Reviewerly/exHarmony huggingface dataset:
Ultrasound GLCM texture analysis of radiation-induced acute toxicity following breast cancer radiotherapy

The aim of this study is to identify ultrasound texture features to characterize radiation-associated acute breast toxicity in women following radiotherapy treatment for breast cancer. We investigated a series of sonographic features obtained from the gray level co-occurrence matrix (GLCM) – a second order statistical method of texture analysis. These features were tested in a pilot study of 42 postradiotherapy patients. The mean follow-up time for the postradiotherapy patients was 6 weeks. Each participant underwent an ultrasound study in which ultrasound scans were performed on the bilateral breast, generating a total of 42 post irradiation and 42 contralateral normal breasts exams. The ultrasound scans of the irradiated breasts were graded as either mild (n=27) or severe (n=15). After specifying the region of interest on the B-mode images and computing the sonographic features based off the severity grading, we observed statistically significant differences in the quantification of
Impact of Artificial Intelligence in the Future of Retail and Customer Engagement

We are witnessing a real revolution in the way we buy and in consumer habits. It's been decades since brands started taking their first steps in ecommerce, followed by the implementation of the first online payments and the phenomenon of .com. However, even at that time, companies had little information about their customers, and these were perceived and treated as a large homogeneous mass. The stage today is different. The user is proactive and has access to a lot of information, which makes him more demanding. For this and other reasons, the consumer should be perceived and treated as an independent individual and whose needs determine the development and marketing of a brand's products and services (now called a centric customer strategy).
Transferability of Compositionality

Compositional generalization is the algebraic capacity to understand and produce large amount of novel combinations from known components. It is a key element of human intelligence for out-of-distribution generalization. To equip neural networks with such ability, many algorithms have been proposed to extract compositional representations from the training distribution. However, it has not been discussed whether the trained model can still extract such representations in the test distribution. In this paper, we argue that the extraction ability does not transfer naturally, because the extraction network suffers from the divergence of distributions. To address this problem, we propose to use an auxiliary reconstruction network with regularized hidden representations as input, and optimize the representations during inference. The proposed approach significantly improves accuracy, showing more than a 20% absolute increase in various experiments compared with baselines. To our best knowledge, this is the first work to focus on the transferability of compositionality, and it is orthogonal to existing efforts of learning compositional representations in training distribution. We hope this work will help to advance compositional generalization and artificial intelligence research.
Single-shot three-dimensional reconstruction for colonoscopic image analysis

We propose a method for the three-dimensional reconstruction from a single-shot colonoscopic image. Extracting a three-dimensional colon structure is an important task in colonoscopy. However, a colonoscope captures only two-dimensional information as colonoscopic images. Therefore, an estimation of three-dimensional information from two-dimensional images has potential demands. In this paper, we integrate deep-learning-based depth estimation to three-dimensional reconstruction. This approach omits the inaccurate corresponding matching from the procedure of conventional three-dimensional reconstruction. We experimentally demonstrated accurate reconstructions with comparisons between a polyp size in three-dimensional reconstruction and an endoscopist's measurement.
Boundary Effects in CNNs: Feature or Bug?

Recent studies have shown that the addition of zero padding drives convolutional neural networks (CNNs) to encode a significant amount of absolute position information in their internal representations, while a lack of padding precludes position encoding. Additionally, various studies have used image patches on background canvases (e.g., to accommodate that inputs to CNNs must be rectangular) without consideration that different backgrounds may contain varying levels of position information according to their color. These studies give rise to deeper questions about the role of boundary information in CNNs, that are explored in this paper: (i) What boundary heuristics (e.g., padding type, canvas color) enable optimal encoding of absolute position information for a particular downstream task?; (ii) Where in the latent representations do boundary effects destroy semantic and location information?; (iii) Does encoding position information affect the learning of semantic representations?; (iv) Does encoding position information always improve performance? To provide answers to these questions, we perform the largest case study to date on the role that padding and border heuristics play in CNNs. We first show that zero padding injects optimal position information into CNNs relative to other common padding types. We then design a series of novel tasks which allow us to accurately quantify boundary effects as a function of the distance to the border. A number of semantic objectives reveal the destructive effect of dealing with the border on semantic representations. Further, we demonstrate that the encoding of position information improves separability of learned semantic features. Finally, we demonstrate the implications of these findings on a number of real-world tasks to show that position information can act as a feature or a bug.
The Effects of the Use of Google Translate on Translation Students’ Learning Outcomes

