Reviewerly/exHarmony huggingface dataset:
ROSE: Robust Searchable Encryption With Forward and Backward Security

Dynamic searchable symmetric encryption (DSSE) has been widely recognized as a promising technique to delegate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">update</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">search</monospace> queries over an outsourced database to an untrusted server while guaranteeing the privacy of data. Many efforts on DSSE have been devoted to obtaining a good tradeoff between security and performance. However, it appears that all existing DSSE works miss studying on what will happen if the DSSE client issues irrational <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">update</monospace> queries carelessly, such as duplicate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">update</monospace> queries and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delete</monospace> queries to remove non-existent entries (that have been considered by many popular database system in the setting of plaintext). In this scenario, we find that (1) most prior works lose their claimed correctness or security, and (2) no single approach can achieve correctness, forward and backward security, and practical performance at the same time. To address this problem, we study for the first time the notion of robustness of DSSE. Generally, we say that a DSSE scheme is robust if it can keep the same correctness and security even in the case of misoperations. Then, we introduce a new cryptographic primitive named key-updatable pseudo-random function and apply this primitive to constructing ROSE, a robust DSSE scheme with forward and backward security. Finally, we demonstrate the efficiency of ROSE and give the experimental comparisons.
A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context

In recent years, big data in health care is commonly used for the prediction of diseases. The most common cancer is breast cancer infections of metropolitan Indian women as well as in women worldwide with a broadly factor occurrence among nations and regions. According to WHO, among 14% of all cancer tumours in women breast cancer is well-known cancer in women in India also. Few researches have been done on breast cancer prediction on Big data. Big data is now triggering a revolution in healthcare, resulting in better and more optimized outcomes. Rapid technological advancements have increased data generation; EHR (Electronic Health Record) systems produce a massive amount of patient-level data. In the healthcare industry, applications of big data will help to improve outcomes. However, the traditional prediction models have less efficiency in terms of accuracy and error rate. This review article is about the comparative assessment of complex data mining, machine learning, deep learning models used for identifying breast cancer because accuracy rate of any particular algorithm depends on various factors such as implementation framework, datasets(small or large),types of dataset used(attribute based or image based)etc. Aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, Because “Early detection is the key to prevention-in case of any cancer”.
Blockchain governance

Blockchain is probably best known as a technology that underpins bitcoin cryptocurrency, taking records (e.g., confirmed financial transactions) and placing them into 'blocks', which are linked to prior blocks - forming a chronological 'chain' of blocks. However, bitcoin blockchain is only one instantiation of blockchain technology and there exist a few qualitative analyses addressing instantiations of blockchain technology. The aim of this study was two-fold: 1) to understand the difference between bitcoin and blockchain; 2) to delineate the need (and role) of governance in blockchain technology. First, fundamental relationships (and differences) between bitcoin and blockchain are presented. Second, drawing on societal blockchain technology concerns, a key element (i.e., governance) and its role in shaping blockchain technology is suggested. This research concludes with possible areas of research (and research questions) that can enable realisation of blockchain governance along the areas philosophical, theoretical, axiological, methodological, axiomatic, method and application dimensions.
The Signature-Based Model for Early Detection of Sepsis from Electronic Health Records in the Intensive Care Unit

Optimal feature selection leads to enhanced efficiency and accuracy when developing both supervised and unsupervised machine-learning models.In this work, a new signature-based regression model is proposed to automatically identify a patient's risk of sepsis based on physiological data streams and to make a positive or negative prediction of sepsis for every time interval since admission to the intensive care unit.The gradient boosting machine algorithm that uses the features at the current time-points and the signature features extracted from the time-series to model the longitudinal effects of sepsis yields the utility function score of 0.360 (officially ranked 1st, team name: 'Can I get your Signature?') on the full test set.The signature method shows a systematic and competitive approach to model sepsis by learning from health data streams.
Selective Fairness in Recommendation via Prompts

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in \urlhttps://github.com/wyqing20/PFRec.
Smart Energy Management System: Blockchain-Based Smart Meters in Microgrids

Smart energy management systems have shown lately efficient solutions to optimize the energy consumption, maximize the energy productions, predict the demand response, and grid self-diagnosis. These systems give insights on energy behaviour within the electrical power grid, where the consumers and producers can have real-time key performance indicators related to the energy that help them to take decisions in order to optimize their consumption. The management of loads and sources is a critical component of the mentioned systems especially when it comes to decentralized energy production within the grid, which create a huge energy market, where prosumers can buy and sell green energy interfacing with a certified agency that approve these transactions. The goal of this paper is to de-sign a blockchain base energy management system which enable a peer-to-peer energy market, the key element of this design is the power meters and their inter-actions with the electrical grid not only as measurement devices but also as digital wallet, that enable the prosumers to sell and buy energy. This concept integrates all blockchain components including cybersecurity, bidding techniques and welfare optimization. A simulation of these optimization techniques, hashing algorithms and their interactions with the smart meters to generate smart contracts and to increase security according to the blockchain concept were proposed.
Lung cancer CT image generation from a free-form sketch using style-based pix2pix for data augmentation

Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative adversarial networks (GANs). However, applying a GAN as a data augmentation technique has not been explored, owing to the quality and diversity of the generated images. To promote such applications by generating diverse images, this study aims to generate free-form lesion images from tumor sketches using a pix2pix-based model, which is an image-to-image translation model derived from GAN. As pix2pix, which assumes one-to-one image generation, is unsuitable for data augmentation, we propose StylePix2pix, which is independently improved to allow one-to-many image generation. The proposed model introduces a mapping network and style blocks from StyleGAN. Image generation results based on 20 tumor sketches created by a physician demonstrated that the proposed method can reproduce tumors with complex shapes. Additionally, the one-to-many image generation of StylePix2pix suggests effectiveness in data-augmentation applications.
Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency

Online class-incremental continual learning aims to learn new classes continually from a never-ending and single-pass data stream, while not forgetting the learned knowledge of old classes. Existing replay-based methods have shown promising performance by storing a subset of old class data. Unfortunately, these methods only focus on selecting samples from the memory bank for replay and ignore the adequate exploration of semantic information in the single-pass data stream, leading to poor classification accuracy. In this paper, we propose a novel yet effective framework for online class-incremental continual learning, which considers not only the selection of stored samples, but also the full exploration of the data stream. Specifically, we propose a gradient-based sample selection strategy, which selects the stored samples whose gradients generated in the network are most interfered by the new incoming samples. We believe such samples are beneficial for updating the neural network based on back gradient propagation. More importantly, we seek to explore the semantic information between two different views of training images by maximizing their mutual information, which is conducive to the improvement of classification accuracy. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on a variety of benchmark datasets. Our code is available on https://github.com/YananGu/DVC.
How do English translations differ from non-translated English writings? A multi-feature statistical model for linguistic variation analysis

Abstract This paper discusses the debatable hypotheses of “Translation Universals”, i. e. the recurring common features of translated texts in relation to original utterances. We propose that, if translational language does have some distinctive linguistic features in contrast to non-translated writings in the same language, those differences should be statistically significant, consistently distributed and systematically co-occurring across registers and genres. Based on the balanced Corpus of Translational English (COTE) and its non-translated English counterpart, the Freiburg-LOB corpus of British English (FLOB), and by deploying a multi-feature statistical analysis on 96 lexical, syntactic and textual features, we try to pinpoint those distinctive features in translated English texts. We also propose that the stylo-statistical model developed in this study will be effective not only in analysing the translational variation of English but also be capable of clustering those variational features into a “translational” dimension which will facilitate a crosslinguistic comparison of translational languages (e. g. translational Chinese) to test the Translation Universals hypotheses.
A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method

Abstract Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset ( T ), false subset ( F ), and indeterminacy subset ( I ). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset ( I ). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.
A new hybrid method for Arabic multi-font text segmentation, and a reference corpus construction

In analytical systems of Arabic text recognition, segmentation is a critical and decisive stage. In this stage, words after being extracted from the text document are segmented into individual characters to allow the feature extraction and classification. The absence of standard corpus for printed Arabic text does not allow a good and objective comparison between different segmentation systems. In this paper, we propose two contributions. The first one is a multi-font reference corpus of printed Arabic text, in which we grouped all segmentation problems and which can be used as a reference to compare different segmentation systems. The second is a hybrid method for segmentation of printed multi-font Arabic text. The proposed method is based on two of the most known techniques in the field: contour based and Template matching techniques. This method is insensitive to the font variability and the characters overlapping. To evaluate the results of this method, we have studied, implemented and tested other methods in literature by using the proposed corpus. The experimental results show that our method gives better segmentation rates.
A 3D shape measurement method based on novel segmented quantization phase coding

Codeword is very important in phase unwrapping based on phase coding method. Each phase coding fringe is marked with a unique codeword and then used to determine the fringe order for absolute phase retrieval. However, the traditional phase coding method is limited by the number of codeword. To overcome this problem, a segmented quantization phase coding and decoding method is proposed to achieve absolute phase retrieval without reducing the difference between adjacent quantized phase values or increasing the quantization levels. The quantized phase is modulated by a specific coding sequence and then embedded into phase coding fringe patterns, thereby greatly improving the accuracy of the decoding. The coding creates multiple segments of the whole region and each segment embeds the special coding sequence. Through the corresponding decoding algorithm, the fringe order can be obtained, and following this, the absolute phase is obtained. Simulation and experimental results demonstrate the feasibility and superiority of the proposed method to measure both a single object and multiple isolated objects.
ULW-DMM: An Effective Topic Modeling Method for Microblog Short Text

