Reviewerly/exHarmony huggingface dataset:
DIAGNOSIS AND CLASSIFICATION ASSISTANCE FROM LYMPHOMA MICROSCOPIC IMAGES USING DEEP LEARNING

The microscopic diagnosis of lymphoma remains challenging. Recent data from our group within the French (nationwide) Lymphopath network shows that 20% of diagnoses are inaccurate, with direct impact on patient care1. Another difficulty is the subclassification of lymphoma subtypes to predict therapeutic response and clinical behaviour. For these, molecular techniques, not affordable for all pathology departments, have become critical. Currently, automated solutions that could help with diagnostic decisions or histological grading are lacking, and it is unreasonable to expect pathologists to be experts on rare tumours if they only see a few cases per year. Digital microscopy offers unique features which are not available in conventional optical microscopy2 and allows automated image analysis and quantification by using computer vision and, in particular, deep learning (DL) approaches.3 Using DL on digital slides we tried to address two questions: 1) Can we train deep neural network (CNN) to distinguish follicular lymphoma (FL) from either follicular hyperplasia or other small B-cell lymphoma subtypes on hematoxylin-eosin stained lymph node digital slides? 2) Can CNN separate germinal center from non germinal center diffuse large B-cell lymphoma (DLBCL) the same way? Patch extraction or patch coordinated extraction was carried with different architectures (Residual Net, GoogleNet, VG-GNet) of whole slides images. A home designed neural network classification was also performed on 125x125pixel non-overlapping tiles of a whole slide image extracted at very low resolution (pyramid level 5 ó 7,68μm/pixel). The latter classifier was a fairly simple CNN architecture. It stacked three blocks of Convolution-MaxPooling with respectively 32, 64 and 128 filters, followed by two 1024-unit fully connected layers. Output of the network is a 2-unit layer with softmax activation to produce class probabilities. As to the diagnosis of FL, the accuracy of our method is superior to that of non expert pathologists, with an average area under the curve (AUC) of 0.95. Furthermore, we trained CNNs to predict GC and non GC phenotype of DLBCL previously determined with Hans' algorithm with an average AUC of 0.882. Our models were validated on independent series of biopsies (training, testing and validation sets) and provide quite similar accuracy irrespective of the architecture we used. These findings strongly suggest that DL models, even on very simple CNNs, can assist pathologists in the diagnosis and subclassification of lymphoma as recently shown is subsets of lung cancers.3 References 1) Laurent C et al. J Clin Oncol. 2017;35:2008-17. 2) Krenacs T et al. Digital microscopy - 2010. Microscopy: Science, Technology, Applications and Education. A. Méndez-Vilas and J. Díaz (Eds.). 3) Coudray N et al. Nat Med. 2018;24(10:1559-67). Keywords: diffuse large B-cell lymphoma (DLBCL); follicular lymphoma (FL).
The Global Omics Observatory Network: Shaping standards for long-term molecular observation

Founded in early 2018 through a collaboration between the EU Horizon 2020 AtlantOS project and Agriculture and Agri-Food Canada, the Global Omics Observatory Network (GLOMICON) is federating long-term ecological observatories employing "omic" (e.g. metagenomcis, metatranscriptomics, metabolomics) techniques to assess biodiversity across scales. GLOMICON is the consolidation of a series of meetings and ad hoc efforts (e.g. the multi-omic sessions at TDWG 2017) and seeks to mainstream multi-omic observation in existing observatory systems. GLOMICON currently networks &amp;gt;40 organisations observing biodiversity from urban and agricultural systems to the depths of the polar ocean. Coordination through GLOMICON allows the long-term observatory community to develop and align their needs, thus approaching standards bodies including the Genomic Standards Consortium (GSC), the Biodiversity Information Standards (TDWG) organisation, and the Earth Science Information Partners (ESIP) with a common voice. Further, as more ecological observatories begin to adopt molecular techniques, GLOMICON offers a community building best practices to facilitate their operationalisation. Vitally, GLOMICON interfaces with established observation networks via organisations such as UNESCO/IOC Global Ocean Observing System through its Biology and Ecosystems Panel. Such interactions have provided invaluable guidance on how to approach global standardisation in a firmly operational and multi-stakeholder environment, while ensuring innovative science can thrive. In this contribution, we will deliver a briefing on GLOMICON's current priorities and efforts to shape molecular standards to become fit-for-purpose in observatory-grade settings. In particular, we will focus on our interactions with other key omic observing networks, including the Genomic Observatories Network, and our joint strategies to progress towards an distributed yet integrated system. We will also note practical steps the network has taken to systematise protocols and best practices, (bio)informatics routines, observatory parameters, and global intercalibration through sample exchange. Lastly, we will note the network's upcoming priorities, which feature the need to develop strategies for sustainability and the extension of coordination efforts between national, regional, and global Earth observation systems.
The substitution mechanism of Au in In-, Fe- and In-Fe-bearing synthetic crystals of sphalerite, based on the data from EPMA and LA-ICP-MS study

