Reviewerly/exHarmony huggingface dataset:
Generalising multistain immunohistochemistry tissue segmentation using end‐to‐end colour deconvolution deep neural networks

A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumour biopsies. Drug development requires a correlative analysis of various biomarkers. To enable that, tissue slides are manually annotated by pathologists, which is a tedious and error-prone task. Automation of this annotation process can improve accuracy and consistency while reducing workload and cost. The authors present a deep learning method to automatically segment digitised slide images with multiple stainings into compartments of tumour, healthy tissue, necrosis, and background. The method is based on using a fully convolutional neural network including a colour deconvolution segment learned end-to-end and helping the network to converge faster and deal with the dataset staining variability. They evaluate the performance of the proposed method using the F1 score, which is the harmonic mean between precision and recall. They report a testing F1 score of 0.88, 0.9, 0.8, and 0.99 for tumour, tissue, necrosis, and background, respectively. They address the task in the context of drug development where multiple stains exist and look into solutions for generalisations over these image populations. They also apply visualisation techniques to help understand the network decisions and gain more trust from pathologists.
A novel Approach for Privacy-Preserving Data Aggregation in Smart Grid

Over the last few years, the rapid development of smart grids and the deployment of smart metering infrastructures on a much larger scale have raised a crucial privacy concern related to the collection of real-time energy consumption data. For these purpose, data aggregation is a widely used technique for privacy preservation. Securing the data aggregation process is a challenge since smart grids are susceptible to many cyber security threats and attacks. Therefore, several approaches have been proposed to address this issue. However, many of them are vulnerable to various types of attacks and suffer from high communication and computation costs in addition to the use of complex key management schemes. To overcome this problem, we propose a secure and privacy-preserving data aggregation scheme based on homomorphic encryption. In this solution, we take advantage of the opportunities offered by ECEL-Gamal and its homomorphic property as well as the ECDSA and ECDHE algorithms to use them in order to aggregate privacy-preserving data. Through detailed analyses, we show that our proposed scheme can meet various security requirements and preserves user privacy. In addition, experiment results demonstrate that our scheme is more efficient than existing competing approaches in terms of communication and computation overhead.
Teaching Programming and Design-by-Contract

This paper summarizes the experience of teaching an introductory course to programming by using a correctness by construction approach at Innopolis University, Russian Federation. In this paper we claim that division in beginner and advanced groups improves the learning outcomes, present the discussion and the data that support the claim.
Standardization of file recovery classification and authentication

Digital forensics can no longer tolerate software that cannot be relied upon to perform specific functions such as file recovery. Indistinct and non-standardized results increase the risk of misinterpretation by digital forensic practitioners, and hinder automated correlation of file recovery results in forensic analysis and tool testing. Treating file recovery results in a clear, distinct manner helps reduce the risk of misunderstandings, incorrect assertions and, ultimately, miscarriages of justice. The root of this problem is a lack of clearly defined software requirements, which compels users and tool testers to make educated guesses and assumptions about how digital forensic tools work. To address this problem, this work applies the core forensic processes of classification, authentication and evaluation to file recovery. Specifically, this work defines a vocabulary for software developers, testers and practitioners to classify, authenticate, evaluate and present results of file recovery operations. This vocabulary can be used by software developers to normalize how file recovery is treated, improving clarity, testability and interoperability of results, and reducing the risk or mistakes in digital investigations. This work also proposes an inaugural set of requirements for applying this vocabulary to file recovery results, providing a foundation for further development by the digital forensic community. This work demonstrates how this vocabulary can be implemented using DFXML, and presents a normalized representation of file recovery results using the Cyber-investigation Analysis Standard Expression (CASE). To demonstrate the more generalized utility of this vocabulary, it is applied to recovery results from versioning file systems and SQLite databases. The formalized vocabulary and forensic methods developed in this work support tool validation as called for in the international standard ISO/IEC 27041 and required for accreditation under the international standard ISO 17025. This work also demonstrates how the European Network of Forensic Science Institutes (ENFSI) Guideline for Evaluative Reporting can be applied to express the results of file recovery classification, authentication and evaluation.
Document listing on repetitive collections with guaranteed performance

