Reviewerly/exHarmony huggingface dataset:
A Soft Label Method for Medical Image Segmentation with Multirater Annotations

In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels' learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater's annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis.
Text Generator using GPT2 Model

Abstract- GPT-2 is state of art algorithm transfer learning with respect to nlp task we can do it like text summarizationand many more applications. The text generation application programming interface is supported by a large-scale unsupervised language model capable of generating paragraphs of text This transformer-based language model, based on OpenAI's GPT-2 model, accepts a sentence or partial sentence as input and predicts subsequent text. GPT-2 uses input textto set the initial context for further text generation. The length of an input string can range from few words to a maximumsequence length of 1024 tokens. The longer an initial input, the more subject context is provided to a model. Generally, longer inputs produce a more coherent text output. It was specifically trained to guess the next word in sentences. GPT-2 isa massive model that contains a massive amount of compressed knowledge from various parts of the internet. It can be usedto forecast the likelihood of a sentence. The model learns an internal representation of the English language, which it can then use to extract features useful for subsequent tasks. We have used a tkinter which is a GUI to display the generated output. Keywords—Text generation, Graphical User Interface, gpt2 transformer,Open AI,tkinter,
The use of distance education to confronting the educational challenges in light of Corona crisis. (Analysis and evaluating study)

The chapter aims to recognize the use of distance education in confronting the challenges of education in light of Corona crisis. It discusses the following topics: introduction. Objectives. and significance of the chapter. Its terms and distance education (its concepts. Characteristics. Technologies. Norms of choosing and the challenges of implementing in light of corona crisis and the needs of its success). The international experience in implementing electronic and distance education after and during corona crisis, analyzing and evaluating the experience of the Kingdom of Saudi Arabia universities to confronting the educational crisis during corona pandemic., designing questionnaire as a tool for collecting data of sample reaches. (352) members were selected randomly from the teaching staff in governmental universities in Saudi Arabia in (1441/1442). In addition to the document analysis card which was developed by the Ministry of Education and Saudi Arabia governmental universities to analyze documents related to distance education. The study reached at many results, including: A complete shift of Saudi governmental universities to distance learning during Corona crisis. The research sample agrees with a high degree that the distance education policy in the universities of Saudi Arabia is clear. The most important reasons for the success of distance education in the experience of Saudi universities: regulation, clarity of the educational policy among universities in the field of using distance education and preparing good technical infrastructure, the training of teaching staff members on the use of distance education in teaching through learning management system (Blackboard), the training of students on the learning management system (Blackboard), the use of control and motivation for learners to ensure their interaction, providing electronic guidance for students, as well as providing technical support for all university employees. The study came out with many recommendations, most notably: Getting benefit from successful international experiences in the field of distance education and the need to harmonize and resettle them according to the Saudi environment and students' needs, switching all traditional courses to electronic courses, and training university teaching staff to design and produce electronic courses, create an administration specialized in educational design made up of qualified cadres of educational technology specialists working on formulating and designing courses, studying reality, designing scenarios, and directing, producing and evaluating them. In addition to the need to pay attention to the technical infrastructure of universities Key words: Distance education, Corona crisis, Experience of the Kingdom of Saudi Arabia, challenges of education.
A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals

Optical deep learning (DL) accelerators have attracted significant interests due to their latency and power advantages. In this article, we focus on incoherent optical designs. A significant challenge is that there is no known solution to perform single-wavelength accumulation (a key operation required for DL workloads) using incoherent optical signals efficiently. Therefore, we devise a hybrid approach, where accumulation is done in the electrical domain, and multiplication is performed in the optical domain. The key technology enabler of our design is the transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently. Through detailed design and evaluation of our design, along with a comprehensive benchmarking study against state-of-the-art RRAM-based designs, we derive the following key results: (1) For a four-layer multilayer perceptron network, our design achieves 115× and 17.11× improvements in latency and energy, respectively, compared to the RRAM-based design. We can take full advantage of the speed and energy benefits of the optical technology because the inference task can be entirely mapped onto our design. (2) For a complex workload (Resnet50), weight reprogramming is needed, and intermediate results need to be stored/re-fetched to/from memories. In this case, for the same area, our design still outperforms the RRAM-based design by 15.92× in inference latency, and 8.99× in energy.
Low-Quality Face Recognition Using an Index-Based Super Resolution Technique

