Reviewerly/exHarmony huggingface dataset:
Bi-gradient Verification for Grad-CAM Towards Accurate Visual Explanation for Remote Sensing Images

Gradient-weighted Class Activation Mapping (Grad-CAM) has been a successful technique to produce visual explanation for CNN-based models. In this paper, we verify its applicability in the task of remote sensing image classification. The results show Grad-CAM gives contradictory localization of the important regions for some remote sensing images. To solve this problem, we propose a new strategy, bidirectional gradient verification (BiGradV), to rectify the visual explanation produced by Grad-CAM. The BiGradV is based on the fact both positive and negative gradients can be sensitive to class discrimination of remote sensing images. It designs an internal feature map occlusion with confidence drop decision to verifying which directional gradients works for certain class. The verified gradients are then used to gain the correct visual explanation. Experiments on both remote sensing image dataset and general image datasets demonstrate our proposed strategy is effective and generalized. It could provide a good complement to the Grad-CAM based methods.
The Development of a Mathematical Model of Lithium-Ion Battery Discharge Characteristics

This paper is devoted to obtaining a mathematical model of the lithium-ion battery discharge capacity at a constant current discharge from the system of physical and chemical processes in a lithium-ion battery equations obtained earlier by the authors. The input of this model is the discharge capacity of a particular instance of a lithium-ion battery and a constant discharge current at which the discharge capacity is determined. The output of the model is determined by the discharge capacity of the considered instance of the lithium-ion battery. The model under consideration for the discharge capacities of a lithium-ion battery is obtained by excluding dynamic variables and unknown constant coefficients from the system of physical and chemical processes in lithium-ion battery equations. This exclusion of dynamic variables is carried out numerically by calculating different dynamics of physical and chemical processes in a lithium-ion battery corresponding to different values of constant coefficients and further approximation of the desired model on these calculated dynamics. The obtained model is tested experimentally.
Evaluation of cloud autoscaling strategies under different incoming workload patterns

Summary Cloud computing provides cost‐effective solutions for deploying services and applications. Although resources can be provisioned on demand, they need to adapt quickly and in a seamless way to the workload intensity and characteristics and satisfy at the same time the desired performance levels. In this paper, we evaluate the effects exercised by different incoming workload patterns on cloud autoscaling strategies. More specifically, we focus on workloads characterized by periodic, continuously growing, diurnal and unpredictable arrival patterns. To test these workloads, we simulate a realistic cloud infrastructure using customized extensions of the CloudSim simulation toolkit. The simulation experiments allow us to evaluate the cloud performance under different workload conditions and assess the benefits of autoscaling policies as well as the effects of their configuration settings.
Utilizing MAR for Remedial Teaching of Compound-Cube-Surface Area at Elementary School in Taiwan

In this paper, the development of the MAR (mobile augmented reality) remedial teaching program is described. It allowed students to manipulate augmented objects through AURASMA app via internet and find leads to solve geometry problems regarding compound-cube-surface area. In order to foster students' spatial abilities, the program provided specific functions included partition, complementation, recombination, and multi-perspective to promote their “mental image” as well as “visualization.” The proposed program was evaluated with the quasi-experimental pre-test/post-test control group design to probe into students' learning performance. Moreover, students' error types of problem-solving were analyzed using their solving track recorded in the sheets. The results showed that the MAR remedial teaching program truly benefit students on solving geometry problems regarding compound-cube-surface area, and there are still some questions needed to investigate in the future, such as how students were affected by visual-obscuration to inexactly carry out their counts.
Efficiently User-Independent Ultrasonic-Based Gesture Recognition Algorithm