We consider document listing on string collections, that is, finding in which strings a given pattern appears. In particular, we focus on repetitive collections: a collection of size N over alphabet [1,σ] is composed of D copies of a string of size n, and s edits are applied on ranges of copies. We introduce the first document listing index with size O˜(n+s), precisely O((nlg⁡σ+slg2⁡N)lg⁡D) bits, and with useful worst-case time guarantees: Given a pattern of length m, the index reports the ndoc>0 strings where it appears in time O(mlg1+ϵ⁡N⋅ndoc), for any constant ϵ>0 (and tells in time O(mlg⁡N) if ndoc=0). Our technique is to augment a range data structure that is commonly used on grammar-based indexes, so that instead of retrieving all the pattern occurrences, it computes useful summaries on them. We show that the idea has independent interest: we introduce the first grammar-based index that, on a text T[1,N] with a grammar of size r, uses O(rlg⁡N) bits and counts the number of occurrences of a pattern P[1,m] in time O(m2+mlg2+ϵ⁡r), for any constant ϵ>0. We also give the first index using O(zlg⁡(N/z)lg⁡N) bits, where T is parsed by Lempel–Ziv into z phrases, counting occurrences in time O(mlg2+ϵ⁡N).
Speaker Recognition With Random Digit Strings Using Uncertainty Normalized HMM-Based i-Vectors

In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform channel and uncertainty compensation, and we propose a novel method for using the uncertainty in the i-vector estimates. The experiments on RSR2015 part III show that the proposed method attains 1.52% and 1.77% Equal Error Rate (EER) for male and female respectively, outperforming state-of-the-art methods such as x-vectors, trained on vast amounts of data. Furthermore, these results are attained by a single system trained entirely on RSR2015, and by a simple score-normalized cosine distance. Moreover, we show that the omission of channel compensation yields only a minor degradation in performance, meaning that the system attains state-of-the-art results even without recordings from multiple handsets per speaker for training or enrolment. Similar conclusions are drawn from our experiments on the RedDots corpus, where the same method is evaluated on phrases. Finally, we report results with bottleneck features and show that further improvement is attained when fusing them with spectral features.
DroneGraffiti

With advances in robotics and digital arts, new ways for artwork execution emerge. One such way is mural or graffiti drawing by flying robots [Katsu 2018; TsuruRobotics 2018; Vempati et al. 2018]. This is itself a novel approach, which only recently gained traction and moved to full or partial autonomous operations. Key hurdles of drone graffiti are complicated system setup and a long painting process, associated with that only one drone is available at a time. Here we present a multi-drone graffiti dispatch system tested on up to three actual aerial robots operating simultaneously in real-time. The developed task dispatch system proved to be robust and effective. Although the proposed system and approach is best demonstrated by artistic applications, it can be transferred to commercial applications that need precise aerial vehicle operation, such as commercial painting, cleaning, and non-destructive control.
Guard Placement Attacks on Path Selection Algorithms for Tor

Abstract The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack . We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84× higher than what it would be under Tor currently. Our findings indicate that existing location-based path selection algorithms allow guards to achieve disproportionately high selection probabilities relative to the cost required to run the guard. Finally, we propose and evaluate a generic defense mechanism that provably defends any path selection algorithm against guard placement attacks. We run our defense mechanism on each of the three path selection algorithms, and find that our mechanism significantly enhances the security of these algorithms against guard placement attacks with only minimal impact to the goals or performance of the original algorithms.
Privacy and Linkability of Mining in Zcash

With the growth in popularity for cryptocurrencies the need for privacy preserving blockchains is growing as well. Zcash is such a blockchain, providing transaction privacy through zero-knowledge proofs. In this paper we analyze transaction linkability in Zcash based on the currency minting transactions (mining). Using predictable usage patterns and clustering heuristics on mining transactions an attacker can link to publicly visible addresses over 84% of the volume of the transactions that use a ZK-proof. Since majority of Zcash transactions are not yet using ZK-proofs, we show that overall 95.5% of the total number of Zcash transactions are potentially linkable to public addresses by just observing the mining activity.
CerebroVis: Designing an Abstract yet Spatially Contextualized Cerebral Artery Network Visualization

