Reviewerly/exHarmony huggingface dataset:
Training Generative Adversarial Networks With Weights

The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training. Despite the continuous efforts and improvements, there are still open issues regarding their convergence properties. In this paper, we propose a simple training variation where suitable weights are defined and assist the training of the Generator. We provide theoretical arguments which indicate that the proposed algorithm is better than the baseline algorithm in the sense of creating a stronger Generator at each iteration. Performance results showed that the new algorithm is more accurate and converges faster in both synthetic and image datasets resulting in improvements ranging between 5% and 50%.
Quantifying Information Leakage of Deterministic Encryption

In order to protect user data while maintaining application functionality, encrypted databases can use specialized cryptography such as property-revealing encryption, which allows a property of the underlying plaintext values to be computed from the ciphertext. One example is deterministic encryption which ensures that the same plaintext encrypted under the same key will produce the same ciphertext. This technology enables clients to make queries on sensitive data hosted in a cloud server and has considerable potential to protect data. However, the security implications of deterministic encryption are not well understood.
Impediments to Introducing Continuous Integration for Model-Based Development in Industry

Model-based development and continuous integration each separately are methods to improve the productivity of development of complex modern software systems. We investigate industrial adoption of these two phenomena in combination, i.e., applying continuous integration practices in model-based development projects. Through semi-structured interviews, eleven engineers at three companies with different modelling practices share their views on perceived and experienced impediments to this adoption. We find some cases in which this introduction is undesired and expected to not be beneficial. For other cases, we find and categorize several impediments and discuss how they are dealt with in industrial practice. Model synchronization and tool interoperability are found the most challenging to overcome and the ways in which they are circumvented in practice are detrimental for introducing continuous integration.
A Framework for Efficient Lattice-Based DAA

Currently standardized Direct Anonymous Attestation (DAA) schemes have their security based on the factoring and the discrete logarithm problems, and are therefore insecure against quantum attackers. This paper presents a quantum-safe lattice-based Direct Anonymous Attestation protocol that can be suitable for inclusion in a future quantum-resistant TPM. The security of our proposed scheme is proved in the Universal Composability (UC) model under the assumed hardness of the Ring-SIS, Ring-LWE, and NTRU problems. The signature size of our proposed DAA scheme is around 2MB, which is (at least) two orders of magnitude smaller compared to existing post-quantum DAA schemes.
Integrated multimodal disaster mitigation management for urban areas: a preliminary study for 2-d flood modeling

Abstract Flooding in urban areas is a common phenomenon in megacities due to the high population and settlement growth. Lack of knowledge from urban societies triggers them to intervene in the capacity of the river for housing. This impact affects various disasters, both quality and quantity of water. Kali Belik experiences flooding every year, although several solutions have been installed to mitigate, for example, Embung. Integrated multimodal disaster mitigation management is proposed to solve the problems and answer the research gaps. The preliminary research only focuses on flood analysis. The analysis uses 2-D modeling with HEC-RAS. The study concludes that the main factor of flooding in the research area is decreasing river capacity due to social behavior. Further research would be conducted to obtain a comprehensive understanding and solution by involving the government, academics, and society.
Explanation by Progressive Exaggeration

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g. saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a tuning knob to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.
Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities

The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.
Challenge and future trends of distributed measurement systems based on Blockchain technology in the European context

In these years, Blockchain technology is growing in importance thanks to cryptocurrencies. This technology offers many features such as the immutability of data stored in the data chain, the replicability of data in nodes, the management without a central repository, etc. These features are widely adopted also in other fields such as in distributed measurement systems. Although the Blockchain technology is widespread worldwide, there is still a legislative gap in the adoption of this technology in various states. This paper will examine the challenge and future trends on distributed measurement systems based on Blockchain technology in the European context. In particular, critical aspects of Blockchain technology will be examined under GDPR point of view and in particular in the management of data and privacy. The advantages and disadvantages of using Blockchain technology in applications such as the Internet of Things and smart contracts will be discussed and some aspects of legal metrology in Blockchain networks will be analyzed. At the end, future trends in Blockchain technology applied to distributed measurement systems will be shown.
Necessary and Sufficient Geometries for Gradient Methods

We study the impact of the constraint set and gradient geometry on the convergence of online and stochastic methods for convex optimization, providing a characterization of the geometries for which stochastic gradient and adaptive gradient methods are (minimax) optimal. In particular, we show that when the constraint set is quadratically convex, diagonally pre-conditioned stochastic gradient methods are minimax optimal. We further provide a converse that shows that when the constraints are not quadratically convex---for example, any $\ell_p$-ball for $p < 2$---the methods are far from optimal. Based on this, we can provide concrete recommendations for when one should use adaptive, mirror or stochastic gradient methods.
Explaining Neural Networks via Perturbing Important Learned Features.

