Reviewerly/exHarmony huggingface dataset:
Joint PLDA for simultaneous modeling of two factors

Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class of the sample and another one that is assumed independent across samples and models the within-class variability. In this work, we propose a generalization of PLDA that enables joint modeling of two sample-dependent factors: the class of interest and a nuisance condition. The approach does not change the basic form of PLDA but rather modifies the training procedure to consider the dependency across samples of the latent variable that models within-class variability. While the identity of the nuisance condition is needed during training, it is not needed during testing since we propose a scoring procedure that marginalizes over the corresponding latent variable. We show results on a multilingual speaker-verification task, where the language spoken is considered a nuisance condition. The proposed joint PLDA approach leads to significant performance gains in this task for two different data sets, in particular when the training data contains mostly or only monolingual speakers.
Voxel printing using procedural art-directable technologies

A procedural art-directable workflow is developed for voxel 3D printing using existing digital effects technologies. Customised for the Stratasys J750's unique materials, the system produces large-scale prosthetic eyes as a case study for film and display work.
LBP-Haar Cascade Based Real-Time Pedestrian Protection System Using Raspberry Pi

People detection has always posed a challenge in the image processing domain, and a lot of research has been going on to solve the problems posed due to the large number of variants present in detecting a human. In this paper, we introduce a system that integrates various components to perform pedestrian detection, safe distance calculation and a risk assessment for pedestrians, with a warning if the pedestrian is potentially in danger. The proposed system is for middle income cars, where a web camera is connected to a Raspberry Pi 3. The processor performs the calculations based on live webcam feed and gives an alert to the driver via an Android Application. This project uses a custom LBP Cascade classifier written in OpenCV, for pedestrian detection. A comparison of results between this custom classifier with the standard Haar classifier is shown here. This system is tested on an Indian dataset collected by us and on the Penn-Fudan Database for Pedestrian Detection and Segmentation dataset for comparing the results.
INTERDISCIPLINARY INTEGRATION AS A CONDITION FOR THE MODERNIZATION OF TEACHER EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 1183-1188ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0363Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Moiré–Ronchigram analysis applied in the characterization of aberration surfaces and optical surface parameters from 3D wavefronts

In this work, we propose the construction of the Moir&#x00E9;-Ronchigram as a pattern obtained from simple Ronchigrams, superposed and with a slight degree of rotation, to obtain a 3D wavefront. The aberrations in the obtained wavefront are fitted to the aberration Zernike polynomial of degree 12 in order to obtain a polynomial expression of a high degree of efficiency, which is subsequently compared with the general equation of quadrics (considering the coefficients accordingly) to obtain essential parameters (as focus, eccentricity, or F number) that allow us to understand and manipulate the optical elements under test optimally. Finally, we compare our results analyzing optical surfaces to make a weak statistic of our proposed method.
Security and Compliance

As you have seen, most blockchains are decentralized, and the identity of each party is normally protected; however, most blockchain-related code involves storing some confidential data such as a user’s personal information, passwords, cryptocurrency, and wallets.
Forensic Analysis on Kindle and Android

In this chapter, we conduct a forensic analysis of Amazon’s Kindle Fire HD and report on our findings.
You Can't Teach a New Phone Old Tricks

The smartphone is an incredible computing platform. Loaded with powerful processing, vast data storage, near-global connectivity, built-in batteries, and a rich array of sensors, these devices reliably service the needs of billions of users every day. However, when tasked to run just a single application continuously without any human interaction, the smartphone platform becomes surprisingly unreliable. Over the course of a four-month deployment of Android-phone-based cellular gateways in Zanzibar, Tanzania, all 16 deployed phones failed despite significant engineering efforts, and six phones became physically damaged. This article examines what went wrong and how mobile computing platforms could adapt to support more traditional embedded computing roles and workloads.
AugmentedBook: A Collaborative E-Learning Augmented Reality Platform

This paper introduces an augmented reality-based framework (called AugmentedBook) for e-learning that allows the creation of collaborative notes, illustrative media (i.e. video, 2D or 3D image, audio) for mobile devices or Google glass. The augmented content can be added to real-world educational support to make it more comprehensive, interactive and collaborative. In this platform, students and teachers can add collaborative notes to any part of the educational support system. They can also find illustrative media and indicate the pertinence of the result. Using our AugmentedBook platform, students can also download the enriched support using a mobile device. Our framework solves the problem of standard integration of augmented reality applications in education, offering a distributed framework which is e-learning compliant.
The Discrete Cosine Transform on Triangles

The discrete cosine transform is a valuable tool in analysis of data on undirected rectangular grids, like images. In this paper it is shown how one can define an analogue of the discrete cosine transform on triangles. This is done by combining algebraic signal processing theory with a specific kind of multivariate Chebyshev polynomials. Using a multivariate Christoffel-Darboux formula it is shown how to derive an orthogonal version of the transform.
Tiny Vehicle Detection from UAV Imagery