Research subject . Sphalerite (ZnS) is a widespread mineral that can be found in various depositional environments. During formation, this mineral can accumulate minor and trace impurities, with gold being one of the most valuable component. The issue of the chemical state of Au in sphalerite has been much discussed recently. Methods. Samples of In-, Fe- and In-Febearing sphalerite with a composition ranging from 0 to 2.5 mol.% In2S3 and 0 – 40 mol.% FeS were synthesized in an Ausaturated system using gas transport and salt flux techniques. The resulting products were subsequently investigated using EPMA and LA-ICP-MS. Results. All the elements under investigation are found to be homogeneously distributed within the sphalerite matrix. After quenching, sphalerite is shown to retain Au. Our data indicates that the observed increase in Au concentration is caused by the presence of In (up to 1.02 wt % Au) and, to a lesser extent, by that of Fe (up to ≈600 ppm Au). These elements substitute Zn in the crystal structure of sphalerite following the scheme Au+ + In3+(Fe3+) ↔ 2Zn2+, which is in good agreement with previous data obtained using the XAS method. Conclusions. A higher sulphur fugacity in the system leads to a more significant accumulation of Au in sphalerite. The concentration of Au in pure sphalerite does not exceed 10 ppm under our experimental conditions and does not depend on the activity of sulphur in the system.
Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%.
Indexing the bijective BWT

The Burrows-Wheeler transform (BWT) is a permutation whose applications are prevalent in data compression and text indexing. The bijective BWT is a bijective variant of it that has not yet been studied for text indexing applications. We fill this gap by proposing a self-index built on the bijective BWT . The self-index applies the backward search technique of the FM-index to find a pattern P with O(|P| lg|P|) backward search steps.
Moving Target Defense in Preventing SQL Injection

The database stores important information about the user, which make it a core part of the website. Therefore, database injection has become a serious cyber-attack. Traditional database injection defenses are passive defenses, which cannot detect new vulnerability before it is exposed. The Moving Target Defense (MTD) method that emerged in recent years has become a breakthrough to solve this problem. This paper mainly establishes the model to verify the possibility of dynamic defense application in database injection defense. This paper first introduces the related concepts SQLI and MTD, then we build models to compare the attack surface of the traditional static defense model and MTD one. It is concluded that with certain conditions, the dynamic defense model has a smaller attack surface, which indicate stronger defense ability.
Increasing Motivation for Playing Blockchain Games Using Proof-of-Achievement Algorithm

Satoshi Nakamoto introduced a peer-to-peer electronic payment system in the paper “Bitcoin: A Peer-to-Peer Electronic Cash System” [12] in 2009. The payment system introduced in this paper (now known as blockchain technology) had a big impact on digital data transfer in a point that using blockchain technology, digital data transfer can be done without having third parties to give trust to the transfer process. In this paper, we focus on one of the key components of the blockchain technology, “consensus algorithm”. We propose a consensus algorithm “Proof-of-Achievement (PoA)” which is an algorithm optimized for blockchain games, focusing on the number of tasks achieved in the game. Using the PoA algorithm, we aim to increase the motivation for playing blockchain games. Through a user study, this paper discusses the effects of the PoA algorithm.
Coverless Text Steganography Based on Maximum Variable Bit Embedding Rules

As a carrier of information hiding, text is difficult to hide secret because of its less redundancy. The robustness and security of some traditional methods based text are not high. Based on Markov chain model, a coverless text steganography algorithm was proposed in this paper. According to the characteristic and value of transition probability in the model, the algorithm used the maximum variable bit embedding instead of the usual fixed bit embedding. In order to verify the effectiveness of the algorithm, different datasets and algorithms were selected to test. By comparing with results, the proposed model had higher concealment and hidden capacity.
Public Transportation Analysis Based on Social Media Data