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition, they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities, we designed CerebroVis, a novel abstract-yet spatially contextualized-cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/e5sxt.
An Improved Non-Interactive Zero-Knowledge Range Proof for Decentralized Applications

Blockchain is the core technology underlying the first decentralized cryptocurrency, Bitcoin, introduced by Nakamoto in 2008. Since then, blockchain technology has many more advancements that are being developed and experimented. In particular, recent blockchain platforms such as Ethereum offer general and executable scripts, namely smart contracts, that can be employed to develop decentralized applications (DApps) in many domains beyond payment. However, the transparency of blockchain data raises concerns for many applications that require high privacy level. Therefore, many privacy enhancing technologies have been applied to DApp development, including zero knowledge proof (ZKP). This paper focuses on a particular kind of ZKP, called zero knowledge range proof (ZKRP), that has been applied in blockchain-based payments for banks. ZKRP allows a user to convince other people that a secret value actually lies within an interval without revealing any information about the secret. Here we introduce a new ZKRP which has the following remarkable features: (1) Non-interactive: No communication is required between a user and a verifier during the proof. (2) Range-flexibility: There is no limitation on the lower bound and the upper bound of the range except that they are natural numbers. (3) Efficiency: Our scheme is modified from that of Pang et al. (2010), yet achieves better security and is more efficient than their scheme. We believe our new ZKRP can be beneficial to the development of DApps and can extend the application scope to more scenarios.
Construction of Parallel Piecewise-Linear Interval Models for Nonlinear Dynamical Objects

The paper is devoted to the developing of the methodological framework for modeling of nonlinear dynamical objects. Such a framework allows performing the coordinate transformation and formulating the algorithm of its usage to transform the series mathematical models of the nonlinear dynamical objects into the interval piecewise-defined linear models and then translate them into parallel ones. The abovementioned algorithm is based on reducing domains size where the nonlinearities of studied objects exist. We offer to reduce these domains by defining intervals on which nonlinear functions can be bounded on the bottom and on the top by linear functions. Thus, we suggest replacing nonlinear functions with their top and bottom piecewise-linear boundaries which are determined by minimization of the above-mentioned domains' areas. Previously suggested boundaries are used to define the transfer functions of the considered objects but one can use them for the stability analysis and the synthesis of closed-loop systems with desired dynamic as well. Then, we apply partial fraction decomposition of the transfer functions to perform transformation of the mathematical models into parallel form. Usage of the proposed approach is one of the ways to decrease calculation time and to improve multi-core CPU usage while simulation of nonlinear dynamical objects is being performed as well as to simplify the study of transients and steady-state modes of considered objects and to construct closed-loop control systems for a wide range of the nonlinear dynamical objects.
Simple noninterference from parametricity

In this paper we revisit the connection between parametricity and noninterference. Our primary contribution is a proof of noninterference for a polyvariant variation of the Dependency Core Calculus of in the Calculus of Constructions. The proof is modular: it leverages parametricity for the Calculus of Constructions and the encoding of data abstraction using existential types. This perspective gives rise to simple and understandable proofs of noninterference from parametricity. All our contributions have been mechanised in the Agda proof assistant.
Machine Learning for Feature Selection and Cluster Analysis in Drug Utilisation Research

Machine learning methods are increasingly used in health data mining. We describe current unsupervised learning methods for phenotyping and discovery and illustrate their application for detecting features and sub-groups related to drug use within a population. Patient representation or phenotyping and discovery is one of the main branches of health data analysis. Phenotyping concerns identifying features that are representative of the population from raw patient data. Discovery involves analysing these features, for example, to identify patterns in the population such as sub-groups and to predict outcomes. Most studies use unsupervised learning methods for phenotyping as they are suited for data-driven feature extraction. We describe some of the commonly used methods and demonstrate their use in feature selection followed by cluster analysis. Unsupervised learning methods can be used to extract the features of and identify sub-groups within specific populations. We demonstrate the potential of these methods and highlight the associated challenges, which researchers may find useful in understanding the suitability of these methods for analysing health data.
Multi-Objective Local Pollination-Based Gray Wolf Optimizer for Task Scheduling Heterogeneous Cloud Environment