Low-quality face recognition (LQFR), unlike high-quality face recognition is very challenging. This is due to the standoff between the subject and the camera and small face size. The most common way to overcome the mentioned problems is using super-resolution (SR) techniques. In this paper, we show that the efficiency of a FR system degrades significantly in low-resolution images. We propose a novel FR approach using an index-based super-resolution method to improve performance and computational load of a FR system. To achieve this goal, first we apply face quality assessment (FQA) to select appropriate face images with high quality from sequence of images. Then, we introduce Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) as an index based on which we decided whether or not to use super-resolution in a LQFR system. We also conduct experiments to explore factors which affect FR performance including super-resolution methods, face image size (pixels), face quality and change of the sequence of the conventional LQFR stages. To demonstrate the efficiency of the proposed approach, we compare face identification rate on various deep CNN face recognition. Experimental results show that the proposed method increases the average identification rate to 71.20% on SCFace dataset.
&lt;b&gt;Improvement in Rapidness of Earthquake Early Warning Using Ocean Bottom Seismic Data&lt;/b&gt;

This study proposes an earthquake early warning (EEW) method for ocean bottom seismic (OBS) data systems of railways. Because the conventional Shinkansen systems adopts only the S-wave alarm method for the OBS data, we demonstrate that the P-wave alarm method can estimate epicentral distance and magnitude using the OBS data but cannot accurately determine back azimuth. Considering this, we propose a concrete method for how to judge whether a warning is issued applying the OBS data. The proposed method enables us to improve the rapidness of EEW and the safety of railways during earthquakes.
Threshold effect for probabilistic entanglement swapping

The basic entanglement swapping protocol allows to project two qubits, which have never interacted, onto a maximally entangled state. For deterministic swapping, the key ingredient is the maximal entanglement that was initially contained in two pairs of qubits and the capacity of projecting onto a Bell basis. Thus the basic and deterministic entanglement swapping scheme involves three maximal level of entanglement. In this work we propose probabilistic entanglement swapping processes performed with different amounts of initial entanglement. Besides that we suggest a non Bell measuring-basis, to introduce a third entanglement level in the process. Additionally, we propose the unambiguous state extraction scheme as the local mechanism for probabilistically achieving the EPR projection. The combination of these three elements allows us to design four strategies for performing probabilistic entanglement swapping. Surprisingly, we find a twofold entanglement threshold effect related to the concurrence of the measuring-basis. Specifically, the maximal probability of accomplishing a EPR projection becomes a constant for concurrences higher than or equal to threshold entanglement value. Thus, we show that maximal entanglement in the measuring-basis is not required for attaining the EPR projection.
Using Simulated Retests to Estimate the Reliability of Diagnostic Assessment Systems

Abstract As diagnostic classification models become more widely used in large‐scale operational assessments, we must give consideration to the methods for estimating and reporting reliability. Researchers must explore alternatives to traditional reliability methods that are consistent with the design, scoring, and reporting levels of diagnostic assessment systems. In this article, we describe and evaluate a method for simulating retests to summarize reliability evidence at multiple reporting levels. We evaluate how the performance of reliability estimates from simulated retests compares to other measures of classification consistency and accuracy for diagnostic assessments that have previously been described in the literature, but which limit the level at which reliability can be reported. Overall, the findings show that reliability estimates from simulated retests are an accurate measure of reliability and are consistent with other measures of reliability for diagnostic assessments. We then apply this method to real data from the Examination for the Certificate of Proficiency in English to demonstrate the method in practice and compare reliability estimates from observed data. Finally, we discuss implications for the field and possible next directions.
Fusion of Various Sentiment Analysis Techniques for an Effective Contextual Recommender System

In recent years, Recommendation Systems (RSs) have become a vital component of numerous websites and online applications in a variety of domains. Consider, for instance, the e-commerce websites where RSs predominate. In part, the problem of information overload is mitigated by these RSs. However, RSs still have a few issues, such as data sparsity, which leads to another issue known as cold-start. The cold-start problem occurs when the user-item matrix is incredibly sparse. Additionally, RSs has a problem known as the long-tail problem, in which the system is incapable of providing suggestions due to insufficient or invalid ratings for often purchased products. The cold-start problem can be solved by providing recommendations based on captured user preferences and user feedback. User attitudes can be gleaned from the analysis of textual user reviews of purchased products. Sentiment analysis(SA), also known as opinion mining, is the study of people’s opinions, feelings, judgments, feedbacks, and emotions conveyed through written language regarding entities and their features. Usually these sentiments are derived and based out of various contexts. A context in sentiment analysis is a mood-based natural attribute.
On the Impact of Vote Delegation