This paper investigates the effects of uncontrolled use of Google Translate (GT) on the development of students’ translation skills. It aims to find out if the current patterns of GT use by translation students could impact their learning outcomes, and if so, in what ways. The study uses think-aloud data from actual translation efforts of four students. Analysis of the four subjects’ translations show that their overreliance on GT for first drafts reduced their propensity to search for equivalents. However, when students post-edit poorly constructed GT sentences, they carry out a process of analysis and synthesis similar to the process of translating from scratch. The study concludes that post-editing GT translation is beneficial for translation students’ development of translation skills, but it warns against introducing or allowing its use at the elementary stages of translation training.
Feature selection using ant lion optimization algorithm in text categorization

This is Big Data decade with extensive increase in the textual information where the text classification is the significant approach for processing and organizing textual information. Text categorization refers to the process of spontaneously allotting documents to the relevant classes. The key features of these text classification issue is tremendous increase in higher dimensionality of text information. Meta-Heuristics Approaches are effortlessly employed to obtain optimal solutions for high dimensional datasets in text categorization. However, some of these approaches like genetic algorithm and particle swarm optimization gives a sub-optimal solutions, the convergence time is more compared to other approaches and cannot guarantee the global maxima to the text categorization. Thus, in this paper, a nature-inspired optimization approach depending on catching mechanism of antlions in the environment known as Ant Lion Optimizer (ALO) Approach, is applied to resolve higher dimensionality issues prior to text classification. The precision and recall values for the proposed is comparatively effective when compared with the existing text categorization dimensionality reduction techniques.
SAFENet: A Secure, Accurate and Fast Neural Network Inference

The advances in neural networks have driven many companies to provide prediction services to users in a wide range of applications. However, current prediction systems raise privacy concerns regarding the user's private data. A cryptographic neural network inference service is an efficient way to allow two parties to execute neural network inference without revealing either party’s data or model. Nevertheless, existing cryptographic neural network inference services suffer from huge running latency; in particular, the latency of communication-expensive cryptographic activation function is 3 orders of magnitude higher than plaintext-domain activation function. And activations are the necessary components of the modern neural networks. Therefore, slow cryptographic activation has become the primary obstacle of efficient cryptographic inference. In this paper, we propose a new technique, called SAFENet, to enable a Secure, Accurate and Fast nEural Network inference service. To speedup secure inference and guarantee inference accuracy, SAFENet includes channel-wise activation approximation with multiple-degree options. This is implemented by keeping the most useful activation channels and replacing the remaining, less useful, channels with various-degree polynomials. SAFENet also supports mixed-precision activation approximation by automatically assigning different replacement ratios to various layer; further increasing the approximation ratio and reducing inference latency. Our experimental results show SAFENet obtains the state-of-the-art inference latency and performance, reducing latency by 38%∼61% or improving accuracy by 1.8%∼4% over prior techniques on various encrypted datasets.
Research on tor-based anonymous DDoS attack

Based on Tor, an anonymous DDoS attack is proposed, and the attacker of this attack is very difficult to trace due to the strong anonymity of Tor. Through conducting experiments in different network environment and making analyzation of security, the experiments results demonstrate the effectiveness of this attack, and the strategy to defense the attack is also discussed.
Using the iCC framework for solving unconstrained LSGO problems

Abstract Evolutionary computation is a branch of computational intelligence that is inspired by nature. This algorithm family uses fundamental rules of natural selection to find a better solution when solving optimization problems. The productivity of these methods decreases when the number of parameters to be optimized is extra-large. This area of research is known as large-scale global optimization. Cooperative coevolution is a structure for evolutionary algorithms which tries to solve the curse of dimensionality problem. The cooperative coevolution efficiency of application largely depends on the two settings: the size of each subcomponent(subproblem) and the grouping of variables. The actual work proposes improved cooperative coevolution denoted as iCC. The iCC approach dynamically resizes the groups. iCC starts with a predefined set of subproblems and reduces them gradually during the optimization process. A novel metaheuristic has been developed which is called iCC-SHADE for black-box optimization problems with a large number of variables. The proposed method has been tested on fifteen optimization tasks from the LSGO CEC’2013 competition benchmark. The experimental results have demonstrated that iCC-SHADE has statistically better performance than CC-SHADE with a static number of subproblems. Also, the effectiveness of iCC-SHADE has been tested in comparison with other modern metaheuristics. The Wilcoxon rank-sum test was used to compare the effectiveness of investigated metaheuristics.
CompOFA – Compound Once-For-All Networks for Faster Multi-Platform Deployment

