Reviewerly/exHarmony huggingface dataset:
Automated Segmentation of Breast Arterial Calcifications from Digital Mammography

Breast arterial calcifications (BACs) are formed when calcium is deposited in the walls of arteries in the breast. The accurate segmentation of BACs is a critical step for risk assessment of cardiovascular disease from a mammogram. This paper evaluates the performance of three deep learning architectures, YOLO, Unet and DeepLabv3+, on detecting BACs in digital mammography. In comparison, a simple Hessian-based multiscale filter is developed to enhance BACs pattern, then a self-adaptive thresholding algorithm is applied to obtain the binary mask of BACs. As BACs are relatively small in size, we developed a new metric to better evaluate the small object segmentation. In this study, 135 digital mammographic images containing labelled BACs were obtained, in which 80% for training deep learning networks and 20% for validation. The results show that our Hessian-based filtering method achieves a highest accuracy on validation data, and DeepLabv3+ falls behind with little effectiveness. We conclude simple filtering technique is effective in BACs extraction, and DeepLabv3+ is an expensive alternative in terms of its computational cost and configuration complexity.
Collaborative Transportation of Cable-Suspended Payload using Two Quadcopters with Human in the loop

We study the problem of collaborative transportation of cable-suspended payload using two quadcopters. While previous works on transportation using quadcopters emphasize more on autonomous control and generating complex trajectory, in this paper a master-slave strategy is implemented where the master quadcopter is controlled by human and the slave quadcopter tries to stabilize the oscillations of the payload. Two quadcopters with a cable-suspended payload system is under-actuated with coupled dynamics and hence, manual control is difficult. We use Lagrangian mechanics on a manifold for deriving equations of motion and apply variation based linearization to linearize the system. We designed a Lyapunov based controller to minimize the oscillations of the payload while transportation, leading to an easier manual control of master quadcopter.
Incremental focal loss GANs

Generative Adversarial Networks (GANs) have achieved inspiring performance in both unsupervised image generation and conditional cross-modal image translation. However, how to generate quality images at an affordable cost is still challenging. We argue that it is the vast number of easy examples that disturb training of GANs, and propose to address this problem by down-weighting losses assigned to easy examples. Our novel Incremental Focal Loss (IFL) progressively focuses training on hard examples and prevents easy examples from overwhelming the generator and discriminator during training. In addition, we propose an enhanced self-attention (ESA) mechanism to boost the representational capacity of the generator. We apply IFL and ESA to a number of unsupervised and conditional GANs, and conduct experiments on various tasks, including face photo-sketch synthesis, map↔aerial-photo translation, single image super-resolution reconstruction, and image generation on CelebA, LSUN, and CIFAR-10. Results show that IFL boosts learning of GANs over existing loss functions. Besides, both IFL and ESA make GANs produce quality images with realistic details in all these tasks, even when no task adaptation is involved.
Model to Personalize the Teaching-Learning Process in Virtual Environments Using Case-Based Reasoning

In recent years, new research has appeared in the area of education, which has focused on the use of information technology and the Internet to promote online learning, breaking many barriers of traditional education such as space, time, quantity and coverage.
A Note on Laplace Transforms of Some Common Distributions Used in Counting Processes Analysis

An important problem of actuarial risk management is the calculation of the probability of ruin.Using probability theory and the definition of the Laplace transform one obtains expressions, in the classical risk model, for survival probabilities in a finite time horizon.Then explicit solutions are found with the inversion of the double Laplace transform; using algebra, the Laplace complex inversion formula and Matlab, for the exponential claim amount distribution.
Type-2 fuzzy self-tuning of modified fractional-order PID based on Takagi–Sugeno method

Abstract In this paper, Takagi–Sugeno (TS) fuzzy technique is combined with interval type-2 fuzzy sets (IT2-FSs) to design a new adaptive self-tuning fractional-order PID (FOPID) controller. TS fuzzy technique is used to construct a modified FOPID controller (TSMFOPID). IT2-FSs are used as a tuner for TSMFOPID to update their gains under parameter uncertainty change and to compensate the controlled system undesired effects of the sudden disturbances. Three types of IT2-FSs tuning methods are used for TSMFOPID. The first one is to tune the proportional–integral–derivative gains of the TSMFOPID. The second one deals with tuning the fractional orders of integral and derivative effects. The last one tunes the five parameters of the TSMFOPID. The responses of the fractional orders of the integral and derivative actions on the controlled system driven by TSMFOPID are given and discussed. The three tuning methods via IT2FST for TSMFOPID controller are applied to load frequency control as a case study of a power system comprising a single area. Comparative studies of the tuning type-1 fuzzy sets (T1FSTs) and IT2FST methods for controlled system using TSMFOPID are carried out and the results are discussed. The results prove that the proposed IT2FST for TSMFOPID controller is very useful for considered application over disturbance changes and parameter uncertainties.
Exploring Identity-Based Crime Victimizations: Assessing Threats and Victim Services among a Sample of Professionals

