Reviewerly/exHarmony huggingface dataset:
Quantitative analysis of facies variation using ground-based lidar and hyperspectral imaging in Mississippian limestone outcrop near Jane, Missouri

Ground-based hyperspectral imaging is useful for geologic mapping because of its high spectral and spatial resolutions at a millimeter to centimeter scale. We have used hyperspectral and terrestrial laser scanner (TLS) data collected in close range to a roadcut near Jane, Missouri, that contains a subvertical outcrop of Lower Mississippian limestone. The outcrop consists of the Compton, Northview, and Pierson Formations, which we evaluated for facies heterogeneity. The sequence near Jane, Missouri, was deposited in shelf margin with high-frequency sea-level fluctuations. These fluctuations introduced lithologic and geometric heterogeneity to the facies, and debris flows brought in carbonate mounds referred to as outrunner blocks. These are important to interpret accurately because of their equivocal depositional origin, which is highly debated by previous workers. We combined hyperspectral data with TLS for an integrated spatial analysis of geometric and compositional variations in facies by accurate, point cloud-registered mineralogical mapping. We mapped several carbonate facies based on spectral signatures of calcite, silt, and clay particles and distinguished pure limestone outrunner blocks from surrounding mud-prone limestone facies with various proportions of silt and clay (a total of approximately 60%). By tracing the classified facies from combined hyperspectral and TLS imagery, we produced a lithostratigraphic framework, which indicates rapid changes in lithology and the presence of shale baffles that vary the character of the Compton through Pierson interval and contribute to heterogeneity in this outcrop. The data suggest a lower energy depositional environment and support the hypothesis of transported outrunner blocks in a distally steepened ramp system. The information that we have evaluated in our study could help to explain reservoir heterogeneity in equivalent carbonate fields.
An adaptive replica placement approach for distributed key‐value stores

Summary The use of distributed key‐value stores (KVS) has experienced fast adoption by various applications in recent years due to key advantages such as hypertext transfer protocol‐based RESTful application programming interface, high availability and elasticity. Due to great scalability characteristics, KVS systems commonly use consistent hashing as data placement mechanism. Although KVS systems offer many advantages, they were not designed to dynamically adapt to changing workloads which often include data access skew. Furthermore, the underlying physical storage nodes may be heterogeneous and do not expose their performance capabilities to higher level data placement layers. In this paper, we address those issues and propose an essential step toward a dynamic autonomous solution by leveraging deep reinforcement learning. We design a self‐learning approach that incrementally changes the data placement, improving the load balancing. Our approach is dynamic in the sense that is capable of avoiding hot spots, that is, overloaded storage nodes when facing different workloads. Also, we design our solution to be pluggable. It assumes no previous knowledge of the storage nodes capabilities, thus different KVS deployments may make use of it. Our experiments show that our method performs well on changing workloads including data access skew aspects. We demonstrate the effectiveness of our approach through experiments in a distributed KVS deployment.
Agile operational research

As project management has become a critical subject in modern-world organisations, Operational Research (OR) needs to incorporate mechanisms to deal with rapid, unplanned changes as well as confusing information and stakeholders with conflicting values. Agile methods are widely used and tested in Software Engineering (SE) to deal with problems of the characteristics above. Because of this, after establishing that both OR interventions, as well as SE developments, have common stages and information evolution, this proposal aims to pose the challenge of applying agility to manage OR projects. Guidelines to adapt Agile Methodologies to OR are proposed, and a case vignette is studied as an initial test. Finally, future lines of work are considered to define how the larger project in which this proposal is embedded will continue.
SemiSupervised Learning for Class Association Rule Mining Using Genetic Network Programming

Data mining extracts useful knowledge from big data. The extracted knowledge in data mining is often represented by association rules, and association rules can be also used for classification. However, when association rules for classification (called class association rules) are extracted, a large number of data with class labels are necessary, which requires a lot of cost of manual annotation. Therefore, this paper proposes a semisupervised learning method for rule extraction, where a small number of labeled data and a large number of unlabeled data are used to efficiently extract class association rules. In detail, this paper proposes two types of classifiers using class association rules: one is a classifier based on semisupervised learning, and the other is that based on both supervised and semisupervised learning. The second method builds several classifiers using supervised learning and semisupervised learning and the classification results of these classifiers are integrated to make the final decision. As an association rule mining method, Genetic Network Programming (GNP), which is one of the graph‐based evolutionary algorithms is used. GNP has shown distinguished classification ability in some applications; therefore, one of the other objectives of this paper is to extend the applicability of GNP to data mining based on semisupervised learning. In the experiments, the classification accuracy is evaluated using some benchmark datasets by comparing with some conventional methods. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Human Visual Perception Based Image Quality Assessment for Video Prediction