A low-complexity gesture recognition algorithm robust to temporal variations of motions is proposed for ultrasonic sensing based free air gesture human-computer interface in this paper. During training and testing, it aligns the features extracted from the range-Doppler map of each frame with the template sequence of each class by dynamic time warping in advance. For each class, a two-class random forest that makes prediction according to the aligned features is trained. Experiments show that the proposed classifier trained by 6 people has a better leave-one-out cross validation accuracy compared with the competitors. It can identify 8 gestures with 93.9% accuracy in 37 ms on PC. Its model size is 5.8 Mbytes.
Detection of Suspicious Human Activity based on CNN-DBNN Algorithm for Video Surveillance Applications

Detection of suspicious human actions in automated video surveillance applications, is of great practical importance. Those kind of unusual activities in human is very difficult to acquire and classify to predict. In our proposed work, automatic tracking and detecting unusual movement's problems in closed circuit videos was resolved. Firstly, the videos are converted into frames. Then from the obtained frames, humans are detected from the video using a background subtraction method. Then the features are extracted using a convolutional neural network (CNN). The features thus extracted are fed to a Discriminative Deep Belief Network (DDBN). Labeled videos of some suspicious activities are also fed to the DDBN and their features are also extracted. Then the features extracted using Convolutional Neural Network (CNN) are compared against these features extracted from the labeled sample video of classified suspicious actions using a Discriminative Deep Belief Network (DDBN) and various suspicious activities are detected from the given video and results shows increase accuracy of 90% for the proposed framework for classification.
Topic enhanced word embedding for toxic content detection in Q&amp;A sites

Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better results in toxic content classification. An empirical study was also conducted to analyze the topics of the insincere questions on Quora, and we found that topics on "religion", "gender" and "politics" has a higher proportion of insincere questions.
Modular reality: Analogues of patching in immersive space

Despite decades of virtual reality (VR) research, current creative workflows remain far from VR founder Jaron Lanier’s musically inspired dream of collaboratively ‘improvising reality’ from within. Drawing inspiration from modular synthesis as a distinctive musically immersed culture and practice, this article presents a new environment for visual programming within VR that supports live, fine-grained, multi-artist collaboration, through a new framework for operational transformations on graph structures. Although presently focused on audio synthesis, it is articulated as a first step along a path to synthesising worlds.
Revised anonymous authentication protocol for adaptive client‐server infrastructure

Summary Rapid evolution in information and communication technologies has facilitated us to experience mobile communication in our daily routine. Mobile user can only avail the services from the server, once he/she is able to accomplish authentication process successfully. In the recent past, several researchers have contributed diverse authentication protocols for mobile client‐server environment. Currently, Lu et al designed two‐factor protocol for authenticating mobile client and server to exchange key between them. Lu et al emphasized that their scheme not only offers invincibility against potential security threats but also offers anonymity. Although this article reveals the facts that their protocol is vulnerable against client and server impersonation, man‐in‐the‐middle, server key breach, anonymity violation, client traceability, and session‐specific temporary attacks, therefore, we have enhanced their protocol to mitigate the above mention vulnerabilities. The enhanced protocol's security strength is evaluated through formal and informal security analysis. The security analysis and performance comparison endorses the fact that our protocol is able to offer more security with least possible computation complexity.
PLMIX: an <tt>R</tt> package for modelling and clustering partially ranked data

The PLMIX package offers a comprehensive framework aimed at endowing the R statistical environment with some recent methodological advances in modelling and clustering partially ranked data. The usefulness of the PLMIX package can be motivated from several perspectives: (i) it contributes to fill the gap concerning Bayesian estimation of ranking models in R, by focusing on the Plackett–Luce model and its extension within the finite mixture approach as the generative sampling distribution; (ii) it addresses computational complexity by combining the flexibility of R routines and the speed of compiled C++ code, with possibly parallel execution; (iii) it covers the fundamental phases of ranking data analysis allowing for a more careful and critical application of ranking models in real contexts; (iv) it provides effective tools for clustering heterogeneous partially ranked data. Specific S3 classes and methods are also supplied to enhance the usability and foster exchange with other packages. The functionality of the novel package is illustrated with several applications to simulated and real datasets.
Identifying Simple Shapes to Classify the Big Picture