In the past decade, great progress has been made in general object detection based on deep convolutional neural networks. However, object detection from Unmanned Aerial Vehicles (UAV) imagery received not so much concern. In this paper, a densely connected feature mining network is proposed for high accuracy detection. Specifically, multi-scale predictions are used to enhance the feature representation of the tiny vehicles. Furthermore, a streamlined one-stage detection network is used to achieve satisfactory trade-off between speed and accuracy. Finally, a improved distance metric function is integrated into the priors clustering process, which can lead to a better preliminary location before training. The proposed architecture is evaluated on the highly competitive UAV benchmark (UAVDT). The experimental results show that the proposed dense-darknet network has achieved a competitive performance of 42.03% mAP (mean Average Precision) and good generalization ability on the other UAV benchmarks.
The High Speed 3D Measurement Based on Interval Line Structured Light Method for Translucent Objects

Nowadays, modern manufacturing fields may encounter plastic, nylon, porcelain and other minerals which have translucent surface that would contrast the blurring of projection. It is one of the biggest bottleneck problems in the using of new materials. There are many optical methods indicates solving this problem in the past few years where cameras are used. Among all the optical methods, epipolar line projection method is the most efficient category that could acquire dense point clouds of object surface whose accuracy is equivalent to traditional methods. This paper presents a high speed 3D profile measurement based on triangular pattern motion blur method using interval line projection which derives from epipolar line projection. The experiments indicate that the proposed method can achieve high-speed 3D measurement of translucent objects. The measurement time of translucent objects is 0.091 s.
INVESTIGATION AND ANALYSIS OF ALGORITHMS FOR EVALUATING THE ACQUISITION OF KNOWLEDGE ON TEACHING STUDENTS AT THE HIGHER EDUCATION INSTITUTIONS

INVESTIGATION AND ANALYSIS OF ALGORITHMS FOR EVALUATING THE ACQUISITION OF KNOWLEDGE ON TEACHING STUDENTS AT THE HIGHER EDUCATION INSTITUTIONS
Imitation-Regularized Offline Learning

We study the problem of offline learning in automated decision systems under the contextual bandits model. We are given logged historical data consisting of contexts, (randomized) actions, and (nonnegative) rewards. A common goal is to evaluate what would happen if different actions were taken in the same contexts, so as to optimize the action policies accordingly. The typical approach to this problem, inverse probability weighted estimation (IPWE), requires logged action probabilities, which may be missing in practice due to engineering complications. Even when available, small action probabilities cause large uncertainty in IPWE, rendering the corresponding results insignificant. To solve both problems, we show how one can use policy improvement (PIL) objectives, regularized by policy imitation (IML). We motivate and analyze PIL as an extension to Clipped-IPWE, by showing that both are lower-bound surrogates to the vanilla IPWE. We also formally connect IML to IPWE variance estimation and natural policy gradients. Without probability logging, our PIL-IML interpretations justify and improve, by reward-weighting, the state-of-art cross-entropy (CE) loss that predicts the action items among all action candidates available in the same contexts. With probability logging, our main theoretical contribution connects IML-underfitting to the existence of either confounding variables or model misspecification. We show the value and accuracy of our insights by simulations based on Simpson's paradox, standard UCI multiclass-to-bandit conversions and on the Criteo counterfactual analysis challenge dataset.
A PROJECT MANAGEMENT ACTIVITY FOR STUDENT TEAMS IN A COMPUTER SCIENCE UNDERGRADUATE CAPSTONE PROJECT COURSE: TASK PLANNING

A PROJECT MANAGEMENT ACTIVITY FOR STUDENT TEAMS IN A COMPUTER SCIENCE UNDERGRADUATE CAPSTONE PROJECT COURSE: TASK PLANNING
The Secret Life of Granite: A Story of Emplacement, Alteration, Subduction and Metamorphism

"The Secret Life of Granite: A Story of Emplacement, Alteration, Subduction and Metamorphism." Applied Earth Science, 128(2), p. 38
COLLABORATIVE LEARNING AND TECHNOLOGIES IN HIGHER EDUCATION. CHARACTERISTICS, ADVANTAGES AND DISADVANTAGES FOR ITS IMPLEMENTATION

COLLABORATIVE LEARNING AND TECHNOLOGIES IN HIGHER EDUCATION. CHARACTERISTICS, ADVANTAGES AND DISADVANTAGES FOR ITS IMPLEMENTATION
Beating Fredman-Komlós for Perfect k-Hashing.

