Reviewerly/exHarmony huggingface dataset:
Adaptive Neural Networks Accounted for by Five Instances of “Respondent-Based” Conditioning

Neural Networks may be made much faster and more efficient by reducing the amount of memory and computation used. In this paper, a new type of neural network called an Adaptive Neural Network is introduced. The proposed neural network is comprised of five unique pairings of events. Each pairing is a module and the modules are connected within a single neural network. The pairings are a simulation of respondent conditioning. The simulations do not necessarily represent conditioning in actual organisms. In the theory presented here, the pairings in respondent conditioning become aggregated together to form a basis for operant conditioning. The specific pairings are as follows. The first pairing is between the reinforcer and the neural stimulus that elicits the behavior. This pairing strengthens and makes salient that eliciting neural stimulus. The second pairing is that of the now salient neural stimulus with the external environmental stimulus that precedes the operant behavior. The third is the pairing of the environmental stimulus event with the reinforcing stimulus. The fourth is the pairing of the stimulus elicited by the drive with the reinforcement event, changing the strength of the reinforcer. The fifth pairing is that after repeated exposure the external environmental stimulus is paired with the drive stimulus. This drive stimulus is generated by an intensifying drive. Within each module, a “0” means no occurrence of a pairing A of Stimuli A and a “1” means an occurrence of a pairing A of Stimuli A. Similarly, a “0” means no occurrence of a pairing Band a “1” means an occurrence of a pairing B, and so on for all 5 pairings. To obtain an output one multiplies the values of pairings through E. In one trial or instance, all 5 pairings will occur. The results of the multiplications are then accumulated and divided by the number of instances. The use of these simple respondent pairings as a basis for neural networks reduces errors. Examples of problems that may be addressable by such networks are included.
Inferentialist ethics for analyzing mathematical interactions in the age of artificial intelligence

This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 18K13162 and 18H05751.
Sequences of lower bounds for entropic uncertainty relations from bistochastic maps

Abstract Given two orthonormal bases <?CDATA $\mathcal{A}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">A</mml:mi> </mml:math> and <?CDATA $\mathcal{B}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">B</mml:mi> </mml:math> , the basic form of the entropic uncertainty principle is stated in terms of the sum of the Shannon entropies of the probabilities of measuring <?CDATA $\mathcal{A}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">A</mml:mi> </mml:math> and <?CDATA $\mathcal{B}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">B</mml:mi> </mml:math> onto a given quantum state. State independent lower bounds for this sum encapsulate the degree of incompatibility of the observables diagonal in the <?CDATA $\mathcal{A}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">A</mml:mi> </mml:math> and <?CDATA $\mathcal{B}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">B</mml:mi> </mml:math> bases, and are usually derived by extracting as much information as possible from the unitary operator U connecting the two bases. Here we show a strategy to derive sequences of lower bounds based on alternating sequences of measurements onto <?CDATA $\mathcal{A}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">A</mml:mi> </mml:math> and <?CDATA $\mathcal{B}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">B</mml:mi> </mml:math> . The problem can be mapped into the multiple application of bistochastic processes that can be described by the powers of the unistochastic matrices directly derivable from U . By means of several examples we study the applicability of the method. The results obtained show that the strategy can allow for an advantage both in the pure state and in the mixed state scenario. The sequence of lower bounds is obtained with resources which are polynomial in the dimension of the underlying Hilbert space, and it is thus suitable for studying high dimensional cases.
Pengujian UTAUT Model dalam Pemanfaatan Literasi Informasi Perpustakaan Perguruan Tinggi

Exist an information technology hope to increase use of information literacy. In order for use of information literacy to improve well, the technology used must be able will be accepted and used properly by users. It hope that use of information technology can simplify information literacy activities in college libraries. There are still some differences in the problems that occur in earlier studies about use of information systems, so this study aims to decide how much use of university library information literacy using UTAUT model with descriptive quantitative method. The results of this study show that users are more likely will be interested in utilizing the college library information system for other things than using it for scientific literacy activities. In addition, the moderating effects of age, gender, experience also didnâ€™t have a significant effect. Overall, this model can answer several problems in this study.
CT image denoising based on locally adaptive thresholding

The noise in reconstructed X-ray Computed Tomography (CT) slices is complex, non-stationary and indefinitely distributed. Subsequent image processing is needed in order to achieve a good-quality medical diagnosis. It requires a sufficiently great ratio between the detailed contrasts and the noise component amplitude. This paper presents an adaptive method for noise reduction in CT images, based on the local statistical evaluation of the noise component in the domain of Repagular Wavelet Transformation (RWT). Considering the spatial dependence of the noise strength, the threshold constant for processing the high frequency coefficients in the proposed shrinkage method is a function of the local standard deviation of the noise for each pixel of the image. Experimental studies have been conducted using different images in order to evaluate the effectiveness of the proposed algorithm.
Applying Possessive pronoun in Writing Sentences: Exploring Students’ Ability and Problems in a Mixed Method Study

