Reviewerly/exHarmony huggingface dataset:
Feasibility of fully automated classification of whole slide images based on deep learning

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.
Context-Aware Zero-Shot Recognition

We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transferring knowledge from the objects belonging to semantically similar seen categories, we aim to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior. Specifically, we leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories. We integrate our context-aware zero-shot learning framework into the traditional zero-shot learning techniques seamlessly using a Conditional Random Field (CRF). The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods.
Robust adaptation regularization based on within-class scatter for domain adaptation

In many practical applications, the assumption that the distributions of the data employed for training and test are identical is rarely valid, which would result in a rapid decline in performance. To address this problem, the domain adaptation strategy has been developed in recent years. In this paper, we propose a novel unsupervised domain adaptation method, referred to as Robust Adaptation Regularization based on Within-Class Scatter (WCS-RAR), to simultaneously optimize the regularized loss, the within-class scatter, the joint distribution between domains, and the manifold consistency. On the one hand, to make the model robust against outliers, we adopt an l2,1-norm based loss function in virtue of its row sparsity, instead of the widely-used l2-norm based squared loss or hinge loss function to determine the residual. On the other hand, to well preserve the structure knowledge of the source data within the same class and strengthen the discriminant ability of the classifier, we incorporate the minimum within-class scatter into the process of domain adaptation. Lastly, to efficiently solve the resulting optimization problem, we extend the form of the Representer Theorem through the kernel trick, and thus derive an elegant solution for the proposed model. The extensive comparison experiments with the state-of-the-art methods on multiple benchmark data sets demonstrate the superiority of the proposed method.
Differentially Private Confidence Intervals

Confidence intervals for the population mean of normally distributed data are some of the most standard statistical outputs one might want from a database. In this work we give practical differentially private algorithms for this task. We provide five algorithms and then compare them to each other and to prior work. We give concrete, experimental analysis of their accuracy and find that our algorithms provide much more accurate confidence intervals than prior work. For example, in one setting (with ε = 0.1 and n = 2782) our algorithm yields an interval that is only 1/15th the size of the standard set by prior work.
Passive Image Forgery Detection Based on the Demosaicing Algorithm and JPEG Compression

Multimedia files play an important role in everyday life.Today, the majority of the population owns state-of-the-art cameras integrated into their mobile devices.Technological development not only facilitates the generation of multimedia content, but also the intentional manipulation of it, and this is where forensic techniques of detecting manipulation on images and videos take on great importance.Although historically there has been confidence in the integrity of images, the advance of technology has begun to erode this confidence.This work proposes a digital image authentication method based on the quadratic mean error of the Color Filter Array interpolation pattern estimated from the analysed image.For the evaluation of the proposed method, experiments were carried out with public databases of forged images that are widely developed for research purposes.The results of the experiments demonstrate the efficiency of the proposed method.
Feature selection based on weighted conditional mutual information

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.
Pengembangan Media Pembelajaran Matematika Berbasis Macromedia Flash Materi Luas dan Keliling untuk Meningkatkan Motivasi Belajar Siswa

One of the computer-based mathematics learning media developed is Macromedia Flash-based learning media. Macromedia Flash-based learning media that have been available so far have not been students to be actively involved in using the media. This has encouraged researchers to develop new Macromedia Flash-based media. This research is development research. The development model used in 4D, namely Defines, Design, Develop and Disseminate. The subjects consisted of 60 students in grade 4 at SD Mardirahayu 01 and 02 Ungaran. The research instrument consisted of media validation sheets, material validation sheets, student response sheets, and motivation questionnaires. Research data were analyzed qualitatively and quantitatively. The results showed that the media developed were valid and could increase students' motivation in learning mathematics in a broad and mobile material.
Simultaneous Deep Stereo Matching and Dehazing with Feature Attention

Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and clear image from a hazy stereo image pair. Both tasks are synergistically formulated by fusing depth information from the matching cost and haze transmission. To learn the optimal fusion of depth-related features, we present a novel encoder-decoder architecture that extends the core idea of attention mechanism to the simultaneous stereo matching and dehazing. As a result, our method estimates high-quality disparity for the stereo images in scattering media, and produces appearance images with enhanced visibility. Finally, we further propose an effective strategy for adaptation to camera-captured images by distilling the cross-domain knowledge. Experiments on both synthetic and real-world scenarios including comparisons with state-of-the-art methods demonstrate the effectiveness and flexibility of our approach.
Artificial Motivation for Cognitive Software Agents

