Reviewerly/exHarmony huggingface dataset:
Cirquent Calculus in a Nutshell

This paper is a brief presentation of cirquent calculus, a novel proof system for resource-conscious logics. As such, it is a refinement of sequent calculus with mechanisms that allow to explicitly account for the possibility of sharing of subexpressions/subresources between different expressions/resources. This is achieved by dealing with circuit-style constructs, termed cirquents, instead of formulas, sequents or other tree-like structures. The approach exhibits greater expressiveness, flexibility and efficiency compared to the more traditional proof-theoretic approaches. The need for substantially new deductive tools that could overcome the limitations of sequent calculus while dealing with resource logics surfaced with the birth of computability logic, a game-semantically conceived logic of computational resources and tasks, acting as a formal theory of computability in the same sense as classical logic is a formal theory of truth.Cirquent calculus offers elegant axiomatizations for certain basic fragments of computability logic that have been shown to be inherently unaxiomatizable in sequent calculus or other traditional systems. The paper provides an iformal account on the main characteristic features of cirquent calculus, motivations for it, semantics, advantages over sequent calculus, as well as how cirquent calculus relates to classical and linear logics. Out of several existing cirquent calculus systems, only the simplest and most basic one, CL5, is presented in full detail.
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision

The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change. With the progress of efficient deep learning techniques, e.g., model compression, researchers can obtain efficient models with fewer parameters and smaller latency. However, most of the existing efficient deep learning methods do not explicitly consider energy consumption as a key performance indicator. Furthermore, existing methods mostly focus on the inference costs of the resulting efficient models, but neglect the notable energy consumption throughout the entire life cycle of the algorithm. In this paper, we present the first large-scale energy consumption benchmark for efficient computer vision models, where a new metric is proposed to explicitly evaluate the full-cycle energy consumption under different model usage intensity. The benchmark can provide insights for low carbon emission when selecting efficient deep learning algorithms in different model usage scenarios.
The Design of a Land Suitability Model on Rice Production Estimation using Remote Sensing Method in Merauke District Papua

The area of agricultural land in Merauke Regency according to data from Bappeda (Agency for Regional Development) of Merauke Regency is for about 4.6 million hectares in 2015 and within the next 5 years will be cultivated as much as 1.2 hectares, especially for rice field and secondary crops plants [1].This study has purpose to estimate the value of rice production by using dry-milled rice with a land suitability approach using decision tree analysis and remote sensing methods in Merauke Regency. Remote sensing that has been corrected geometrically and radio-metrically is analyzed by using decision tree analysis to derive information on paddy/rice and non-rice field land use and which is reclassified by using field unit information based on field observations which will result in accuracy of use and producer. The rice field data is then processed to derive information on the rotation pattern of rice by using a decision tree analysis that is using input data on land characteristics which will produce total accuracy. Information on rice field area and rotation patterns are complemented by land productivity (tons / ha) sourced from BPS (Central Statistics Agency) data and interviews with land cultivators and local residents (farmers) were used to calculate the total rice production value on dry-milled rice The results of calculations by using this method are expected to have a fairly large surplus of calculations, both data from BPS (Central Bureau of Statistics) and data from interviews with land cultivators and local residents (farmers). Thus, these results are expected to show that the land suitability approach by using remote sensing methods for estimating rice production can be used to produce information on rice field area and rotation patterns with moderate to high accuracy.
Auxiliary Equipment for Breast Cancer Screening Using a Cordless ProbeVerification of Operation of Probe Scanning Control System

Early detection is effective for breast cancer. However, in the present breast cancer examination, the examination accuracy depends on the skill of the medical technologist. Therefore, in this study, we will develop an auxiliary device for breast examination in order to obtain ultrasound images with clear tumor size and location. This paper describes a position control system with force control. Control of position and force prevents unnecessary loading during scanning. The operation verification result of the constructed control system is also described.
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

We present a summary of domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.
Students’ Problems in Semantic Meaning Analysis of Literary Works in English Handbooks