We say a subset C subseteq {1,2,...,k}^n is a k-hash code (also called k-separated) if for every subset of k codewords from C, there exists a coordinate where all these codewords have distinct values. Understanding the largest possible rate (in bits), defined as (log_2 |C|)/n, of a k-hash code is a classical problem. It arises in two equivalent contexts: (i) the smallest size possible for a perfect hash family that maps a universe of N elements into {1,2,...,k}, and (ii) the zero-error capacity for decoding with lists of size less than k for a certain combinatorial channel. A general upper bound of k!/k^{k-1} on the rate of a k-hash code (in the limit of large n) was obtained by Fredman and Komlos in 1984 for any k >= 4. While better bounds have been obtained for k=4, their original bound has remained the best known for each k >= 5. In this work, we present a method to obtain the first improvement to the Fredman-Komlos bound for every k >= 5, and we apply this method to give explicit numerical bounds for k=5, 6.
PWGAN

Generative adversarial network (GAN) plays an important part in image generation. It has great achievements trained on large scene data sets. However, for small scene data sets, we find that most of methods may lead to a mode collapse, which may repeatedly generate the same image with bad quality. To solve the problem, a novel Wasserstein Generative Adversarial Networks with perceptual loss function (PWGAN) is proposed in this paper. The proposed approach could be better to reflect the characteristics of the ground truth and the generated samples, and combining with the training adversarial loss, PWGAN can produce a perceptual realistic image. There are two benefits of PWGAN over state-of-the-art approaches on small scene data sets. First, PWGAN ensures the diversity of the generated samples, and basically solve mode collapse problem under the small scene data sets. Second, PWGAN enables the generator network quickly converge and improve training stability. Experimental results show that the images generated by PWGAN have achieved better quality in visual effect and stability than state-of-the-art approaches.
Sistem Pendukung Keputusan Penerimaan Beasiswa Peningkatan Prestasi Akademik (PPA) Berbasis Web

Determination of the decision that recipient of Academic Achievement Improvement scholarships in the Informatics Engineering is still done conventionally by the student division at the institutional and study program level, namely by collecting files, inputting and calculating data in Microsoft Excel, and selecting based on the data calculation results. The large number of scholarship applicants results in the inefficiency of the scholarship admission process so that it needs a system that can support the decision to receive scholarships. The proposed web-based system uses the weighted product method in determining the ranking of scholarship applicants. The criteria used in the problem of determining the scholarship include the Grade Point Average (GPA), parents' income, and the number of dependents. The results of applying weighted products to sample data of scholarship applicants in the Informatics Engineering in 2017 provide ranking information for scholarship applicants where Student 3 gets first rank, which means getting the first priority as a recipient of scholarship. The system that was built has displayed the results of the application of weigted products and graphical displays that make it easier for users to obtain information about the registration of scholarships.
PRESENT Cipher Implemented on an ARM-Based System on Chip

Ultra lightweight cryptography is a research area very active nowadays, mainly because it is widely used in application fields such as IoT (Internet of Things), intelligent sensors networks and smart grids, specially where embedded low-energy systems are involved. In this paper, the PRESENT ultra-lightweight cryptographic algorithm was implemented on a PSoC (Programmable System on Chip), it was totally performed in software by means of using C programming language, and using modular functions. The percentage of use of each implemented C function was analyzed as well as their execution time in order to define which of them are able to be implemented using the digital programmable blocks of the used SoC, and thus designing a new prototype optimized in space and in time.
Fully automatic colorization for anime character considering accurate eye colors

In this paper, we propose a method to colorize line drawings of anime characters' faces with colors from a reference image. Previous studies using reference images often fail to realize fully-automatic colorization, especially for small areas, e.g., eye colors in the resulting image may differ from the reference image. The proposed method accurately colorizes eyes in the input line drawing using automatically computed hints. The hints are round patches used to specify the positions and corresponding colors extracted from the eye areas of a reference image.
The Prediction of The Professionalization of Teachers Based on The Role of Professional Leadership of Managers by Mediating Trust and Cooperation among Teachers

Objective: The current research aims to predict the professionalization of teachers based on the role of professional leadership of managers by mediating trust and cooperation among teachers in the city of Babol, Mazandaran, Iran.Method: The purpose of the current study is applicable, and according to its data collection method it has a descriptive-correlational research design. The statistical population of the study includes all teachers of the first and second grade in both the primary and high schools (N=3100) in the city of Babol in the school year of 2018-19 that a sample of 200 teachers (Female=115 and Male=85) were chosen using stratified random sampling method. All of the participants completed the surveys: professional leadership of managers, professionalization of teachers, trust and cooperation among teachers. To test the reliability of these tools, Cronbach's alpha was applied. Then, data were analyzed through correlation and path analysis using SPSS and LISREL software.Results: The findings indicated that there is a positive significant relationship between the professionalization of teachers and the professional leadership of managers. Professional leadership of managers has a direct and significant effect on the professionalization of teachers. In addition, professional leadership of managers by mediating trust and cooperation among teachers has an indirect and significant effect on the professionalization of teachers.Conclusions: The results of this research indicate the importance of the professional leadership role of managers in the professional development of teachers.
Security threats and their mitigation in big data recommender systems