The purpose of this research was to find out the students' ability in applying Possessive pronoun in writing sentences and the problems encounter it. This mixed method study employs an explanatory design to reveals it. 53 students out of 105 students from1st semester EFL students from one reputable University in Pekanbaru, Indonesia, were invited to this study. These 53 students were selected using simple random sampling and enrolled for an essay test and interview to see the students' ability and explaining the problems. The data analysis using SPSS showed that the average score of students was 52.98. Meanwhile for the median is 48, the mode is 20. The score of Standard Deviation is 27.93, Variance is 780.25, and Range is 84. Z-Score was found 41.5%, which is means higher than average and 58.5% while, students' ability was indicated below the average. It showed that the students were low ability in applying possessive pronoun in writing sentences. The study also found the common problems, i.e., (1) students still mixed up between possessive pronoun and possessive adjectives. (2) students used the wrong pattern in using a possessive pronoun. (3) students did not understand clearly about a possessive pronoun, (4) experiencing difficulties in learning possessive pronoun.
한국어 학습자의 숙달도별 어휘 사용 양상

This study is a statistical analysis of Korean language learners’ vocabulary usage according to the learning proficiency. This study was analyzed using a large Korean learner corpus built by the National Institute of Korean Language. Korean language learners used 249 words and 97 types on average, and as Korean language proficiency increased, the number of words increased by 163 and word types by 54 on average. In addition, as a result of analyzing the use of educational vocabulary of Korean language learners, as the learner''s level improved, the percentage of beginner’s vocabulary decreased and the percentage of intermediate and advanced vocabulary increased. At all levels, the basic vocabulary was used a lot. As the learning level improved, the types of words used increased, but the use of high-level words did not increase significantly. The use of high frequency vocabulary by Korean language learners decreased more slowly than Koreans’ use of modern language. The ratio of use of educational vocabulary between advanced Korean learners and modern Korean is very similar. However, advanced Korean language learners used fewer advanced vocabularies than Koreans. Among the high frequency vocabulary, vocabulary with basic semantic function showed consistent usage regardless of Korean language proficiency. Also, the vocabulary usage pattern by theme was different according to the level of Korean language proficiency. (Cyber Hankuk University of Foreign Studies)
Penggunaan Media Visual dalam Meningkatkan Aktivitas dan Keterampilan Menulis Laporan

This study aims to describe the improvement of student report writing activities and skills through visual media. This research data in the form of qualitative data and quantitative data. Research procedures through stages: 1) observation and identification of problems, 2) pre-action activities, 3) planning research, 4) choosing a process approach using visual media to increase the activity and value of report writing skills, 5) making three-dimensional visual learning media to be projected through the LCD of the projector, 6) making observation sheets, 7) implementing research (observation, analysis and reflection). Research activities were carried out 2 times without using visual media and using visual media. Data collection was carried out by 2 researchers with observers. The results of the study found that after the research was conducted, it appeared that there was an improvement in student processes and grades. The results showed an increase in activity indicators (an average of 60 with sufficient qualifications (before the use of the media), to 80 with good qualifications (after the use of the media). Improved values ​​can be seen from an average of 54.17 (before) with almost enough qualifications being 76.25 with good qualifications (after). This explains that visual media can improve student activities and grades in writing reports based on visual objects.&#x0D; Keywords: Activities, Skills, Writing, Reports, Visual Media
2015 개정 교육과정 기본어휘 목록 및 고등학교 영어 교과서 어휘동사 사용 분석: COCA 핵심학업동사에 기초하여

This paper analyses the lexical verbs in the guided word list of the 2015 Revised English Curriculum and high school English textbooks on the basis of the high frequency 200 lexical verbs extracted from the core academic words in the Corpus of Contemporary American English [COCA]. Lexical verbs are key words in determining sentence structures and meanings. In particular, this research focuses on academic verbs the knowledge of which is important for EFL learners’ academic achievement. The following questions are dealt with in this paper: 1) What changes occur in the distribution of high frequency core academic verbs in the guided word list? 2) Which verbs are classified as career development and technical words? 3) What are the properties of the academic verbs used in the high school textbooks of <English>, <English I> and <English II> in terms of frequency? and 4) With regard to EFL learners’ vocabulary learning, why should we be concerned with academic verbs classified as elementary-level words? The findings reveal that 35% of the 200 core academic verbs are used in the series of high school English textbooks, which means that a thorough learning of the general lexical verbs is important from a pedagogical perspective in the EFL situation. Furthermore, a diverse use of more core academic verbs is required in high school English textbooks.
Dialetheic Conditional Modal Logic

