Reviewerly/exHarmony huggingface dataset:
An Efficient and Secure Data Integrity Auditing Scheme with Traceability for Cloud-Based EMR

Cloud computing provides an effective way to manage and share massive medical data for Electronic Medical Record (EMR), hence establishing cloud-based EMR has attracted more and more attention. The data integrity and privacy issue in cloud-based EMR should be emphasized since users do not want their medical records to be damaged or disclosed to others. To address the concern in this paper, we propose an efficient and secure Data Integrity Auditing scheme with Traceability for cloud-based EMR (DIAT), which provides data integrity and privacy. We store multiple copies so that data can be recovered quickly from damage as long as one copy remains intact; meanwhile, data cannot be leaked to unauthorized entities since it is stored as ciphertext. For supporting data dynamics, we have designed a two-dimensional data structure, called Dynamic Mapping Hash Table (DMHT). It does not require large auxiliary validation information, nor does it affect the sequence numbers of other blocks. Moreover, data traceability is achieved by organizing all versions of a data block as a chain so that doctors are enabled to track the changes of patients condition in their records. In addition, formal security analysis and experiment results confirm that DIAT is provably secure and efficient.
Supervised approaches for explicit search result diversification

Diversification of web search results aims to promote documents with diverse content (i.e., covering different aspects of a query) to the top-ranked positions, to satisfy more users, enhance fairness and reduce bias. In this work, we focus on the explicit diversification methods, which assume that the query aspects are known at the diversification time, and leverage supervised learning methods to improve their performance in three different frameworks with different features and goals. First, in the LTRDiv framework, we focus on applying typical learning to rank (LTR) algorithms to obtain a ranking where each top-ranked document covers as many aspects as possible. We argue that such rankings optimize various diversification metrics (under certain assumptions), and hence, are likely to achieve diversity in practice. Second, in the AspectRanker framework, we apply LTR for ranking the aspects of a query with the goal of more accurately setting the aspect importance values for diversification. As features, we exploit several pre- and post-retrieval query performance predictors (QPPs) to estimate how well a given aspect is covered among the candidate documents. Finally, in the LmDiv framework, we cast the diversification problem into an alternative fusion task, namely, the supervised merging of rankings per query aspect. We again use QPPs computed over the candidate set for each aspect, and optimize an objective function that is tailored for the diversification goal. We conduct thorough comparative experiments using both the basic systems (based on the well-known BM25 matching function) and the best-performing systems (with more sophisticated retrieval methods) from previous TREC campaigns. Our findings reveal that the proposed frameworks, especially AspectRanker and LmDiv, outperform both non-diversified rankings and two strong diversification baselines (i.e., xQuAD and its variant) in terms of various effectiveness metrics.
A Qualitative Exploratory Study of the Knowledge, Forensic, and Legal Challenges from the Perspective of Police Cybercrime Specialists

Abstract The extant literature has demonstrated that there is a need for more empirical research on the subject of policing cybercrime. This article makes a contribution and offers a qualitative exploration of the problem of policing cybercrime from an international perspective. It structures the analysis around three main challenges: the knowledge, forensic, and legal challenges. A literature review supports the evidence that these issues are core challenges when policing cybercrime. Through 13 semi-structured interviews with cybercrime specialists from 8 countries, these three challenges are further investigated. A thematic analysis of the interviews, whilst confirming some of the previous literature findings, also outlines novel findings. Interview analysis shows that in some areas little recent progress has been made, in particular the recording of cybercrime. However, in other areas new creative solutions have been implemented, including filling the policing skills gap with civilian specialists. The article concludes by offering recommendations for addressing some of the challenges for policing cybercrime.
Virtual Reality, Augmented Reality, and Mixed Reality in Education

Virtual reality (VR), augmented reality (AR), and mixed reality (MR) are three different technologies developed in the last decades of the 20th century. They combine hardware and software solutions. They permit the creation of three-dimensional (3D) virtual worlds and virtual objects. This chapter describes how VR, MR, and AR technologies find positive application fields in educational environments. They support different learning styles, offering potential help in teaching and in learning paths.
Nonstandard conditionally specified models for nonignorable missing data

