Reviewerly/exHarmony huggingface dataset:
Sacred Excess: Organizational Ignorance in an Age of Toxic Data

Actors in data-intensive industries at times deliberately induce and reproduce organizational ignorance by engaging in over-production of data. This observation leads the paper to make two claims. First, members of these industries fetishize data excess not in order to reduce, but in order to reproduce and stabilize organizational ignorance. Second, in this process of fetishization, organizational ignorance gives rise to forms of collective effervescence similar to that found in totemistic religions. This effervescence allows organizational actors to draw defining lines around that which is marked as awe-inspiring, dangerous and off-limits, namely the sacred. In reviewing organizational ignorance from the perspective of the sacred, this paper proposes that, paradoxically, contemporary forms of data creation allow companies and industries to organize themselves around ignorance as opposed to the promise of knowledge and insight. The paper uses this theoretical proposal in order to outline the contours of an alternative ontology of organizational ignorance, one that understands this phenomenon in terms of excessive presence of data and information.
Indirect diffusion based level set evolution for image segmentation

In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart.
Remote-Sensing Image Denoising With Multi-Sourced Information

In this paper, a new multi-source-information-based method for remote-sensing image denoising is proposed. Different from the conventional denoising algorithm, the proposed method employs the features of similar reference images from the different band, different sensors, or from multi-temporal images when denoising a target image. The noise-free reference images as a prior is introduced into the denoising object function. The prior's information about the reference image is explored in two aspects: dictionary learning and edge-feature prediction. For dictionary learning, we improve the basis training process by incremental singular value decomposition. For edge-feature prediction, we construct the relationship between gradients of the target image and the reference image by linear ridge regression. The new denoising object function employs both the sparsity of the coefficient and the edge similarity between the target image and the reference image. We also present the optimization scheme for the proposed denoising model. Some typical cases based on different feature relations between a target image and a reference image are comprehensively discussed. Reasonably utilizing the similarity between the target image and the reference image, the proposed algorithm smooths out more noise and conserves more detail at the same time. Better performance of the proposed method is confirmed when compared with other state-of-the-art reference-based denoising methods.
Fuzzy Knowledge-Based Prediction Through Weighted Rule Interpolation

Fuzzy rule interpolation (FRI) facilitates approximate reasoning in fuzzy rule-based systems only with sparse knowledge available, remedying the limitation of conventional compositional rule of inference working with a dense rule base. Most of the existing FRI work assumes equal significance of the conditional attributes in the rules while performing interpolation. Recently, interesting techniques have been reported for achieving weighted interpolative reasoning. However, they are either particularly tailored to perform classification problems only or employ attribute weights that are obtained using additional information (rather than just the given rules), without integrating them with the associated FRI procedure. This paper presents a weighted rule interpolation scheme for performing prediction tasks by the use of fuzzy sparse knowledge only. The weights of rule conditional attributes are learned from a given rule base to discriminate the relative significance of each individual attribute and are integrated throughout the internal mechanism of the FRI process. This scheme is demonstrated using the popular scale and move transformation-based FRI for resolving prediction problems, systematically evaluated on 12 benchmark prediction tasks. The performance is compared with the relevant state-of-the-art FRI techniques, showing the efficacy of the proposed approach.
Image-attribute reciprocally guided attention network for pedestrian attribute recognition

Pedestrian attribute recognition is a fundamental yet challenging task in intelligent surveillance. Previous work focus on either discovering the discriminative visual features, or exploring the semantic dependencies among the attributes. However, the learning of comprehensive features by exploiting the relationship between the visual appearances and the attributes are not fully considered. To this end, we propose an image-attribute reciprocal guidance representation method, which investigates image-guided feature and attribute-guided feature to learn the comprehensive features for different attributes. To adaptively optimize the weight distributions of both features, we further develop a fusion attention mechanism. Besides, we present a focal cross-entropy loss to address the attribute imbalance problem. The whole framework is called Image-Attribute Reciprocally Guided Attention Network (IA2-Net), which is an end-to-end model. Extensive experiments on benchmark PETA and RAP datasets show that IA2-Net is very competitive with state-of-the art approaches.
Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification

This work takes the lead to study the aspect-level sentiment classification <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in the domain adaptation scenario</i> . Given a document of any domains, the model needs to figure out the sentiments with respect to fine-grained aspects in the documents. Two main challenges exist in this problem. One is to build a robust document modeling across domains; the other is to mine the domain-specific aspects and make use of the sentiment lexicon. In this paper, we propose a novel approach Neural Attentive model for cross-domain Aspect-level sentiment CLassification (NAACL), which leverages the benefits of the supervised deep neural network as well as the unsupervised probabilistic generative model to strengthen the representation learning. NAACL jointly learns two tasks: (i) a domain classifier, working on documents in both the source and target domains to recognize the domain information of input texts and transfer knowledge from the source domain to the target domain. In particular, a weakly supervised Latent Dirichlet Allocation model (wsLDA) is proposed to learn the domain-specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aspect</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sentiment lexicon</i> representations that are then used to calculate the aspect/lexicon-aware document representations via a multi-view attention mechanism; (ii) an aspect-level sentiment classifier, sharing the document modeling with the domain classifier. It makes use of the domain classification results and the aspect/sentiment-aware document representations to classify the aspect-level sentiment of the document in domain adaptation scenario. NAACL is evaluated on both English and Chinese datasets with the out-of-domain as well as in-domain setups. Quantitatively, the experiments demonstrate that NAACL has robust superiority over the compared methods in terms of classification accuracy and F1 score. The qualitative evaluation also shows that the proposed model is capable of reasonably paying attention to those words that are important to judge the sentiment polarity of the input text given an aspect.
Minimal digital chaotic system

Over the past few decades, many works have been devoted to designing simple chaotic systems based on analog electronic circuits. However, the same attention is not observed in digital chaotic systems. This paper presents a design of a digital chaotic system using a digit complement. This special case of fixed-point number representation allows us to reduce the silicon area and the number of logic elements to perform the arithmetic operations. The design presents a configurable number of bits, and it is based on the logistic map. The proposed circuit has been implemented on a reconfigurable hardware, FPGA Cyclone V, showing that the number of logic elements has been significantly reduced compared to other works in the literature.
Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations

We propose data generating structures which can be represented as the nonlinear autoregressive models with single and finite mixtures of scale mixtures of skew normal innovations. This class of models covers symmetric/asymmetric and light/heavy-tailed distributions, so provide a useful generalization of the symmetrical nonlinear autoregressive models. As semiparametric and nonparametric curve estimation are the approaches for exploring the structure of a nonlinear time series data set, in this article the semiparametric estimator for estimating the nonlinear function of the model is investigated based on the conditional least square method and nonparametric kernel approach. Also, an Expectation–Maximization-type algorithm to perform the maximum likelihood (ML) inference of unknown parameters of the model is proposed. Furthermore, some strong and weak consistency of the semiparametric estimator in this class of models are presented. Finally, to illustrate the usefulness of the proposed model, some simulation studies and an application to real data set are considered.
Modelling user attitudes using hierarchical sentiment-topic model

Uncovering the latent structure of various hotly discussed topics and the corresponding sentiments from different social media user groups (e.g., Twitter) is critical for helping organizations and governments understand how users feel about their services and facilities, along with the events happening around them. Although numerous research texts have explored sentiment analysis on the different aspects of a product, fewer works have focused on why users like or dislike those products. In this paper, a novel probabilistic model is proposed, namely, the Hierarchical User Sentiment Topic Model (HUSTM), to discover the hidden structure of topics and users while performing sentiment analysis in a unified way. The goal of the HUSTM is to hierarchically model the users' attitudes (opinions ) using different topic and sentiment information, including the positive, negative, and neutral. The experiment results on real-world data sets show the high quality of the hierarchy obtained by the HUSTM in comparison to those discovered using other state-of-the-art techniques.
Evaluating Login Challenges as aDefense Against Account Takeover