An estimated 26 million American citizens per year have been victims of an identity-based crime. This study contributes to the scholarship on identity-based crime by examining serious identity-based crime victimization and the services available to help victims. This study investigated the interaction between victimized individuals and organizations and explored unmet needs for victim services. Interviews from focus groups with fifty professionals (e.g., private investigators, fraud examiners, victim service providers, and executives of firms offering victim services and protection services) working in identity-based crime victim services were used. The current study addressed two questions: 1) How do experts in the field of identity-based crime define the victims of identity theft and fraud and 2) How can the services for victims of identity-based crime be improved or expanded? Results show that professionals have a three-victim model of identity-based crime victimization, are greatly concerned about the rise in synthetic identity crime, and that identity-based crime victimization is present in a greater percentage of the population than most national estimates.
Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Extreme and rare events, such as abnormal spikes in air pollution or weather conditions can have serious repercussions. Many of these sorts of events develop from spatio-temporal processes, and accurate predictions are a most valuable tool in addressing their impact, in a timely manner. In this paper, we propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, by introducing bias into formerly random processes. This spatio-temporal bias includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under-or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposal provides an advantage over random resampling strategies in imbalanced spatio-temporal forecasting tasks. Additionally, we also find that valuing an observation's recency is more useful when over-sampling; while valuing its spatial distance to other cases with extreme values is more beneficial when under-sampling.
On the Cardinality Spectrum and the Number of Latin Bitrades of Order 3

By a (Latin) unitrade of order k, we call a subset of vertices of the Hamming graph H(n, k) that intersects any maximal clique at either 0 or 2 vertices. A bitrade is a bipartite unitrade, i.e., a unitrade that can be split into two independent subsets. We study the cardinality spectrum of bitrades in the Hamming graph H(n, k) with k = 3 (ternary hypercube) and the growth of the number of such bitrades as n grows. In particular, we determine all possible small (up to 2.5·2n) and large (from 14·3n−3) cardinalities of bitrades of dimension n and prove that the cardinality of a bitrade takes only values equivalent to 0 or 2n modulo 3 (this result can be treated in terms of a ternary Reed-Muller type code). A part of the results are valid for an arbitrary k. For k = 3 and n → ∞ we prove that the number of nonequivalent bitrades is not less than 2(2/3−o(1))n and not greater than $$2^{\alpha^n}$$, α < 2 (the growth order of the double logarithm of this number remains unknown). Alternatively, the studied set Bn of bitrades of order 3 can be defined as follows: B0 consists of three rationals - 1, 0, 1; Bn consists of ordered triples (a, b, c) of elements from Bn−1 such that a + b + c = 0.
Research on Scheduling of Cloud Manufacturing Resources Based on Bat Algorithm and Cellular Automata

To solve the optimization problem of cloud manufacturing resources scheduling, the optimal model, which based on the object of accomplishing the task at the lowest cost and shortest time, is built. A new algorithm of cloud manufacturing resources scheduling, which combined the bat algorithm with cellular automata, is proposed. The ROV coding technique was applied to the bat algorithm for coding and decoding of the individual position vector and the velocity vector, So the scheduling problem with continuous is converted into discrete problem. The objective function is solved by using the bat algorithm, and the optimal solution is obtained. The final results proves the effectiveness and feasibility of the algorithm.
A Multiple Coefficients Trial Method to Solve Combinatorial Optimization Problems for Simulated-annealing-based Ising Machines

When solving a combinatorial optimization problem with Ising machines, we have to formulate it onto an energy function of Ising model. Here, how to determine the penalty coefficients in the energy function is a great concern if it includes constraint terms. In this paper, we focus on a traveling salesman problem (TSP, in short), one of the combinatorial optimization problems with equality constraints. Firstly, we investigate the relationship between the penalty coefficient and the accuracy of solutions in a TSP. Based on it, we propose a method to obtain a TSP quasi-optimum solution, which is called multiple coefficients trial method. In our proposed method, we use an Ising machine to solve a TSP by changing a penalty coefficient every trial, the TSP solutions can converge very fast in total. Compared to naive methods using simulated-annealing-based Ising machines, we confirmed that our proposed method can reduce the total number of annealing iterations to 1/10 to 1/1000 to obtain a quasi-optimum solution in 32-city TSPs.
Intuitive Control of Virtual Robots using Transformed Objects as Multiple Viewports