With the popularity of social media, including micro-blog, mining effective information in short texts has become an increasingly important issue.However, due to the sparseness, high dimensionality and large amount of data, mining this information is a very challenging task.In this paper, we propose a method to extend the Dirichlet multinomial mixture (DMM) topic model by combining the user-LDA topic model based on internal data expansion with the potential feature vector representation of words trained on a very large external corpus (we refer to it as ULW-DMM).The experimental results show that the ULW-DMM model produces a relatively large improvement in topic consistency and classification tasks for topic modeling of microblog short texts.
Character n-Gram Embeddings to Improve RNN Language Models

This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the field of word embedding construction (Wieting et al. 2016). Our proposed method constructs word embeddings from character ngram embeddings and combines them with ordinary word embeddings. We demonstrate that the proposed method achieves the best perplexities on the language modeling datasets: Penn Treebank, WikiText-2, and WikiText-103. Moreover, we conduct experiments on application tasks: machine translation and headline generation. The experimental results indicate that our proposed method also positively affects these tasks
Scalable Fine-Grained Proofs for Formula Processing

We present a framework for processing formulas in automatic theorem provers, with generation of detailed proofs. The main components are a generic contextual recursion algorithm and an extensible set of inference rules. Clausification, skolemization, theory-specific simplifications, and expansion of ‘let’ expressions are instances of this framework. With suitable data structures, proof generation adds only a linear-time overhead, and proofs can be checked in linear time. We implemented the approach in the SMT solver veriT. This allowed us to dramatically simplify the code base while increasing the number of problems for which detailed proofs can be produced, which is important for independent checking and reconstruction in proof assistants. To validate the framework, we implemented proof reconstruction in Isabelle/HOL.
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation.

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on $k$ workers (PLAyBOOK), for asynchronous parallel BO. We demonstrate empirically the efficacy of PLAyBOOK and its variants on synthetic tasks and a real-world problem. We undertake a comparison between synchronous and asynchronous BO, and show that asynchronous BO often outperforms synchronous batch BO in both wall-clock time and number of function evaluations.
Mapping an Enterprise Network by Analyzing DNS Traffic

Enterprise networks are becoming more complex and dynamic, making it a challenge for network administrators to fully track what is potentially exposed to cyber attack. We develop an automated method to identify and classify organizational assets via analysis of just 0.1% of the enterprise traffic volume, specifically corresponding to DNS packets. We analyze live, real-time streams of DNS traffic from two organizations (a large University and a mid-sized Government Research Institute) to: (a) highlight how DNS query and response patterns differ between recursive resolvers, authoritative name servers, web-servers, and regular clients; (b) identify key attributes that can be extracted efficiently in real-time; and (c) develop an unsupervised machine learning model that can classify enterprise assets. Application of our method to the 10 Gbps live traffic streams from the two organizations yielded results that were verified by the respective IT departments, while also revealing new knowledge, attesting to the value provided by our automated system for mapping and tracking enterprise assets.
Inverse engineering of shortcut pulses for high fidelity initialization on qubits closely spaced in frequency

High-fidelity qubit initialization is of significance for efficient error correction in fault tolerant quantum algorithms.Combining two best worlds, speed and robustness, to achieve high-fidelity state preparation and manipulation is challenging in quantum systems, where qubits are closely spaced in frequency.Motivated by the concept of shortcut to adiabaticity, we theoretically propose the shortcut pulses via inverse engineering and further optimize the pulses with respect to systematic errors in frequency detuning and Rabi frequency.Such protocol, relevant to frequency selectivity, is applied to rare-earth ions qubit system, where the excitation of frequency-neighboring qubits should be prevented as well.Furthermore, comparison with adiabatic complex hyperbolic secant pulses shows that these dedicated initialization pulses can reduce the time that ions spend in the excited state by a factor of 6, which is important in coherence time limited systems to approach an error rate manageable by quantum error correction.The approach may also be applicable to superconducting qubits, and any other systems where qubits are addressed in frequency.
Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems

Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences.
Multi-Level Elasticity for Data Stream Processing

This paper investigates reactive elasticity in stream processing environments where the performance goal is to analyze large amounts of data with low latency and minimum resources. Working in the context of Apache Storm, we propose an elastic management strategy which modulates the parallelism degree of applications' components while explicitly addressing the hierarchy of execution containers (virtual machines, processes and threads). We show that provisioning the wrong kind of container may lead to performance degradation and propose a solution that provisions the least expensive container (with minimum resources) to increase performance. We describe our monitoring metrics and show how we take into account the specifics of an execution environment. We provide an experimental evaluation with real-world applications which validates the applicability of our approach.