In this paper, we study the efficacy of login challenges at preventing account takeover, as well as evaluate the amount of friction these challenges create for normal users. These secondary authentication factors-presently deployed at Google, Microsoft, and other major identity providers as part of risk-aware authentication-trigger in response to a suspicious login or account recovery attempt. Using Google as a case study, we evaluate the effectiveness of fourteen device-based, delegation-based, knowledge-based, and resource-based challenges at preventing over 350,000 real-world hijacking attempts stemming from automated bots, phishers, and targeted attackers. We show that knowledge-based challenges prevent as few as 10% of hijacking attempts rooted in phishing and 73% of automated hijacking attempts. Device-based challenges provide the best protection, blocking over 94% of hijacking attempts rooted in phishing and 100% of automated hijacking attempts. We evaluate the usability limitations of each challenge based on a sample of 1.2M legitimate users. Our results illustrate that login challenges act as an important barrier to hijacking, but that friction in the process leads to 52% of legitimate users failing to sign-in-though 97% of users eventually access their account in a short period.
TSRAM: A time-saving k-degree anonymization method in social network

Social networks provide an attractive environment in order to have low cost and easy communication; however, analyzing huge amounts of produced data can considerably affect the user's privacy. In other words, an efficient algorithm should intelligently take the user's privacy into account while extracting data for useful information. In recent years, many studies have been conducted on social network privacy preservation for data publishing. However, the current algorithms are not one-time scan; that is, for every level of anonymization, the data set must be scanned again and this is a time-consuming operation. In order to address the above mentioned issue, the present research introduces a time-saving k-degree anonymization method in social network (TSRAM) that anonymizes the social network graph without having to rescan the data set for different levels of anonymity. First, it produces a tree from the data set. Then, the anonymized degree sequence of the graph is computed based on the tree. The proposed method employs an efficient approach to partition the node degrees. It takes advantage of partitioning the graph bottom-up nodes based on the anonymization levels. Moreover, it uses two effective criteria to increase the utility of the anonymized graph. Comparing to other similar techniques, the results show that TSRAM is effective, not only to make the degree sequence anonymization of the graph one-time scan, but also to preserve the utility of the anonymized graph.
Application of fuzzy logic and geometric average: A Cu sulfide deposits potential mapping case study from Kapsan Basin, DPR Korea

In this paper, two kinds of knowledge-driven methods, one using the fuzzy logic and another using geometric average, were applied to create the mineral potential maps for Cu sulfide deposits in the greenfield Kapsan Basin, DPR Korea. The ore geology studies for the study area have revealed that Cu sulfide deposits of hydrothermal genesis in Kapsan Basin are closely associated with Jurassic intrusions and faulting tectonics. Based on the conceptual model of Cu sulfide deposits and the available spatial datasets in the study area, we used five independent evidential maps for Cu sulfide deposits potential mapping. They include: (1) faults; (2) aeromagnetic anomaly; (3) Cu geochemical data; (4) Pb geochemical data; and (5) Zn geochemical data. The evidential map values were transformed into continuous values of the [0, 1] range using the non-linear fuzzy membership functions; logistic sigmoid and fuzzy Gaussian functions. Because the fuzzy logic and geometric average methods can use the same fuzzification methodology based on suitable membership functions, it is very economic and efficient to simultaneously apply two predictive models for mineral potential mapping of the study area. The preparation of these evidential layers were performed using spatial analyses supported in ArcGIS 10.4 GIS platform based on geological, geophysical and geochemical spatial datasets. The validation and comparative analysis results for the two predictive models demonstrated that most of known mineral occurrences are distributed in areas with high potential values. The target areas classified by the fuzzy logic occupy 15% of the study area and contain 78% of the total number of known mineral occurrences. Compared with the fuzzy logic, the resulting areas by the geometric average occupy 13% of the study area, but contain 93% of the total number of known mineral occurrences. Although the total number of known mineral occurrences is relatively low for the application of ROC (receiver operating characteristics) technique, the areas under the ROC curve (AUC) obtained by two predictive models were greater than 0.5, suggesting that both predictive models and their resulting potential maps are useful for evaluating the prospectivity of Cu sulfide deposits in Kapsan Basin.
BigBlueBot

Chatbots are becoming quite popular, with many brands developing conversational experiences using platforms such as IBM's Watson Assistant and Facebook Messenger. However, previous research reveals that users' expectations of what conversational agents can understand and do far outpace their actual technical capabilities. Our work seeks to bridge the gap between these expectations and reality by designing a fun learning experience with several goals: explaining how chatbots work by mapping utterances to a set of intents, teaching strategies for avoiding conversational breakdowns, and increasing desire to use chatbots by creating feelings of empathy toward them. Our experience, called BigBlueBot, consists of interactions with two chatbots in which breakdowns occur and the user (or chatbot) must recover using one or more repair strategies. In a Mechanical Turk evaluation (N=88), participants learned strategies for having successful human-agent interactions, reported feelings of empathy toward the chatbots, and expressed a desire to interact with chatbots in the future.
A Survey of Statistical Machine Learning Elements in Genetic Programming