Prediction is an important ability of robot to imitate human intelligence. In recent years, video prediction in the computer vision field provides the prediction-coding implementation for robots, which allows robots to predict a reasonable future scene based on a little continuous video information by generating multi-frame images. Since the images of the predicted generation vary based on different algorithms, it tends to cause different types and degrees of distortion, thereby reducing the prediction-oriented operability of robots. The image quality assessment(IQA) for video prediction task must consider whether it is consistent with human visual perception to judge the rationality, not just get some shallow information like PSNR or SSIM method. Based on the two alternative forced choice (2AFC) experiment of human visual perception, this paper proposes a perceptual assessment metric based on convolutional feature extraction networks for video prediction task. The assessment results are consistent with the law that the video prediction quality declines over time, and more correspond to the human perception. In addition, the VggNet used as feature extraction network is more sensitive to image quality, so it will be easier to assess similar images.
Pattern Recognition to Automate Chronic Patients Follow-Up and to Assist Outpatient Diagnostics

Pattern Recognition detects templates in images and signals of all sorts, including what comes from sensors, communications, and information management devices. The Internet of Things is increasingly available in the population, coincidentally with soaring proportions of help-dependent people. Engineering development, in addition to consumer products and industrial goods, is offering a growing number and variety of home care and personal devices. Considering personal assistants will be in great shortage in the very near future, automatic detection of problems – both environmental and medical – is a must. The shortage of qualified helpers at home – eventually and partially substituted by automatic systems – calls for psychological adaptation and cultural changes. Once restricted to small groups of well-cared-for citizens, the benefits of medicine in the twenty-first century can be shared by ever-larger numbers of individuals, thanks to technology dissemination and Pattern Recognition. In addition to magic, placebo, and personal interaction with a physician, people of the present century will be able to rely upon technology to live longer lives. A system is first described – SIMIC – to help perform cardiac failure patient follow-up for elderly and chronic condition persons. Secondly, a personal learned assistant for physicians – PRAXIS – is described, giving him or her the support of all previous cases treated, organized in a knowledge base to reduce diagnostic errors and to increase diagnostics as well as patient management efficiency.
A Source and Ownership Identification Framework for Mobile-Based Messenger Applications

This paper proposed a transform domain watermarking scheme for ownership and source identification for mobile phone-based messenger applications. In this scheme, text message is used as a watermark signal, which contains user’s 10-digit contact number and secret data representing specific messenger application (i.e. Facebook Messenger, WhatsApp, etc.). Walsh orthogonal codes are used to generate the application-specific watermark signal, which is then merged with user’s cell number to generate complete watermark data. Block-wise slantlet transform is performed on each uniform non-overlapping block of the host image for watermark embedding to achieve robustness against signal processing attacks. Three copies of watermark are inserted to make the scheme more reliable. The correct app identification enables us to know, whether a message is forwarded/not. The correct user identification enables us to know the origin of message. Experimental results signified that the method is robust enough against general signal processing attacks and the objective of the method (successful detection of the user and messenger) is achieved.
Fast and Area Efficient Implementation of RSA Algorithm

Efficient hardware implementations of public-key cryptosystems have been gaining interest in the past few decades. To achieve the goal, a high frequency as well as low latency Rivest-Shamir-Adleman (RSA) cryptosystem is reported in this paper. To configure such cryptosystem shift-add multiplier have been re-constructed and binary digit based modular exponentiation circuitry is proposed. Such exponentiation circuitry has been implemented through binary bit distribution technique, where, most significant bit (MSB) has been discarded for the implementation, owing to increase the operating frequency. The functionality of the reported algorithms were justified and compared in Hardware Description Language (HDL), simulated in Modelsim and synthesized in Xilinx ISE 14.2 platform. The proposed hardware implementation of RSA algorithm has a maximum frequency of operation of 545 MHz and 298 MHz for the bit sizes of 8 and 64 respectively. The proposed method shows improvements in terms of speed as well as in number of Look-up-tables (LUTs). Moreover, application-specific integrated circuit (ASIC) implementation of such cryptosystem of RSA was carried out through Encounter® RTL Compiler v11.10-p005_1 of Cadence® tool.
Secure Image Transmission Based on a Proposed Chaotic Maps