Significance We consider data-analysis settings where data are missing not at random. In these cases, the two basic modeling approaches are 1) pattern-mixture models, with separate distributions for missing data and observed data, and 2) selection models, with a distribution for the data preobservation and a missing-data mechanism that selects which data are observed. These two modeling approaches lead to distinct factorizations of the joint distribution of the observed-data and missing-data indicators. In this paper, we explore a third approach, apparently originally proposed by J. W. Tukey as a remark in a discussion between Rubin and Hartigan, and reported by Holland in a two-page note, which has been so far neglected.
Explicit song lyrics detection with subword-enriched word embeddings

In this paper, we investigate the problem of automatically detecting explicit song lyrics, i.e., determining if the lyrics of a given song could be offensive or unsuitable for children. The problem can be framed as a binary classification task, and in this work we propose to tackle it with the fastText classifier, an efficient linear classification model leveraging a peculiar distributional text representation that, by exploiting subword information in building the embeddings of the words, enables to cope with words not seen at training time. We assess the performance of the fastText classifier and word representations with a lyrics dataset of over 800K songs, annotated with explicit information, that we assembled from publicly available resources. The evaluation shows that the fastText classifier is effective for explicit lyrics detection, substantially outperforming a reference approach for the task, and that the subword information effectively contributes to this result.
Economics of Blockchain Storage

Miners in a blockchain system are suffering from the ever-increasing storage costs, which in general have not been properly compensated by the users' transaction fees. In the long run, this may lead to less participation of miners and jeopardize the blockchain security. In this paper, we study the economics of blockchain storage and identify the incentive issues related to this storage cost problem. More specifically, we model the interactions among users (who generate transactions) and miners in two stages, where the users set the transaction fees in Stage 1, and the miners select which transactions to include in Stage 2. Through characterizing the Nash equilibrium of the two-stage game, we find that the transaction fees indeed cannot cover the storage costs under the current practice in general, due to the negative externality and the unfair delay-based pricing. We also identify that a longer block interval can alleviate the concern by raising the transactions fees at the expense of larger delay.
EXplainable AI (XAI) approach to image captioning

This article presents an eXplainable AI (XAI) approach to image captioning. Recently, deep learning techniques have been intensively used to this task with relatively good performance. Due to the ‘black-box’ paradigm of deep learning, however, existing approaches are unable to provide clues to explain the reasons why specific words have been selected when generating captions for given images, hence leading to generate absurd captions occasionally. To overcome this problem, this article proposes an explainable image captioning model, which provides a visual link between the region of an object (or a concept) in the given image and the particular word (or phrase) in the generated sentence. The model has been evaluated with two datasets, MSCOCO and Flickr30K, and both quantitative and qualitative results are presented to show the effectiveness of the proposed model.
Anytime Frequent Itemset Mining of Transactional Data Streams

Mining frequent itemsets from transactional data streams has become very essential in today's world with many applications such as stock market analysis, retail chain analysis, web log analysis, etc. Various algorithms have been proposed to efficiently mine single-port and multi-port transactional streams within the constraints of limited time and memory. However, all of them are budget algorithms, i.e., they are not capable of handling varying inter-arrival rate of transactions and high speed streams. They are constrained by a maximum limit to the inter-arrival rate of transactions, beyond which they fail to process. Also, these algorithms are not capable of giving immediate mining results, even with compromised accuracy if required. The above two properties characterize an anytime algorithm. We propose AnyFI, which is the first anytime frequent itemset mining algorithm for data streams. AnyFI uses a novel data structure - BFI-forest, which is capable of handling transactions arriving at variable rate. It maintains itemsets in BFI-forest in such a way that it can give a mining result almost immediately when the time allowance to mine is very less and can refine its accuracy with increase in time allowance. We also propose MPAnyFI which extends AnyFI into a parallel framework for anytime frequent itemset mining of multi-port data streams over commodity clusters. It uses AnyFI at each computing node of the cluster. Our extensive experimental analysis shows that AnyFI can handle high stream speeds close to 60,000 trans/sec with recall close to 100%. They also show the efficiency of MPAnyFI.
Humour in multimodal times

Abstract WhatsApp groups have become a popular way of communication among relatives, colleagues, friends and acquaintances regardless of different backgrounds, such as culture, education or age. When conveyed in digital settings, humour is usually evoked through multimodal elements that can modify the illocutionary force of the text or send complete hilarious messages that sparkle conversations. This paper seeks to analyse the expression of humour in a WhatsApp group made up of users over 65, a cohort that has not yet attracted considerable attention from research in the field. This is part of a broader research project that aims to investigate communicative exchanges in WhatsApp groups from a cross-generational perspective.
The Use of Evolutionary Methods for the Determination of a DC Motor and Drive Parameters Based on the Current and Angular Speed Response

