Reviewerly/exHarmony huggingface dataset:
Visualizing narrative patterns in online news media

Abstract News media play an important role in shaping social reality, and their multimedia narrative content, in particular, can have widespread repercussions in the public’s perception of past and present phenomena. Being able to visually track changes in media coverage over time could offer the potential for aiding social change, as well as furthering accountability in journalism. In this paper, we explore how visualizations could be used to examine differences in online media narrative patterns over time and across publications. While there are existing means of visualizing such narrative patterns over time, few address the aspect of co-occurrence of variables in media content. Comparing co-occurrences of variables chronologically can be more useful in identifying patterns and possible biases in media coverage than simply counting the individual occurrences of those variables independently. Here, we present a visualization, called time-sets , which has been designed to support temporal comparisons of such co-occurrences. We also describe an interactive prototype tool we have developed based on time-sets for analysis of multimedia news datasets, using an illustrative case study of news articles published on three online sources over several years. We then report on a user study we have conducted to evaluate the time-sets visualization, and discuss its findings.
Random Number Generators Based on Discrete-time Chaotic Maps

A novel tent map based random number generator (RNG) is introduced. Feasibility verification results from the experiment and simulation, show that the generated binary sequence fulfill statistical randomness tests without post processing, are presented. In the proposed RNG, bit sequences are produced by analyzing the distribution of the underlying signal. The proposed design can be applied for the other discrete-time chaotic maps and can be implemented in the integrated circuit.
A structure-guided approach to the prediction of natural image saliency

The structure of a scene provides global contextual information in directing gaze and complements local object information in saliency prediction. In this study, we explore how visual attention can be affected by scene structures, namely openness, depth and perspective. We first build an eye tracking dataset with 2500 natural scene images and collect gaze data via both eye tracking and mouse tracking. We make observations on scene layout properties and propose a set of scene structural features relating to visual attention. The set of complementary features are then integrated for saliency prediction. Our features are independent of and can work together with many computational modules, and this work demonstrates the use of Multiple kernel learning (MKL) as an example to integrate the features at low- and high-levels. Experimental results demonstrate that our model outperforms existing methods and our scene structural features can improve the performance of other saliency models in outdoor scenes.
Data Stream Trajectory Analysis Using Sequential Possibilistic Gaussian Mixture Model

Data stream processing has gained much attention lately, in the era of big data. Streaming clustering is an effective tool to recognize normal baseline and to detect outliers in sequentially presented data. Perhaps more importantly would be the ability to predict that incoming data indicates movement towards a likely anomaly. In this paper, a Gaussian Mixture Model (GMM) is employed to represent different patterns in the data stream. The Sequential Possibilistic One-Means (SP1M) is used for initialization, and is incorporated into the GMM framework to recognize new mixture components in the data stream. The new proposed algorithm is called Sequential Possibilistic Gaussian Mixture Model (SPGMM). Furthermore, two methods of trajectory analysis, the “maximum typicality decline” and the “trend value measurement,” are used together with SPGMM to detect early signs of pattern changes before unusual pattern data arrive in the stream. The proposed SPGMM is tested on synthetic and real-world datasets, and is shown to have excellent performance on predicting early signs of pattern changes in these sequential streams.
Variable selection and nonlinear effect discovery in partially linear mixture cure rate models

Survival data with long-term survivors are common in clinical investigations. Such data are often analyzed with mixture cure rate models. Existing model selection procedures do not readily discriminate nonlinear effects from linear ones. Here, we propose a procedure for accommodating nonlinear effects and for determining the cure rate model composition. The procedure is based on the Least Absolute Shrinkage and Selection Operators (LASSO). Specifically, by partitioning each variable into linear and nonlinear components, we use LASSO to select linear and nonlinear components. Operationally, we model the nonlinear components by cubic B-splines. The procedure adds to the existing variable selection methods an ability to discover hidden nonlinear effects in a cure rate model setting. To implement, we ascertain the maximum likelihood estimates by using an Expectation Maximization (EM) algorithm. We conduct an extensive simulation study to assess the operating characteristics of the selection procedure. We illustrate the use of the method by analyzing data from a real clinical study.
Approach of Agile Methodologies in the Development of Web-Based Software