The rebel of global networked resource is Cloud computing and it shared the data to the users easily. With the widespread availability of network technologies, the user requests increase day by day. Nowadays, the foremost complication in Cloud technology is task scheduling. The cargo position and arrangement of the tasks are the two important parameters in the Cloud domain, which can provide the Quality of Service (QoS). In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm. In the hybrid concept, Gray Wolf Optimizer (GWO) algorithm and Flower Pollination Algorithm (FPA) are combined and used. In the presence of GWO, the best searching factor is used to increase the convergence speed and FPA is used to distribute the data to the next packet of candidate solution using local pollination concept. Chaotic mapping and OBL are used to provide a suitable initial candidate for task solutions. Therefore, the experiments delivered better task scheduling results in low and high heterogeneities of physical machines. Ultimately, the comparison with the simulation results had shown the minimum convergence speed of makespan and energy consumption.
Personality Driven Multi-task Learning for Image Aesthetic Assessment

With the prevalence of convolutional neural networks (CNNs), assessing the aesthetics of an image has gained great advances recently. Individual users often have different aesthetic preferences on images, which we believe are mainly affected by their personality traits. However, most of the current aesthetics models predict a generic aesthetic score based on handcrafted and/or learned feature representations, which are unified and thus cannot reflect the individual differences during image aesthetic rating. In this paper, we propose an end-to-end personality driven multi-task deep learning model to address this problem. Firstly, both image aesthetics and personality traits are learned from the proposed multi-task model. Then the personality features are employed to modulate the aesthetics features, producing the optimal generic image aesthetics scores. The experimental results on two public databases show that the proposed method is superior to the state-of-the-art approaches.
Hyper dimension shuffle

In distributed query processing, data shuffle is one of the most costly operations. We examined scaling limitations to data shuffle that current systems and the research literature do not solve. As the number of input and output partitions increases, naïve shuffling will result in high fan-out and fan-in. There are practical limits to fan-out, as a consequence of limits on memory buffers, network ports and I/O handles. There are practical limits to fan-in because it multiplies the communication errors due to faults in commodity clusters impeding progress. Existing solutions that limit fan-out and fan-in do so at the cost of scaling quadratically in the number of nodes in the data flow graph. This dominates the costs of shuffling large datasets. We propose a novel algorithm called Hyper Dimension Shuffle that we have introduced in production in SCOPE, Microsoft's internal big data analytics system. Hyper Dimension Shuffle is inspired by the divide and conquer concept, and utilizes a recursive partitioner with intermediate aggregations. It yields quasilinear complexity of the shuffling graph with tight guarantees on fan-out and fan-in. We demonstrate how it avoids the shuffling graph blow-up of previous algorithms to shuffle at petabyte-scale efficiently on both synthetic benchmarks and real applications.
Global Filter–Wrapper method based on class-dependent correlation for text classification

Text classification suffers from the high dimensionality and sparseness of the feature space. Feature selection (FS) is known as an important stage of the pre-processing phase. Most recently, Point-wise Mutual Information (PMI), a common concept in information theory, has been used as an effective and widely adapted approach for FS. The FS method proposed in this paper uses a hybrid approach to propose a new global PMI-based FS method. In it, the advantages of both the filter approach and the wrapper approach are combined in a different way. In the first phase, the ranking-based filter approach is implemented for FS by applying the information gain method. In the second phase, the subset selection-based filter approach is implemented for FS by introducing the global PMI-based FS method designed based on two basic principles: (1) class-dependent assumption for computing the correlation between pairs of features, and (2) an embedded wrapper approach. The results showed that the hybrid proposed method can produce better results than the state-of-the-art FS methods in both classification performance and dimension reduction.