English handbooks used in High schools fill with literary works. It is either in poem, prose, or drama. These literary works are human’s creatures both in spoken and written language which fills with implicit meaning beneath the stylistic language used in that literary works. Besides, semantics is a branch of linguistics that studies meaning. This study aimed to find out problems faced by students in the analysis of the semantics meaning of literary works found in English handbooks. This study applied the qualitative descriptive method. The result showed that the problems faced by students lied beneath the determination of semantics meanings and their types found in words, phrases, clauses, and sentences in literary works.
Controllable Facial Caricaturization With Localized Deformation and Personalized Semantic Attentions

The facial caricature shows the distinct characteristics of a person via exaggerations of both shape and appearance. This paper presents a novel framework that automatically generates vivid facial caricatures by encoding personalized semantic information. To this end, we first design a part-based scheme for geometry warping, which composes local semantic deformation into a global warping field, equipped with sufficient warping freedom of different facial components. Second, under the scheme of Part-based Warping, we design a photo-to-caricature translation network called PbWarpGAN, and adopt several novel losses to capture the personalized characteristics of each input face and preserve its identity better. Third, based on PbWarpGAN, we develop a user-friendly interface by introducing an attention scheme on each facial component, allowing ordinary users to adjust the automatically generated caricature by PbWarpGAN according to their preference conveniently. Experimental results show that our PbWarpGAN is more effective in capturing personalized characteristics than counterparts, and provides an efficient tool for caricature designing application.
Graph Fourier Transform based Audio Zero-watermarking

The frequent exchange of multimedia information in the present era projects an increasing demand for copyright protection. In this work, we propose a novel audio zero-watermarking technology based on graph Fourier transform for enhancing the robustness with respect to copyright protection. In this approach, the combined shift operator is used to construct the graph signal, upon which the graph Fourier analysis is performed. The selected maximum absolute graph Fourier coefficients representing the characteristics of the audio segment are then encoded into a feature binary sequence using K-means algorithm. Finally, the resultant feature binary sequence is XOR-ed with the watermark binary sequence to realize the embedding of the zero-watermarking. The experimental studies show that the proposed approach performs more effectively in resisting common or synchronization attacks than the existing state-of-the-art methods.
Studi Komparatif Hasil Belajar Bahasa Indonesia Secara Luring Dengan Daring Siswa Kelas V SD

This thype of research is ex-post facto research by conducting comparative. A comprative study was conducted because of problems with student online learning outcomes which have decreased since transfer of learning from offlineto online. The population and research sample were all students of class V SD Gugus II, Sabbangparu District, Wajo Regency. The number of population and sample are the same, namely as many as 66 students, and for the technique of determining the offline data sample using online cluster random sampling. Data collection techniques used were documentation and interview. The data analysis technique using independent sample t-test. The results showed that the price of t count > t table so that the hypothesis H 1 was accepted and H 0 was rejected. The hypothesis is that there are differences in learning outcomes of Indonesian Language Offline and Online students of class V SD. Study conclusion there was a difference between the results of learning Indonesian offline and online students of Class V SD Gugus II, Sabbangparu sub-distric, Wajo distric. After knowing the differences in student learning outcomes offline and online, the teacher must have innovations to harmonize the learning process carried out both offline and online so that the learning outcomes achieved by student are as good.
Analysis and improvement for a class of variance reduced methods

Stochastic variance reduced methods have recently surged into prominence for solving large scale optimization problems in machine learning. However, how to choose the step sizes is still a problem to work out. Inspired by SVRG-BB (stochastic variance reduced gradient with Barzilai-Borwein step size), we propose an adaptive step size which is based on local estimation of Lipschitz constant for variance reduced methods. A framework was given of how to select the crucial parameter for different algorithms in our step size by solving a minimax problem.Then we adapt this step size to SARAH (stochastic recursive gradient algorithm) and SVRG (stochastic variance reduced gradient),which leads to two algorithms SARAH-AS (SARAH with adaptive step size) and SVRG-AS (SVRG with adaptive step size), respectively. Both of them converge linearly in the strongly convex case. Furthermore, we provide a novel perspective to explore why SARAH+ performs well in practice. Numerical experiments on standard datasets demonstrate the efficiency of our adaptive step size for stochastic variance reduced methods.
On the evolution and future development of online education