In this paper, the integration of Leap Motion controller with RoboAnalyzer software has been reported. Leap Motion is a vision based device that tracks the motion of human hands, which was processed and used to control a virtual robot model in RoboAnalyzer, an established robot simulation software. For intuitive control, the robot model was copied and transformed to be placed at four different locations such that the user watches four different views in the same graphics environment. This novel method was used to avoid multiple windows or viewports and was observed to have a marginally better rendering rate. Several trials of picking up cylindrical objects (pegs) and moving them and placing in cylindrical holes were carried out and it was found that the manipulation was intuitive, even for a novice user.
Fuzzy c-means clustering method with the fuzzy distance definition applied on symmetric triangular fuzzy numbers

The conventional fuzzy c-means (FCM) clustering method can be applied on data, where data features are crisp; however, when the features are fuzzy, the conventional FCM cannot be utilized. Recently, some researchers applied FCM on fuzzy numbers when the used metric has a crisp value. Since differen ce between two fuzzy numbers can be represented by a fuzzy value better than crisp one, in this paper, it is going to extend the FCM method for clustering symmetric triangular fuzzy numbers, where the used metric has a fuzzy value. It will be shown that the proposed fuzzy distance expresses the distance between two fuzzy numbers much better than crisp metrics. Then the proposed method has been applied on simulated and various real data, where it is compared with several new methods. The experimental results show better performance of the proposed method in compare to other ones.
Tangibly understanding intangible complexities: Designing for distributed autonomous organizations

Abstract New digital technologies such as Blockchain and smart contracting are rapidly changing the face of value exchange, and present new opportunities and challenges for designers. Designers and data specialists are at the forefront of exploring new ways of exchanging value, using Blockchain, cryptocurrencies, smart contracting and the direct exchanges between things made possible by the Internet of Things (Tallyn et al. 2018; Pschetz et al. 2019). For researchers and designers in areas of Human Computer Interaction (HCI) and Interaction Design to better understand and explore the implications of these emerging and future technologies as Distributed Autonomous Organisations (DAOs) we delivered a workshop at the ACM conference Designing Interactive Systems (DIS) in Edinburgh in 2017 (Nissen et al. 2017). The workshop aimed to use the lens of DAOs to introduce the principle that products and services may soon be owned and managed collectively and not by one person or authority, thus challenging traditional concepts of ownership and power. This workshop builds on established HCI research exploring the role of technology in financial interactions and designing for the rapidly changing world of technology and value exchange (Kaye et al. 2014; Malmborg et al. 2015; Millen et al. 2015; Vines et al. 2014). Beyond this, the HCI community has started to explore these technologies beyond issues of finance, money and collaborative practice, focusing on the implications of these emerging but rapidly ascending distributed systems in more applied contexts (Elsden et al. 2018a). By bringing together designers and researchers with different experiences and knowledge of distributed systems, the aim of this workshop was two-fold. First, to further understand, develop and critique these new forms of distributed power and ownership and second, to practically explore how to design interactive products and services that enable, challenge or disrupt existing and emerging models.
Real-Time Multiple Pedestrian Tracking Based on Object Identification

In this paper, we present a novel real-time multiple pedestrian tracking based on object identification. The proposed method decides whether an object detected in the current frame is the same one in the previous frame, and up-dates the coordinate of multiple objects and the corresponding histogram during the tracking process. Experimental results show that the proposed multiple object tracking method out-performs existing methods in challenging situation with partial occlusions.
A COMPARATIVE STUDY ON GOOGLE APP ENGINE AMAZON WEB SERVICES AND MICROSOFT WINDOWS AZURE