Standard modal logic for alethic modalities analyses modalities as ranging over all possible worlds (the Leibnizian universe). This leaves very little room in the space of worlds to entertain impossible things. My proposal is to liberate the Leibnizian universe and reinforce the relative aspect of possibility; worlds are possible with respect to some worlds, and impossible for others. The central idea is to isolate relative possibility (Kripke) from conditionality (Lewis/Stalnaker). To accommodate counterpossibles, I provide a dialetheic conditional modal logic, a theory that is dialetheic at every level, in the logic as well as in the set theory behind it.
Performance Analysis of Multiple Kalman Filter Systems

Various Kalman Filters have been used in literature to remodel noise and follow a target in Cartesian coordinates whose measurements come in polar coordinates. In addition, since the target to be tracked in real life is not bound to a single motion model, systems enabling the interaction of multiple motion models have been used. In this article, these filters and their motion models are defined and the performance of these filters and multiple filter systems are evaluated by simulations with various scenarios. In this simulations, all types of Kalman Filters show similar performances, but DCMKF (Debiased Consistent Converted Measurement Kalman Filter) is faster than others. Among multiple filters systems, IMM (Interacting Multiple Model) outperforms CUSUM(Cumulative Sum).
Mitigating large adversarial perturbations on X-MAS (X minus Moving Averaged Samples)

We propose the scheme that mitigates the adversarial perturbation $\epsilon$ on the adversarial example $X_{adv}$ ($=$ $X$ $\pm$ $\epsilon$, $X$ is a benign sample) by subtracting the estimated perturbation $\hat{\epsilon}$ from $X$ $+$ $\epsilon$ and adding $\hat{\epsilon}$ to $X$ $-$ $\epsilon$. The estimated perturbation $\hat{\epsilon}$ comes from the difference between $X_{adv}$ and its moving-averaged outcome $W_{avg}*X_{adv}$ where $W_{avg}$ is $N \times N$ moving average kernel that all the coefficients are one. Usually, the adjacent samples of an image are close to each other such that we can let $X$ $\approx$ $W_{avg}*X$ (naming this relation after X-MAS[X minus Moving Averaged Samples]). By doing that, we can make the estimated perturbation $\hat{\epsilon}$ falls within the range of $\epsilon$. The scheme is also extended to do the multi-level mitigation by configuring the mitigated adversarial example $X_{adv}$ $\pm$ $\hat{\epsilon}$ as a new adversarial example to be mitigated. The multi-level mitigation gets $X_{adv}$ closer to $X$ with a smaller (i.e. mitigated) perturbation than original unmitigated perturbation by setting the moving averaged adversarial sample $W_{avg} * X_{adv}$ (which has the smaller perturbation than $X_{adv}$ if $X$ $\approx$ $W_{avg}*X$) as the boundary condition that the multi-level mitigation cannot cross over (i.e. decreasing $\epsilon$ cannot go below and increasing $\epsilon$ cannot go beyond). With the multi-level mitigation, we can get high prediction accuracies even in the adversarial example having a large perturbation (i.e. $\epsilon$ $>$ $16$). The proposed scheme is evaluated with adversarial examples crafted by the FGSM (Fast Gradient Sign Method) based attacks on ResNet-50 trained with ImageNet dataset.
Detecting stuffing of a user's credentials at her own accounts

We propose a framework by which websites can coordinate to detect credential stuffing on individual user accounts. Our detection algorithm teases apart normal login behavior (involving password reuse, entering correct passwords into the wrong sites, etc.) from credential stuffing, by leveraging modern anomaly detection and carefully tracking suspicious logins. Websites coordinate using a novel private membership-test protocol, thereby ensuring that information about passwords is not leaked; this protocol is highly scalable, partly due to its use of cuckoo filters, and is more secure than similarly scalable alternatives in an important measure that we define. We use probabilistic model checking to estimate our credential-stuffing detection accuracy across a range of operating points. These methods might be of independent interest for their novel application of formal methods to estimate the usability impacts of our design. We show that even a minimal-infrastructure deployment of our framework should already support the combined login load experienced by the airline, hotel, retail, and consumer banking industries in the U.S.
An anti-peep passcode keypad based on hybird image and fuzzy adaptive visual distance adjustment