With human civilization entering the Internet era, the way people receive education is also experiencing the transformation of network and information. Online education has been seen in the field of vision in the way of "Internet plus education", which has strongly impacted traditional education mode. Online education makes the in school education develop towards the direction of information, and the out of school education develop towards the direction of online. With the coming of the first year of the national online course in 2020, online education for the first time has achieved full coverage from preschool to graduate, vocational and interest education. Online education has various forms and different contents. On the whole, it abandons the original consistent teaching methods and integrates various personalized teaching methods, so that teachers and students can interact with each other in all time and space. These advantages make it seize the market opportunity and win the success in today’s Internet economy society. In the future, online education will be more closely combined with artificial intelligence, virtual reality, big data, cloud computing, blockchain and other technologies, and will develop in a more intelligent direction, pay more attention to the content and effect of education, promote the realization of educational equity, and become the leading role of education.
Digitalization of post-industrial society in the context of social development

The article explores the technology development as a factor of the evolutionary and creative formation of society. The field of science and technology is constantly expanding due to the exponential growth in the number of technologies and due to the increasing expansion of technologies into the natural environment of mankind. The relevance of considering and socio-philosophical analysis and reflection of technology as a phenomenon of social reality is of particular importance in the modern world in the context of a global pandemic, which brought the digitalization of post-industrial society to a whole new level. Attention is drawn to the fact that technogenic risks increase in the post - industrial phase of social development. In addition, the article breaks down the concept of a post-industrial society under the catalytic influence of digitalization processes. The article substantiates the importance that post-material values acquire in the context of the development of modern society.
Training Dynamic based data filtering may not work for NLP datasets

The recent increase in dataset size has brought about significant advances in natural language understanding.These large datasets are usually collected through automation (search engines or web crawlers) or crowdsourcing which inherently introduces incorrectly labeled data.Training on these datasets leads to memorization and poor generalization.Thus, it is pertinent to develop techniques that help in the identification and isolation of mislabelled data.In this paper, we study the applicability of the Area Under the Margin (AUM) metric to identify and remove/rectify mislabelled examples in NLP datasets.We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points and leads to the loss of a large amount of relevant language information.We show that models rely on the distributional information instead of relying on syntactic and semantic representations.
Abstract Mining

IMPORTANCE: The marked explosion and fragmentation of bibliographic databases that include large parts pertaining to medical subspecialties has created the opportunity to identify new areas of research using the citations at the interface of subspecialty information. Bibliographic databases such as PubMed are useful to researchers when they wish to identify specific citations of their interest. However, they are not useful because of their size for the purpose of identifying new areas of research.
Improvements and recent updates of persistent fault analysis on block ciphers

Persistence is an intrinsic nature of many errors yet has not been caught enough attractions for years. In this chapter, the feature of persistence is applied to fault attacks (FAs), and the persistent FA is proposed. Different from traditional FAs, adversaries can prepare the fault injection stage before the encryption stage, which relaxes the constraint of the tight-coupled time synchronization. The persistent fault analysis (PFA) is elaborated on different implementations of AES-128, specially fault-hardened implementations based on dual modular redundancy (DMR). Our experimental results show that PFA is quite simple and efficient in breaking these typical implementations. To show the feasibility and practicability of our attack, a case study is illustrated on a few countermeasures of masking. This work puts forward a new direction of FAs and can be extended to attack other implementations under more interesting scenarios.
Quantum watermarking based on threshold segmentation using quantum informational entropy