In recent years, Deep Artificial Neural Networks (DNNs) have demonstrated their ability in solving visual classification problems. However, an impediment is transparency where it is difficult to interpret why an object is classified in a particular way. Furthermore, it is also difficult to validate whether a learned model truly represents a problem space. Learning Classifier Systems (LCSs) are an Evolutionary Computation technique capable of producing human-readable rules that explain why an instance has been classified, i.e. the system is fully transparent. However, because they can encode complex relationships between features, they are not best suited to domains with a large number of input features, e.g. classification in pixel images. Thus, the aim of this work is to develop a novel DNN-LCS system where the former extracts features from pixels and the latter classifies objects from these features with clear decision boundaries. Results show that the system can explain its classification decisions on curated image data, e.g. plates have elliptical or rectangular shapes. This work represents a promising step towards explainable artificial intelligence in computer vision.
Why Jenny can't figure out which of these messages is a covert information operation

We view foreign interference in US and UK elections via social manipulation through the lens of usable security. Our goal is to provide advice on what interventions on the socio-technical election system are likely to work, and which are likely to fail. Strategies that the usable security literature indicates are likely to work are those that (1) avoid overloading the user's primary task; (2) help people understand negative consequences of their actions; and (3) support the long-term education of users with analytic reasoning skills and adequate background knowledge. Several of the responses to election interference proposed by governments and technology companies so far do not abide by these recommendations and are likely to be ineffective.
Sketching Manipulators for Localized Blendshape Editing

We introduce a method that localizes the direct manipulation of blendshape models for facial animation with a customized sketch-based interface. Direct manipulation methods address the problem of cumbersome weight editing process using the traditional tools with a practical “pin-and-drag” operation directly on the 3D facial models. However, most of the direct manipulation methods have a global deformation impact, which lead to unintuitive and unexpected results. To this end, we propose a new way to localize the theory of direct manipulation method using geodesic circles for confining the edits to the local geometry. Inspired by artists’ brush painting on canvas, we additionally introduce a sketch-based interface as an application that provides direct manipulation and produces expressive facial poses efficiently and intuitively. Our method allows the artists to simply sketch directly onto the 3D facial model and automatically produces the expeditious manipulation until the desired facial pose is obtained. We show that localized blendshape direct manipulation has the potential to reduce the time-consuming blendshape editing process to an easy freehand stroke drawing. Finally, we present a wide range of facial poses on various models that are created rapidly using our method.
Graphical Flow-based Spark Programming

Abstract Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domain-specific graphical tools. The approach follows the flow-based programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flow-based programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises early-stage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate code-abstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications.
Imperfections Underlying the Manifesto for Agile Software Development

There is an assumption that Agile principles are sacrosanct and therefore Agile movement is in some ways accepting few criticisms. As a result, academic studies providing evidence about persistent difficulties, deterioration situations or even complete failure in this regard are still scarce. Although, there is a limited number of studies on the limitations, context dependencies and assumptions of the Agile Software Development and the Agile Manifesto in particular, the number of studies on the imperfections of the manifesto is very few. Holding the aim of opening gates to questioning of the underpinning "sacrosanct" Agile principles for the sake of agility itself, this study puts forwards that the manifesto has some imperfections. For the investigation of the imperfections, a reading was made by the author on the Agile Manifesto. The study then exposes and comprehensively discusses the identified issues along with their reasoning, to fulfill the gap emerging from the lacking resources on the area.
A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms

This paper presents a novel image processing algorithm for automated microcalcifications (MCs) detection in digital mammograms. In order to improve the detection accuracy and reduce false positive (FP) numbers, two scales of sub-images are considered for detecting varied sized MCs, and different processing algorithms are used on them. The main contributions of this research work include: use of multifractal analysis based methods to analyze mammograms and describe MCs texture features; development of adaptive α values selection rules for better highlighting MCs patterns in mammograms; application of an effective SVM classifier to predict the existence of tiny MC spots. A full-field digital mammography (FFDM) dataset INbreast is used to test our proposed method, and experimental results demonstrate that our detection algorithm outperforms other reported methods, reaching a higher sensitivity (80.6%) and reducing FP numbers to lower levels.
A Paper Recommendation System Based on User Interest and Citations