Abstract Natural selection has imbued biological agents with motivations moving them to act for survival and reproduction, as well as to learn so as to support both. Artificial agents also require motivations to act in a goal-directed manner and to learn appropriately into various memories. Here we present a biologically inspired motivation system, based on feelings (including emotions) integrated within the LIDA cognitive architecture at a fundamental level. This motivational system, operating within LIDA’s cognitive cycle, provides a repertoire of motivational capacities operating over a range of time scales of increasing complexity. These include alarms, appraisal mechanisms, appetence and aversion, and deliberation and planning.
A Survey on Machine Learning Adversarial Attacks

It is becoming notorious several types of adversaries based on their threat model leverage vulnerabilities to compromise a machine learning system. Therefore, it is important to provide robustness to machine learning algorithms and systems against these adversaries. However, there are only a few strong countermeasures, which can be used in all types of attack scenarios to design a robust artificial intelligence system. This paper is structured and comprehensive overview of the research on attacks to machine learning systems and it tries to call the attention from developers and software houses to the security issues concerning machine learning.
On-Line Signature Partitioning Using a Population Based Algorithm

Abstract The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.
CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

We live in an era of privacy concerns. As smart devices such as smartphones, service robots and surveillance cameras spread, preservation of our privacy becomes one of the major concerns in our daily life. Traditionally, the problem was resolved by simple approaches such as image masking or blurring. While these provide effective ways to remove identities from images, there are certain limitations when it comes to a matter of recognition from the processed images. For example, one may want to get ambient information from scenes even when privacy-related information such as facial appearance is removed or changed. To address the issue, our goal in this paper is not only to modify identity from faces but also keeps facial attributes such as color, pose and facial expression for further applications. We propose a novel face de-identification method based on a deep generative model in which we design the output vector from an encoder to be disentangled into two parts: identity-related part and the rest representing facial attributes. We show that by solely modifying the identity-related part from the latent vector, our method effectively modifies the facial identity to a completely new one while the other attributes that are loosely related to personal identity are preserved. To validate the proposed method, we provide results from experiments that measure two different aspects: effectiveness of personal identity modification and facial attribute preservation.
Distributed Machine Learning Oriented Data Integrity Verification Scheme in Cloud Computing Environment

Distributed Machine Learning (DML) is one of the core technologies for Artificial Intelligence (AI).However, in the existing distributed machine learning framework, the data integrity is not taken into account.If network attackers forge the data, modify the data, or destroy the data, the training model in the distributed machine learning system will be greatly affected, and the training results are led to be wrong.Therefore, it is crucial to guarantee the data integrity in the DML.In this paper, we propose a distributed machine learning oriented data integrity verification scheme (DML-DIV) to ensure the integrity of training data.Firstly, we adopt the idea of Provable Data Possession (PDP) sampling auditing algorithm to achieve data integrity verification so that our DML-DIV scheme can resist forgery attacks and tampering attacks.Secondly, we generate a random number, namely blinding factor, and apply the discrete logarithm problem (DLP) to construct proof and ensure privacy protection in the TPA verification process.Thirdly, we employ identity-based cryptography and two-step key generation technology to generate data owner's public/private key pair so that our DML-DIV scheme can solve the key escrow problem and reduce the cost of managing the certificates.Finally, formal theoretical analysis and experimental results show the security and efficiency of our DML-DIV scheme.
Entanglement-based quantum deep learning

Abstract Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger–Horne–Zeilinger entanglement states. An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed.
A Multi-Label Classification With Hybrid Label-Based Meta-Learning Method in Internet of Things