Attributing the output of a neural network to the contribution of given input elements is one way of shedding light on the black box nature of neural networks. We propose a novel input feature attribution method that finds an input perturbation that maximally changes the output neuron by exclusively perturbing important hidden neurons (i.e. learned features) on the path to output neuron. Given an input, this is achieved by 1) pruning unimportant neurons, and subsequently 2) finding a local input perturbation that maximizes the output in the pruned network. Since our method considers the importance of hidden neurons (high-level features), it inherently considers interdependencies between multiple input elements, which is vital for input feature attribution. We propose PruneGrad, an efficient gradient-based solution for the pruning and perturbation steps of our method. The efficacy of our method is evaluated by quantitatively benchmarking against other attribution methods using 1) sanity checks, 2) pixel perturbation, and 3) Remove and Retrain (ROAR). Our results show that while most of the existing attribution methods are prone to fail or get mediocre results in at least one benchmark, our proposed method achieves state of the art results in all three benchmarks. The results are further supported by comparative visual evaluation.
Using Adapted JSEG Algorithm with Fuzzy C Mean for Segmentation and Counting of White Blood Cell and Nucleus Images

In this paper, an adapted unsupervised segmentation approach is proposed to fully automate the segmentation of white blood cells and their nuclei. Segmentation and counting of white blood cells from microscope images are challenging tasks, especially the segmentation of white blood cell nuclei from the cell wall and cytoplasm because of the need to consider intra-class variations arising from non-uniform illumination, stage of maturity, colour distribution, scale, and overlapped cells with other components of the blood. We propose the use of the JSEG algorithm based on colour-texture distribution, and adapted region growing using the Fuzzy C Mean to segment and count WBCs and their nuclei. First, colours in the image are quantized to represent differentiated regions in the image. Image pixel colours are then replaced by their corresponding colour class labels, thus forming a class-map of the image. A criterion for “good” segmentation using this spatial class-map is applied to local image windows resulting in J-images, which can be segmented using adapted region growing based on the Fuzzy C Mean algorithm. The Fuzzy C Mean is also employed for counting each white blood cell in images. Performance of the proposed method is evaluated on a combined dataset of 10 types of white blood cell with 200 digital images collected from 3 datasets. It achieves an average segmentation accuracy using four indices for WBC segmentation: jaccard distance, rand index, boundary detection error and F-value indices, 0.002, 0.93, 10.11, 0.93, respectively, while for WBC nuclei segmentation, it achieves indices values, 0.015, 0.88, 14.11, 0.90, respectively. The segmentation accuracy of the proposed method is also compared and benchmarked with the other existing techniques for segmentation of white blood cells over the same datasets and the results show that the proposed method is superior to other approaches.
Proof Systems In Blockchains: A Survey

Blockchain is a prime example of disruptive technology in multiple levels. With the advent of blockchains becomes obsolete the need for a mutually trusted third party acting as intermediary between agents which do not necessarily trust each other in transactions of any kind, including political or shareholder voting, crowdfunding, financial deals, logistics and supply chain management, and contract formulation. An integral part of the blockchain stack is the proof system, namely the mechanism efficiently verifying the claims of various blockchain stakeholders. Thus, trust is effectively established in a literally trustless environment with purely computational means. This is especially critical in the digital formulation of smart contracts where clauses are to be strictly upheld by intelligent agents. The most prominent proof systems recently proposed in the scientific literature are reviewed. Additionally, the applications of blockchain technology to smart contracts is discussed. The latter allows clause re-negotiation, increasing thus the flexibility factor in transactions. As a concrete example, a simple smart contract written in Solidity, a high level language for the Ethereum Virtual Machine, is presented.
Time-aware Adversarial Networks for Adapting Disease Progression Modeling

Modeling patient disease progression using their historical Electronic Health Records (EHRs) is critical to assist clinical decision making and provide prompt medications. Long-Short Term Memory (LSTM) has been widely applied to handle large-scale sequential data such as EHRs. However, when applied to EHRs, the standard LSTM encounters one major limitation: lack of the generalizability given the intricate heterogeneity among different patient groups. To overcome this limitation, we propose a domain adaptation (DA) framework named Time-aware Adversarial Networks, which combines two temporal LSTM-based recurrent components, a label predictor and a domain classifier, to extract an invariant feature representation across different patient groups (domains) through an adversarial learning process. The label predictor learns the temporal developing patterns of the disease while the domain classifier identifies the temporal changes of feature distributions across patient groups. We evaluate the proposed DA framework on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs among different patient groups across age, race, and gender. Our results demonstrate that our proposed DA framework can significantly improve the performance for the disease progression modeling while standard machine learning models are severely impacted by the data heterogeneity in EHRs.
Analysis of Spatial Domain Image Steganography Based on Pixel-Value Differencing Method