Determination of the seven parameters of a Direct Current (DC) motor and drive is presented, based on the speed and current step responses. The method is extended for the motor and drive parameter determination in the case of a controlled drive. The influence of a speed controller on the responses is considered in the motor model with the use of the measured voltage. Current limitation of the supply unit is also considered in the DC motor model. For parameter determination, a motor model is used, which is determined with two coupled differential equations. Euler’s first-order and Runge–Kutta fourth-order methods are used for the motor model simulations. For parameter determination, evolutionary methods are used and compared to each other. Methods used are Genetic Algorithm, Differential Evolutions with two strategies, Teaching–Learning-Based Optimization, and Artificial Bee Colony. To improve results, deviation of the motor model simulation time is used and Memory Assistance with three different approaches is analyzed to shorten the calculation time. The tests showed that Differential Evolution (DE)/rand/1/exp is the most appropriate for the presented problem. The division of the motor model simulation time improves the results. For the presented problem, short-term memory assistance can be suggested for calculation time reduction.
The Challenge of Augmented Reality in Surgery

Imaging has revolutionized surgery over the last 50 years. Diagnostic imaging is a key tool for deciding to perform surgery during disease management; intraoperative imaging is one of the primary drivers for minimally invasive surgery (MIS), and postoperative imaging enables effective follow-up and patient monitoring. However, notably, there is still relatively little interchange of information or imaging modality fusion between these different clinical pathway stages. This book chapter provides a critique of existing augmented reality (AR) methods or application studies described in the literature using relevant examples. The aim is not to provide a comprehensive review, but rather to give an indication of the clinical areas in which AR has been proposed, to begin to explain the lack of clinical systems and to provide some clear guidelines to those intending pursue research in this area.
Does Fiat-Shamir Require a Cryptographic Hash Function?

The Fiat-Shamir transform is a general method for reducing interaction in public-coin protocols by replacing the random verifier messages with deterministic hashes of the protocol transcript. The soundness of this transformation is usually heuristic and lacks a formal security proof. Instead, to argue security, one can rely on the random oracle methodology, which informally states that whenever a random oracle soundly instantiates Fiat-Shamir, a hash function that is "sufficiently unstructured" (such as fixed-length SHA-2) should suffice. Finally, for some special interactive protocols, it is known how to (1) isolate a concrete security property of a hash function that suffices to instantiate Fiat-Shamir and (2) build a hash function satisfying this property under a cryptographic assumption such as Learning with Errors. In this work, we abandon this methodology and ask whether Fiat-Shamir truly requires a cryptographic hash function. Perhaps surprisingly, we show that in two of its most common applications—building signature schemes as well as (general-purpose) non-interactive zero-knowledge arguments—there are sound Fiat-Shamir instantiations using extremely simple and non-cryptographic hash functions such as sum-mod-p or bit decomposition. In some cases, we make idealized assumptions (i.e., we invoke the generic group model), while in others, we prove soundness in the plain model. On the negative side, we also identify important cases in which a cryptographic hash function is provably necessary to instantiate Fiat-Shamir. We hope this work leads to an improved understanding of the precise role of the hash function in the Fiat-Shamir transformation.
PaDE-NPC: Parameter Adaptive Differential Evolution With Novel Parameter Control for Single-Objective Optimization

Single objective real-parameter optimization problems exist in many areas of the real world, and Differential Evolution (DE) is a powerful population based stochastic optimization approach for tackling such problems. There are many different mutation strategies mentioned in the literature, and each of them has its own advantage. In this paper, we propose combined mutation strategies which can make a full use of the advantage of each mutation strategy regarding a population diversity indicator during the evolution. Furthermore, Novel Parameter Control (NPC) for the three control parameters including the scale factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> , crossover rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CR$ </tex-math></inline-formula> and population size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PS$ </tex-math></inline-formula> are also proposed in the paper. Different from employing the fitness value as a weight in recent proposed state-of-the-art DE variants, our PaDE-NPC algorithm can tackle a large optimization problems especially for those the fitness differences are unavailable; Moreover, a platform based population size reduction scheme is also involved in the NPC, which can get a better perception of the landscape at the early stage of the evolution while obtaining a balance between exploration and exploitation in the later part of the evolution. The novel PaDE-NPC algorithm is verified under 58 benchmark functions from CEC2013 and CEC2017 test suits for real-parameter optimization competitions and experiment results show that our proposed PaDE-NPC algorithm outperforms these recently proposed powerful DE variants.
SeGShare: Secure Group File Sharing in the Cloud using Enclaves