It is difficult for researchers to access useful scientific articles from an excessive number of articles. This difficulty has led to article recommendation systems. Many studies have been conducted on the article recommendation. Article recommendation studies in the literature generally do not take into account the interest of users. The innovation of this study is to propose research articles by using bibliography and creating a user profile. The proposed approach aims to personalize scientific recommendations based on article-bibliographic relationships and user profile. In our study, candidate recommendation articles were clustered by K-Means clustering method according to their bibliographies. In our study, K-NN algorithm is used to determine which cluster is closer to the user's profile. Once the cluster to which the user belongs is determined, other articles in the cluster are recommended to the user. Experimental results show that the method is successful.
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection

Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge. In this paper, we propose to leverage results from optimal control to scale BO to higher dimensional control tasks and to reduce the need for manually selecting the optimization domain. The contributions of this paper are twofold: 1) We show how we can make use of a learned dynamics model in combination with a model-based controller to simplify the BO problem by focusing onto the most relevant regions of the optimization domain. 2) Based on (1) we present a method to find an embedding in parameter space that reduces the effective dimensionality of the optimization problem. To evaluate the effectiveness of the proposed approach, we present an experimental evaluation on real hardware, as well as simulated tasks including a 48-dimensional policy for a quadcopter.
Semi-Auto Sketch Colorization Based on Conditional Generative Adversarial Networks

The sketch plays an important role in the animation industry. Auto or semi-auto colorizing sketches will improve animators' efficiency and reduce the production costs. In this paper, we propose a semi-auto sketch colorization method based on conditional generative adversarial networks, which can support user interaction by adding scribbles to guide the colorization process. In addition, we apply a pre-model to extract high-level features of sketches in order to make good use of sketches' unique texture information. Furthermore, the loss function in our method is specially designed that can reduce blend and overflow in the result. At last, we use joint bilateral filter to smooth the output and generate a cleaner and vivid coloring sketch. Experimental results show that every module in our method can make a contribution to the final results. Moreover, the comparison with PaintsChainer demonstrates that our method can avoid large areas of leakage in the background and have cleaner skin for characters.
Disclose or Exploit? A Game-Theoretic Approach to Strategic Decision Making in Cyber-Warfare

Today, countries are engaged in the cyber-arms race. With over 16 K new hardware/software vulnerabilities reported in 2018 alone, an important question confronts senior government decision makers when their cyber-warfare units discover a new vulnerability. Should they disclose the vulnerability to the vendor who produced the vulnerable product? Or should they “stockpile” the vulnerability, holding it for developing exploits (i.e., cyber-weapons) that can be targeted at an adversary? Choosing the first option may be important when the affected company is a corporation in the nation state that discovers the vulnerability and/or if that nation state would have a big exposure to that vulnerability. Choosing the second option has obvious advantages to the discovering nation's defense. We formulate the cyber-competition between countries as a repeated cyber-warfare game (RCWG), where two countries (players) compete over a series of vulnerabilities by deciding, at the time of vulnerability discovery, 1) whether to exploit or disclose it and 2) how long to exploit it if they decide to exploit. We define the equilibrium state of the RCWG as a pure strategy Nash equilibrium, and propose a learning-while-competing framework to compute the pure strategy Nash equilibrium of the formulated RCWG. Although testing our results with real data in the murky world of cyber-warfare is challenging, we were able to obtain real statistics from other sources and demonstrate the effectiveness of our proposed algorithm through a set of simulation results under different scenarios using these third-party statistics. We also report on our DiscX system that can help support government decision makers in their decision whether to disclose or exploit a vulnerability that they find.