We examine vote delegation on blockchains where preferences of agents are private information. One group of agents (delegators) does not want to participate in voting and either abstains under conventional voting or can delegate its votes to a second group (voters) who decides between two alternatives. We show that free delegation favors minorities, that is, alternatives that have a lower chance of winning ex-ante. The same occurs if the number of voting rights that actual voters can exert is capped. When the number of delegators increases, the probability that the ex-ante minority wins under free and capped delegation converges to the one under conventional voting—albeit non-monotonically.
You Can Sign but Not Decrypt: Hierarchical Integrated Encryption and Signature

Recently, Chen et al. (ASIACRYPT 2021) introduced a notion called hierarchical integrated signature and encryption (HISE), which provides a new principle for combining public key schemes. It uses a single public key for both signature and encryption schemes, and one can derive a decryption key from the signing key but not vice versa. Whereas, they left the dual notion where the signing key can be derived from the decryption key as an open problem. In this paper, we resolve the problem by formalizing the notion called hierarchical integrated encryption and signature (HIES). Similar to HISE, it features a unique public key for both encryption and signature components and has a two-level key derivation mechanism, but reverses the hierarchy between signing key and decryption key, i.e. one can derive a signing key from the decryption key but not vice versa. This property enables secure delegation of signing capacity in the public key reuse setting. We present a generic construction of HIES from constrained identity-based encryption. Furthermore, we instantiate our generic HIES construction and implement it. The experimental result demonstrates that our HIES scheme is comparable to the best Cartesian product combined public-key scheme in terms of efficiency, and is superior in having richer functionality as well as retaining merits of key reuse.
Similarities in Challenges faced by Developers: Investigations on Stack Overflow and GitHub

A large amount of rich data available in today’s world encounters a lot of opportunities to analyze the data and identify some valuable patterns from them. However, dealing with such data requires automated frameworks and knowledge of programming languages. The most common and widely used programming languages among developers are Java and Python, as evident from the queries and issues posted on Stack Overflow and GitHub. Despite the popularity of both Java and Python, the challenges in transitioning from one technology to another technology are hard for individuals and industries. In this paper, we aim to investigate similarities in the challenges faced by developers while dealing with both programming languages. To achieve this goal, we formulated two research questions (RQs) for understanding the topics and issues asked and faced by developers. We have also identified the temporal trend of asking new questions on Stack Overflow for Java and Python programming languages (PLs). Our results revealed the changing trend, from 2015 onwards, from Java to Python and inclined towards Python from the number of new posts on Stack Overflow. We analyzed 18,892 Stack Overflow questions related to Java and Python PLs and 42,674 issues from 22 different GitHub repositories, 11 for each PL. Our results indicate that questions asked on Stack Overflow are co-related to issues posted by developers on GitHub during real-time development for a respective PL.
Augmenting Simulation Data with Sensor Effects for Improved Domain Transfer

Simulation provides vast benefits for the field of robotics and Human-Robot Interaction (HRI). This study investigates how sensor effects seen in the real domain can be modeled in simulation and what role they play in effective Sim2Real domain transfer for learned perception models. The study considers introducing naive noise approaches such as additive Gaussian and salt and pepper noise as well as data-driven sensor effects models into simulation for representing Microsoft Kinect sensor capabilities and phenomena seen on real world systems. This study quantifies the benefit of multiple approaches to modeling sensor effects in simulation for Sim2Real domain transfer by their object classification improvements in the real domain. User studies are conducted to address hypotheses by training grounded language models in each of the sensor effects modeling cases and evaluated on the robot’s interaction capabilities in the real domain. In addition to grounded language performance metrics, user study evaluation includes surveys on the human participant’s assessment of the robot’s capabilities in the real domain. Results from this pilot study show benefits to modeling sensor noise in simulation for Sim2Real domain transfer. This study also begins to explore the effects that such models have on human-robot interactions.
A lightweight convolutional neural network built on inceptio-residual and reduction modules for deep facial recognition in realistic conditions