Public participation has become an important part of traffic planning and policy-making, but due to the limits of project cycle and cost, collecting and processing public opinions on a large scale seems to be a huge challenge. Relevant government departments should accept public opinions and make scientific and reasonable policies. This study focuses on how to use big data from social network platforms for content analysis. Data about public opinions are collected from the internet by means of a web crawler. A latent Dirichlet allocation (LDA) topic model is built to summarize public opinions and traffic problems. This paper also uses the Nanjing subway system as an example to introduce the whole procedure of content analysis and sum up the spatiotemporal properties of data. According to the results, some corresponding measures for the Nanjing metro system are proposed to improve the operation and management.
A Survey of Privacy-Preserving Techniques for Blockchain

Blockchain technology is an important innovation of fintech area. It mainly consists of distributed data storage, P2P propagation, consensus mechanism and encryption algorithm. From the cryptocurrency to IoT (Internet of things), the blockchain technology has been applied to many areas. However, it faces some challenges, especially for personal privacy preservation. Though there exist some studies on the privacy issue of blockchain, it still lacks a systematic review of the privacy preserving techniques for blockchain technology. This paper focuses on some methods to protect personal private data in blockchain and newly developing areas combined with blockchain. Further, we discuss the limitation of existing techniques and future development direction.
Avatar Embodiment, Behavior Replication, and Kinematics in Virtual Reality

The reproduction of users’ nonverbal behavior through avatars is an important aspect for virtual reality (VR) simulations and an effective method to further enhance the user’s immersion and presence, to induce virtual body ownership, or to develop multi-user applications that support social interaction with high behavioral realism. This chapter focuses on the understanding of tracking and replicating human behavior in the context of real-time VR simulations. The chapter covers basic concepts of behavior tracking and replication of body motion, facial expressions, and gaze along with guidelines for the implementation. As a reader, you will be guided through the concepts and gather knowledge to include virtual embodiment in your projects.
The revival of values and meanings of the teacher education: Reflexive-creative approach

The purpose of the article is to determine the values and meanings of a modern teacher in the national and foreign professional education. The results of the research work: the article provides the theoretical analysis and the analysis of the research results which allowed: to clarify the contents of the concept â€˜creativityâ€™, to define it as a significant side of every future teacherâ€™s activity; to consider pedagogical values as norms; to determine the basis of creative interaction between a teacher and a future teacher; to determine the strategy of acquisition of both pedagogical values and meanings and methods of construction of knowledge bases on pedagogical disciplines; to identify the possibility of objective evaluation of the results of pedagogical education; to reveal that a lecture is the main organisational form of pedagogical education both in Russian and foreign experience. Conclusions and recommendations: the socio-pedagogical, group and personal pedagogical values of any future teacher are determined.&#x0D; Keywords: creativity, pedagogical education, reflection, sense, value.
Intelligent computational techniques for multimodal data

This guest editorial introduces the special issue on “Intelligent Computational Techniques for Multimodal Data”. The goal of this special issue was to solve a variety of real-life problems which have uncertainty, imprecision, vagueness, resulting in high performance applications or prototypes for real time system. The special issue touched different hot topics related to Computer Vision, Computational Biology, Multimedia data mining, High-dimensional multimedia data, Deep convolution network, Deep semantic preserving hashing, High-dimensional multimedia classification, Deep CNN and extended residual units, particle swarm optimization, Cyberbullying detection on social multimedia, Multimedia detection algorithm of malicious nodes, Multimedia based fast face recognition, Diversifying personalized mobile multimedia application, Multimedia image edge extraction algorithm, Rumour veracity detection.
The Attack Case of ECDSA on Blockchain Based on Improved Simple Power Analysis

Blockchain is an emerging distributed computing technology of de-centralization. The cryptography is used to ensure integrity, anonymity, privacy and immutability. Security of blockchain rely on cryptographic algorithms. However, cryptographic primitives typically get broken or weakened due to increase computational power and advanced cryptanalysis of the side channel methods. The cryptographic algorithms of blockchain will face side channel attacks. In this paper, we present the systematic analysis of threats on broken digital signature of the transaction data. And an improved SPA against ECDSA is presented with the power feature model in this paper. An attack case is given, and the private key of ECDSA can be recovered by using the proposed attack method with a power trace. Then the countermeasure of equivalent power consumption at atomic level is given by adding empty operations in point doubling and addition operations for hardware devices of blockchain.
Ham-Sandwich Cuts and Center Transversals in Subspaces

