Reviewerly/exHarmony huggingface dataset:
In Hardware We Trust

Data processing and communication in almost all electronic systems are based on Central Processing Units (CPUs). In order to guarantee confidentiality and integrity of the software running on a CPU, hardware-assisted security architectures are used. However, both the threat model and the non-functional platform requirements, i.e. performance and energy budget, differ when we go from high-end desktop computers and servers to low-end embedded devices that populate the internet of things (IoT). For high-end platforms, a relatively large energy budget is available to protect software against attacks. However, measures to optimize performance give rise to microarchitectural side-channel attacks. IoT devices, in contrast, are constrained in terms of energy consumption and do not incorporate the performance enhancements found in high-end CPUs. Hence, they are less likely to be susceptible to microarchitectural attacks, but give rise to physical attacks, exploiting, e.g., leakage in power consumption or through fault injection. Whereas previous work mostly concentrates on a specific architecture, this paper covers the whole spectrum of computing systems, comparing the corresponding hardware architectures, and most relevant threats.
"Document-while-doing": a documentation tool for Fab Lab environments

(2019). 'Document-while-doing': a documentation tool for Fab Lab environments. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 2019-2030.
Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort

Scientific Named Entity Referent Extraction is often more complicated than traditional Named Entity Recognition (NER). For example, in polymer science, chemical structure may be encoded in a variety of nonstandard naming conventions, and authors may refer to polymers with conventional names, commonly used names, labels (in lieu of longer names), synonyms, and acronyms. As a result, accurate scientific NER methods are often based on task-specific rules, which are difficult to develop and maintain, and are not easily generalized to other tasks and fields. Machine learning models require substantial expert-annotated data for training. Here we propose polyNER: a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based word vector classifier. Evaluation on materials science publications shows that the polyNER approach enables improved precision or recall relative to a state-of-the-art chemical entity extraction system at a dramatically lower cost: it required just two hours of expert time, rather than extensive and expensive rule engineering, to achieve that result. This result highlights the potential for human-computer partnership for constructing domain-specific scientific NER systems.
Correlation of Quadratic Boolean Functions: Cryptanalysis of All Versions of Full $$\mathsf {MORUS}$$

We show that the correlation of any quadratic Boolean function can be read out from its so-called disjoint quadratic form. We further propose a polynomial-time algorithm that can transform an arbitrary quadratic Boolean function into its disjoint quadratic form. With this algorithm, the exact correlation of quadratic Boolean functions can be computed efficiently. We apply this method to analyze the linear trails of $$\mathsf {MORUS}$$ (one of the seven finalists of the CAESAR competition), which are found with the help of a generic model for linear trails of $$\mathsf {MORUS}$$ -like key-stream generators. In our model, any tool for finding linear trails of block ciphers can be used to search for trails of $$\mathsf {MORUS}$$ -like key-stream generators. As a result, a set of trails with correlation $$2^{-38}$$ is identified for all versions of full $$\mathsf {MORUS}$$ , while the correlations of previously published best trails for $$\mathsf {MORUS}$$ -640 and $$\mathsf {MORUS}$$ -1280 are $$2^{-73}$$ and $$2^{-76}$$ respectively (ASIACRYPT 2018). This significantly improves the complexity of the attack on $$\mathsf {MORUS}$$ -1280-256 from $$2^{152}$$ to $$2^{76}$$ . These new trails also lead to the first distinguishing and message-recovery attacks on $$\mathsf {MORUS}$$ -640-128 and $$\mathsf {MORUS}$$ -1280-128 with surprisingly low complexities around $$2^{76}$$ . Moreover, we observe that the condition for exploiting these trails in an attack can be more relaxed than previously thought, which shows that the new trails are superior to previously published ones in terms of both correlation and the number of ciphertext blocks involved.
Improving Knowledge Sharing in Distributed Software Development

Distributed Software Development has become an established software development paradigm that provides several advantages but it presents significant challenges to share and understand the knowledge required for developing software.Organizations are expected to implement appropriate practices to address knowledge management.From the existing studies, it is been analyzed that there were problems of collaboration between distributed team members which effects knowledge sharing.Documentation problem (such as missing, poor and outdated documents) and knowledge vaporization (as much of the conversation and communication is done via chat and retrieving it later is a great headache) is a major challenge in Distributed Software Development in knowledge sharing.Our main objective is to improve knowledge sharing between distributed team members and prevent knowledge vaporization and reduced documentation problem that will help in improving software development process in a distributed environment.To eliminate these challenges we proposed a framework which deals with documentation and knowledge vaporization problems and evaluated it through industrial case study and evaluate the framework performance in real-life context where actually the problem arises, we conducted the interviews and analyzed the data using thematic analysis and SUS questioner we came to the conclusion on team members response that they are satisfied with our proposed solution and it improved their knowledge sharing process.Our intention was to improve the knowledge process with our proposed solution and the evaluation showed that we resolved these problems.
Improving the combination of results in the ensembles of prototype selectors