Image steganography is a technique of embedding secret information inside a cover image and transmits through a public channel without revealing the presence of a message. So that no one except the intended recipient can recognize secret message within the carrier image. Image steganography based on pixel value differencing (PVD) is one of the most important steganographic methods in spatial domain. To embed secret information, PVD method utilizes the difference value of each pixel pair on cover image. In this paper, we study the basic concepts of image steganography based on PVD method and latest research developments in image steganography using PVD. We also presented a detailed analysis of these techniques on the basis of some performance parameters such as payload capacity, imperceptibility, and robustness.
Amortized Bethe Free Energy Minimization for Learning MRFs

We propose to learn deep undirected graphical models (i.e., MRFs) with a non-ELBO objective for which we can calculate exact gradients. In particular, we optimize a saddle-point objective deriving from the Bethe free energy approximation to the partition function. Unlike much recent work in approximate inference, the derived objective requires no sampling, and can be efficiently computed even for very expressive MRFs. We furthermore amortize this optimization with trained inference networks. Experimentally, we find that the proposed approach compares favorably with loopy belief propagation, but is faster, and it allows for attaining better held out log likelihood than other recent approximate inference schemes.
Market dynamics, cyclical patterns and market states

Purpose In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors’ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies’ market but also highlights the existence of nonlinearity propriety as a stylized fact.
Emotional Text Analysis Based on Ensemble Learning of Three Different Classification Algorithms

In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logistic regression, support vector machine and K-Neighborhood algorithm. Compared with single classification algorithm, this algorithm shows better accuracy. The experimental results show that the model has good generalization performance and robustness.
Optimal Dislocation with Persistent Errors in Subquadratic Time

We study the problem of sorting N elements in the presence of persistent errors in comparisons: In this classical model, each comparison between two elements is wrong independently with some probability up to p, but repeating the same comparison gives always the same result. In this model, it is impossible to reliably compute a perfectly sorted permutation of the input elements. Rather, the quality of a sorting algorithm is often evaluated w.r.t. the maximum dislocation of the sequences it computes, namely, the maximum absolute difference between the position of an element in the returned sequence and the position of the same element in the perfectly sorted sequence. The best known algorithms for this problem have running time O(N2) and achieve, w.h.p., an optimal maximum dislocation of $O(\log N)$ for constant error probability p. Note that no algorithm can achieve maximum dislocation $o(\log N)$ w.h.p., regardless of its running time. In this work we present the first subquadratic time algorithm with optimal maximum dislocation. Our algorithm runs in $\widetilde {O}(N^{3/2})$ time and it guarantees $O(\log N)$ maximum dislocation with high probability for any p ≤ 1/16.
Gender and User Feedback: An Exploratory Study

Through app stores, users can submit feedback in the form of user reviews. Previous work has found that these reviews contain useful information such as user requirements and bug reports; and has presented approaches for automatically extracting this information. However, the differences in the feedback submitted by female and male users and its consequences with respect to algorithm bias have not been studied so far. In this paper, we take a step in this direction and report on an exploratory study that investigates 919 reviews from eight countries written by users with usernames identified by manual analysis as female or male. We contribute initial evidence of a possible imbalance in the number of female and males users writing app reviews. Additionally, while this disproportion exists, the analyzed feedback between female and male users is similar in terms of the expressed sentiment, content, rating, timing and length. These variables are commonly used when prioritizing user feedback for their later use during software evolution. Although we need a larger sample size to generalize our results, the similarities we report hint that gender bias is not a threat for feedback processing algorithms which exclusively take into account the characteristics studied in this work.
Building a Massive Corpus for Named Entity Recognition Using Free Open Data Sources

With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large enough dataset is available for training. Nonetheless, human-annotated datasets are often expensive to produce, especially when labels are fine-grained, as is the case of Named Entity Recognition (NER), a task that operates with labels on a word-level. In this paper, we propose a method to automatically generate labeled datasets for NER from public data sources by exploiting links and structured data from DBpedia and Wikipedia. Due to the massive size of these data sources, the resulting dataset - SESAME - is composed of millions of labeled sentences. We detail the method to generate the dataset, report relevant statistics, and design a baseline using a neural network, showing that our dataset helps building better NER predictors.
Memeing to learning: Exploring meaning-making in a language-learning chat group

Meaning-making in a new era increasingly defined by digital communication is a complicated ensemble of both verbal and non-verbal modes. Following this trend of conveying information multimodally, digital memes have become a new and viral way to communicate and make meaning. Memeing, as a multimodal and social practice, may hold potential for language learners. This study explores the impact of memes on second language learning, examining how memes support meaning-making and language learning during intercultural encounters in an online chat group. Using a case study design, this article focuses on three Chinese L2 learners varying in language proficiency, learning motivation, and memeing engagement. The naturally-occurring memeing data (from 2016 to 2018) of a university-level student-run Chinese-English intercultural chat group was collected and analyzed together with individual interviews. The findings show that meaning-making and language learning were empowered by memeing in the intercultural chat group through remixing linguacultural messages, constructing identities, and developing social connections. This study concludes with meme-related pedagogical activities and a discussion of future research.