We propose a new quantum watermarking scheme based on threshold selection using informational entropy of quantum image. The core idea of this scheme is to embed information into object and background of cover image in different ways. First, a threshold method adopting the quantum informational entropy is employed to determine a threshold value. The threshold value can then be further used for segmenting the cover image to a binary image, which is an authentication key for embedding and extraction information. By a careful analysis of the quantum circuits of the scheme, that is, translating into the basic gate sequences which show the low complexity of the scheme. One of the simulation-based experimental results is entropy difference which measures the similarity of two images by calculating the difference in quantum image informational entropy between watermarked image and cover image. Furthermore, the analyses of peak signal-to-noise ratio, histogram and capacity of the scheme are also provided.
Chapter 10. Student-teacher e-mail interaction as asynchronous dialogue in an academic setting

This study analyses a corpus of e-mails exchanged between university students and their teachers, aiming to understand some similarities and differences between computer mediated communication (CMC) and face-to-face dialogue. Methodologically, we combine corpus analysis with systemic functional linguistics to identify patterns of experiential meaning as expressed through verbs, or Processes (Halliday and Matthiessen 2004), looking at how students and teachers co-construct meaning and build rapport via e-mail. The results illustrate both similarities (informality, negotiation, near-synchronicity) and differences (personal disclosure, paralinguistic features) between e-mails and face-to-face dialogue. This seems to indicate that the two should be used in complementary ways to maximize the pragmatic effectiveness of student-teacher interaction, also harnessing the potential of other, more recent CMC technologies, like social networks.
An Efficient Tracker for Thyroid Nodule Detection and Tracking During Ultrasound Screening

Thyroid tumor is a common disease in clinic. Junior doctors could easily miss or get false detection due to the unclear boundary and similarity between nodules and tissues during thyroid screening. In this paper, we propose an efficient tracker for simultaneously detecting and tracking nodules to assist doctors in examination and improve their work efficiency. An attention based fusion block which adaptively combines the features of previous and current frames is introduced to acquire better detection and tracking result. To increase the detection accuracy, we propose an advanced post-processing strategy instead of using general post-processing methods to train the network to obtain the best prediction. Moreover, a minibatch self-supervised learning module is embedded to reduce the false positive rate (FPR) by strengthening the ability of distinguishing nodules from similar tissues. The proposed framework is validated on a dataset of 1555 thyroid ultrasound movies with 13314 frames. The result of 91% recall with 3.8% FPR running at 30 fps demonstrates the effectiveness of our method.
ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems

Due to the surge of cloud-assisted AI services, the problem of designing resilient prediction serving systems that can effectively cope with stragglers and minimize response delays has attracted much interest. The common approach for tackling this problem is replication which assigns the same prediction task to multiple workers. This approach, however, is inefficient and incurs significant resource overheads. Hence, a learning-based approach known as parity model (ParM) has been recently proposed which learns models that can generate ``parities’’ for a group of predictions to reconstruct the predictions of the slow/failed workers. While this learning-based approach is more resource-efficient than replication, it is tailored to the specific model hosted by the cloud and is particularly suitable for a small number of queries (typically less than four) and tolerating very few stragglers (mostly one). Moreover, ParM does not handle Byzantine adversarial workers. We propose a different approach, named Approximate Coded Inference (ApproxIFER), that does not require training any parity models, hence it is agnostic to the model hosted by the cloud and can be readily applied to different data domains and model architectures. Compared with earlier works, ApproxIFER can handle a general number of stragglers and scales significantly better with the number of queries. Furthermore, ApproxIFER is robust against Byzantine workers. Our extensive experiments on a large number of datasets and model architectures show significant degraded mode accuracy improvement by up to 58% over ParM.
BREAST CANCER SEGMENTATION OF MAMMOGRAPHICS IMAGES USING GENERATIVE

Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.
Anomaly detection in time-series data environment

Typical label data detect anomaly due to the relationship between inputs and labels, but time-series data are more demanding in detecting anomalies because they detect anomaly based on time-varying values. To solve this problem, this paper proposed Stacked-Autoencoder based data detection technique with ICS dataset among time series data. The Loss value was calculated as CDF and determined to be a suspicious event if it was greater than the arbitrarily specified threshold value. The experiment was carried out by designating 0.5, 0.7, 0.9 and 0.98, and 0.98 showed the best result with an accuracy of about 96%.