The current inclusion of agile methodologies in web-oriented projects has been considered on a large-scale by software developers. However, the benefits and limitations go beyond the comforts that project managers delimit when choosing them. Selecting a methodology involves more than only the associated processes or some documentation. Based on the above, we could define as the main concerns the approach with which we identify the methodology, the needs of the company, the size, and qualities of the project, and especially the characteristics of agile development that they possess. However, there are several difficulties in selecting the most appropriate methodology due to the features in common; Will it be suitable for my project? What challenges will be presented in the process? Will my team understand each stage? Will I be able to deliver software that satisfies the client? Project managers create these questions, which seem manageable but have huge effects. This paper presents a systematic literature review based on the analysis of the approaches of six web development methodologies. The aim of the study is to analyze the approaches presented by relevant methodologies, identifying their common agile characteristics and managing to contrast both its benefits and limitations during a project. As a result, we could itemize five common features, which are presented within the processes; (1) flexibility, (2) constant communication of the workgroup, (3) use of UML, (4) the inclusion of the end-user and (5) some documentation.
GANReDL: Medical Image Enhancement Using a Generative Adversarial Network with Real-Order Derivative Induced Loss Functions

Deep (convolutional) neural networks (DCNN) have recently gained popularity, and shown improved performance in the field of image enhancement (de-noising and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in DCNN mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details, and are therefore error-prone with respect to fine-scale, possibly clinically relevant details. In this article, we present GANReDL, a generative adversarial network (GAN) for image enhancement equipped with a real-order derivative induced loss functions (ReDL) which we will show gives improved images, in particular wrt to the reconstruction of fine-scale details. To the best of our knowledge, this is the first framework that incorporates non-integer order derivatives in loss functions. To this aim, we propose a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and propose a new loss function motivated by real-order derivatives which is capable of also capturing global image features rather than pixel-wise features only. We show, with several numerical experiments, that GANReDL is better in reconstructing the high-frequency image details, and therefore show improved performance for image enhancement over other state-of-the-art methods.
Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks

To measure the life log of humans and enjoy virtual or augmented reality video games, several wearable devices have been developed that allow users to intuitively input commands. However, monitoring and estimating three-dimensional human motions for extended periods using the wearable devices is difficult. Therefore, this study aims to develop a method that estimates the joint angles of the upper human body using a wearable suit implanted with strain sensors with a nonlinear characteristic. We used a convolutional neural network (CNN) to estimate the joint angles. We established a CNN estimator based on the training data of two adult males and confirmed that this estimator could estimate the joint angles of other adult males. To monitor the caretakers in a care facility, we measure the care-working motion, such as motions that care workers transform the elder persons, estimate each joint angle, and visualize the motions on Unity.
Three New Geochemical Reference Materials for Mineral Exploration and Environmental Contamination Studies: SdAR‐L2, SdAR‐M2 and SdAR‐H1

The preparation and characterisation of three new sediment‐based reference materials SdAR‐L2 (blended sediment), SdAR‐M2 (metal‐rich sediment) and SdAR‐H1 (metalliferous sediment) described in this paper involved collaboration between the United States Geological Survey (USGS) and International Association of Geoanalysts (IAG). Sediments and soils from six locations in the western United States were mixed according to a USGS‐designed blending programme that calculates the proportions of source materials required to match target mass fractions and minimise the number of elements with mass fractions below instrument detection limits. For selected element mass fractions, NIST SRMs 2709–2711 provided target values. By using this approach, the three reference materials retained a natural mineralogy that is so essential for matching matrix and dissolution characteristics. Geochemical characterisation of these materials was carried out through the IAG Geo PT proficiency testing programme. Three independent rounds of Geo PT testing were used, each providing results from over eighty laboratories. Contributed results were statistically evaluated to produce reference values for over forty elements in each material. Reference and information values as well as mineralogical compositions are presented to establish these samples as reference materials, especially for use in mineral exploration and environmental contamination monitoring studies.
Linear Order Statistic Neuron

Herein, a generalization of the ordered weighted average (OWA) is put forth relative to pattern recognition. The resultant linear order statistic neuron (LOSN) is unique in that it bridges fuzzy sets, specifically fuzzy data/information aggregation, with neural networks. This article discusses the gradient descent-based optimization and geometric interpretation of the LOSN. An advantage is that the LOSN is an efficient shared weight encoding of N! perceptrons, relative to N inputs. Open source codes are provided to facilitate reproducible research. Experiments are conducted to both validate the method and show its non-linear geometric expression.
A polynomial-time fragment of epistemic probabilistic argumentation

Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory. However, in practice, this approach can be severely limited by the fact that probabilities are defined by adding an exponential number of terms. We show that this exponential blowup can be avoided in an interesting fragment of epistemic probabilistic argumentation and that some computational problems that have been considered intractable can be solved in polynomial time. We give efficient convex programming formulations for these problems and explore how far our fragment can be extended without losing tractability.
ARCode: Augmented Reality Application for Learning Elementary Computer Programming

This paper presented ARCode, the system that employed augmented reality (AR) to motivate and help learners with programming. Since programming is an abstract task, many need help with programming, both technically and emotionally. ARCode is the game-based learning system that shows how each command works by using AR animations, and focuses on the logical order of commands. Learners can collaborate in the real world while practicing individually in the application. In our experiment, we found that the treatment group had significantly improved on their scores for all topics except for Selection, while the control group did not. The user satisfaction survey suggested that more than 80% of users accepted our system as a useful, enjoyable, and collaborative learning system. The primary results showed the system's potential to help learners better learn programming with high motivation.
Quaternion Hadamard Transform and QR Decomposition Based Robust Color Image Watermarking

A robust watermarking method depending on Quaternion Hadamard Transform (QHT) and QR decomposition is proposed. The QHT can preserve the relationship among the rgb component and color information. The entire host image is initially processed with Quaternion Hadamard Transform to obtain the QHT transformed image; and then the QHT transformed image is partitioned into $4\times 4$ sub-blocks. QR decomposition is applied on each sub-block to attain two matrices named as $\boldsymbol{Q}$ matrix and R matrix. The $\boldsymbol{Q}$ matrix coefficients are chosen to embed the binary watermark data. A one-bit data is embedded in the second, third and fourth element of the first row of $4\times 4 \boldsymbol{Q}$ coefficients. The proposed method's performance is validated using the metrics embedding rate and Normalized cross-correlation (NCC) on different varieties of attacks. The embedding rate is 0.0625, thus the embedding rate of the proposed method is superior to the existing methods. The robustness of the proposed Color Image Watermarking algorithm is validated by testing it with several attacks.
Exploiting depth information to increase object tracking robustness

Object tracking is a critical component of many computer vision applications. However, despite years of research, it is still considered a difficult problem as most tracking algorithms fail when the objects of interest are similar in appearance to their surroundings or they are occluded. In this paper the widely used 2D mean-shift tracking algorithm is extended to 3D space, in order to exploit the extra depth information provided by 3D video content to increase tracking accuracy and robustness. The performance of the proposed 3D tracking algorithm is compared to the traditional 2D mean-shift algorithm in terms of tracking accuracy and speed. Results show that in general the tracking accuracy is improved while requiring more processing time. Nonetheless, the algorithm executes at a rate which is faster than typical video capturing rates and thus has no impact on the performance of the tracking system.
Sun Tracking Solar Panel Without Microcontroller

Today we are living in an era where we need to use renewable energy as much as possible because day by day the pollution is increasing. So one of the most usable and efficient renewable energy source is solar panel or solar energy. We have already seen a lot of solar panel systems like static solar panel and sun tracking solar panel. Sun tracking systems are mainly of 2 types, one is single axis solar tracker and dual axis solar tracker. Already many paper has been published on different types of solar panels and already seen the latest one is sun tracking system based on microcontroller. In our project we are trying to make a sun tracking scalable system without microcontroller which can be cheaper than other solar panel circuits in our approach. We hope that this proposal of our system can be efficient in future. This paper proposes a solar system which can track the sun using the reference of two small solar panel and track the path by tracking the difference of two voltages of two reference panels and moves the system structure using stepper motor.
Detecting security leaks in hybrid systems with information flow analysis