With the widespread adoption of Internet connected devices and the application of Internet of Things (IoT), more and more research efforts focusing on using machine learning techniques in recognizing activities from IoT sensors, especially in solving multi-label classification problems.Without considering the associations among labels, traditional approaches aim to transform the original multi-label classification problem into several single-label classification problems.The loss of information among labels will damage the classification performance.In this paper, we proposed a novel hybrid label-based meta-learning algorithm for multi-label classification based on an ensemble of a cluster algorithm and generalized linear mixed model (GLMM).In this algorithm, the clustering phase is performed to catch the association among labels and to reduce the computational complexity from vast label subsets simultaneously, and the GLMM phase is performed to solve dependence of a subject with multi-labels in training data.The numerical results show that the proposed algorithm outperforms others, especially for cases with relatively large number of labels.
Split-gate cavity coupler for silicon circuit quantum electrodynamics

Coherent charge-photon and spin-photon coupling has recently been achieved in silicon double quantum dots (DQDs). Here, we demonstrate a versatile split-gate cavity-coupler that allows more than one DQD to be coupled to the same microwave cavity. Measurements of the cavity transmission as a function of level detuning yield a charge cavity coupling rate of gc/2π= 58 MHz, a charge decoherence rate of γc/2π= 36 MHz, and a cavity decay rate of κ/2π= 1.2 MHz. The charge cavity coupling rate is in good agreement with device simulations. Our coupling technique can be extended to enable simultaneous coupling of multiple DQDs to the same cavity mode, opening the door to long-range coupling of semiconductor qubits using microwave frequency photons.
Bad smell detection using quality metrics and refactoring opportunities

Abstract Bad smells are bad practices in developing software. These poor solutions significantly influence the understandability and maintainability of source code. Therefore, bad smell detection plays a vital role in the refactoring, maintaining, and measuring the quality of large and complex software systems. Researchers believe that bad smells should be precisely identified and addressed. However, bad smell detection is complicated by issues such as informal and inconsistent specifications of bad smells and high false positive rates in the detection process, all of which affect the success rate in detection. In this paper, we present a new method to detect bad smells in code by addressing the aforementioned issues. Our proposed method is a multi‐step process using software quality metrics and refactoring opportunities. In this method, after obtaining the bad smell formal specifications based on software metrics, we utilize them to achieve a set of candidates for each bad smell. Afterwards, each of the instances will be examined and compared with the corresponding refactoring situations specified for that bad smell. This examination strikes out the false positives created in the previous step. The evaluation of this method on four open‐source systems demonstrates the improved effectiveness of bad smell detection in code.
Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.
Built to last! Embedding open science principles and practice into European universities

The purpose of this article is to examine the cultural change needed by universities, as identified by LERU in its report Open Science and its role in universities: a roadmap for cultural change.1 It begins by illustrating the nature of that cultural change. Linked to that transformation is a necessary management change to the way in which organizations perform research. Competition is not the only, or necessarily the best, way to conduct this transformation. Open science brings to the fore the values of collaboration and sharing. Building on a number of Focus on Open Science Workshops held over five years across Europe, the article identifies best practice in changing current research practices, which will then contribute to the culture change necessary to deliver open science. Four case studies, delivered at Focus on Open Science Workshops or other conferences in Europe, illustrate the advances that are being made: the findings of a Workshop on Collaboration and Competition at the OAI 11 meeting in Geneva in June 2019; alternative publishing platforms, exemplified by UCL Press; open data, FAIR data and reproducibility; and a Citizen Science Workshop held at the LIBER Conference in Dublin in June 2019.
Quantum Science and Quantum Technology

Quantum science and quantum technology are of great current interest in multiple frontiers of many scientific fields ranging from computer science to physics and chemistry, and from engineering to mathematics and statistics. Their developments will likely lead to a new wave of scientific revolutions and technological innovations in a wide range of scientific studies and applications. This paper provides a brief review on quantum communication, quantum information, quantum computation, quantum simulation, and quantum metrology. We present essential quantum properties, illustrate relevant concepts of quantum science and quantum technology, and discuss their scientific developments. We point out the need for statistical analysis in their developments, as well as their potential applications to and impacts on statistics and data science.
A Survey of Adversarial Learning on Graphs

Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give appropriate definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and offer insights for the relevant researchers. Latest advances in graph adversarial learning are summarized in our GitHub repository https://github.com/EdisonLeeeee/Graph-Adversarial-Learning.