Prototype selection is one of the most common preprocessing tasks in data mining applications. The vast amounts of data that we must handle in practical problems render the removal of noisy, redundant or useless instances a convenient first step for any real-world application. Many algorithms have been proposed for prototype selection. For difficult problems, however, the use of only a single method would unlikely achieve the desired performance. Similar to the problem of classification, ensembles of prototype selectors have been proposed to overcome the limitations of single algorithms. In ensembles of prototype selectors, the usual combination method is based on a voting scheme coupled with an acceptance threshold. However, this method is suboptimal, because the relationships among the prototypes are not taken into account. In this paper, we propose a different approach, in which we consider not only the number of times every prototype has been selected but also the subsets of prototypes that are selected. With this additional information we develop GEEBIES, which is a new way of combining the results of ensembles of prototype selectors. In a large set of problems, we show that our proposal outperforms the standard boosting approach. A way of scaling up our method to large datasets is also proposed and experimentally tested.
Economic impact of energy saving techniques in cloud server

In recent years, lot of research has been carried in the field of cloud computing and distributed systems to investigate and understand their performance. Economic impact of energy consumption is of major concern for major companies. Cloud Computing companies (Google, Yahoo, Gaikai, ONLIVE, Amazon and eBay) use large data centers which are comprised of virtual computers that are placed globally and require a lot of power cost to maintain. Demand for energy consumption is increasing day by day in IT firms. Therefore, Cloud Computing companies face challenges towards the economic impact in terms of power costs. Energy consumption is dependent upon several factors, e.g., service level agreement, virtual machine selection techniques, optimization policies, workload types etc. We address a solution for the energy saving problem by enabling dynamic voltage and frequency scaling technique for gaming data centers. The dynamic voltage and frequency scaling technique is compared against non-power aware and static threshold detection techniques. This helps service providers to meet the quality of service and quality of experience constraints by meeting service level agreements. The CloudSim platform is used for implementation of the scenario in which game traces are used as a workload for testing the technique. Selection of better techniques can help gaming servers to save energy cost and maintain a better quality of service for users placed globally. The novelty of the work provides an opportunity to investigate which technique behaves better, i.e., dynamic, static or non-power aware. The results demonstrate that less energy is consumed by implementing a dynamic voltage and frequency approach in comparison with static threshold consolidation or non-power aware technique. Therefore, more economical quality of services could be provided to the end users.
Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.
A Closer Look Tells More

Face authentication is vulnerable to media-based virtual face forgery (MVFF) where adversaries display photos/videos or 3D virtual face models of victims to spoof face authentication systems. In this paper, we propose a liveness detection mechanism, called FaceCloseup, to protect the face authentication on mobile devices. FaceCloseup detects MVFF-based attacks by analyzing the distortion of face regions in a user's closeup facial videos captured by built-in camera on mobile device. It can detect MVFF-based attacks with an accuracy of 99.48%.
Nonparametric Bayesian Deep Networks with Local Competition

The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inference. To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition. In this context, our main technical innovation consists in an inferential setup that leverages solid arguments from Bayesian nonparametrics. We infer both the needed set of connections or locally competing sets of units, as well as the required floating-point precision for storing the network parameters. Specifically, we introduce auxiliary discrete latent variables representing which initial network components are actually needed for modeling the data at hand, and perform Bayesian inference over them by imposing appropriate stick-breaking priors. As we experimentally show using benchmark datasets, our approach yields networks with less computational footprint than the state-of-the-art, and with no compromises in predictive accuracy.
Doubly Semi-Implicit Variational Inference

We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference in models where the prior and the posterior can be expressed as an intractable infinite mixture of some analytic density with a highly flexible implicit mixing distribution. We provide a sandwich bound on the evidence lower bound (ELBO) objective that can be made arbitrarily tight. Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact. We evaluate DSIVI on a set of problems that benefit from implicit priors. In particular, we show that DSIVI gives rise to a simple modification of VampPrior, the current state-of-the-art prior for variational autoencoders, which improves its performance.
A compact network learning model for distribution regression

Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Our proposed distribution regression network (DRN) achieves higher prediction accuracies while using fewer parameters than traditional neural networks.
Advanced Tabu Search Algorithms for Bipartite Boolean Quadratic Programs Guided by Strategic Oscillation and Path Relinking