Since the last two decades, face recognition has been a significant area in the fields of biometrics and machine learning that uses the different appearances and geometric-based facial features to recognize the living substances present in a frame or video. In this article, we have proposed an inceptio-residual convolutional neural network inspired by the ResNet architecture for deep facial feature extraction and recognition. Besides that, some of the well-known lite or moderately dense and highly accurate ImageNet benchmarked convolutional neural networks, such as VGG-19 BN, ResNet 18/34/50, SE-ResNet50, ResNeXt50, Inception V2/V3/V4, Xception50, and DualPathNet68, have been utilized for face recognition. In conjunction with that, the proposed one was tested along those above-standard networks with respect to various performance metrics. Based on the results, we concluded that the proposed Incepto-residual convolutional neural network has performed better than the ImageNet benchmarked Convolutional Neural Networks.
Optimal non-asymptotic concentration of centered empirical relative entropy in the high-dimensional regime

This document establishes the optimal non-asymptotic concentration of the Kullback–Leibler divergence between the empirical distribution and the true distribution around its mean in the regime of K≫n, where K and n are the alphabet size and sample size, respectively.
Feasibility Study of Post Quantum Cryptography in TLS 1.3

This paper focuses on optimized constant-time software implementations of three NIST PQC KEM Finalists, CRYSTALS-Kyber, NTRU, and Saber, targeting ARMv8 microprocessor cores.All optimized implementations include explicit calls to Advanced Single-Instruction Multiple-Data instructions (a.k.a.NEON instructions).Benchmarking is performed using two platforms: 1) MacBook Air, based on an Apple M1 System on Chip (SoC), including four high-performance 'Firestorm' ARMv8 cores, running with the frequency of around 3.2 GHz, and 2) Raspberry Pi 4, singleboard computer, based on the Broadcom SoC, BCM2711, with four 1.5 GHz 64-bit Cortex-A72 ARMv8 cores.In each case, only one core of the respective SoC is being used for benchmarking.The obtained results demonstrate substantial speed-ups vs. the best available implementations written in pure C. For the 'Firestorm' core of Apple M1, NEON implementations outperform pure C implementations in the case of decapsulation by factors varying in the following ranges: 1.55-1.74for Saber, 2.96-3.04 for Kyber, and 7.24-8.49for NTRU, depending on an algorithm's variant and security level.For encapsulation, the corresponding ranges are 1.37-1.60 for Saber, 2.33-2.45for Kyber, and 3.05-6.68for NTRU.These uneven speed-ups of the three lattice-based KEM finalists affect their rankings for optimized software implementations targeting ARMv8.
File Browser Design that Enhances Immersion in VR using Snap Turns

Primary QuestionIt would seem that a large portion of virtual reality content being produced currently is intended purely for consumption (Bye, 2015).Very few virtual reality experiences allow the user to create something with it.I initially thought it was strange that developers would write code, pull the headset down over their eyes to test it, take it off to adjust their work, put it back on to test again… it seemed that this all should be done with the headset on.If it is such a powerful and versatile medium, why not use it to build the experiences themselves?Even as the concept is suggested, the worms start to exit the can.Creating a game, for example, may require 3D modeling, image editing, and coding.While each have tools on traditional 2D computer screens, no interfaces have been created that are designed for these tasks in the volume of virtual reality.A user can't even see their keyboard to type.They're now moving their body in ways that weren't the case with the previous mouse and keyboard system.It would seem initially that everything we know about digital interaction design is thrown out the window as the majority of our current interfaces are perceived as the two dimensional screens of computer monitors and smartphone screens.This thought process led me to my initial question: How can the two dimensional paradigm of operating systems be most ergonomically redesigned for head mounted displays?One of the interesting caveats of this question is the prospect of design.It proposes the creation of workflow -to design a process for design.In order to create this new medium of interaction, a method of creation must first be invented.
STUDY ON “GUMMA-KEN AGATSUMA-GUN SHOKUKO KUMIAI KIYAKUSYO” PUBLISHED IN 1887

This article considers the wages of craftsmen described in the “Agatsuma shokuko kumiai kiyaku” in the “Gunma-ken Agatsuma shokuko kumiai kiyaku-syo” published in June 1887. The following points will be clarified. From this material, we can find out the wages of construction jobs and the artificial number of jobs in 1887. Under this agreement, carpenters, joiners, etc. could find a close relationship between wages and quantity per unit.