Reliability and security are two important subjects in modern digital communications, each with a variety of sub-disciplines. Recently, security is a prime important issue, and encryption is one of the best alternative ways to ensure security. This work is devoted to proposing a secured image transmission system has robust encryption technique and high immunity against channel impairments. The proposed system gains these features by developing a proposed chaotic system, called Nahrain. The proposed chaotic system was tested using Lyapunov exponent, 0–1, bifurcation diagram tests to confirm the hyperchaotic behavior. While the National Institute of Standard and Technology (NIST) suite tests were used to fruition the randomness statistical properties. The simulation results show that NHS has hyperchaotic behavior and the system can be synchronized by using the nonlinear control system. The analysis results that acquired by using MATLAB for LE test was 0.7008, for 0–1 test was 0.9864, for the Mono Bit test was 0.5787, and for Run test within a block was 0.5917. As a consequence, the proposed chaotic system may be used to improve effective encryption and synchronization algorithms for secure image transmission applications.
Minimax Efficient Finite-Difference Gradient Estimators

We consider stochastic gradient estimation when only noisy measurements of the function are available. A standard approach is to use the finite difference method or its variants. While natural, it is open to our knowledge whether its statistical accuracy is the best possible. This paper argues that this is nearly so in a well-defined minimax sense. In particular, we show that central finite-difference is an optimal zeroth-order derivative estimator among the class of linear estimators, over the worst-case scenario among a wide class of twice differentiable functions. This optimality is achieved exactly for any finite sample in the single-dimensional case, and nearly (up to a multiplicative factor) in the multi-dimensional case. We also show that the same estimator is nearly optimal among the much larger class of all (nonlinear) estimators. We utilize elementary techniques for the linear minimax results and Le Cam's method for the general minimax counterparts.
Country Selection on Web Forms: A Comparison of Dropdown Menus, Radio Buttons and Text Field with Autocomplete

Many forms on the web include obligatory country fields. Usually country form-fields are implemented using a long drop-down menu. This preliminary study set out to investigate whether the typical country drop-down menu is as efficient as common practices indicate. A controlled within-groups experiment with N = 17 participants was conducted comparing radio buttons, drop down lists and a text field with autocomplete. The results show that the mean time to select country by inputting the prefix of the country in a text field with autocomplete was the fastest although not significantly faster than the drop-down menu. However, both were significantly faster to use than radio buttons. The results support the choice of mechanisms used on some websites where country selection is implemented with a multi-mode input control that can be used either as a drop-down menu or by inputting the country prefix according to the users' preferences.
Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a proximity-network, a novel space-efficient data structure to facilitate scalable event mining. This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections. We probabilistically model the proximity network as a generative process with sparsity-inducing regularization. This allows us to efficiently and effectively extract high-quality and interpretable news events. Experiments on three different news corpora demonstrate that the proposed method is effective and robust at generating high-quality event descriptors and attributes. We briefly detail many interesting applications from our proposed framework such as news summarization, event tracking and multi-dimensional analysis on news. Finally, we explore a case study on visualizing the events for a Japan Tsunami news corpus and demonstrate ProxiModel's ability to automatically summarize emerging news events.
An Attention-Based User Preference Matching Network for Recommender System

Click-through rate prediction (CTR) is an essential task in recommender system. The existing methods of CTR prediction are generally divided into two classes. The first class is focused on modeling feature interactions, the second class is focused on solving time-series problems. However, the existing models of the second class are not able to handle time-series problems with user feedback information, so we propose PMN to solve this kind of problem. To be able to take full advantage of historical user behavior along with the user feedback, PMN uses the attention mechanism to get the user historical behavior representation and the user preference representation from the original input. Specially, user preference representation is derived from the user feedback information and it explicitly shows the user's attitude towards the candidate, which greatly improve the model performance. Finally, we introduced user preference baselines to solve the problem of inconsistent scoring standards for different users. In this paper, we focus on the CTR prediction modeling in the scenario of video recommendation in Video On Demand (VOD) service. Experimental results on multiple data sets have shown that our PMN model is effective.
An Enhanced SqueezeNet Based Network for Real-Time Road-Object Segmentation