File sharing applications using cloud storage are increasingly popular for personal and business use. Due to data protection concerns, end-to-end encryption is often a desired feature of these applications. Many attempts at designing cryptographic solutions fail to be adopted due to missing relevant features. We present SeGShare, a new architecture for end-to-end encrypted, group-based file sharing using trusted execution environments (TEE), e.g., Intel SGX. SeGShare is the first solution to protect the confidentiality and integrity of all data and management files; enforce immediate permission and membership revocations; support deduplication; and mitigate rollback attacks. Next to authentication, authorization and file system management, our implementation features an optimized TLS layer that enables high throughput and low latency. The encryption overhead of our implementation is extremely small in computation and storage resources. Our enclave code comprises less than 8500 lines of code enabling efficient mitigation of common pitfalls in deploying code to TEEs.
Evaluation of factors affecting the decision to adopt blockchain technology: A logistics company case study using Fuzzy DEMATEL

Blockchain practices have been attracting attention in industries other than financial services, since blockchain is not only an information technology, but also an institutional technology owing to its new currency economics and distributed structures. Today, supply chains, power, and food/agriculture have emerged as promising areas in terms of their potential to incorporate blockchain technology for improving processes and reducing costs. Logistics corporations, especially, have been concentrating on developing efficiency in integrated data, fleet management, and communication issues, to achieve cost advantages. Experts from a well-known logistics company in Turkey contributed to our study by helping to assess critical factors for successful blockchain technology implementation. Our research topic included determining whether blockchain technology is suitable for this company. Fuzzy decision-making trial and evaluation laboratory (DEMATEL) was used to determine and evaluate the critical factors to encourage blockchain technology adoption, based on the company’s requirements. For the company experts, the factors affecting the decision to adopt blockchain technology were, in order of priority: cryptocurrency, instant money transfer, privacy, real time processing, smart contract, security, authentication, transparency, immutability, traceability, distributed ledger, reduced delays, and peer-to-peer networks.
Large-Scale Evaluation of Keyphrase Extraction Models

Keyphrase extraction models are usually evaluated under different, not directly comparable, experimental setups. As a result, it remains unclear how well proposed models actually perform, and how they compare to each other. In this work, we address this issue by presenting a systematic large-scale analysis of state-of-the-art keyphrase extraction models involving multiple benchmark datasets from various sources and domains. Our main results reveal that state-of-the-art models are in fact still challenged by simple baselines on some datasets. We also present new insights about the impact of using author- or reader-assigned keyphrases as a proxy for gold standard, and give recommendations for strong baselines and reliable benchmark datasets.
Improvement of evolution process of dandelion algorithm with extreme learning machine for global optimization problems

Dandelion Algorithm (DA) is a novel swarm intelligent optimization algorithm. In evolutionary process of DA, the quality of the seeds generated by dandelions is uneven, and the excellent seeds are expected to be retained and evaluated, while the poor seeds should be discarded without evaluation. In order to determine whether a seed is excellent or not, an improvement of evolution process of dandelion algorithm with extreme learning machine (ELMDA) is proposed in this paper. In ELMDA, firstly, the dandelion population can be partitioned into excellent dandelions and poor dandelions based on fitness values. Then, the excellent dandelions and poor dandelions are assigned corresponding labels (i.e. +1 if excellent or -1 if poor), which can be regarded as a training set, and the training model is built based on ELM. Finally, the model is applied to classify the seeds as excellent or poor, and the excellent seeds are chosen to participate in evolution process. Meanwhile, the robustness of the proposed algorithm is analyzed in this paper. Experimental results performed on test functions show that the proposed algorithm is competitive to its peers. Moreover, the proposed algorithm is demonstrated on three engineering designed problems, and the results indicate that the proposed algorithm has better performance in solving them.