The emergence of CNNs in mainstream deployment has necessitated methods to design and train efficient architectures tailored to maximize the accuracy under diverse hardware & latency constraints. To scale these resource-intensive tasks with an increasing number of deployment targets, Once-For-All (OFA) proposed an approach to jointly train several models at once with a constant training cost. However, this cost remains as high as 40-50 GPU days and also suffers from a combinatorial explosion of potentially sub-optimal model configurations. We find that the cost of this one-shot training is dependent on the size of the model design space, and hence seek to speed up the training by constraining the design space to configurations with better accuracy-latency trade-offs. We incorporate the insights of compound relationships between model dimensions to build CompOFA, a design space smaller by several orders of magnitude. Through experiments on ImageNet, we demonstrate that even with simple heuristics we can achieve a 2x reduction in training time and 216x speedup in model search/extraction time compared to the state of the art, without loss of Pareto optimality! We also show that this smaller design space is dense enough to support equally accurate models for similar diversity of hardware and latency targets, while also reducing the complexity of the training and subsequent extraction algorithms. Source code is at https//github.com/compofa-blind-review/compofa-iclr21
End-to-end classification on basal-cell carcinoma histopathology whole-slides images

The high incidence of BCC skin cancer caused that the amount of work for pathologists has risen to unprecedented levels. Acquiring outlined annotations for training deep learning models classifying BCC is often tedious and time consuming. End-to-end learning provides relief in labelling data by using a single label to predict an clinical outcome. We compared multiple-instance-learning (MIL) and a streaming performance for detecting BCC in 420 slides collected from 72 BCC positive patients. This resulted in an ROC with AUC of 0.96 and 0.98 for respectively streaming and MIL. Saliency and probability maps showed that both methods were capable of classifying classifying BCC in an end-to-end way with single labels.
A comprehensive review on meta-heuristic algorithms and their classification with novel approach

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.
EFEKTIFITAS PEMANFAATAN GOOGLE FORMULIR SEBAGAI SISTEM PENGUMPULAN DOKUMEN PENYALURAN TUNJANGAN PROFESI GURU MADRASAH DI KANTOR KEMENTERIAN AGAMA KABUPATEN CILACAP

This article is intended to get an overview of the process of collecting the distribution of documents for the professional madrasah teacher . the Ministry of Religion Cilacap Regency and to find out the effectiveness of the service applying the document collection policy using Google forms media. The research method and analysis uses a qualitative descriptive approach. The results showed that the process of collecting documents the professional madrasah teacher could be seen from 3 aspects. First, responsiveness . the Ministry of Religion Cilacap Regency as a service provider and the professional madrasah teacher as service users showed a positive response. The second, development, is the innovation that has been carried out by the Ministry of Religion Cilacap Regency from being initially based on manual to being based online. Third, the adequacy in which all stages of the filing program stage have been running quite effectively and smoothly. From the results of the discussion of these three aspects, it was concluded that the service system for collecting documents on the distribution of the professional madrasah teacher at the Ministry of Religion Cilacap Regency was effective.
An Improved Fitting Method for Predicting the Zernike Coefficient–Wavelength Curves