The Ham-Sandwich theorem is a well-known result in geometry. It states that any d mass distributions in R^d can be simultaneously bisected by a hyperplane. The result is tight, that is, there are examples of d+1 mass distributions that cannot be simultaneously bisected by a single hyperplane. In this abstract we will study the following question: given a continuous assignment of mass distributions to certain subsets of R^d, is there a subset on which we can bisect more masses than what is guaranteed by the Ham-Sandwich theorem?We investigate two types of subsets. The first type are linear subspaces of R^d, i.e., k-dimensional flats containing the origin. We show that for any continuous assignment of d mass distributions to the k-dimensional linear subspaces of R^d, there is always a subspace on which we can simultaneously bisect the images of all d assignments. We extend this result to center transversals, a generalization of Ham-Sandwich cuts. As for Ham-Sandwich cuts, we further show that for d-k+2 masses, we can choose k-1 of the vectors defining the k-dimensional subspace in which the solution lies.The second type of subsets we consider are subsets that are determined by families of n hyperplanes in R^d. Also in this case, we find a Ham-Sandwich-type result. In an attempt to solve a conjecture by Langerman about bisections with several cuts, we show that our underlying topological result can be used to prove this conjecture in a relaxed setting.
Safe Policy Search Using Gaussian Process Models

We propose a method to optimise the parameters of a policy which will be used to safely perform a given task in a data-efficient manner. We train a Gaussian process model to capture the system dynamics, based on the PILCO framework. The model has useful analytic properties, which allow closed form computation of error gradients and the probability of violating given state space constraints. Even during training, only policies that are deemed safe are implemented on the real system, minimising the risk of catastrophic failure.
Niching Particle Swarm Optimizer with Entropy-Based Exploration Strategy for Global Optimization

As a kind of evolutionary algorithms, particle swarm optimization is famous for its simplicity and efficiency in optimization. However, for complex problems, PSO is prone to be trapped into the local optima. To address this issue, a particle swarm optimizer with niching strategy and entropy-based exploration strategy (PSO-NE) is proposed in this paper. To be specific, on one hand, a distance based niching strategy and the competitive learning strategy are adopted to design the exploitation in PSO-NE; on the other hand, the exploration in PSO-NE is achieved by an entropy based exploring strategy. With such kind of designs, the exploitation and exploration in PSO-NE can be dependently adjusted, which is beneficial for balancing these two factors. To validate the effectiveness of the proposed algorithm, extensive experiments have been conducted based on 28 benchmarks from CEC’ 2013. The proposed algorithm shows its competitive performance with comparing to six other typical variants of PSO.
A Geometric Perspective on the Transferability of Adversarial Directions

State-of-the-art machine learning models frequently misclassify inputs that have been perturbed in an adversarial manner. Adversarial perturbations generated for a given input and a specific classifier often seem to be effective on other inputs and even different classifiers. In other words, adversarial perturbations seem to transfer between different inputs, models, and even different neural network architectures. In this work, we show that in the context of linear classifiers and two-layer ReLU networks, there provably exist directions that give rise to adversarial perturbations for many classifiers and data points simultaneously. We show that these ``transferable adversarial directions'' are guaranteed to exist for linear separators of a given set, and will exist with high probability for linear classifiers trained on independent sets drawn from the same distribution. We extend our results to large classes of two-layer ReLU networks. We further show that adversarial directions for ReLU networks transfer to linear classifiers while the reverse need not hold, suggesting that adversarial perturbations for more complex models are more likely to transfer to other classifiers. We validate our findings empirically, even for deeper ReLU networks.
User Data as Public Resource: Implications for Social Media Regulation

Revelations about the misuse and insecurity of user data gathered by social media platforms have renewed discussions about how best to characterize property rights in user data. At the same time, revelations about the use of social media platforms to disseminate disinformation and hate speech have prompted debates over the need for government regulation to assure that these platforms serve the public interest. These debates often hinge on whether any of the established rationales for media regulation apply to social media. This paper argues that the public resource rationale that has been utilized in traditional media regulation in the U.S. applies to social media. The public resource rationale contends that, when a media outlet utilizes a public resource – such as the broadcast spectrum, or public rights of way – the outlet must abide by certain public interest obligations that may infringe upon its First Amendment rights. This paper argues that aggregate user data can be conceptualized as a public resource that triggers the application of a public interest regulatory framework to social media sites and other digital platforms that derive their revenue from the gathering, sharing, and monetization of massive aggregations of user data.
Weighted adjacent matrix for K-means clustering

K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.