence conditions is investigated. Channel transmittance is introduced into the system model according to the log-negative Weibull distribution. We have adopted the trusted noise model and included the channel phase distortion in the excess noise calculations. We have also evaluated the secret key rate (SKR) using Gaussian modulation (GM) and discrete modulation (DM) protocols. We have reported the superiority of GM in achieving the highest SKR over long distances with the optimum choice of the modulation variance under Eve’s attack on the PRP. Moreover, we analyzed phase and amplitude attacks on the PRP and showed that phase attacks are more severe and deteriorate the communication link more rapidly than amplitude attacks.
High-efficiency simplification method of irregular FOV for accelerating the imaging process in the Risley-Prism system

The Risley-Prism system, which has advantages in large FOV (field of view), high resolution and flexible imaging, faces limitations in image processing speed due to the non-convex characteristics of the FOV. Here, we proposed a simplified FOV model using inscribed rectangle and its property in area size, equivalent resolution and dynamic characteristics are studied, shows high consistency with original FOV. The simulation and experimental results show that the simplified FOV can reduce the calculation time of the total area of multi sub-FOVs from 3500 ms to 7.4 ms, and the image distortion correction time can also be reduced by 88.9%∼96.9%.
Comparative Analysis of Image Denoising Using Different Filters

The quality of images is often hampered due to the presence of noise. There are different image denoising techniques that can be used. One such technique is the use of filters. Filters are used for enhancing the appearance of images by eliminating unwanted information. We provide a detailed comparative analysis of different filters that can be used in denoising images containing various noises, in this paper. Four different noises, Speckle, Salt and Pepper, Gaussian, and Poisson, have been considered and different filters like Bilateral, Wiener, Mean, and Median have been applied to images containing each of them. The different filtered output images have been compared with the original image using their structural similarity index. Through observation and experimentation, new combinations of filters like Multiple Mean and Median-Mean have been introduced. The processing time has been calculated to decide upon the performance of different filters. A conclusion has been drawn as to which filter has to be used for denoising images containing different noises.
Lexical simplification benchmarks for English, Portuguese, and Spanish

Even in highly-developed countries, as many as 15–30% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. In this study, we present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures, to enable compilation of comparable datasets in other languages and domains. As the first multilingual lexical simplification dataset, where instances in all three languages were selected and annotated using comparable procedures, this is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs. non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find that a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages, according to all evaluation measures. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese, thus posing a question if such an architecture can be used for successful lexical simplification in other languages, especially the low-resourced ones.
Image signal denoising method of grating linear displacement sensor based on NLM

To improve the measurement resolution and accuracy of the grating displacement sensor, this paper proposes an image signal denoising method of grating linear displacement sensors based on NLM. Firstly, the optical system is designed to load the displacement information into the fringe image based on the grating pitch; Then, the principle of NLM image denoising is studied, and the inverse ratio kernel NLM denoising algorithm is designed; Thirdly, the effect of denoising algorithm is compared with the typical kernel function; Finally, the optimized image is used for displacement demodulation. The experimental results show that the SNR of the image is 30.1370db after denoising by this algorithm, and the high-precision displacement stage is set at 20 nm, 40 nm, and, 60 nm. Using the method in this paper, the positioning can be calculated to 22.7 nm, 38.2 nm, and 63.3 nm, and the sub-pixel positioning of the image and the nanopositioning measurement of the displacement are completed.
An optimized cost-based data allocation model for heterogeneous distributed computing systems

&lt;span lang="EN-US"&gt;Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.&lt;/span&gt;