Broadband transmitted wavefront measurement is a detection method that uses transmitted wavefronts at a few specific wavelengths though a function of Zernike coefficients and wavelength to predict other transmitted wavefronts at any wavelength within a certain range. This method is used when the designed wavelength of the interferometer and the detection wavelength are different. The function of the Zernike coefficients and wavelength can be expressed by the Conrady formula which can be used to describe a monotonic function curve or a function curve with an inflection point. Two methods, i.e., solving and fitting, can be used to determine the coefficients of the Conrady formula. Owing to the inevitable errors in the actual measurements, the fitting method is preferred to determine the coefficients of the Conrady formula. However, by analyzing the Conrady formula, it was found that the curve obtained by fitting the Conrady formula is a monotonic curve. This method cannot be used to obtain a curve with an inflection point or a slow-changing curve in the long waveband. In this paper, we propose an improved data fitting approach that transforms the Conrady formula into a polynomial formula to fit a given set of data for determining the Conrady formula coefficients. The correctness of the method was verified via simulations. Finally, the feasibility of the proposed method was verified by changing the light source and using the Fizeau interferometer to measure the transmitted wavefront of the doublet lens. To ascertain the feasibility of using visible light to predict the invisible light band, a near-infrared 1064 nm light source was added to the experimental set-up to broaden the range of the transmitted wavefront prediction waveband. Experimental results showed that the conversion polynomial fitting method can be used to obtain monotonic curves and the curve with an inflection point and a slow-changing curve in the long waveband, thereby mitigating the limitation of directly fitting the Conrady formula using a set of data points. Furthermore, using this method, we verified that the visible light can be used to predict the transmitted wavefront at any wavelength in the invisible light band. This observation is highly significant for the practical applications of the broadband transmitted wavefront measurement technique.
Few-shot weakly supervised detection and retrieval in histopathology whole-slide images

In this work, we propose a deep learning system for weakly supervised object detection in digital pathology whole slide images. We designed the system to be organ- and object-agnostic, and to be adapted on-the-fly to detect novel objects based on a few examples provided by the user. We tested our method on detection of healthy glands in colon biopsies and ductal carcinoma in situ (DCIS) of the breast, showing that (1) the same system is capable of adapting to detect requested objects with high accuracy, namely 87% accuracy assessed on 582 detections in colon tissue, and 93% accuracy assessed on 163 DCIS detections in breast tissue; (2) in some settings, the system is capable of retrieving similar cases with little to none false positives (i.e., precision equal to 1.00); (3) the performance of the system can benefit from previously detected objects with high confidence that can be reused in new searches in an iterative fashion.
MEMBACA PUISI DENGAN METODE MEMBACA ESTETIK PADA SISWA SMK N 1 SLAWI

This dedication shows that the lack of stability of the concept and aesthetic reading process is one of the obstacles that inhibits the fostering of the appreciation of the literature of students of SMK N 1 Slawi. The purpose of this dedication is 1) Identifying the ability to read literary texts, especially reading poetry in students of SMK N 1 Slawi. 2) Describe the application of aesthetic reading in literary learning, especially reading poetry at SMK N 1 Slawi. The method used in this service is a qualitative instrumental approach. Where this dedication will produce new methods in reading aesthetics. The sample in this service is class XI student of SMK N 1 Slawi. Data collection techniques used were observation, interviews, observations on all parties involved in the implementation of this service. The results of this dedication of 10% of students are able to read poetry well and by 90% of students have not been able to read poetry well in the first meeting. There was an increase in the ability to read poetry after being given aesthetic reading stimulus by the team, that is 70% of students were skilled in reading poetry and 30% of students had difficulty in determining gestures and appreciation.
Research on the Impact of Rooftop Photovoltaic on Reducing Carbon Dioxide Emissions

Abstract At present, the biggest environmental problem facing mankind is the rapid change of climate. In order to be able to mitigate climate change, we must attach importance to carbon reduction actions. China has taken many measures to adjust the energy structure to reduce petrochemical energy consumption and carbon emissions as much as possible. In the development of the power industry, the universal application of renewable resources such as solar energy can adjust and upgrade the traditional thermal power structure. The effective development of renewable resources such as solar energy can greatly reduce carbon dioxide emissions, which is an important measure to reduce carbon emissions. The establishment of solar photovoltaic power generation systems on the roof can improve the development level of China’s photovoltaic power generation systems. At the same time, this can also reduce the energy consumption of coal-fired power, which is of great help in reducing carbon emissions. In the process of researching rooftop photovoltaic power generation systems, we need to analyze the importance of solar photovoltaic power generation. Meanwhile, we also need to explore the practical application of rooftop photovoltaic power generation systems, and conduct research and analysis on the factors affecting rooftop photovoltaic carbon emission reductions. Only in this way can we accurately grasp the impact of rooftop photovoltaics on carbon dioxide emissions, and provide a corresponding reference for promoting the widespread application of rooftop photovoltaic power generation systems.