Information flow analysis is an effective way to check useful security properties, such as whether secret information can leak to adversaries. Despite being widely investigated in the realm of programming languages, information-flow-based security analysis has not been widely studied in the domain of cyber-physical systems (CPS). CPS provide interesting challenges to traditional type-based techniques, as they model mixed discrete-continuous behaviors and are usually expressed as a composition of state machines. In this paper, we propose a lightweight static analysis methodology that enables information security properties for CPS models. We introduce a set of security rules for hybrid automata that characterizes the property of non-interference. Based on those rules, we propose an algorithm that generates security constraints between each sub-component of hybrid automata, and then transforms these constraints into a directed dependency graph to search for non-interference violations. The proposed algorithm can be applied directly to parallel compositions of automata without resorting to model-flattening techniques. Our static checker works on hybrid systems modeled in Simulink/Stateflow format and decides whether or not the model satisfies non-interference given a user-provided security annotation for each variable. Moreover, our approach can also infer the security labels of variables, allowing a designer to verify the correctness of partial security annotations. We demonstrate the potential benefits of the proposed methodology on two case studies.
SAR image synthesis based on conditional generative adversarial networks

In recent years, synthetic aperture radar (SAR) has played an increasingly important role in the military and civil fields. Since the SAR image reflects the scattering characteristics of the target， it is of great significance to achieve multi-angle fusion of the target. However, there is a problem of angular loss in real SAR images. Through the electromagnetic simulation method, SAR images of 0–360° can be obtained, but the similarity to real images is low. Here, the authors combine electromagnetic simulation with conditional generative adversarial networks (cGANs). The image obtained by the electromagnetic simulation is taken as the input of the cGANs, and then the generator generates photorealistic SAR images containing the label information. Thereby, authors’ method complement the missing angles in the real SAR image dataset. Finally, they qualitatively and quantitatively evaluated the synthetic images generated through their model to verify the quality of the dataset.
Constructing multi-target controlled phase gate in circuit QED and its applications

We present a one-step scheme in circuit quantum electrodynamics (QED) to realize multi-target controlled phase gates by using one microwave cavity that is coupled capacitively to a superconducting flux qutrit which plays the control role and simultaneously controls n target qutrits embedded in the microwave cavity. A strong classical field is imposed on the control qutrit so as to yield two dressed states, one of which is coded as a logical state. The controlled phase gates can be employed to achieve the creation of entangled states deterministically without any measurement, including the Bell state, the GHZ state, and the cluster state. Numerical simulations demonstrate that the high-fidelity creation of the entangled state is feasible with the constructed controlled phase gate under the present circuit QED technology.
Co-Occurrence Neural Network

Convolutional Neural Networks (CNNs) became a very popular tool for image analysis. Convolutions are fast to compute and easy to store, but they also have some limitations. First, they are shift-invariant and, as a result, they do not adapt to different regions of the image. Second, they have a fixed spatial layout, so small geometric deformations in the layout of a patch will completely change the filter response. For these reasons, we need multiple filters to handle the different parts and variations in the input. We augment the standard convolutional tools used in CNNs with a new filter that addresses both issues raised above. Our filter combines two terms, a spatial filter and a term that is based on the co-occurrence statistics of input values in the neighborhood. The proposed filter is differentiable and can therefore be packaged as a layer in CNN and trained using back-propagation. We show how to train the filter as part of the network and report results on several data sets. In particular, we replace a convolutional layer with hundreds of thousands of parameters with a Co-occurrence Layer consisting of only a few hundred parameters with minimal impact on accuracy.
Quality Metrics for Hybrid Software Development Organizations – A Case Study

As the software industry transitions from a license-based model to a subscription-based Software-as-a-Service (SaaS) model, many software development organizations are transforming to a hybrid development model using agile and CI/CD practices for some products while continuing to use waterfall methods for other products. The traditional metrics used for measuring software quality in the waterfall or agile paradigms do not apply to this hybrid organization. Our paper presents a framework for defining a comprehensive set of quality metrics for hybrid organizations. The metrics we define are for all major milestones during the software development life cycle and are effective for all development methodologies. This framework has been implemented by all on-prem products in Cisco Systems, Inc.
Probabilistic Atlases to Enforce Topological Constraints

Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain or heart which have limited or predictable range of deformations. Here we propose an encoding-decoding CNN architecture that can exploit rough atlases that encode only the topology of the target structures that can appear in any pose and have arbitrarily complex shapes to improve the segmentation results. It relies on the output of the encoder to compute both the pose parameters used to deform the atlas and the segmentation mask itself, which makes it effective and end-to-end trainable.