Modern genetic programming (GP) operates within the statistical machine learning (SML) framework. In this framework, evolution needs to balance between approximation of an unknown target function on the training data and generalization, which is the ability to predict well on new data. This paper provides a survey and critical discussion of SML methods that enable GP to generalize.
Automatic smart contract generation using controlled natural language and template

Smart contracts, which are widely recognized as key components of blockchain technology, enable automatic execution of agreements. Since each smart contract is a computer program that autonomously runs on a blockchain platform, their development requires much effort and care compared with the development of more common programs. In this paper, we propose a technique to automatically generate a smart contract from a human-understandable contract document that is created using a document template and a controlled natural language (CNL). The automation is based on a mapping from the document template and the CNL to a formal model that can define the terms and conditions in a contract including temporal constraints and procedures. The formal model is then translated into an executable smart contract. We implemented a toolchain that generates smart contracts of Hyperledger Fabric from template-based contract documents via a formal model. We then evaluated the feasibility of our approach through case studies of two types of real-world contracts in different domains.
Pre-Attention and Spatial Dependency Driven No-Reference Image Quality Assessment

The excessive emulation of the human visual system and the lack of connection between chromatic data and distortion have been the major bottlenecks in developing image quality assessment. To address this issue, we develop a new no-reference (NR) image quality assessment (IQA) metric that accounts for the impact of pre-attention and spatial dependency on the perceived quality of distorted images. The resulting model, dubbed the Pre-attention and Spatial-dependency driven Quality Assessment (PSQA) predictor, introduces the pre-attention theory to emulate early phase visual perception by refining luminance-channel data. Chromatic data are also processed concurrently by transforming images from RGB to the perceptually optimized SCIELAB color space. Considering that the gray-tone spatial dependency matrix conveys important texture properties that are closely related to visual quality, this matrix, as a mathematical solution for subsequent visual process emulation, is calculated along with its statistical features on both gray and color channels. To clarify the influence of different regression procedures on model output, support vector regression and AdaBoosting Back Propagation (BP) neural networks are adopted separately to train the prediction models. We thoroughly evaluated PSQA on four public image quality databases: LIVE, TID2013, CSIQ, and VCL. The experimental results show that PSQA delivers highly competitive performance compared with top-rank NR and full-reference IQA metrics.
On the performance of web services, google cloud messaging and firebase cloud messaging

Smartphones and other connected devices rely on data services, such as Web Services (WS), Google Cloud Messaging (GCM) and Firebase Cloud Messaging (FCM), to share the information they collect or use. Traditionally, these services were classified according to the average number of bytes transmitted or their delivery time. However, when dealing with battery-operated devices, another important parameter to be taken into account is their power consumption. Furthermore, software designers and developers often do not consider the efficiency of a data communication system, but are simply concerned about ease-of-use and response time. In this paper, we compare FCM, GCM and two types of WS, namely Simple Object Access Protocol (SOAP) and REpresentational State Transfer (REST) WS in terms of delay, data efficiency, and power consumption. The final outcome is that RESTful WS outperforms all others, making GCM and FCM a viable alternative only when the amount of data to be transmitted is very limited, or when the mobile application requires the advanced services offered by FCM or GCM only.
Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach

The core security of proof-of-work (PoW)-based blockchain networks, relies on PoW consensus algorithm and requires miners solving a crypto-puzzles of hash computation. However, the mining process in the blockchain is resource-intensive where only the first miner who completes full PoW will be rewarded. Therefore, miners exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this brief, we propose a new reputation-based mechanism for the PoW computation in the blockchain, in which miners are incentivized to conduct honest mining. Based on the game theory, we design a reputation-based algorithm to encourage honest mining of miners, and thereby increase the overall revenue of the pool. In addition, numerical illustrations are also presented to support the performance of our proposed mechanism.