The bipartite Boolean quadratic programming problem (BBQP) is a generalization of the well-studied NP-hard Boolean quadratic programming problem and can be regarded as a unified model for many graph theoretic optimization problems, including maximum weight-induced subgraph problems, maximum weight biclique problems, matrix factorization problems, and maximum cut problems on bipartite graphs. This paper introduces three main algorithms for solving the BBQP, based on three variants of tabu search, the first two consisting of strategic oscillation–tabu search (SO-TS) algorithms, which use destructive and constructive procedures to guide the search into unexplored and promising areas. The third algorithm, whichDoes also incorporates the SO-TS algorithms as solution improvement methods, uses a path relinking (PR) algorithm that is capable of further enhancing search performance. Experimental results demonstrate that all three algorithms perform very effectively compared with the best methods in the literature, and the PR algorithm joined with tabu search is able to discover new best solutions for two-thirds of the large problem instances and match the previous best known solutions for the other instances. Additional analysis discloses the contributions of the key ingredients of each of the proposed algorithms.
What does Google recommend when you want to compare insurance offerings?

Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google’s top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.
EvalNE : a framework for evaluating network embeddings on link prediction

In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks. Link prediction is one of the most popular choices for evaluating the quality of network embeddings. However, the complexity of this task requires a carefully designed evaluation pipeline in order to provide consistent, reproducible and comparable results. EvalNE simplifies this process by providing automation and abstraction of tasks such as hyper-parameter tuning and model validation, edge sampling and negative edge sampling, computation of edge embeddings from node embeddings, and evaluation metrics. The toolbox allows for the evaluation of any off-the-shelf embedding method without the need to write extra code. Moreover, it can also be used for evaluating any other link prediction method, and integrates several link prediction heuristics as baselines.
Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K -nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.
Overview of ImageCLEFlifelog 2019: Solve My Life Puzzle and Lifelog Moment Retrieval.

This paper describes ImageCLEFlifelog 2019, the third edition of the Lifelog task. In this edition, the task was composed of twosubtasks (challenges): the Lifelog Moments Retrieval (LMRT) challengethat followed the same format as in the previous edition, and the SolveMy Life Puzzle (Puzzle), a brand new task that focused on rearranginglifelog moments in temporal order. ImageCLEFlifelog 2019 received noticeably higher submissions than the previous editions, with ten teamsparticipating resulting in a total number of 109 runs.
Deep hash for latent image retrieval

With the development of the era of internet, an increasing number of images flow into people’s daily life, and it’s really a challenge to quickly search interesting images in such a huge image database. The most advanced method is using a deep neural network to get hash code of images to achieve fast image retrieval at present. However, people usually use the single pooling method to screen the image pixels when designing the neural network, and some effective information of the image will be gradually lost due to the single pooling method with the number of network deepening. Aiming at this problem, this paper proposes a convolutional neural network combining multiple pooling methods to preserve the effective information of the image as much as possible. To verify the effectiveness of the proposed method, many experiments are carried out on the CIFRA-10, NUS-WIDE and MNIST datasets. The experimental results show that the proposed method is better than most existing hash-based image retrieval methods.
Security tokens: architecture, smart contract applications and illustrations using SAFE

Purpose Blockchain technologies have pervaded modern crowdfunding and capital sourcing through a variety of financial instruments implemented as smart contracts. Smart contracts provide a unique mechanism not only to create a unique one-of-a-type financial instrument, but also to enable unique innovations atop existing financial instruments due to underlying efficiencies. The smartness comes from the flexibility that programs provide which can create extremely unique financial instruments that are often complex to implement, yet easy to create, maintain through versioning, trade and destroy. The purpose of this paper is to describe the security token architecture as an application of smart contracts. Further, the author illustrates the implementation and design of a commonly used financial instrument known as Simple Agreement for Future Equity (SAFE) using the security token architecture proposed and smart contract functionality. The author then models the transaction using relational algebra, and, models the utility maximization. The author shows how on account of reduced information asymmetry between the investors and SAFE users (i.e. startups) utility is positive when smart contract-based security tokens are deployed for each state in the SAFE contract. Design/methodology/approach Using an existing well-adopted instrument called a SAFE contract, the author illustrates the architecture of a smart contract-based security token system. The author illustrates how different components of a SAFE contract can be implemented as a smart contract and discusses the advantages and disadvantages of applying blockchain-based smart contracts to design SAFE instruments. The author deploys two methods: a state space diagram to explain state transitions and a utility model to explain the utilities. Findings The key findings of this research study are the design of a security token architecture, which can be used to convert any the physical or contract-based financial instrument to a smart contract that runs on the blockchain. However, there are limitations to the implementation of the same which can be overcome. The model illustrates the positive utilities derived for all economic actors, i.e. the contractors, the utility providers, etc., in the market. Originality/value This paper is an original paper. For the very first time, the author explored the architecture of a security token system. Using a well-known financial instrument, namely the SAFE, the author describes various components, e.g. the four contracts that form SAFE and then model the utilities for the system.