Point cloud image segmentation plays an important role in self-driving. SqueezeSeg network has good performance in terms of accuracy and calculation speed on point cloud segmentation. However, potential details might be lost during the computational processing of SqueezeSeg and other similar kinds of networks. In this work, we try to retain the detailed information of the image by combining PointSeg network and the conditional random field in order to capture more data information and improve the recall rate. These two processes can complement and fully play their respective advantages. The proposed method has been tested on KITTI dataset. Simulation results demonstrate that our method can overcome the shortcomings of the SqueezeSeg network and similar kinds of networks on the extraction of detailed information.
Differentially Private Reinforcement Learning

With remarkable performance and extensive applications, reinforcement learning is becoming one of the most popular learning techniques. Often, the policy $$\pi ^*$$ released by reinforcement learning model may contain sensitive information, and an adversary can infer demographic information through observing the output of the environment. In this paper, we formulate differential privacy in reinforcement learning contexts, design mechanisms for $$\epsilon $$ -greedy and Softmax in the K-armed bandit problem to achieve differentially private guarantees. Our implementation and experiments illustrate that the output policies are under good privacy guarantees with a tolerable utility cost.
Political Economy of Rentierism in the Middle East and Disruptions from the Digital Space

Rentierism in the Middle East and North Africa (MENA) region had emanated both from significant external rent and from the statist model of development feeding each other, where legitimacy was secured through rent distribution. The rent-led resource imbalance between the state and the society, as well as intra-societal inequalities in the region, has been less recognized and studied. The flow of external rent in tandem with internal rent-seeking has perpetuated the wealth and power of the political and economic elites and limited economic opportunities of the larger population. The rentierism that bred on vertical controls and network of privileges is set to be disrupted from flows and connectivity generated in the growth of digital commerce in the region.
Exploiting Blockchain and Smart Contracts for Data Exploration As a Service

Digital transformation and the adoption of ICT technologies in the factory of the future are growing faster and faster. In particular, data exploration methods and techniques are enabling the development of data-intensive Remote Monitoring Services for anomaly detection and predictive maintenance purposes. Remote Monitoring Services involve different actors across organizations. The Original Equipment Manufacturer explores high volume of data collected by sensors on the monitored machines to provide anomaly detection and predictive maintenance services. Insurance agencies may provide support to sustain maintenance costs. Spare parts suppliers can schedule the delivery of mechanical parts required for maintenance interventions. In this scenario, trust among participants becomes a critical issue. On the one hand, providers of anomaly detection and predictive maintenance services as well as insurance agencies must trust the way machines have been used by collecting and analysing sensors data. On the other hand, owners of monitored machines must trust the use of collected data to implement services, based on which maintenance costs are calculated. The goal of this paper is to leverage blockchain and smart contracts to ensure the required level of trust when implementing data exploration for Remote Monitoring Services. Events occurring on the monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation. Moreover, trust-demanding services are implemented as smart contracts, to guarantee the required level of trustworthiness among participants. The approach is integrated with a tool for data exploration in the digital factory, and has been validated taking into account performances and cost requirements.
Target-Topic Aware Doc2Vec for Short Sentence Retrieval from User Generated Content

This paper proposes a new method of supplementing the context of short sentences for the training phase of Doc2Vec. Since CGM (Consumer Generated Media) sites and SNS sites become widespread, the importance of similarity calculation between a given query and a short sentence is increasing. As an example, a search by the query "sad" should find actual expressions such as "I needed a handkerchief" on a movie review site. Doc2Vec is one of the most widely used methods for vectorization of queries and sentences. However, Doc2Vec often exhibits low accuracy if the training data consists of short sentences, because they lack context. We modified Doc2Vec with the hypothesis that other posts for the same topic (i.e. reviews for the same movie in online movie review sites) may share the same background. Our method uses target-topic IDs instead of sentence IDs as the context in the training phase of the Doc2Vec with the PV-DM model; this model estimates the next term from a few previous terms and context. The model trained with item IDs vectorizes a sentence more accurately than a model trained with sentence IDs. We conducted a large-scale experiment using 1.2 million movie review posts and a crowdsourcing-based evaluation. The experimental result demonstrates that our new method achieves higher precision and nDCG than previous Doc2Vec variants and traditional topic modeling methods.