Big data recommender systems are very vulnerable to attacks, especially to profile injection attacks. So, we should use security mechanisms to protect big data recommender systems from different kinds of attacks. These vulnerabilities and attacks may decrease users&apos; trust in accuracy of recommender systems. In addition, issues related to big data recommender systems and their security problems are based on security challenges in Hadoop architecture which is called Hadoop Distributed File System (HDFS). In this chapter, we investigate a number of known attack models, examine their influence and suggest some solutions to combat them. Furthermore, we represent different methods that are used by attackers to modify an attack so it is not recognized as an attack. We consider important issues in creating secure big data recommender systems, focusing on attack models and their effect on different big data recommender approaches. We know the general effect on systems&apos; ability to predict accurately, and we also know the amount of knowledge that attackers need to know about the system to deploy a realistic attack. In this chapter, we show that the two approaches, i.e. user-based and item-based algorithms, are particularly vulnerable to attack patterns, but hybrid algorithms that are the combination of both user-based and item-based algorithms may present higher stability. Also, we study the basics of relevant research and advanced schemas, and discuss future thoughts.
DISJUNCTIVE BASES : NORMAL FORMS AND MODEL THEORY FOR MODAL LOGICS

We present the concept of a disjunctive basis as a generic framework for normal forms in modal logic based on coalgebra. Disjunctive bases were defined in previous work on completeness for modal fixpoint logics, where they played a central role in the proof of a generic completeness theorem for coalgebraic mu-calculi. Believing the concept has a much wider significance, here we investigate it more thoroughly in its own right. We show that the presence of a disjunctive basis at the level entails a number of good properties for a coalgebraic mu-calculus, in particular, a simulation theorem showing that every alternating automaton can be transformed into an equivalent nondeterministic one. Based on this, we prove a Lyndon theorem for the full fixpoint logic, its fixpoint-free fragment and its one-step fragment, a Uniform Interpolation result, for both the full mu-calculus and its fixpoint-free fragment, and a Janin-Walukiewicz-style characterization theorem for the mu-calculus under slightly stronger assumptions. We also raise the questions, when a disjunctive basis exists, and how disjunctive bases are related to Moss' coalgebraic modalities. Nabla formulas provide disjunctive bases for many coalgebraic modal logics, but there are cases where disjunctive bases give useful normal forms even when nabla formulas fail to do so, our prime example being graded modal logic. We also show that disjunctive bases are preserved by forming sums, products and compositions of coalgebraic modal logics, providing tools for modular construction of modal logics admitting disjunctive bases. Finally, we consider the problem of giving a category-theoretic formulation of disjunctive bases, and provide a partial solution.
Jumping in at the deep end

Recent years has seen an explosion in Machine Learning (ML) research. The challenge is now to transfer these new algorithms into the hands of artists and TD's in visual effects and animation studios, so that they can start experimenting with ML within their existing pipelines. This paper presents some of the current challenges to experimentation and deployment of ML frameworks in the post-production industry. It introduces our open-source "ML-Server" client / server system as an answer to enabling rapid prototyping, experimentation and development of ML models in post-production software. Data, code and examples for the system can be found on the GitHub repository page:
Skeleton Joint Difference Maps for 3D Action Recognition with Convolutional Neural Networks

Action recognition is a leading research topic in the field of computer vision. This paper proposes an effective method for action recognition task based on the skeleton data. Four features are proposed based on the joint differences from 3D skeleton data. From the differences of 3D coordinates of corresponding joints in successive frames, three maps are extracted related to x, y and z coordinates respectively and then these maps are encoded into 2D color images, named as Joint Difference Maps (JDMs). The fourth JDM is formed by mapping the individual x, y and z difference maps into red, green and blue values. Hence, the 3D action recognition problem is converted into 2D image classification problem. It enables us to fine tune CNNs to learn informative features for 3D action recognition problem. The proposed method achieved 79.30% recognition rate on UTD MHAD dataset.
Impact of Disruptive Technologies on the Human Attitude

The article deals with current problems of IT deployment in different segments of human activities. In the introduction the similarity of Industrial Revolution in 18 century and IT revolution today are compared. Following chapter deals with the impact of broad deployment of IT on human mental and decision process. Following chapter discuss changes in capability of human to memorize information and to judge based on information memorized and impact of IT on these abilities. The filtering model of human information processing introduce simple novelty approach using two separated filters – the knowledge filter and emotional filter. The conclusion emphasizes the thread of changes in people way of information processing, especially impact on people attitude and simple perception management.