Abstract Because traditional Android mobile phone passcode keypad without anti-peeping function, so we propose a kind of anti-peep code passcode keypad based on hybrid image and fuzzy adaptive visual distance adjustment. First, replace the key bitmaps with hybrid images to solve the problem that password keyboard doesn’t have anti-peeping function. Then, the fuzzy logic and adaptive principle are used to solve the user’s use problem. Finally, it is found through experiments that the time to refresh 10 hybrid images is between 0.38s and 0.48s, and the blur effect of the image can be automatically adjusted according to the distance. The results show that the anti-peep code keyboard makes up for the problem that the traditional passcode keypad has no anti-peeping function.
Sea surface ships detection method of UAV based on improved YOLOv3

For satellite remote sensing image, ship detection is affected by factors such as cloud, weather and sea clutter, and there are problems such as high false alarm rate and missed detection rate. A deep neural network (DT-YOLO) for real-time detection of surface ships in unmanned aerial vehicle (UAV) aerial photography is proposed. DT-YOLO firstly improves the traditional YOLOv3 algorithm by constructing a deep neural network using densely connected modules and transition modules to extract ship features and designs five different scale convolution feature maps. After upsampling, they are merged with the corresponding scale feature maps to form a multi-scale feature pyramid to perform ship prediction tasks. Then the k-means algorithm is used to cluster the target frame dimensions, determine the target frame parameters, and improve the positioning accuracy of the model; In the detection network, the non-maximum suppression algorithm (NMS) is optimized by attenuating the confidence score linearly, which effectively mitigates the problem of mutual occlusion detection of ships. Finally, the self-constructed sea surface ship dataset is established to test the performance of the algorithm in different scenes by using multi-scale training and data enhancement strategies. The results show that the proposed method improves the detection accuracy under the premise of ensuring real-time performance, especially in the detection of larger targets and mutually occluded ships.
GAMBARAN HASIL PEMERIKSAAN TROMBOSIT DARAH PADA PENDERITA DBD SESUDAH MENGKONSUMSI JAMBU BIJI (psidium guajava) DI RST SOLOK TAHUN 2018

Dengue or dengue fever is the most dominant disease suffered by the people of Indonesia. It can be detected through hematocrit values and platelet counts of patients. Usually, the patient's hematocrit value increases and blood plasma volume decreases. On the other hand, the platelet count of the patient decreases due to bone marrow suppression and the presence of antibodies against platelet deficiency. The purpose of this study was to determine the hematocrit value and platelet count of patients with dengue fever. This study was conducted on 10 patients with dengue fever at the Solok Army Hospital in the December 2018 - January 2019 period. The results of this study found that the total platelet count of patients when starting treatment was 38,300 cells / mm3. While the mean hematocrit when starting treatment is 43.5%. It can be concluded that dengue fever sufferers have increased hematocrit values and decreased platelet counts.
How to Add Webmentions to Blogger

If you want to connect your Blogger account to other  #IndieWebsites you have to do two things:Add an h-card to your siteSet up webmentions with BridgyWhat are
Crossref grant IDs: a global, open database of funding information and identifiers

33 slides.-- Presentation delivered within the session Identifiers in research information management
Variational Metric Scaling for Metric-Based Meta-Learning

Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial role in the performance of metric-based meta-learning algorithms. However, there still lacks a principled method for learning the metric scaling parameter automatically. In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. Firstly, we propose a stochastic variational method to learn a single global scaling parameter. To better fit the embedding space to a given data distribution, we extend our method to learn a dimensional scaling vector to transform the embedding space. Furthermore, to learn task-specific embeddings, we generate task-dependent dimensional scaling vectors with amortized variational inference. Our method is end-to-end without any pre-training and can be used as a simple plug-and-play module for existing metric-based meta-algorithms. Experiments on mini-ImageNet show that our methods can be used to consistently improve the performance of existing metric-based meta-algorithms including prototypical networks and TADAM. The source code can be downloaded from https://github.com/jiaxinchen666/variational-scaling.
Learning Controllable Disentangled Representations with Decorrelation Regularization

A crucial problem in learning disentangled image representations is controlling the degree of disentanglement during image editing, while preserving the identity of objects. In this work, we propose a simple yet effective model with the encoder-decoder architecture to address this challenge. To encourage disentanglement, we devise a distance covariance based decorrelation regularization. Further, for the reconstruction step, our model leverages a soft target representation combined with the latent image code. By exploiting the real-valued space of the soft target representations, we are able to synthesize novel images with the designated properties. We also design a classification based protocol to quantitatively evaluate the disentanglement strength of our model. Experimental results show that the proposed model competently disentangles factors of variation, and is able to manipulate face images to synthesize the desired attributes.