In today Internet has grown to be persistent in daily livelihood furthermore Cloud computing is a rising model where computing resources offered over the Internet as scalable, on-demand (Web) services.An association deploy internet service needs to use enormous amounts of money on infrastructure needs to serve feasible users which is not a problem for large venture but when it comes to Small and Medium Enterprises or Enterprises affordability becomes a huge factor with the huge infrastructure come problems like machines failure, hard drive noises, software bugs, etc.Here might be a big problem for such a community.Cloud Computing is the ultimate solution to this problem.Rather than buying, installing and operating its own systems, an organization can rely on a cloud provider to do this for them.Cloud Computing key market leaders like Google, Amazon and Microsoft etc, these providers introduce new operating and business models that allow customers to pay for the resources they completely use, instead of making tremendous upfront investments.The purpose of this paper is to analyze most popular platforms, The Google App Engine, Amazon Web Services, and Windows Azure Platform.
Stereoscopic Image Quality Assessment Weighted Guidance by Disparity Map Using Convolutional Neural Network

In this paper, we propose a new two-column dense Convolutional Neural Network (CNN) for stereoscopic image quality assessment. The input of one column is the cyclopean image which conforms to the binocular combination and rival mechanism in our brain. The input of other column is the disparity map which provides some compensation information for the cyclopean image. More importantly, we employ the features of disparity map to guide and weight the feature maps obtained from the cyclopean image, which is implemented by modifying the structure of Squeeze and Excitation block. This weighting strategy recalibrates the importance of feature maps extracted from cyclopean image. At the end of CNN, we combine the outputs from the two-column through 'Concat', and then process them to get the final quality score of the stereoscopic image. Experimental results demonstrate that the proposed method can achieve high consistent alignment with subjective assessment.
Constructing the image of Islam in contemporary Russian print media: the language strategies and politics of misrepresentation

This article examines Russian print media coverage from 2010 to 2016, specifically newspaper articles, addressing topics related to Islam. It seeks to identify the main linguistic strategies employed to construct a negative image of Islam within a broader discursive strategy of differentiation between ‘radical’ and ‘traditional’ Islam. The research is based on critical discourse analysis and corpus linguistics, and draws on a corpus containing 18,308 articles from six national newspapers: Nezavisimaia Gazeta, Kommersant, Komsomolskaia Pravda, Moskovskii Komsomolets, Rossiiskaia Gazeta, and Novaia Gazeta. Through an analysis of the lexical compatibility patterns of the lexeme ‘Islam’ (e.g. ‘Islam + verb’, ‘Muslim/Islamic + noun’), the article identifies the four most obvious discursive strategies aimed at creating a negative image of Islam: 1) defining ‘correct’ Islam; 2) homogenising Muslims via ‘big numbers’; 3) criminalising neutral connotations (e.g. ‘Muslim celebration’); 4) misunderstanding specific Islamic terms.
Efficient Isolated Speech to Sign Conversion Based on the Adaptive Rate Processing

This paper proposes a novel approach, based on the adaptive rate processing and analysis, of achieving a visual transformation of the isolated speech. The idea is to smartly combine the adaptive rate signal processing and classification for realizing an effective speech recognition and its transformation in sign. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The K-Nearest Neighbor (KNN) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are the classification decisions. The classification decision is transformed into a systematic sign. The system functionality is tested for a case study and results are presented. A 7.9times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.6% is achieved. It shows that the proposed approach can be employed in potential applications like industrial and noisy environments, integration of people with impaired hearing, etc.
Convolutional Neural Network Based Up-Sampling for Depth Video Intra Coding

Depth video contains depth and disparity information of a scene, which is critical for 3D video systems. In this paper, a convolutional neural network (CNN) based block upsampling method is proposed to improve the efficiency of depth video intra coding. For each largest coding tree in a depth map, it is down-sampled before sent into encoder and recovered into the original size in an intelligent way after low-resolution coding. A novel texture-assisted CNN (TACNN) is presented to handle the depth block up-sampling. The network is made up of several residual coding units and the features of texture block are extracted to assist the reconstruction of the corresponding depth block. Experimental results show that the proposed method achieves competitive rate-distortion performance compared with the state-of-the-art approaches.
An Ontology for Security Patterns

Security is a fundamental requirement that we must keep in mind when developing a system. We approach the secure construction of software through the use of security patterns, as a way to mitigate their threats. We propose an ontological approach to security patterns, with the aim of adding semantics to the elements that surround security patterns. We have added ontological descriptions to pattern descriptions to make their use more precise, to allow the development of appropriate tools to present to the developer the relevant patterns in each stage and to be able to build better pattern catalogs. A final objective would be the construction of a complete catalog where each pattern includes ontological descriptions. Our contributions are (i) a representation of security patterns in the form of ontology; (ii) examples through queries on the use of the ontology and (iii) a discussion of the possible uses of this ontology for secure software development.